Get started with jointVIP

The purpose of the jointVIP

The joint variable importance plot (jointVIP for short) is designed to help identify which variables to prioritize based on both treatment and outcome for adjustment. This plot should be used during the design stage of the observational study prior to the analysis phase.

Traditionally, balance tables and Love plots are used to show standardized mean differences and identify variables with high treatment imbalance. However, adjusting only based on treatment imbalance is not advised since they may not be confounders. The jointVIP helps researchers prioritize variables that contribute marginal bias on both dimensions that can be used in observational study design. Additionally, bias curves based on simple one variable (unadjusted) omitted variable bias framework are plotted for the ease of comparison.

Set up for example

You can install the released version of jointVIP with:

# install once its on CRAN!
# install.packages("jointVIP")

# devtools::install_github("ldliao/jointVIP")

# load jointVIP package
library(jointVIP)

# data to use for example
library(causaldata)

# matching method shown in example
library(MatchIt)
library(optmatch)

# weighting method shown in example
library(WeightIt)
library(optweight)

# load data for estimating earnings from 1978
# treatment is the NSW program
pilot_df = cps_mixtape
analysis_df = nsw_mixtape

transform_earn <- function(data, variables){
  data = data.frame(data)
  log_variables = sapply(variables, 
                         function(s){paste0('log_',s)})
  data[,log_variables] =
    apply(data[,variables], 2, 
        function(x){ifelse(x == 0, 
                           log(x + 1), 
                           log(x))})
  return(data)
}

pilot_df <- cps_mixtape
pilot_df <- transform_earn(pilot_df, c('re74', 're75', 're78'))

analysis_df <- nsw_mixtape
analysis_df <- transform_earn(analysis_df, c('re74', 're75', 're78'))

Specifying parameters for the create_jointVIP() function

To visualize for the jointVIP, following parameters must be specified:

• treatment the treatment variable name; 0 for control and 1 for treated
• outcome the outcome of interest
• covariates covariates of interest that are common for the two pilot_df and analysis_df — the variables all should occur prior to treatment and be potential confounders
• pilot_df pilot dataset consists of external controls
• analysis_df analysis dataset consists of both treated and controls of interest

The new_jointVIP is a jointVIP object.

treatment = 'treat'
outcome = 'log_re78'
covariates = c(names(analysis_df)[!names(analysis_df) %in% c(treatment,
                                           outcome, "data_id",
                                           "re74", "re75",
                                           "re78")])

new_jointVIP = create_jointVIP(treatment = treatment,
                               outcome = outcome,
                               covariates = covariates,
                               pilot_df = pilot_df,
                               analysis_df = analysis_df)

Diagnostics with a jointVIP object

The summary() function outputs the maximum absolute bias, the number of variables are above the desired bias tolerance (measured in absolute always), and the number of variables that can be plotted.

summary(new_jointVIP, 
        smd = "cross-sample",
        use_abs = TRUE,
        bias_tol = 0.01)
#> Max absolute bias is 0.113
#> 2 variables are above the desired 0.01 absolute bias tolerance
#> 8 variables can be plotted

The print() function outputs the variables and its associated bias above the absolute bias_tol desired.

print(new_jointVIP,
      smd = "cross-sample",
      use_abs = TRUE,
      bias_tol = 0.01)
#>           bias
#> log_re75 0.113
#> log_re74 0.045

plot(new_jointVIP)

By visualizing with the jointVIP, several important aspects stand out. First, the most important variables are the log_re75 and log_re74. Traditional methods, such as the Love plot or balance table would indicate nodegree and hisp variables to be more important to adjust than log_re74, but these variables show low marginal bias contribution using the jointVIP.

Matching examples

Following matching examples are performed to illustrate the utility of the jointVIP. Based on the plot shown above, the only variables that need adjustment are the log_re75 and log_re74. Post-match results are plotted to help visualize for comparison.

# 1:1 optimal matching w/o replacement
m.out <- matchit(
  treat ~ log_re75 + log_re74,
  data = analysis_df,
  method = "optimal",
  distance = "mahalanobis"
)
optmatch_df <- match.data(m.out)[, c(treatment, outcome, covariates)]

# ordering for the weightit
ordered_analysis_df = analysis_df[order(analysis_df$treat, decreasing = T),]

optwt <- weightit(treat ~ log_re74 + log_re75,
                  data = ordered_analysis_df,
                  method = "optweight", estimand = "ATE",
                  tols = 0.005, include.obj = TRUE)
# summary(optwt)

optwt_df = ordered_analysis_df[, c(covariates, treatment, outcome)]

Post-matching/weighting assessment with post_jointVIP object

Below are the examples showing how to plot after matching. The main function to use is the create_post_jointVIP() function, which takes in the original jointVIP object new_jointVIP in our example. The post-matched data frame need to be specified as post_analysis_df argument.

The functions: summary(), print(), and plot() all provide comparison between original and post jointVIPs. Note that the post-matched data frames contain the pair-matched individuals — for post-weighted data, an additional processing step multiplying the weight by the original data.frame subsetted on all the covariates.

All methods yielded satisfactory results based on desired bias tolerance.

post_optmatch_jointVIP <- create_post_jointVIP(new_jointVIP, 
                                               post_analysis_df = optmatch_df)

summary(post_optmatch_jointVIP)
#> Max absolute bias is 0.113
#> 2 variables are above the desired 0.01 absolute bias tolerance
#> 8 variables can be plotted
#> 
#> Max absolute post-bias is 0.005
#> Post-measure has 0 variable(s) above the desired 0.005 absolute bias tolerance

print(post_optmatch_jointVIP)
#>           bias post_bias
#> log_re75 0.113     0.005
#> log_re74 0.045     0.003

plot(post_optmatch_jointVIP, 
     plot_title = "Post-match jointVIP using optimal matching")

post_optwt_jointVIP = create_post_jointVIP(new_jointVIP,
                                           post_analysis_df = optwt_df,
                                           wts = optwt$weights)
summary(post_optwt_jointVIP)
#> Max absolute bias is 0.113
#> 2 variables are above the desired 0.01 absolute bias tolerance
#> 8 variables can be plotted
#> 
#> Max absolute post-bias is 0.005
#> Post-measure has 0 variable(s) above the desired 0.005 absolute bias tolerance

print(post_optwt_jointVIP)
#>           bias post_bias
#> log_re75 0.113     0.003
#> log_re74 0.045     0.003

plot(post_optwt_jointVIP, 
     plot_title = "Post-weighting jointVIP using optimal weighting")