Abstract: Objective This study aimed to evaluate the application effectiveness of various statistical methods in Alzheimer's disease clinical trials based on the complementary sensitivity analysis framework recommended by the European Medicines Agency (EMA), providing a statistical practice reference meeting international regulatory standards for similar studies. Methods Based on longitudinal data from a phase III clinical trial for the treatment of Alzheimer's disease, the change from baseline in Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) score from baseline at week 26 (with a decrease indicating cognitive improvement) was used as the primary outcome measure. This study employed a mixed-effects model with repeated measures (MMRM) under the assumption of missing at random (MAR) as the primary analyses, while using critical point analyses, rank-based analyses, and slope-based analyses for sensitivity verification. A systematic comparison of the efficacy estimation results from the four analytical methods was conducted to explore the consistency and robustness differences in research conclusions under different analytical strategies. Results The primary analysis results from MMRM showed that the improvement in ADAS-Cog score from baseline at week 26 in the treatment group was superior to that in the placebo group LSMD = -3.22, 95%CI: -4.55--1.88, P < 0.001. Both rank-based analyses and slope-based analyses indicated significant differences between groups (both P < 0.001). Critical point analyses revealed that the treatment effect was negative under most reasonable missing hypothesis scenarios, with the effect value only potentiallyturning positive in extreme non-random missing scenarios and no overall reversal of efficacy conclusions. Conclusion The combination of MMRM, critical point analysis, rank-based analysis, and slope-based analysis can effectively address the challenge of missing data in clinical trials for progressive diseases. This strategy fully complies with international regulatory standards, verifies efficacy conclusions from multiple perspectives, and enhances the credibility and scientific validity of clinical trial results. It can provide a feasible reference for statistical analysis of missing data in similar clinical trials for progressive diseases.