Dynamic treatment regimens in small n, sequential, multiple assignment, randomized trials: An application in focal segmental glomerulosclerosis

Abstract

Focal segmental glomerulosclerosis (FSGS) is a rare kidney disease with an annual incidence of 0.2-1.8 cases per 100,000 individuals. Most rare diseases like FSGS lack effective treatments, and it is difficult to implement clinical trials to study rare diseases because of the small sample sizes and difficulty in recruitment. A novel clinical trial design, a small sample, sequential, multiple assignment, randomized trial (snSMART) has been proposed to efficiently identify effective treatments for rare diseases. In this work, we review and expand the snSMART design applied to studying treatments for FSGS. The snSMART is a multistage trial that randomizes participants to one of three active treatments in the first stage and then re-randomizes those who do not respond to the initial treatment to one of the other two treatments in the second stage. A Bayesian joint stage model efficiently shares information across the stages to find the best first stage treatment. In this setting, we modify the previously presented design and methods (Wei et al. 2018) such that the proposed design includes a standard of care as opposed to three active treatments. We present Bayesian and frequentist models to compare the two novel therapies to the standard of care. Additionally, we show for the first time how we should estimate and compare tailored sequences of treatments or dynamic treatment regimens (DTRs) and contrast the results from our methods to existing methods for analyzing DTRs from a SMART. We also propose a sample size calculation method for our snSMART design when implementing the frequentist model with Dunnett's correction.

Keywords: Clinical trial; Effect estimation; Small sample size.

Fig. 1.

A small n, sequential, multiple assignment, randomized trial (snSMART) design. Subjects are allocated to one of the three first stage treatment groups A, B, C at time 0. R represents equal randomization to the following treatments. Based on the response status at time t, patients either continue the initial treatment or are re-randomized to one of the other two treatments. Subgroups 1 through 9 denote the treatment paths that any one patient may follow. Second stage responses can be obtained at time 2t. The combination of two treatment paths, one for responders and another for non-responder sharing the same first stage treatment defines a DTR.

Fig. 2.

Left column: The absolute values of means of bias of DTR response rate estimates across Scenarios 1 to 4. Right column: The means of root mean squared error (rMSE) of DTR response rate estimates across Scenarios 1 to 4.

Fig. 3.

Power curve using JSRM with Dunnett’s approach. Two pair-wise comparisons (active treatment A vs. standard of care C and active treatment B vs. standard of care C) are performed for each run. Power is estimated by the proportion of runs in which one or both of the p values from the two pair-wise comparisons after Dunnett’s correction are smaller than the nominal α.