plot.SumStat_sga: Plot the distribution of propensity scores and subgroup...

In siyunyang/PSweight.sga: PSweight.sga: Propensity Score Weighting for Causal Subgroup Analysis

Plot the distribution of propensity scores and subgroup balance statistics via Connect-S plot

Description

Summarize the SumStat_sga object, plot the subgroup balance statistics under weighting versus no weighting. In a Connect-S plot (Yang et al. 2021), each row represents a subgroup and each column represents a confounder; each dot is shade-coded according to the value of the ASMD corresponding to the specific subgroup and confounder. Dark gray and black dots in the Connect-S plot flag meaningful covariate imbalance. The last two columns show the subgroup sample size and estimated variance inflation due to weighting.

Usage

## S3 method for class 'SumStat_sga'
plot(x, varlist = NULL, base = FALSE, plotsub = FALSE, ...)

Arguments

x	a SumStat_sga object obtained with SumStat function.
varlist	an optional vector specifying the variables to be plotted on the x-axis by index or variable names. The default plots all main effect variables specified by ps.formula in the SumStat_sga function, excluding subgrouping variables.
base	an indicator to specify whether to print the unadjusted ASMDs before weighting. The default is FALSE.
plotsub	an indicator to specify whether to treat subgrouping variables as confounders and print them on the x-axis. The default is FALSE.
...	further arguments passed to or from other methods.

Details

For the Connect-S plot, the shade is categorized based on the common ASMD threshold of 0.1 and 0.2 following Austin and Stuart (2015), with darker shade implying more severe imbalance.

Value

Plot of the indicated type.

References

Yang, S., Lorenzi, E., Papadogeorgou, G., Wojdyla, D. M., Li, F., & Thomas, L. E. (2021). Propensity score weighting for causal subgroup analysis. Statistics in medicine, 40(19), 4294-4309.

Austin, P.C. and Stuart, E.A. (2015). Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Statistics in Medicine, 34(28), 3661-3679.

siyunyang/PSweight.sga documentation built on Aug. 16, 2022, 5:23 a.m.