Addressing missing data in clinical trials

Abstract

The reliability and interpretability of results from clinical trials can be substantially reduced by missing data. Frequently used approaches to address these concerns, such as upward adjustments in sample sizes or simplistic methods for handling missing data, including last-observation-carried-forward, complete-case, or worst-case analyses, are usually inadequate. Although rational imputation methods may be useful to treat missingness after it has occurred, these methods depend on untestable assumptions. Thus, the preferred and often only satisfactory approach to addressing missing data is to prevent it. Procedures should be in place to maximize the likelihood that outcome data will be obtained at scheduled times of evaluation for all surviving patients who have not withdrawn consent. To meaningfully reduce missing data, it is important to recognize and address many factors that commonly lead to higher levels of missingness.

Figure. Change from baseline in 6-minute walk distance over 48 months after initiation of treatment in patients with pulmonary arterial hypertension

At each scheduled visit, the average change from baseline in 6-minute walk distance is plotted separately for the subgroups that will and will not remain under follow-up at the time of the next scheduled visit. The numbers above the squares represent, at each scheduled visit, the number of patients in the subgroup that will remain under follow-up at the time of the next scheduled visit. Hence, these are the numbers of patients who contribute to the calculations of the means and the 95% CIs for that subgroup.