The Effect of Missingness on the Analysis of Alzheimer’s Disease Clinical Trials of Disease Modifying Agents

Patients often withdraw from clinical trials for various reasons, including adverse health effects, lack of drug efficacy, and death. The resulting missing data poses a problem for analysis of the data. The goal of this project was to understand the effect of various types of missing data on the analysis of a clinical trial for a degenerative disease such as Alzheimer’s Disease. Data were simulated based on the expected longitudinal progression of a clinical trial for an Alzheimer’s Disease Disease Modifying (ADDM) drug, with varying degrees of variation and missingness. Missingness was simulated as Missing Completely at Random (MCAR), Missing at Random (MAR) and Missing Not at Random (MNAR). Analysis of these simulated data showed that complete cases analysis and multiple imputation (MI) are more appropriate for this type of clinical study than the standard Last Observation Carried Forward (LOCF) approach, yielding higher power and lower bias. We present recommendations for use of specific methods for various combinations of the standard deviation, degree of missingness, type of missingness, and other parameters.