A Review of Methods and Practices for Missing Data in Sequential Multiple Assignment Randomized Trials (SMARTs): An Ancillary Study of a Scoping Review

TL;DR

This review examines current statistical methods for handling missing data in SMARTs, highlighting limited methodology specific to SMARTs and gaps in practice.

Contribution

It provides a comprehensive review of existing methods and reports on how missing data is addressed in published SMART studies.

Findings

Most methods assume missing at random (MAR)

Only one paper addresses all SMART-specific missingness types

Median attrition in SMARTs was 18.1%

Abstract

Background: Missing data poses an acute threat to sequential multiple assignment randomized trial (SMART) analyses because of the sequential treatment structure and response-dependent re-randomization. Objectives: This study aimed to (1) review the current statistical methods for handling missing data in SMARTs, and (2) characterize how missing data is reported and handled in published SMARTs. Methods: We conducted a narrative review of statistical methods developed for missing data in SMARTs. Additionally, we conducted a pre-specified secondary extraction of a previously published scoping review of SMARTs focused on missing data. Extraction captured attrition rates, methods for handling missingness, and planned versus performed missing data analyses. Results: Seven methodological papers were identified; nearly all assume missing at random (MAR), and only one addresses the full set of SMART-specific missingness types. Across 30 published SMARTs, median overall attrition was 18.1% (range 0.6%-56.5%). Methods used to address missing data were described in 80% of the manuscripts; mixed-model methods were most common (30%). Among 14 studies with paired protocols, sensitivity analyses were pre-specified in 2 (14%). Conclusions: SMART-specific methodology for missing data is limited, and a substantial gap exists between available methodology and current SMART practice.