Two-level approaches to missing data in longitudinal trials with daily patient-reported outcomes

Abstract

In longitudinal clinical trials with daily patient-reported outcomes, the analysis endpoints are often defined as the averaged daily diary outcomes in a treatment cycle (such as a month or a week). Conventional methods often deal with missing data at the cycle level by imputing the average, and the cycle average is treated as missing if the number of days with available outcomes in the treatment cycle is less than a certain number. This was the method used for a case study of a phase 3 clinical trial evaluating a treatment for insomnia with daily patient-reported outcomes. Such methods may introduce bias. Motivated by this, we propose methods to impute missing daily outcomes in this paper. Specifically, we define a two-level missing pattern for clinical trials with daily patient-reported outcomes, and propose two-level methods to impute missing data at daily base. Other than the standard methods by multiple imputations, we derive analytic formulas for the proposed two-level methods to reduce computational intensity and improve the estimates of variances. The proposed two-level methods provide more powerful approaches to estimate the treatment difference compared to the conventional cycle-level methods, which are evaluated by theoretical development and simulation studies. In addition, the methods are applied to the motivating phase 3 trial evaluating a treatment for insomnia with daily patient-reported outcomes.