Propensity Score-Integrated Matching Method

In psrwe: PS-Integrated Methods for Incorporating Real-World Evidence in Clinical Studies

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Introduction

In the psrwe, PS-integrated matching method (Chen, et al., 2021) is also implemented for leveraging real-world evidence in evaluation of diagnostic tests for low-prevalence diseases. This example is based on PS matching and stratification on an important baseline covariate (e.g., disease stage) that may have a major impact on the sensitivity of the diagnostic.

Note that this example is only for demonstrating PS matching on low-prevalent disease, and when the resource may be very limited. For different scenarios, other PS-integrated approaches may be more appropriate.

data(ex_dta)
dta_ps <- psrwe_est(ex_dta,
                    v_covs = paste("V", 1:7, sep = ""),
                    v_grp = "Group",
                    cur_grp_level = "current",
                    ps_method = "logistic")
dta_ps

PS-integrated matching method

The propensity score (PS) estimation above is based on psrwe_est() may provide stratification. This matching example psrwe_match() below will match the RWD (real-world data) to the current study with $2:1$ ratio (ratio = 2) based on the covariate V1 (strata_covs = "V1"). The (categorical) covariate V1 will be used to create strata, then the data will be matched within each stratum based on PS values based on the nearest neighbor algorithm.

Please see Section of Demo example below for the other option using a different matching algorithm and package.

dta_ps_match <- psrwe_match(dta_ps,
                            ratio = 2,
                            strata_covs = "V1",
                            seed = 123)
dta_ps_match

The returned object dta_ps_match will be used to calculate discounting parameters for the study design. Note that the results are based on two stages indicated by V1 rather than five strata originally set by dta_ps above.

ps_bor_match <- psrwe_borrow(dta_ps_match,
                             total_borrow = 30)
ps_bor_match

PSCL and outcome analyses

The PSCL analysis (Wang, et al., 2020) can be done below with the same steps as other PSCL examples.

rst_cl <- psrwe_compl(ps_bor_match,
                      outcome_type = "binary",
                      v_outcome    = "Y_Bin")
rst_cl

The outcome analysis can be done in the same way. Note that typically the Wilson score method will be used for constructing confidence intervals.

oa_cl <- psrwe_outana(rst_cl, method_ci = "wilson", mu = 0.40)
oa_cl

Demo example

The script in "psrwe/demo/sec_4_3_ex.r" source file has the full example for the PS matching, which can be run via the demo("sec_4_3_ex", package = "psrwe").

Note that the R package optim may provide other matching algorithms. However, it may need additional license permission. Please check the package announcement to see if the package is turned on.

References

1. Chen, W.-C., Li, H., Wang, C., Lu, N., Song, C., Tiwari, R., Xu, Y., and Yue, L.Q. (2021). Evaluation of Diagnostic Tests for Low Prevalence Diseases: A Statistical Approach for Leveraging Real-World Data to Accelerate the Study. Journal of Biopharmaceutical Statistics, 31(3), 375-390.

2. Wang, C., Lu, N., Chen, W. C., Li, H., Tiwari, R., Xu, Y., and 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.

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psrwe documentation built on Aug. 21, 2026, 9:14 a.m.