arXiv subjects

Kelley M. Kidwell

Publications and source records attributed to Kelley M. Kidwell.

3 recordsLinked to original sources

arXiv2025

Statistical Design and Rationale of the Biomarkers for Evaluating Spine Treatments (BEST) Trial

Chronic low back pain (cLBP) is a prevalent condition with profound impacts on functioning and quality of life. While multiple evidence-based treatments exist, they all have modest average treatment effects$\unicode{x2013}$potentially due to individual variation in treatment response and the diverse etiologies of cLBP. This multi-site sequential, multiple-assignment randomized trial (SMART) investigated four treatment modalities with two stages of randomization and aimed to enroll 630 protocol completers. The primary objective was to develop a precision medicine approach by estimating optimal treatment or treatment combinations based on patient characteristics and initial treatment response. The analysis strategy focuses on estimating interpretable dynamic treatment regimes and identifying subgroups most responsive to specific interventions. Broad eligibility criteria were implemented to enhance generalizability and recruitment, most notably that participants could be eligible to enroll even if they could not be assigned to one (but no more) of the study interventions. Enrolling participants with restrictions on the treatment they could be assigned necessitated modifications to standard minimization methods for balancing covariates. The BEST trial represents one of the largest SMARTs focused on clinical decision-making to date and the largest in cLBP. By collecting an extensive array of biomarker and phenotypic measures, this trial may identify potential treatment mechanisms and establish a more evidence-based approach to individualizing cLBP treatment in clinical practice.

stat.AP↗

arXiv2020

Power prior models for treatment effect estimation in a small n, sequential, multiple assignment, randomized trial

A small n, sequential, multiple assignment, randomized trial (snSMART) is a small sample, two-stage design where participants receive up to two treatments sequentially, but the second treatment depends on response to the first treatment. The treatment effect of interest in an snSMART is the first-stage response rate, but outcomes from both stages can be used to obtain more information from a small sample. A novel way to incorporate the outcomes from both stages applies power prior models, in which first stage outcomes from an snSMART are regarded as the primary data and second stage outcomes are regarded as supplemental. We apply existing power prior models to snSMART data, and we also develop new extensions of power prior models. All methods are compared to each other and to the Bayesian joint stage model (BJSM) via simulation studies. By comparing the biases and the efficiency of the response rate estimates among all proposed power prior methods, we suggest application of Fisher's exact test or the Bhattacharyya's overlap measure to an snSMART to estimate the treatment effect in an snSMART, which both have performance mostly as good or better than the BJSM. We describe the situations where each of these suggested approaches is preferred.

stat.ME↗

arXiv2020

Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen (DTR) is a sequence of pre-specified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that are tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial (SMART) is a research tool which allows for the construction of effective DTRs. We derive easy-to-use formulae for computing the total sample size for three common two-stage SMART designs in which the primary aim is to compare mean end-of-study outcomes for two embedded DTRs which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a SMART can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a SMART. The SMART design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for DTR effect analyses using a longitudinal outcome from a SMART, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a SMART aimed at developing a DTR for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.

stat.ME↗