Missing data imputation and sensitivity analysis in longitudinal randomized controlled trials of Alzheimer's disease_Chinese Journal of Evidence-Based Medicine

Authors：

LI Xingxing ¹ , LIU Junlin ¹ , XIAO Shifu ^2,3 , WANG Tao ⁴ , WANG Rui ¹ ,  HE Jia ¹

1. Faculty of Health Services, Naval Medical University, Shanghai 200433, P. R. China;
2. Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, P. R. China;
3. Alzheimer’s Disease and Related Disorders Center, Shanghai Jiaotong University, Shanghai 201108, P. R. China;
4. Department of Psychiatry & Neurology, Wuxi Branch of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Wuxi 214115, P. R. China;

Corresponding?author：

HE Jia, Email: hejia63@yeah.net

Keywords：

Alzheimer's disease; Randomized controlled trial; Missing data; Multiple imputation; Sensitivity analysis

DOI：

10.7507/1672-2531.202601144

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective Clinical trials for Alzheimer's disease feature long follow-up periods and high dropout rates, with missing outcome data being commonplace, which can impair the accuracy of treatment effect estimation. This study aimed to compare the applicability and performance of several commonly used and regulatory-recommended missing data handling strategies, including the mixed-effects model for repeated measures (MMRM), standard multiple imputation (MI), reference-based imputation (RBI), and δ-adjusted multiple imputation, in Alzheimer’s disease clinical trials. Methods The data were derived from a multicenter, randomized, double-blind, parallel-group, placebo-controlled clinical trial for Alzheimer’s disease. The endpoint was the Alzheimer's disease assessment scale-cognitive (ADAS-Cog) score, and the change from baseline in ADAS-Cog score at Week 26 was the primary outcome, and the difference between the treatment and placebo groups was estimated. The primary analysis used MMRM under the missing at random (MAR) assumption. Sensitivity analyses were performed using standard MI, reference-based imputation (J2R, CR, CIR), and δ-adjusted multiple imputation. Effect estimates, standard errors, confidence intervals, and P-values were compared across methods. Results Treatment effect estimates were consistent in direction across all methods. Compared with MMRM and MI under the MAR assumption, RBI yielded more conservative estimates under the missing not at random (MNAR) assumption. Under conservative δ settings the conclusions remained robust (all P-values <0.001), indicating that the findings were stable against deviations from MAR to MNAR. Conclusion In this clinical trial dataset, treatment effect inferences show good consistency and robustness across multiple missing data handling methods and missing data mechanisms. The analytical workflow proposed in this paper can serve as a reference for missing data handling and sensitivity analysis in clinical trials for neurodegenerative diseases.

Citation： LI Xingxing, LIU Junlin, XIAO Shifu, WANG Tao, WANG Rui, HE Jia. Missing data imputation and sensitivity analysis in longitudinal randomized controlled trials of Alzheimer's disease. Chinese Journal of Evidence-Based Medicine, 2026, 26(5): 539-546. doi: 10.7507/1672-2531.202601144 Copy

1.	Kueper JK, Speechley M, Montero-Odasso M. The Alzheimer's disease assessment scale-cognitive subscale (adas-cog): modifications and responsiveness in pre-dementia populations. a narrative review. J Alzheimers Dis, 2018, 63(2): 423-444.
2.	Chen J, Hunter S, Kisfalvi K, et al. A hybrid approach of handling missing data under different missing data mechanisms: VISIBLE 1 and VARSITY trials for ulcerative colitis. Contemp Clin Trials, 2021, 100: 106226.
3.	Khan NA, Torralba KD, Aslam F. Missing data in randomised controlled trials of rheumatoid arthritis drug therapy are substantial and handled inappropriately. RMD Open, 2021, 7(2): e001708.
4.	Jüni P, Altman DG, Egger M. Systematic reviews in health care: assessing the quality of controlled clinical trials. BMJ, 2001, 323(7303): 42-46.
5.	Rubin DB. Inference and missing data. Biometrika, 1976, 63(3): 581-592.
6.	Sassi-Sayadi M, Verweij P, Cornelisse P. Regulatory experiences with the use of multiple imputation for missing data in a phase 3 confirmatory trial. Ther Innov Regul Sci, 2026, 60(1): 8-14.
7.	Siddiqui O. MMRM versus MI in dealing with missing data--a comparison based on 25 NDA data sets. J Biopharm Stat, 2011, 21(3): 423-436.
8.	Liu H, Tan AYS, Mehrabi NF, et al. Astrocytic proteins involved in regulation of the extracellular environment are increased in the Alzheimer's disease middle temporal gyrus. Neurobiol Dis, 2025, 204: 106749.
9.	Li X, Yang Z, Yuan C, et al. Impute the missing data by combining retrieved dropouts and return to baseline method. PLoS One, 2025, 20(5): e0323496.
10.	Ionan AC, Paterniti M, Mehrotra DV, et al. Clinical and statistical perspectives on the ICH E9(R1) estimand framework implementation. Stat Biopharm Res, 2022, 15(3): 554-559.
11.	Ionan AC, Polverejan E, Gomatam S, et al. ICH E9(R1) addendum 5-year anniversary - where are we with its implementation and where to go next. Stat Biopharm Res, 2025.
12.	Mütze T, Bell J, Englert S, et al. Principles for defining estimands in clinical trials-a proposal. Pharm Stat, 2025, 24(1): e2432.
13.	Hu H, Brys M, Ruberg SJ, et al. Estimand endpoints for longitudinal measures of continuous disease progression with an Alzheimer's disease example. Ther Innov Regul Sci, 2025, 59(6): 1413-1420.
14.	Tan PT, Cro S, Van Vogt E, et al. A review of the use of controlled multiple imputation in randomised controlled trials with missing outcome data. BMC Med Res Methodol, 2021, 21(1): 72.
15.	Cro S, Morris TP, Kenward MG, et al. Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: a practical guide. Stat Med, 2020, 39(21): 2815-2842.
16.	Jin M, Fang Y. On reference-based imputation for analysis of incomplete repeated binary endpoints. J Biopharm Stat, 2022, 32(5): 692-704.
17.	Cro S, Quartagno M, White IR, et al. Reference-based multiple imputation for longitudinal binary data. Stat Med, 2025, 44(3-4): e10301.
18.	Zhang Y, Zimmer Z, Xu L, et al. Missing data imputation with baseline information in longitudinal clinical trials. Stat Biopharm Res, 2022, 14(2): 242-248.
19.	Tang Y. On the multiple imputation variance estimator for control-based and delta-adjusted pattern mixture models. Biometrics, 2017, 73(4): 1379-1387.
20.	Wang B, Du Y. Improving the mixed model for repeated measures to robustly increase precision in randomized trials. Int J Biostat, 2023, 20(2): 585-598.
21.	Wang S, Schwartz PF, Mancuso JP. Comprehensive implementations of multiple imputation using retrieved dropouts for continuous endpoints. BMC Med Res Methodol, 2025, 25(1): 47.
22.	Rubin DB, Service ET. Multiple imputations in sample surveys-a phenomenological Bayesian approach to nonresponse. 1978.
23.	Rawlings AM, Sang Y, Sharrett AR, et al. Multiple imputation of cognitive performance as a repeatedly measured outcome. Eur J Epidemiol, 2017, 32(1): 55-66.
24.	Cro S, Roger JH, Carpenter JR. Handling partially observed trial data after treatment withdrawal: introducing retrieved dropout reference-base centred multiple imputation. Pharm Stat, 2024, 23(6): 1095-1116.
25.	Yiu S. Sequential linear regression for conditional mean imputation of longitudinal continuous outcomes under reference-based assumptions. Computation Stat, 2024, 39(6): 3263-3285.
26.	Liu GF, Pang L. Control-based imputation and delta-adjustment stress test for missing data analysis in longitudinal clinical trials. Stat Biopharm Res, 2017, 9(2): 186-194.
27.	Tang Y. An efficient multiple imputation algorithm for control-based and delta-adjusted pattern mixture models using SAS. Stat Biopharm Res, 2017, 9(1): 116-125.
28.	Wang Y, Tu W, Kim Y, et al. Statistical methods for handling missing data to align with treatment policy strategy. Pharm Stat, 2023, 22(4): 650-670.
29.	Wang G, Liu L, Li Y, et al. Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease. Alzheimers Dement (N Y), 2022, 8(1): e12286.
30.	Liu S, Yang S, Zhang Y, et al. Multiply robust estimators in longitudinal studies with missing data under control-based imputation. Biometrics, 2024, 80(1): ujad036.
31.	Fang Y, Jin M. Sequential modeling for a class of reference-based imputation methods in clinical trials with quantitative or binary outcomes. Stat Med, 2022, 41(8): 1525-1540.
32.	European Medicines Agency. Guideline on missing data in confirmatory clinical trials. 2010.

1. Kueper JK, Speechley M, Montero-Odasso M. The Alzheimer's disease assessment scale-cognitive subscale (adas-cog): modifications and responsiveness in pre-dementia populations. a narrative review. J Alzheimers Dis, 2018, 63(2): 423-444.
2. Chen J, Hunter S, Kisfalvi K, et al. A hybrid approach of handling missing data under different missing data mechanisms: VISIBLE 1 and VARSITY trials for ulcerative colitis. Contemp Clin Trials, 2021, 100: 106226.
3. Khan NA, Torralba KD, Aslam F. Missing data in randomised controlled trials of rheumatoid arthritis drug therapy are substantial and handled inappropriately. RMD Open, 2021, 7(2): e001708.
4. Jüni P, Altman DG, Egger M. Systematic reviews in health care: assessing the quality of controlled clinical trials. BMJ, 2001, 323(7303): 42-46.
5. Rubin DB. Inference and missing data. Biometrika, 1976, 63(3): 581-592.
6. Sassi-Sayadi M, Verweij P, Cornelisse P. Regulatory experiences with the use of multiple imputation for missing data in a phase 3 confirmatory trial. Ther Innov Regul Sci, 2026, 60(1): 8-14.
7. Siddiqui O. MMRM versus MI in dealing with missing data--a comparison based on 25 NDA data sets. J Biopharm Stat, 2011, 21(3): 423-436.
8. Liu H, Tan AYS, Mehrabi NF, et al. Astrocytic proteins involved in regulation of the extracellular environment are increased in the Alzheimer's disease middle temporal gyrus. Neurobiol Dis, 2025, 204: 106749.
9. Li X, Yang Z, Yuan C, et al. Impute the missing data by combining retrieved dropouts and return to baseline method. PLoS One, 2025, 20(5): e0323496.
10. Ionan AC, Paterniti M, Mehrotra DV, et al. Clinical and statistical perspectives on the ICH E9(R1) estimand framework implementation. Stat Biopharm Res, 2022, 15(3): 554-559.
11. Ionan AC, Polverejan E, Gomatam S, et al. ICH E9(R1) addendum 5-year anniversary - where are we with its implementation and where to go next. Stat Biopharm Res, 2025.
12. Mütze T, Bell J, Englert S, et al. Principles for defining estimands in clinical trials-a proposal. Pharm Stat, 2025, 24(1): e2432.
13. Hu H, Brys M, Ruberg SJ, et al. Estimand endpoints for longitudinal measures of continuous disease progression with an Alzheimer's disease example. Ther Innov Regul Sci, 2025, 59(6): 1413-1420.
14. Tan PT, Cro S, Van Vogt E, et al. A review of the use of controlled multiple imputation in randomised controlled trials with missing outcome data. BMC Med Res Methodol, 2021, 21(1): 72.
15. Cro S, Morris TP, Kenward MG, et al. Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: a practical guide. Stat Med, 2020, 39(21): 2815-2842.
16. Jin M, Fang Y. On reference-based imputation for analysis of incomplete repeated binary endpoints. J Biopharm Stat, 2022, 32(5): 692-704.
17. Cro S, Quartagno M, White IR, et al. Reference-based multiple imputation for longitudinal binary data. Stat Med, 2025, 44(3-4): e10301.
18. Zhang Y, Zimmer Z, Xu L, et al. Missing data imputation with baseline information in longitudinal clinical trials. Stat Biopharm Res, 2022, 14(2): 242-248.
19. Tang Y. On the multiple imputation variance estimator for control-based and delta-adjusted pattern mixture models. Biometrics, 2017, 73(4): 1379-1387.
20. Wang B, Du Y. Improving the mixed model for repeated measures to robustly increase precision in randomized trials. Int J Biostat, 2023, 20(2): 585-598.
21. Wang S, Schwartz PF, Mancuso JP. Comprehensive implementations of multiple imputation using retrieved dropouts for continuous endpoints. BMC Med Res Methodol, 2025, 25(1): 47.
22. Rubin DB, Service ET. Multiple imputations in sample surveys-a phenomenological Bayesian approach to nonresponse. 1978.
23. Rawlings AM, Sang Y, Sharrett AR, et al. Multiple imputation of cognitive performance as a repeatedly measured outcome. Eur J Epidemiol, 2017, 32(1): 55-66.
24. Cro S, Roger JH, Carpenter JR. Handling partially observed trial data after treatment withdrawal: introducing retrieved dropout reference-base centred multiple imputation. Pharm Stat, 2024, 23(6): 1095-1116.
25. Yiu S. Sequential linear regression for conditional mean imputation of longitudinal continuous outcomes under reference-based assumptions. Computation Stat, 2024, 39(6): 3263-3285.
26. Liu GF, Pang L. Control-based imputation and delta-adjustment stress test for missing data analysis in longitudinal clinical trials. Stat Biopharm Res, 2017, 9(2): 186-194.
27. Tang Y. An efficient multiple imputation algorithm for control-based and delta-adjusted pattern mixture models using SAS. Stat Biopharm Res, 2017, 9(1): 116-125.
28. Wang Y, Tu W, Kim Y, et al. Statistical methods for handling missing data to align with treatment policy strategy. Pharm Stat, 2023, 22(4): 650-670.
29. Wang G, Liu L, Li Y, et al. Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease. Alzheimers Dement (N Y), 2022, 8(1): e12286.
30. Liu S, Yang S, Zhang Y, et al. Multiply robust estimators in longitudinal studies with missing data under control-based imputation. Biometrics, 2024, 80(1): ujad036.
31. Fang Y, Jin M. Sequential modeling for a class of reference-based imputation methods in clinical trials with quantitative or binary outcomes. Stat Med, 2022, 41(8): 1525-1540.
32. European Medicines Agency. Guideline on missing data in confirmatory clinical trials. 2010.

Chinese Journal of Evidence-Based Medicine

Missing data imputation and sensitivity analysis in longitudinal randomized controlled trials of Alzheimer's disease

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content