From 8d761a123e8ba9cdd0991e0549124e6e6af5aeb6 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Thu, 12 Sep 2024 09:01:14 +0100 Subject: [PATCH] Creating 10.21105.joss.06971.jats --- .../paper.jats/10.21105.joss.06971.jats | 561 ++++++++++++++++++ 1 file changed, 561 insertions(+) create mode 100644 joss.06971/paper.jats/10.21105.joss.06971.jats diff --git a/joss.06971/paper.jats/10.21105.joss.06971.jats b/joss.06971/paper.jats/10.21105.joss.06971.jats new file mode 100644 index 0000000000..f594fc699e --- /dev/null +++ b/joss.06971/paper.jats/10.21105.joss.06971.jats @@ -0,0 +1,561 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6971 +10.21105/joss.06971 + +snSMART: An R Package for Small Sample, Sequential, +Multiple Assignment, Randomized Trial Data Analysis + + + +https://orcid.org/0000-0003-4838-0842 + +Wang +Sidi + + + + +https://orcid.org/0000-0002-7089-3591 + +Fang +Fang + + + + + +Tamura +Roy + + + + +https://orcid.org/0000-0002-7113-6998 + +Braun +Thomas + + + + +https://orcid.org/0000-0002-1717-4483 + +Kidwell +Kelley M + + + + + +University of Michigan + + + + +University of South Florida + + + + +22 +5 +2024 + +9 +101 +6971 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +rare disease +clinical trial +snSMART +Bayesian + + + + + + Summary +

Small sample, sequential, multiple assignment, randomized trials + (snSMARTs) are multistage trials designed to estimate first stage + treatment effects. By using data from all stages, snSMARTs provide + more precise estimates of these effects. Additionally, the design may + enhance participant recruitment and retention compared to standard + rare disease trials. To support the application of snSMART statistical + methods, we introduce the R package + snSMART.

+
+ + Statement of need +

The design and methods of snSMARTs are applicable to any disorder + or disease that affects a small group and remains stable over the + trial duration. Recent advances include methods for snSMARTs with + three active treatments + (Chao, + Trachtman, et al., 2020; + Wei + et al., 2018, + 2020), + group sequential designs + (Chao, + Braun, et al., 2020), placebo with two dose levels + (Fang + et al., 2021), and continuous outcomes + (Hartman + et al., 2021). Despite these developments, there is a lack of + software to implement these methods. The + snSMART R package addresses this need by + providing sample size calculations and trial data analysis using both + Bayesian and frequentist approaches. To our knowledge, no other R + packages offer similar functionalities.

+
+ + Functionality of the snSMART package +

We have summarized the functionality of all the + snSMART functions included in this package in + Table 1. The BJSM_binary, + BJSM_c, and group_seq + functions implement the Bayesian Joint Stage Modeling (BJSM) methods + to estimate treatment effects across all treatment arms in a snSMART + design with binary outcomes, continuous outcomes, and in a group + sequential trial design, respectively. The + LPJSM_binary function serves as the frequentist + equivalent to BJSM_binary and can be used for + sensitivity analysis. The sample_size function + performs Bayesian sample size calculations for a snSMART design with + binary outcomes, ensuring that the trial is scientifically valid, + ethically responsible, and resource-efficient.

+
+ + snSMART comparing two dose levels with placebo + + +

Summary of the functionality of the snSMART package.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
FunctionDescription
BJSM functions
BJSM_binaryBJSM binary (3AT or P2D snSMART)
BJSM_cBJSM (3AT snSMART with a mapping function and continuous + outcome)
group_seqBJSM (interim analysis and final analysis of group + sequential 3AT snSMART)
Frequentist functions
LPJSM_binaryLPJSM (3AT or P2D snSMART)
Sample size calculation
sample_size3AT snSMART sample size calculation
S3 summary and print methods
for class ’BJSM_binary’Summarize and print ’BJSM_binary’ object
for class ’BJSM_binary_dose’Summarize and print ’BJSM_binary_dose’ object
for class ’BJSM_c’Summarize and print ’BJSM_c’ object
for class ’group_seq’Summarize and print ’group_seq’ object
for class ’LPJSM_binary’Summarize and print ’LPJSM_binary’ object
for class ’sim_group_seq’Summarize and print ’sim_group_seq’ object
+
+

This section details one of the snSMART designs, which investigates + the response rate of an experimental treatment at low and high doses + compared to placebo + (Fang + et al., 2021). In this design (Figure + [fig:snSMART-dose]), + participants are equally assigned to receive either placebo, low dose, + or high dose in the first stage. They continue their initial treatment + for a pre-specified duration until their responses are measured at the + end of stage 1. In the second stage, all participants who initially + received placebo or low dose are re-randomized to either low or high + dose, regardless of their first stage response. Participants who + responded to the high dose are re-randomized between low and high + doses, while non-responders to the high dose continue with the high + dose in the second stage. The main goal of this snSMART is to estimate + and compare first stage response rates for low and high doses to + placebo by modeling the pooled data from both stages.

+ +

Study design of an snSMART with two dose levels and a + placebo. In stage 1, participants are randomized (R) to treatment P + (placebo), L (low dose), or H (high dose) with equal probability. At + time t, response to stage 1 treatment is assessed. Non-responders to + high dose stay on the same treatment in stage 2, while all the other + participants are equally re-randomized to either low or high dose in + stage 2. Interest is in the first stage response rate of placebo, + low and high + doses.

+ +
+

Fang et al. + (2021) + adapted the Bayesian joint stage model (BJSM) from Wei et al. + (2018) + in Equations 1 and + 2. For this design, + + + m=P,L,H + and + + m=L,H, + where + + P + represents placebo, + + L + low dose, and + + H + high dose. The prior distribution for the response rate of placebo, + + + πP, + may be informed by natural history studies or previous trials and + specified as + + πPBeta(ζn,ηn). + It is assumed that the drug doses have a weak tendency for higher + response rates than placebo, modeled as + + log(πL/πP)N(μ,σ2) + and + + log(πH/πP)N(μ,σ2). + The prior distributions for the linkage parameters may vary, specified + as + + β0m,β1mGamma(ω,ψ).

+

The BJSM is specified as follows:

+

+ + Yi1m|πmBernoulli(πm) + + + Yi2m|Yi1m,πm,β1m,β0mBernoulli((β1mπm)Yi1m(β0mπm)1Yi1m) + for + + i=1,...,N; + and + + j=1,2; + where + + Yijm + is the outcome for participant + + i + at stage + + j + for treatment + + m + and takes the value 1 for response to treatment and 0 for no response; + + + N + is the total sample size; + + β0m + and + + β1m + are the linkage parameters for non-responders and responders, + respectively; + + πm + is the first stage response rate for treatment + + + m; + + + β1mπm + is the second stage response rate for first stage responders; and + + + β0mπm + is the second stage response rate for non-responders to treatment + + + m + in the first stage who receive treatment + + + m + in the second stage.

+

To conduct the analysis in R, we can use the + BJSM_binary function. Users specify priors, + MCMC details, and BJSM model type (six beta or two beta). Here, we + assume the prior distribution of + + πP + as + + Beta(3,17), + + + βjm + as + + Gamma(2,2), + and the treatment effect ratio as + + Normal(0.2,100). + Label placebo as 1, low dose as 2, and high dose as 3 in the dataset. + The output is a BJSM_dose_binary object with + posterior samples and estimates of linkage parameters, treatment + response rates, and pairwise response rate differences.

+ BJSM_dose_result <- BJSM_binary( + data = data_dose, prior_dist = c("beta", "gamma"), + pi_prior = c(3, 17), normal.par = c(0.2, 100), beta_prior = c(2, 2), + n_MCMC_chain = 2, n.adapt = 1000, BURN.IN = 10000, + MCMC_SAMPLE = 60000, ci = 0.95 +) +summary(BJSM_dose_result) + Treatment Effects Estimate: + Estimate Std. Error C.I. CI low CI high +trtP 0.08606853 0.04004852 0.95 0.01694565 0.1618828 +trtL 0.39969511 0.06130935 0.95 0.28185110 0.5202667 +trtH 0.73414788 0.07501235 0.95 0.58710144 0.8763916 + +Differences between Treatments: + Estimate Std.Error C.I. CI low CI high +diffPL -0.3136266 0.07345504 0.95 -0.4577648 -0.1696336 +diffLH -0.3344528 0.07967433 0.95 -0.4895912 -0.1785552 +diffPH -0.6480794 0.08559511 0.95 -0.8071207 -0.4772492 + +Linkage Parameter Estimate: + Estimate Std. Error C.I. CI low CI high +beta[1,1] 0.9763364 0.1640819 0.95 0.65222142 1.2973089 +beta[2,1] 0.8560191 0.3257939 0.95 0.23204941 1.4280772 +beta[1,2] 1.0749284 0.1869756 0.95 0.70435649 1.4426901 +beta[2,2] 0.9872268 0.2503916 0.95 0.48669193 1.4458416 +beta[1,3] 0.3824723 0.1899827 0.95 0.05813823 0.7529239 +beta[2,3] 1.0703154 0.1657493 0.95 0.74952233 1.4055420 +

The response rates for placebo, low dose and high dose are + estimated to be trtP 0.09 (95% credible + interval (CI): 0.02 - 0.16), trtL 0.40 (95% CI: + 0.28 - 0.52), and trtH 0.73 (95% CI: 0.59 - + 0.88) respectively. Other estimated outcomes are also clearly + presented in the R output above.

+
+ + Discussion +

We introduced and demonstrated the snSMART + package for analyzing snSMART data using Bayesian methods. BJSM is + often more efficient, but frequentist methods are recommended for + sensitivity analysis. The package will be updated with new designs and + methods to aid in finding effective treatments for rare diseases.

+
+ + Acknowledgments +

This work was supported by a Patient-Centered Outcomes Research + Institute (PCORI) award (ME-1507-31108). We thank Boxian Wei, + Yan-Cheng Chao, and Holly Hartman for contributing their original R + code to the creation of this package. We also thank Mike Kleinsasser + for assisting with the publication of the R package on CRAN.

+
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