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psrwe: Propensity Score-Integrated Methods
for Incorporating Real-World Evidence in Clinical Studies
Chenguang Wang
2026-01-10
Introduction
In the R package psrwe, we implement a
series of approaches for leveraging real-world evidence in clinical
study design and analysis.
Propensity score estimation
The approaches implemented in psrwe are mostly based
on propensity score adjustment. Estimation of propensity scores can be
done by using the function psrwe_est.
data(ex_dta)
dta_ps <- psrwe_est(ex_dta,
v_covs = paste("V", 1:7, sep = ""),
v_grp = "Group",
cur_grp_level = "current",
nstrata = 5,
ps_method = "logistic")
dta_ps
## This is a sing-arm study. A total of 1031 RWD subjects and
## 200 current study subjects are used to estimate propensity
## scores by logistic model. A total of 5 RWD subjects are
## trimmed (trim_ab=both) and excluded from the final
## analysis. (PS values of 5 and 0 RWD are below and above
## current study) The following covariates are adjusted in the
## propensity score model: V1, V2, V3, V4, V5, V6, V7.
##
## The following table summarizes the number of subjects in
## each stratum, and the distance in PS distributions
## calculated by overlapping area:
##
## Stratum N_RWD N_Current Distance
## 1 Stratum 1 729 40 0.5608752
## 2 Stratum 2 156 40 0.7201481
## 3 Stratum 3 78 40 0.8035261
## 4 Stratum 4 50 40 0.8097207
## 5 Stratum 5 13 40 0.7952280
It is extremely important to evaluate the propensity score adjustment
results. In psrwe, functions are provided to visualize
the balance in covariate distributions and propensity score
distributions based on propensity score stratification.
plot(dta_ps, plot_type = "balance")
## Warning: The `.dots` argument of `group_by()` is deprecated as of dplyr 1.0.0.
## ℹ The deprecated feature was likely used in the dplyr package.
## Please report the issue at <https://github.com/tidyverse/dplyr/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning in grid.Call.graphics(C_rect, x$x, x$y, x$width, x$height,
## resolveHJust(x$just, : semi-transparency is not supported on this device:
## reported only once per page
plot(dta_ps, plot_type = "ps")
## Warning in grid.Call.graphics(C_polygon, x$x, x$y, index): semi-transparency is
## not supported on this device: reported only once per page
PS-integrated power prior approach for single arm studies
For single arm studies when there is one external data source, the
function psrwe_powerp allows one to conduct the
analysis proposed in Wang et. al. (2019). The method uses propensity
score to pre-select a subset of real-world data containing patients that
are similar to those in the current study in terms of covariates, and to
stratify the selected patients together with those in the current study
into more homogeneous strata. The power prior approach is then applied
in each stratum to obtain stratum-specific posterior distributions,
which are combined to complete the Bayesian inference for the parameters
of interest.
ps_bor <- psrwe_borrow(dta_ps, total_borrow = 40,
method = "distance")
rst_pp <- psrwe_powerp(ps_bor, v_outcome = "Y_Bin",
outcome_type = "binary",
seed = 1234)
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Results can be further summarized as:
## $Overall
## Type Mean StdErr
## Mean Control 0.3134685 0.02878146
PS-integrated composite likelihood approach for single arm
studies
For single arm studies when there is one external data source, the
function psrwe_cl allows one to conduct the analysis
proposed in Wang et. al. (2020). In this approach, within each
propensity score stratum, a composite likelihood function is specified
and utilized to down-weight the information contributed by the external
data source. Estimates of the stratum-specific parameters are obtained
by maximizing the composite likelihood function. These stratum-specific
estimates are then combined to obtain an overall population-level
estimate of the parameter of interest.
rst_cl <- psrwe_compl(ps_bor, v_outcome = "Y_Bin",
outcome_type = "binary")
summary(rst_cl)
## $Overall
## Type Mean StdErr
## 1 Control 0.3057532 0.02786997
PS-integrated composite likelihood approach for randomized
studies
For randomized studies when there is one external data source that
contains control subjects, the function
psrwe_cl2arm allows one to conduct the analysis
proposed in Chen et. al. (2020). In this approach, a propensity
score-integrated composite likelihood approach is developed for
augmenting the control arm of the two-arm randomized controlled trial
with patients from the external data source. An example is given
below.
data(ex_dta_rct)
dta_ps_rct <- psrwe_est(ex_dta_rct, v_covs = paste("V", 1:7, sep = ""),
v_grp = "Group", cur_grp_level = "current",
v_arm = "Arm", ctl_arm_level = "control")
dta_ps_rct
## This is a randomized study. A total of 1031 RWD subjects
## and 200 current study subjects are used to estimate
## propensity scores by logistic model. A total of 25 RWD
## subjects are trimmed (trim_ab=both) and excluded from the
## final analysis. (PS values of 1 and 0 RWD are below and
## above current study) The following covariates are adjusted
## in the propensity score model: V1, V2, V3, V4, V5, V6, V7.
##
## The following table summarizes the number of subjects in
## each stratum, and the distance in PS distributions
## calculated by overlapping area:
##
## Stratum N_RWD N_RWD_CTL N_Current N_Cur_CTL N_Cur_TRT Distance
## 1 Stratum 1 703 703 41 20 21 0.7205846
## 2 Stratum 2 120 120 34 20 14 0.7190201
## 3 Stratum 3 93 93 38 20 18 0.7666712
## 4 Stratum 4 72 72 43 20 23 0.6971793
## 5 Stratum 5 18 18 44 20 24 0.6175830
ps_bor_rct <- psrwe_borrow(dta_ps_rct, total_borrow = 30,
method = "distance")
ps_bor_rct
## A total of 30 subjects will be borrowed from the RWD. The
## number 30 is split proportional to the distance in PS
## distributions in each stratum. The following table
## summarizes the number of subjects to be borrowed and the
## weight parameter in each stratum:
##
## Stratum N_RWD N_RWD_CTL N_RWD_TRT N_Current N_Cur_CTL N_Cur_TRT Distance
## 1 Stratum 1 703 703 0 41 20 21 0.7205846
## 2 Stratum 2 120 120 0 34 20 14 0.7190201
## 3 Stratum 3 93 93 0 38 20 18 0.7666712
## 4 Stratum 4 72 72 0 43 20 23 0.6971793
## 5 Stratum 5 18 18 0 44 20 24 0.6175830
## Proportion N_Borrow Alpha
## 1 0.2046512 6.139535 0.008733336
## 2 0.2042068 6.126205 0.051051710
## 3 0.2177401 6.532203 0.070238741
## 4 0.1980039 5.940117 0.082501625
## 5 0.1753980 5.261940 0.292329972
rst_cl_rct <- psrwe_compl(ps_bor_rct, v_outcome = "Y_Con",
outcome_type = "continuous")
rst_cl_rct$Effect
## $Stratum_Estimate
## Mean StdErr
## 1 13.826269 13.003388
## 2 -7.758159 10.346000
## 3 15.811210 15.199217
## 4 15.536261 9.432971
## 5 12.630545 11.726722
##
## $Overall_Estimate
## Mean StdErr
## 1 10.63864 5.413684
Demo examples
The full examples for the PSPP and PSCL approaches can be found via
the script demo("sec_4_1_ex", package = "psrwe") and
demo("sec_4_2_ex", package = "psrwe").
Reference
Chen, W.C., Wang, C., Li, H., Lu, N., Tiwari, R., Xu, Y. and Yue,
L.Q., 2020. Propensity score-integrated composite likelihood approach
for augmenting the control arm of a randomized controlled trial by
incorporating real-world data. Journal of Biopharmaceutical Statistics,
30(3), pp.508-520.
Wang, C., Lu, N., Chen, W. C., Li, H., Tiwari, R., Xu, Y., & Yue,
L. Q. (2020). Propensity score-integrated composite likelihood approach
for incorporating real-world evidence in single-arm clinical studies.
Journal of biopharmaceutical statistics, 30(3), 495-507.
Wang, C., Li, H., Chen, W. C., Lu, N., Tiwari, R., Xu, Y., & Yue,
L. Q. (2019). Propensity score-integrated power prior approach for
incorporating real-world evidence in single-arm clinical studies.
Journal of biopharmaceutical statistics, 29(5), 731-748.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.