Skip to content

Commit

Permalink
Update ExampleWorkflow.Rmd
Browse files Browse the repository at this point in the history
  • Loading branch information
@istallworthy
istallworthy committed Sep 17, 2024
1 parent 96ecf79 commit 682ccf7
Showing 1 changed file with 84 additions and 23 deletions.
107 changes: 84 additions & 23 deletions ExampleWorkflow.Rmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -152,13 +152,13 @@ https://istallworthy.github.io/devMSMs/articles/Workflow_Continuous_Exposure.htm
## PHASE 1: Confounder Adjustment
The first phase of the MSM process is focused on eliminating confounding of the relation between exposure and outcome.

### STEP 1: Create Full Balancing Formulas & Conduct Pre-Balance Checking
The first step is to create full balancing formulas that reflect all measured confounders at each exposure time point.
### STEP 1: Create Full Weights Formulas & Conduct Pre-Balance Checking
The first step is to create full weights formulas that reflect all measured confounders at each exposure time point.

#### 1a. Create Full Balancing Formulas at each Exposure Time Point
#### 1a. Create Full Weights Formulas at each Exposure Time Point
Users have the option to specify concurrent confounders to retain and we recommend doing so consistently throughout this workflow.

Please see the Customize Balancing Formulas Vignette at the link below for more detail on custom formulas.
Please see the *Customize Weights Formulas* Vignette at the link below for more detail on custom formulas.
https://istallworthy.github.io/devMSMs/articles/Customize_Balancing_Formulas.html

```{r}
Expand All @@ -178,12 +178,16 @@ print(full_formulas)
#### 1b. Conduct Exploratory Pre-Balance Assessment
The next step is to examine initial imbalance between confounders and exposure prior to IPTW weighting. Users have the option to specify balance threshold(s), which we recommend doing consistently throughout this workflow.

We first specify two balance thresholds and a list of important confounders, which will be held to the higher (first) balancing threshold. These specifications will be used for all balance assessment.
```{r}
balance_thresh <- c(0.05, 0.1)
imp_conf <- c("InRatioCor.6", "InRatioCor.15", "InRatioCor.24", "InRatioCor.35",
"PmEd2")
```


```{r}
prebalance_stats <- assessBalance(obj = obj,
data = data,
balance_thresh = balance_thresh,
Expand Down Expand Up @@ -220,11 +224,11 @@ for (i in 1:5){
```


### STEP 2: Create Simplified Balancing Formulas & Determine Optimal Weighting Method
The next step is to specify shorter, simplified balancing formula for the purposes of determining the weighting method optimal for the data.
### STEP 2: Create Simplified weights formulas & Determine Optimal Weighting Method
The next step is to specify shorter, simplified weights formula for the purposes of determining the weighting method optimal for the data.

#### 2a. Create Simplified Balancing Formulas
First, create shorter, simplified balancing formulas at each exposure time point.
#### 2a. Create Simplified weights formulas
First, create shorter, simplified weights formulas at each exposure time point.

```{r}
type <- "short"
Expand Down Expand Up @@ -254,7 +258,7 @@ weights.cbps <- createWeights(obj = obj,
method = method,
# maxit = 1, # for testing purposes only; makes it run faster
save.out = TRUE)
#
# Read in if you've run them already:
# weights.cbps <- readRDS("/Users/isabella/Library/CloudStorage/Box-Box/BSL General/MSMs/testing/isa/weights/type_short-exposure_ESETA1-method_cbps.rds")
```

Expand Down Expand Up @@ -291,11 +295,15 @@ weights.gbm <- createWeights(obj = obj,
formulas = formulas,
method = method,
save.out = TRUE)
# Read in if you've run already:
# weights.gbm <- readRDS("/Users/isabella/Library/CloudStorage/Box-Box/BSL General/MSMs/testing/isa/weights/type_short-exposure_ESETA1-method_gbm.rds")
print(weights.gbm,
i = 2)
plot(weights.gbm,
i = 1,
save.out = TRUE)
summary(weights.gbm[[1]])
method <- "bart"
Expand Down Expand Up @@ -418,7 +426,7 @@ We identify CBPS as the weighting method that most successfully reduces imbalanc


### STEP 3: Create Updated Formulas & Re-Specify Weights Using Optimal Weighting Method
The next step is to update the short balancing formulas with any imbalanced confounders, and re-specify the IPTW weights.
The next step is to update the short weights formulas with any imbalanced confounders, and re-specify the IPTW weights.

#### 3a. Inspect Balance of Best-Performing Weighting Method
First, assess how well each of the IPTW achieve balance for all measured confounders.
Expand Down Expand Up @@ -545,7 +553,7 @@ print(updated_formulas)


#### 3c. Create Final Balancing Weights
Next, create final balancing weights using the optimal weighting method and updated balancing formulas.
Next, create final balancing weights using the optimal weighting method and updated weights formulas.

```{r}
formulas <- updated_formulas
Expand All @@ -566,7 +574,7 @@ Inspect
print(final_weights,
i = 1)
summary(final_weights[[3]])
summary(final_weights[[1]])
plot(final_weights,
i = 1,
Expand Down Expand Up @@ -600,6 +608,7 @@ plot(trim_weights,
```



##### Sensitvity Analyses
Next, conduct sensitivity analyses using two other quantile values.

Expand All @@ -625,10 +634,10 @@ plot(trim_weights.s2,


### STEP 4: Conduct Final Balance Assessment
Next, conduct a final balance assessment using all measured confounders (i.e., the full balancing formulas).
Next, conduct a final balance assessment using all measured confounders (i.e., the full weights formulas).

#### Main
First, conduct the main balance assessment.
#### Trimmed
Assess balance of trimmed weights

```{r}
balance_thresh <- c(0.05, 0.1)
Expand All @@ -637,27 +646,68 @@ imp_conf <- c("InRatioCor.6", "InRatioCor.15", "InRatioCor.24",
"InRatioCor.35", "PmEd2")
weights <- trim_weights
final_balance_stats <- assessBalance(data = data,
final_balance_stats_trim <- assessBalance(data = data,
obj = obj,
balance_thresh = balance_thresh,
imp_conf = imp_conf,
weights = weights,
save.out = TRUE)
```

Inspect
Summarize and inspect
```{r}
summary(final_balance_stats,
summary(final_balance_stats_trim,
save.out = TRUE)
plot(final_balance_stats,
t = 2,
save.out = TRUE)
summary(trim_weights[[1]])
print(final_balance_stats,
save.out = TRUE)
# average ESS
mean(summary(trim_weights[[1]])$ESETA1.6$effective.sample.size$Total[2],
summary(trim_weights[[2]])$ESETA1.6$effective.sample.size$Total[2],
summary(trim_weights[[3]])$ESETA1.6$effective.sample.size$Total[2],
summary(trim_weights[[4]])$ESETA1.6$effective.sample.size$Total[2],
summary(trim_weights[[5]])$ESETA1.6$effective.sample.size$Total[2]
)
# plot(final_balance_stats_trim,
# t = 2,
# save.out = TRUE)
#
# print(final_balance_stats_trim,
# save.out = TRUE)
```

#### Untrimmed
Assess balance of untrimmed weights

```{r}
weights <- final_weights
final_balance_stats_untrim <- assessBalance(data = data,
obj = obj,
balance_thresh = balance_thresh,
imp_conf = imp_conf,
weights = weights,
save.out = TRUE)
```

Summarize and inspect
```{r}
summary(final_balance_stats_untrim,
save.out = TRUE)
summary(final_weights[[1]])
# plot(final_balance_stats_untrim,
# t = 2,
# save.out = TRUE)
#
# print(final_balance_stats_untrim,
# save.out = TRUE)
```

#### Sensitvity Analyses
Next, conduct the recommended specifying sensitivity analyses to match the main analyses above.
Expand All @@ -670,6 +720,7 @@ final_balance_stats.s1 <- assessBalance(data = data,
save.out = "balance-weighted-exposure_ESETA1-method_cbps-form_update-s1.rds")
summary(final_balance_stats.s1,
save.out = TRUE)
summary(trim_weights.s1[[1]])
weights <- trim_weights.s2
Expand All @@ -679,6 +730,8 @@ final_balance_stats.s2 <- assessBalance(data = data,
save.out = "balance-weighted-exposure_ESETA1-method_cbp-form_update-s2.rds")
summary(final_balance_stats.s2,
save.out = TRUE)
summary(trim_weights.s2[[1]])
```


Expand Down Expand Up @@ -822,6 +875,14 @@ print(results_t1,
save.out = "comparisons_table-outcome_StrDif_Tot_58-exposure_ESETA1-t1-uw.docx")
plot(results_t1,
save.out = "comparisons_plot-outcome_StrDif_Tot_58-exposure_ESETA1-t1-uw.jpeg")
d1 = mice::complete(data, 1)
d2 = mice::complete(data, 2)
d3 = mice::complete(data, 3)
d4 = mice::complete(data, 4)
d5 = mice::complete(data, 5)
mean(sd(d1$StrDif_Tot.58), sd(d2$StrDif_Tot.58), sd(d3$StrDif_Tot.58), sd(d4$StrDif_Tot.58), sd(d5$StrDif_Tot.58))
```

##### 2 dose timing
Expand Down

0 comments on commit 682ccf7

Please sign in to comment.