Introduction

In patient-centered outcomes research, it is vital to assess the heterogeneity of treatment effects (HTE) when making health care decisions for an individual patient or a group of patients. Nevertheless, it remains challenging to evaluate HTE based on information collected from clinical studies that are often designed and conducted to evaluate the efficacy of a treatment for the overall population. The Bayesian framework offers a principled and flexible approach to estimate and compare treatment effects across subgroups of patients defined by their characteristics.

R package beanz provides functions to facilitate the conduct of Bayesian analysis of HTE and a web-based graphical user interface for users to conduct such Bayesian analysis in an interactive and user-friendly manner.

Data accepted by beanz

There are two types of data structures that beanz recognizes:

• Summary treatment effect data: Each row should represent a subgroup with covariates that define the subgroup, estimated treatment effect in the subgroup and variance for the estimation.

• Patient level raw data: Each row should represent a patient with covariates that define the subgroup in which the patient belongs to, treatment indicator and outcome. The outcome can be binary, continuous, or time to event.

The beanz package provides dataset solvd.sub from the SOLVD trial as an example Patient level raw data dataset.

Estimate subgroup effect

If Patient level raw data is provided, the package provides function bzGetSubgrpRaw for estimating subgroup effect for each subgroup. The return value from bzGetSubgrpRaw is a data frame with the format of Summary treatment effect data.

The example is as follows:

var.cov    <- c("lvef", "sodium", "any.vasodilator.use");
var.resp   <- "y";
var.trt    <- "trt";
var.censor <- "censor";
resptype   <- "survival";

subgrp.effect <- bzGetSubgrpRaw(solvd.sub,
                                  var.resp   = var.resp,
                                  var.trt    = var.trt,
                                  var.cov    = var.cov,
                                  var.censor = var.censor,
                                  resptype   = resptype);
print(subgrp.effect);

##   Subgroup lvef sodium any.vasodilator.use    Estimate   Variance   N
## 1        1    0      0                   0 -0.37783038 0.01212786 562
## 2        2    0      0                   1 -0.34655336 0.01004499 695
## 3        3    0      1                   0 -0.79235451 0.03939983 237
## 4        4    0      1                   1 -0.39334304 0.02969421 250
## 5        5    1      0                   0  0.06776454 0.04629163 223
## 6        6    1      0                   1 -0.23655764 0.02400353 341
## 7        7    1      1                   0  0.15435495 0.10365396 104
## 8        8    1      1                   1  0.05947290 0.07761840 123

Bayesian HTE models

The function bzCallStan calls rstan::sampling to draw samples for different Bayesian models. The following models are available in the current version of beanz:

• nse: No subgroup effect model
• fs: Full stratification model
• sr: Simple regression model
• bs: Basic shrinkage model
• srs: Simple regression with shrinkage model
• ds: Dixon-Simon model
• eds: Extended Dixon-Simon model.

The following examples show how No subgroup effect model (nse), Simple regression model* (sr) and Basic shrinkage model (bs) are called:

var.estvar <- c("Estimate", "Variance");

rst.nse <- bzCallStan("nse", dat.sub=subgrp.effect,
                     var.estvar = var.estvar, var.cov = var.cov,
                     par.pri = c(B=1000),
                     chains=4, iter=4000,
                     warmup=2000, seed=1000, cores=1);

## 
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## Warning: Relative effective sample sizes ('r_eff' argument) not specified.
## For models fit with MCMC, the reported PSIS effective sample sizes and 
## MCSE estimates will be over-optimistic.

rst.sr  <- bzCallStan("sr", dat.sub=subgrp.effect,
                     var.estvar = var.estvar, var.cov = var.cov,
                     par.pri = c(B=1000, C=1000),
                     chains=4, iter=4000,
                     warmup=2000,  seed=1000, cores=1);

## 
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## Warning: Relative effective sample sizes ('r_eff' argument) not specified.
## For models fit with MCMC, the reported PSIS effective sample sizes and 
## MCSE estimates will be over-optimistic.

## Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.

rst.bs  <- bzCallStan("bs", dat.sub=subgrp.effect,
                     var.estvar = var.estvar, var.cov = var.cov,
                     par.pri = c(B=1000, D=1),
                     chains=4, iter=4000, warmup=2000,  seed=1000, cores=1);

## 
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## Warning: There were 8 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.

## Warning: Examine the pairs() plot to diagnose sampling problems

## Warning: Relative effective sample sizes ('r_eff' argument) not specified.
## For models fit with MCMC, the reported PSIS effective sample sizes and 
## MCSE estimates will be over-optimistic.

## Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.

Results presentation

sel.grps <- c(1,4,5);
tbl.sub <- bzSummary(rst.sr, ref.stan.rst=rst.nse, ref.sel.grps=1);
print(tbl.sub);

##                Subgroup   Mean    SD   Q025    Q25 Median    Q75   Q975 ProbLT0
## 1            Subgroup 1 -0.401 0.094 -0.589 -0.463 -0.401 -0.339 -0.219       1
## 2            Subgroup 2  -0.38 0.088 -0.549 -0.439  -0.38 -0.321 -0.205       1
## 3            Subgroup 3 -0.489 0.134 -0.757 -0.579 -0.488 -0.399 -0.223       1
## 4            Subgroup 4 -0.467 0.127 -0.716 -0.553 -0.467 -0.382 -0.222       1
## 5            Subgroup 5 -0.063 0.134  -0.32 -0.154 -0.062  0.029  0.197    0.68
## 6            Subgroup 6 -0.041 0.121 -0.277 -0.122 -0.039   0.04  0.192   0.632
## 7            Subgroup 7  -0.15 0.161 -0.465 -0.259 -0.152 -0.042  0.163   0.828
## 8            Subgroup 8 -0.129 0.147 -0.415 -0.227 -0.131 -0.032  0.167   0.813
## 9 No subgroup effect(1) -0.322 0.055 -0.432 -0.359 -0.321 -0.285 -0.215       1

bzPlot(rst.sr, sel.grps = sel.grps, ref.stan.rst=rst.nse, ref.sel.grps=1);

bzForest(rst.sr, sel.grps = sel.grps, ref.stan.rst=rst.nse, ref.sel.grps=1);

tbl.sub <- bzSummary(rst.bs, ref.stan.rst=rst.nse, ref.sel.grps=1);
print(tbl.sub);

##                Subgroup   Mean    SD   Q025    Q25 Median    Q75   Q975 ProbLT0
## 1            Subgroup 1 -0.348 0.094 -0.544 -0.409 -0.345 -0.284 -0.171       1
## 2            Subgroup 2 -0.333 0.087 -0.509 -0.389 -0.332 -0.277 -0.165       1
## 3            Subgroup 3 -0.514 0.187 -0.929 -0.639  -0.49 -0.368 -0.225       1
## 4            Subgroup 4 -0.346 0.129 -0.621 -0.421 -0.341 -0.264 -0.094   0.996
## 5            Subgroup 5  -0.15 0.181 -0.427 -0.287 -0.177 -0.036  0.251   0.795
## 6            Subgroup 6 -0.266 0.122 -0.489 -0.348 -0.275  -0.19 -0.004   0.977
## 7            Subgroup 7 -0.172 0.212   -0.5 -0.318 -0.212 -0.056  0.328    0.81
## 8            Subgroup 8 -0.182 0.192 -0.483 -0.318 -0.215  -0.07  0.265   0.832
## 9 No subgroup effect(1) -0.322 0.055 -0.432 -0.359 -0.321 -0.285 -0.215       1

bzPlot(rst.bs, sel.grps = sel.grps, ref.stan.rst=rst.nse, ref.sel.grps=1);

bzForest(rst.bs, sel.grps = sel.grps, ref.stan.rst=rst.nse, ref.sel.grps=1);

tbl.sub <- bzSummaryComp(rst.sr, sel.grps=sel.grps);
print(tbl.sub);

##     Comparison   Mean    SD   Q025    Q25 Median   Q75  Q975 ProbLT0
## 1 Subgroup 4-1 -0.067 0.159 -0.374 -0.173 -0.067  0.04 0.249   0.668
## 2 Subgroup 5-1  0.337 0.166  0.014  0.224  0.337 0.449 0.665    0.02
## 3 Subgroup 5-4  0.403 0.184  0.053   0.28  0.403 0.526 0.768   0.011

bzPlot(rst.sr, sel.grps = sel.grps);

bzForest(rst.sr, sel.grps = sel.grps);

tbl.sub <- bzSummaryComp(rst.bs, sel.grps=sel.grps);
print(tbl.sub);

##     Comparison  Mean    SD   Q025    Q25 Median   Q75  Q975 ProbLT0
## 1 Subgroup 4-1     0 0.162 -0.327 -0.105  0.002 0.105 0.317   0.496
## 2 Subgroup 5-1 0.201 0.205 -0.148  0.049   0.18 0.334 0.642   0.163
## 3 Subgroup 5-4 0.197 0.224 -0.195  0.037  0.182 0.347 0.672   0.192

bzPlotComp(rst.bs, sel.grps = sel.grps);

bzForestComp(rst.bs, sel.grps = sel.grps);

lst.rst     <- list(nse=rst.nse, sr=rst.sr, bs=rst.bs);
tbl.summary <- bzRptTbl(lst.rst, dat.sub = subgrp.effect, var.cov = var.cov);

## Warning: Accessing looic using '$' is deprecated and will be removed in a
## future release. Please extract the looic estimate from the 'estimates'
## component instead.

## Warning: Accessing looic using '$' is deprecated and will be removed in a
## future release. Please extract the looic estimate from the 'estimates'
## component instead.

## Warning: Accessing looic using '$' is deprecated and will be removed in a
## future release. Please extract the looic estimate from the 'estimates'
## component instead.

print(tbl.summary);

##                         Model Subgroup lvef sodium any.vasodilator.use   Mean
## Subgroup 1 No subgroup effect        1    0      0                   0 -0.322
## Subgroup 2 No subgroup effect        2    0      0                   1 -0.322
## Subgroup 3 No subgroup effect        3    0      1                   0 -0.322
## Subgroup 4 No subgroup effect        4    0      1                   1 -0.322
## Subgroup 5 No subgroup effect        5    1      0                   0 -0.322
## Subgroup 6 No subgroup effect        6    1      0                   1 -0.322
## Subgroup 7 No subgroup effect        7    1      1                   0 -0.322
## Subgroup 8 No subgroup effect        8    1      1                   1 -0.322
##               SD Prob < 0
## Subgroup 1 0.055        1
## Subgroup 2 0.055        1
## Subgroup 3 0.055        1
## Subgroup 4 0.055        1
## Subgroup 5 0.055        1
## Subgroup 6 0.055        1
## Subgroup 7 0.055        1
## Subgroup 8 0.055        1

Predictive distribution

Function bzPredSubgrp generates the predictive distribution of the subgrooup treatment effects.

pred.dist <- bzPredSubgrp(rst.sr,
                                  dat.sub=subgrp.effect,
                                  var.estvar = var.estvar);
head(pred.dist);

##            [,1]       [,2]       [,3]        [,4]        [,5]        [,6]
## [1,] -0.5679901 -0.4997621 -0.4687660 -0.76574757  0.26725847  0.01626289
## [2,] -0.3420504 -0.2521794 -0.7345765 -0.47152359  0.36613344 -0.07645110
## [3,] -0.4338145 -0.2525592 -0.7803938 -0.54795933 -0.01929658 -0.03668858
## [4,] -0.4814998 -0.5537714 -0.6324113 -0.39108840 -0.15506442 -0.12222381
## [5,] -0.4676442 -0.4078786 -0.6636981 -0.51484721 -0.30893251 -0.54646626
## [6,] -0.3273695 -0.2048737 -0.5903188 -0.09467884 -0.34219340 -0.12112388
##             [,7]         [,8]
## [1,]  0.12626214  0.002695502
## [2,]  0.46222121  0.081932323
## [3,] -0.74327267 -0.875854377
## [4,]  0.03560672  0.246599939
## [5,] -0.79005015 -0.039686095
## [6,] -0.12416157 -0.076585617

Graphical User Interface

With package shiny installed, beaz provides a web-based graphical user interface (GUI) for conducting the HTE analysis in an user-friendly interactive manner. The GUI can be started by

bzShiny();

Toolbox

Package beanz provides function bzGailSimon that implements the Gail-Simon test for qualitative interactions:

gs.pval <- bzGailSimon(subgrp.effect$Estimate,
                       sqrt(subgrp.effect$Variance));
print(gs.pval);

## [1] 0.9191656

The result show that there is no significant qualitative interactions according to the Gail-Simon test.