Validity of MMRM-based hypothesis testing under missing-not-at-random mechanisms
Abstract
In randomized clinical trials with longitudinal continuous outcomes, missing-not-at-random (MNAR) missingness often motivates conservative alternatives to mixed models for repeated measures (MMRM). Such caution is important for estimation, but estimation and testing need not require identical assumptions. Moreover, overly conservative primary analyses may reduce power, increase required sample size, and raise trial costs. We investigated the validity of MMRM-based testing under the global null of identical longitudinal outcome distributions across groups. Because valid testing minimally requires treatment-effect estimators to converge to the null under the null hypothesis, we investigated sufficient conditions for this property. We introduced a proportional observation condition requiring ratios of observation probabilities relative to a reference group, conditional on the full outcome vector, to be outcome-independent, and showed that, with arbitrary post-baseline visits and monotone missingness, this condition is sufficient for convergence to the null value. The condition allows observation to depend on unobserved outcomes and permits between-group differences in overall observation probabilities through outcome-independent dropout, making it clinically interpretable while accommodating outcome-dependent MNAR missingness. Synthetic and data-based bootstrap simulations showed negligible bias and empirical test sizes near 0.05, including nonmonotone missingness. Thus, MNAR missingness does not by itself imply that a more conservative primary testing procedure is required. This result does not justify treatment-effect estimation under alternatives, which still requires estimand-based interpretation and sensitivity analyses.
Related
- Asymptotic Learning Theory for Conditional U–Statistics Based on Delta Sequences Under Missing at Random Mechanisms
- Missing Data Imputation under Manifold Hypothesis
- Robustness intervals for competing risks analysis with causes of failure missing not at random
- A Model of Multiple Hypothesis Testing
- Principal-Agent Hypothesis Testing
BibTeX
@misc{maruo2026validity,
title = {{Validity of MMRM-based hypothesis testing under missing-not-at-random mechanisms}},
author = {Kazushi Maruo and Ryota Ishii and Yusuke Yamaguchi and Keisuke Hanada and Naoki Isogawa and Masahiko Gosho},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.22686},
eprint = {2609.22686},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.22686},
}
RIS
TY - UNPB
TI - Validity of MMRM-based hypothesis testing under missing-not-at-random mechanisms
AU - Kazushi Maruo
AU - Ryota Ishii
AU - Yusuke Yamaguchi
AU - Keisuke Hanada
AU - Naoki Isogawa
AU - Masahiko Gosho
PY - 2026
DA - 2026/09/27
PB - arXiv
DO - 10.48550/arxiv.2609.22686
UR - https://arxiv.org/abs/2609.22686
AB - In randomized clinical trials with longitudinal continuous outcomes, missing-not-at-random (MNAR) missingness often motivates conservative alternatives to mixed models for repeated measures (MMRM). Such caution is important for estimation, but estimation and testing need not require identical assumptions. Moreover, overly conservative primary analyses may reduce power, increase required sample size, and raise trial costs. We investigated the validity of MMRM-based testing under the global null of identical longitudinal outcome distributions across groups. Because valid testing minimally requires treatment-effect estimators to converge to the null under the null hypothesis, we investigated sufficient conditions for this property. We introduced a proportional observation condition requiring ratios of observation probabilities relative to a reference group, conditional on the full outcome vector, to be outcome-independent, and showed that, with arbitrary post-baseline visits and monotone missingness, this condition is sufficient for convergence to the null value. The condition allows observation to depend on unobserved outcomes and permits between-group differences in overall observation probabilities through outcome-independent dropout, making it clinically interpretable while accommodating outcome-dependent MNAR missingness. Synthetic and data-based bootstrap simulations showed negligible bias and empirical test sizes near 0.05, including nonmonotone missingness. Thus, MNAR missingness does not by itself imply that a more conservative primary testing procedure is required. This result does not justify treatment-effect estimation under alternatives, which still requires estimand-based interpretation and sensitivity analyses.
ER -