Then, in a final step, we pass est to corTrans() to compute the PFS-OS correlation.
est
corTrans()
Alternatively, one can combine these steps efficiently via corPFSOS(), @@ -270,15 +258,15 @@
corPFSOS()
corPFSOS(data = simData, transition = transition, bootstrap = TRUE, conf_level = 0.95) #> $corPFSOS -#> [1] 0.5678287 +#> [1] 0.5883777 #> #> $lower #> 2.5% -#> 0.5190675 +#> 0.5137962 #> #> $upper #> 97.5% -#> 0.8010183
Figure 1 - Multistate model with indermediate state progession and @@ -180,7 +166,7 @@
The transition hazards are specified as follows:
+ library(simIDM) transitionTrt <- exponential_transition(h01 = 0.3, h02 = 0.28, h12 = 0.5) transitionPl <- exponential_transition(h01 = 0.5, h02 = 0.3, h12 = 0.6) @@ -189,7 +175,7 @@ Scenario - PFS and OS as The package provides functions that return the values of the PFS or OS survival functions for given transition hazards (Constant, Weibull or Piecewise Constant) and pre-specified time points. - + timepoints <- c(0, 0.1, 0.3, 0.7, 1, 5) # OS Survival function for Constant transition hazards: ExpSurvOS(timepoints, h01 = 0.2, h02 = 0.4, h12 = 0.1) @@ -204,7 +190,7 @@ Scenario - PFS and OS as ) #> [1] 1.00000000 0.95094877 0.85849702 0.69546105 0.59109798 0.03945673 There are also functions for PFS survival functions available: - + timepoints <- c(0, 0.1, 0.3, 0.7, 1, 5) # PFS Survival function for Constant transition hazards: ExpSurvPFS(timepoints, h01 = 0.2, h02 = 0.4) @@ -219,7 +205,7 @@ Scenario - PFS and OS as ) #> [1] 1.00000000 0.92311635 0.78662786 0.57120906 0.44932896 0.01499558 For PFS, the hazard ratio under \(H_0\) is known by specification: - + hTrtPFS <- sum(unlist(transitionTrt$hazards[1:2])) hPlPFS <- sum(unlist(transitionPl$hazards[1:2])) hRatioPFS <- hTrtPFS / hPlPFS @@ -227,7 +213,7 @@ Scenario - PFS and OS as #> [1] 0.725 For OS, the ratio of hazard functions is not necessarily constant. An averaged HR can be calculated using avgHRExpOS: - + hRatioOS <- avgHRExpOS(transitionByArm = transitionList, alpha = 0.5, upper = 1000) hRatioOS #> [1] 0.8072368 @@ -242,7 +228,7 @@ Type I Error - Simulation Under - + transitionListNull <- list(transitionPl, transitionPl) nRep <- 100 simNull <- getClinicalTrials( @@ -257,7 +243,7 @@ Type I Error - Simulation Under - + alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
library(simIDM) transitionTrt <- exponential_transition(h01 = 0.3, h02 = 0.28, h12 = 0.5) transitionPl <- exponential_transition(h01 = 0.5, h02 = 0.3, h12 = 0.6) @@ -189,7 +175,7 @@ Scenario - PFS and OS as The package provides functions that return the values of the PFS or OS survival functions for given transition hazards (Constant, Weibull or Piecewise Constant) and pre-specified time points. - + timepoints <- c(0, 0.1, 0.3, 0.7, 1, 5) # OS Survival function for Constant transition hazards: ExpSurvOS(timepoints, h01 = 0.2, h02 = 0.4, h12 = 0.1) @@ -204,7 +190,7 @@ Scenario - PFS and OS as ) #> [1] 1.00000000 0.95094877 0.85849702 0.69546105 0.59109798 0.03945673 There are also functions for PFS survival functions available: - + timepoints <- c(0, 0.1, 0.3, 0.7, 1, 5) # PFS Survival function for Constant transition hazards: ExpSurvPFS(timepoints, h01 = 0.2, h02 = 0.4) @@ -219,7 +205,7 @@ Scenario - PFS and OS as ) #> [1] 1.00000000 0.92311635 0.78662786 0.57120906 0.44932896 0.01499558 For PFS, the hazard ratio under \(H_0\) is known by specification: - + hTrtPFS <- sum(unlist(transitionTrt$hazards[1:2])) hPlPFS <- sum(unlist(transitionPl$hazards[1:2])) hRatioPFS <- hTrtPFS / hPlPFS @@ -227,7 +213,7 @@ Scenario - PFS and OS as #> [1] 0.725 For OS, the ratio of hazard functions is not necessarily constant. An averaged HR can be calculated using avgHRExpOS: - + hRatioOS <- avgHRExpOS(transitionByArm = transitionList, alpha = 0.5, upper = 1000) hRatioOS #> [1] 0.8072368 @@ -242,7 +228,7 @@ Type I Error - Simulation Under - + transitionListNull <- list(transitionPl, transitionPl) nRep <- 100 simNull <- getClinicalTrials( @@ -257,7 +243,7 @@ Type I Error - Simulation Under - + alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
The package provides functions that return the values of the PFS or OS survival functions for given transition hazards (Constant, Weibull or Piecewise Constant) and pre-specified time points.
+ timepoints <- c(0, 0.1, 0.3, 0.7, 1, 5) # OS Survival function for Constant transition hazards: ExpSurvOS(timepoints, h01 = 0.2, h02 = 0.4, h12 = 0.1) @@ -204,7 +190,7 @@ Scenario - PFS and OS as ) #> [1] 1.00000000 0.95094877 0.85849702 0.69546105 0.59109798 0.03945673 There are also functions for PFS survival functions available: - + timepoints <- c(0, 0.1, 0.3, 0.7, 1, 5) # PFS Survival function for Constant transition hazards: ExpSurvPFS(timepoints, h01 = 0.2, h02 = 0.4) @@ -219,7 +205,7 @@ Scenario - PFS and OS as ) #> [1] 1.00000000 0.92311635 0.78662786 0.57120906 0.44932896 0.01499558 For PFS, the hazard ratio under \(H_0\) is known by specification: - + hTrtPFS <- sum(unlist(transitionTrt$hazards[1:2])) hPlPFS <- sum(unlist(transitionPl$hazards[1:2])) hRatioPFS <- hTrtPFS / hPlPFS @@ -227,7 +213,7 @@ Scenario - PFS and OS as #> [1] 0.725 For OS, the ratio of hazard functions is not necessarily constant. An averaged HR can be calculated using avgHRExpOS: - + hRatioOS <- avgHRExpOS(transitionByArm = transitionList, alpha = 0.5, upper = 1000) hRatioOS #> [1] 0.8072368 @@ -242,7 +228,7 @@ Type I Error - Simulation Under - + transitionListNull <- list(transitionPl, transitionPl) nRep <- 100 simNull <- getClinicalTrials( @@ -257,7 +243,7 @@ Type I Error - Simulation Under - + alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
timepoints <- c(0, 0.1, 0.3, 0.7, 1, 5) # OS Survival function for Constant transition hazards: ExpSurvOS(timepoints, h01 = 0.2, h02 = 0.4, h12 = 0.1) @@ -204,7 +190,7 @@ Scenario - PFS and OS as ) #> [1] 1.00000000 0.95094877 0.85849702 0.69546105 0.59109798 0.03945673
There are also functions for PFS survival functions available:
+ timepoints <- c(0, 0.1, 0.3, 0.7, 1, 5) # PFS Survival function for Constant transition hazards: ExpSurvPFS(timepoints, h01 = 0.2, h02 = 0.4) @@ -219,7 +205,7 @@ Scenario - PFS and OS as ) #> [1] 1.00000000 0.92311635 0.78662786 0.57120906 0.44932896 0.01499558 For PFS, the hazard ratio under \(H_0\) is known by specification: - + hTrtPFS <- sum(unlist(transitionTrt$hazards[1:2])) hPlPFS <- sum(unlist(transitionPl$hazards[1:2])) hRatioPFS <- hTrtPFS / hPlPFS @@ -227,7 +213,7 @@ Scenario - PFS and OS as #> [1] 0.725 For OS, the ratio of hazard functions is not necessarily constant. An averaged HR can be calculated using avgHRExpOS: - + hRatioOS <- avgHRExpOS(transitionByArm = transitionList, alpha = 0.5, upper = 1000) hRatioOS #> [1] 0.8072368 @@ -242,7 +228,7 @@ Type I Error - Simulation Under - + transitionListNull <- list(transitionPl, transitionPl) nRep <- 100 simNull <- getClinicalTrials( @@ -257,7 +243,7 @@ Type I Error - Simulation Under - + alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
timepoints <- c(0, 0.1, 0.3, 0.7, 1, 5) # PFS Survival function for Constant transition hazards: ExpSurvPFS(timepoints, h01 = 0.2, h02 = 0.4) @@ -219,7 +205,7 @@ Scenario - PFS and OS as ) #> [1] 1.00000000 0.92311635 0.78662786 0.57120906 0.44932896 0.01499558
For PFS, the hazard ratio under \(H_0\) is known by specification:
+ hTrtPFS <- sum(unlist(transitionTrt$hazards[1:2])) hPlPFS <- sum(unlist(transitionPl$hazards[1:2])) hRatioPFS <- hTrtPFS / hPlPFS @@ -227,7 +213,7 @@ Scenario - PFS and OS as #> [1] 0.725 For OS, the ratio of hazard functions is not necessarily constant. An averaged HR can be calculated using avgHRExpOS: - + hRatioOS <- avgHRExpOS(transitionByArm = transitionList, alpha = 0.5, upper = 1000) hRatioOS #> [1] 0.8072368 @@ -242,7 +228,7 @@ Type I Error - Simulation Under - + transitionListNull <- list(transitionPl, transitionPl) nRep <- 100 simNull <- getClinicalTrials( @@ -257,7 +243,7 @@ Type I Error - Simulation Under - + alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
hTrtPFS <- sum(unlist(transitionTrt$hazards[1:2])) hPlPFS <- sum(unlist(transitionPl$hazards[1:2])) hRatioPFS <- hTrtPFS / hPlPFS @@ -227,7 +213,7 @@ Scenario - PFS and OS as #> [1] 0.725
For OS, the ratio of hazard functions is not necessarily constant. An averaged HR can be calculated using avgHRExpOS:
avgHRExpOS
+ hRatioOS <- avgHRExpOS(transitionByArm = transitionList, alpha = 0.5, upper = 1000) hRatioOS #> [1] 0.8072368 @@ -242,7 +228,7 @@ Type I Error - Simulation Under - + transitionListNull <- list(transitionPl, transitionPl) nRep <- 100 simNull <- getClinicalTrials( @@ -257,7 +243,7 @@ Type I Error - Simulation Under - + alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
hRatioOS <- avgHRExpOS(transitionByArm = transitionList, alpha = 0.5, upper = 1000) hRatioOS #> [1] 0.8072368
+ transitionListNull <- list(transitionPl, transitionPl) nRep <- 100 simNull <- getClinicalTrials( @@ -257,7 +243,7 @@ Type I Error - Simulation Under - + alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
transitionListNull <- list(transitionPl, transitionPl) nRep <- 100 simNull <- getClinicalTrials( @@ -257,7 +243,7 @@ Type I Error - Simulation Under - + alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
+ alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
alphaOS <- 0.04 alphaPFS <- 0.01 criticalOS <- abs(qnorm(alphaOS / 2)) @@ -265,7 +251,7 @@ Type I Error - Simulation Under With the Schoenfeld approximation, preliminary sample sizes can be computed to get an idea of how many events are needed to achieve 80 % power: - + library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
+ library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant: - + empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
library(rpact) # Number of PFS events required for 80 % power via Schoenfeld: schoenfeldPFS <- getSampleSizeSurvival( @@ -296,7 +282,7 @@ Type I Error - Simulation Under empSignificant
+ empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
empSignificant( simTrials = simNull, criticalPFS = criticalPFS, @@ -321,7 +307,7 @@ Type I Error - Simulation Under Sample size and power calculation - simulation under \(H_1\) Next, we simulate a large number of trials under \(H_1\) to compute the empirical power: - + simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
Next, we simulate a large number of trials under \(H_1\) to compute the empirical power:
+ simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
simH1 <- getClinicalTrials( nRep = nRep, nPat = c(800, 800), seed = 1238, datType = "1rowPatient", transitionByArm = transitionList, @@ -332,7 +318,7 @@ Sample size an us to easily estimate further interesting metrics, such as joint power, i.e. the probability that both endpoints in a trial are significant, if each endpoint is analyzed at its planned time-point. - + empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
+ empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
empSignificant( simTrials = simH1, criticalPFS = criticalPFS, @@ -358,7 +344,7 @@ Sample size an It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average. - + # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
It is also possible to derive the median time at which a certain number of events are expected to occur and how many events of the second endpoint have occurred at that time on average.
+ # median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
# median time point for 329 PFS events to have occurred: timePointsPFS <- lapply(simH1, getTimePoint, eventNum = 329, typeEvent = "PFS", diff --git a/main/authors.html b/main/authors.html index ccac604b..63a1f10b 100644 --- a/main/authors.html +++ b/main/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • simIDMAuthors and Citation • simIDM - - + + diff --git a/main/favicon-16x16.png b/main/favicon-16x16.png index cee8627270de43b9cffeca7d2161d89fa5922b4a..41722759d69e2527996ca63fd6b1bbe86cffc496 100644 GIT binary patch delta 103 zcmdnYv6*8-3=`K2MlL=vgOGzkuQsPK2{U?|=o%R68XANcnphbbS(#X98yHv_7;O72 olfl5ipjzS@QIe8al4_NkpOTqY$zWt)gk8h&bxW>HKEj+20O#Z$1^@s6 delta 103 zcmdnYv6*8-3=`LL9&SDhkxTp6iEU0}5@z%^)-^QJH8cz{G_f)@vobQ!HZZU ng)^r@N diff --git a/main/favicon-32x32.png b/main/favicon-32x32.png index 13b995708358f931e2d07e2423f758c408f06a08..71ec60e65bfd7372fdb749b6a80e68bc5fd46e5e 100644 GIT binary patch delta 101 zcmdnQzlncB1=kBkEsekrd2W+85m*Lka%-_!enRmd;qX`96kU5 delta 101 zcmdnQzlncB1=n*PZaxdqr90*|Y-}oH^ETEsG}1LR3^6pZGBvX@GSoIOure?>5%T>v m0|SFRc?MtW?ChKk%19*4Ldpg-6uP<=K}yko*kC} diff --git a/main/index.html b/main/index.html index 50847030..19affd70 100644 --- a/main/index.html +++ b/main/index.html @@ -21,8 +21,8 @@ - - + + simIDM - 0.1.0.9000 + 0.1.0.9001 @@ -80,18 +80,6 @@ Details Changelog - - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports diff --git a/main/news/index.html b/main/news/index.html index dc908584..9ea9ddd2 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -1,5 +1,5 @@ -Changelog • simIDMChangelog • simIDMPull Request • simIDMOS Hazard Function from Constant Transition Hazards — ExpHazOS • simIDMQuantile function for OS survival function induced by an illness-death model — ExpQuantOS • simIDMOS Survival Function from Constant Transition Hazards — ExpSurvOS • simIDMPFS Survival Function from Constant Transition Hazards — ExpSurvPFS • simIDMSingle Piecewise Exponentially Distributed Event Time — PCWInversionMethod • simIDM - Versions latest-tag -main -v0.1.0 -v0.0.5 -v0.0.4 -v0.0.2 -v0.0.1 - Reports @@ -118,7 +106,7 @@ Value Examples PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948
PCWInversionMethod(haz = c(1.1, 0.5, 0.4), pw = c(0, 7, 10), LogU = log(1 - runif(1))) -#> [1] 1.008584 +#> [1] 2.948948