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

2026-04-26

AI Summary

Sequential multiple assignment randomized trials (SMARTs) present unique challenges for causal inference due to complex missing data patterns arising from response-dependent re-randomization. This study systematically evaluates the extent to which existing statistical methods address SMART-specific missingness through a narrative review and predefined secondary data extraction, complemented by an analysis of reporting practices in 30 empirical studies. It provides the first comprehensive synthesis of missing data methodologies tailored to SMART designs, revealing that only one of seven methodological papers fully accounts for all relevant missing data types. Among empirical studies, the median attrition rate was 18.1%, and merely 14% pre-specified sensitivity analyses for missing data, highlighting a substantial gap between methodological advances and their implementation in practice.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Nonmonotonic Reasoning
• Machine Learning: Causal Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

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.

Problem

• missing data
• sequential multiple assignment randomized trials
• attrition
• missingness handling
• SMART

Innovation

• missing data
• sequential multiple assignment randomized trials
• missing at random
• attrition
• sensitivity analysis