An Alternative Way to Classify Missing Data Mechanism in Clinical Trials—A Dialogue on Missing Data

Abstract

Statisticians in pharmaceutical field are constantly challenged by missing data caused by patient dropout in clinical trials. What the targeted population parameter for statistical inference should be when missing data are present has been a much-debated point. Many missing data methods aim at the so-called hypothetical parameter, i.e., treatment effect of a drug assuming no patients dropout from a clinical trial for the drug. Other methods intend to combine all dropout information into the treatment effect estimate. We believe that patient dropouts should not be treated equally when determining the population parameter of treatment effect. The objective of clinical trials, after all, is to evaluate a drug's effect on patients. Dropouts due to drug-related reasons such as drug-induced adverse experience are part of the drug's attributes, while dropout due to non-drug-related reasons, such as protocol deviation, are not inherent characteristics of the drug. Hence we propose to classify the patient dropouts into two classes: intrinsic (drug-related) and extrinsic (non-drug-related) dropouts. The former should be taken into account when defining the population parameter of the treatment effect, while the latter should not be. This classification will help determine a target population parameter that depicts a fair picture of a drug's effect, while the common classification of missing data as missing completely random (MCAR), missing at random (MAR), and missing not at random (MNAR) will help define appropriate statistical approach to analysis when missing data exist. Other related issues, such as statistical inference under this classification and implementing the classification in real clinical trials, are also touched upon here.