psrwe: Propensity Score-Integrated Approaches for Incorporating Real-World Evidence in Clinical Studies
Chenguang Wang
2020-09-01
## Loading required package: psrwe
## Loading required package: rstan
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## rstan (Version 2.21.2, GitRev: 2e1f913d3ca3)
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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 rwe_ps.
data(ex_dta)
dta_ps <- rwe_ps(ex_dta,
v_covs = paste("V", 1:7, sep = ""),
v_grp = "Group",
cur_grp_level = "current",
nstrata = 5)
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.
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PS-integrated power prior approach for single arm studies
For single arm studies when there is one external data source, the function rwe_ps_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_dist <- rwe_ps_dist(dta_ps)
post_smps <- rwe_ps_powerp(dta_ps,
total_borrow = 40,
v_distance = ps_dist$Dist[1:dta_ps$nstrata],
outcome_type = "binary",
v_outcome = "Y")
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## Warning: Examine the pairs() plot to diagnose sampling problems
The mixing of posterior samples should be checked to ensure the convergence of the posterior sampling.
traceplot(post_smps$stan_rst, pars = c("theta", "thetas"))
Results can be further summarized as:
## $overall_mean
## [1] 0.3092625
##
## $overall_variance
## [1] 0.0008288703
##
## $theta_by_stratum
## Strata Theta Variance
## 1 1 0.4057454 0.004544006
## 2 2 0.2594251 0.003561362
## 3 3 0.2063270 0.003229119
## 4 4 0.3506278 0.004614629
## 5 5 0.3241875 0.004484550
PS-integrated composite likelihood approach for single arm studies
For single arm studies when there is one external data source, the function rwe_ps_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.
ps_borrow <- rwe_ps_borrow(total_borrow = 40, ps_dist)
rst_cl <- rwe_ps_cl(dta_ps, v_borrow = ps_borrow, v_outcome = "Y")
summary(rst_cl)
## $overall_mean
## [1] 0.3009473
##
## $jackknife_variance
## [1] 0.0007589453
##
## $theta_by_stratum
## Strata N1 N0 Theta Variance
## 1 1 40 720 0.4010585 0.003486166
## 2 2 40 143 0.2503177 0.003297067
## 3 3 40 95 0.1924889 0.002852081
## 4 4 40 57 0.3440490 0.004661073
## 5 5 40 16 0.3168223 0.004414814
PS-integrated composite likelihood approach for randomized studies
For randomized studies when there is one external data source that contains control subjects, the function rwe_ps_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_2arm <- rwe_ps(ex_dta_rct,
v_covs = paste("V", 1:7, sep = ""),
v_grp = "Group",
cur_grp_level = "current",
nstrata = 5)
rst_2arm <- rwe_ps_cl2arm(dta_ps_2arm,
v_arm = "Arm",
trt_arm_level = 1,
outcome_type = "continuous",
v_outcome = "Y",
total_borrow = 40)
print(rst_2arm)
## $treatment
## $treatment$overall_mean
## [1] 368.4005
##
## $treatment$jackknife_variance
## [1] 19.15712
##
## $treatment$theta_by_stratum
## Strata N1 N0 Theta Variance
## 1 1 21 21 386.4337 101.98887
## 2 2 15 15 353.5991 50.85397
## 3 3 22 22 374.9533 102.84782
## 4 4 19 19 367.9795 69.01982
## 5 5 23 23 355.6684 105.97465
##
##
## $control
## $control$overall_mean
## [1] 358.1482
##
## $control$jackknife_variance
## [1] 10.25328
##
## $control$theta_by_stratum
## Strata N1 N0 Theta Variance
## 1 1 19 720 373.8150 41.71388
## 2 2 25 143 362.4157 32.31339
## 3 3 18 95 356.2292 99.33514
## 4 4 21 57 354.5110 37.34027
## 5 5 17 16 340.8876 47.54278
##
##
## $effect
## $effect$Estimate
## [1] 10.1551
##
## $effect$Variance
## [1] 6.897587
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.