The impact of analytic method on interpretation of outcomes in longitudinal clinical trials

Abstract

Various analytical strategies for addressing missing data in clinical trials are utilised in reporting study results. The most commonly used analytical methods include the last observation carried forward (LOCF), observed case (OC) and the mixed model for repeated measures (MMRM). Each method requires certain assumptions regarding the characteristics of the missing data. If the assumptions for any particular method are not valid, results from that method can be biased. Results based on these different analytical methods can, therefore, be inconsistent, thereby making interpretation of clinical study results confusing. In this investigation, we compare results from MMRM, LOCF and OC in order to illustrate the potential biases and problems in interpretation. Data from an 8-month, double-blind, randomised, placebo-controlled (placebo; n= 137), outpatient depression clinical trial comparing a serotonin-noradrenalin reuptake inhibitor (SNRI; n= 273) with a selective serotonin reuptake inhibitor (SSRI; n= 274) were used. The study visit schedule included efficacy and safety assessments weekly to week 4, bi-weekly to week 8, and then monthly. Visitwise mean changes for the 17-item Hamilton Depression Rating Scale (HAMD17) Maier subscale (primary efficacy outcome), blood pressure, and body weight were analysed using LOCF, MMRM and OC. Last observation carried forward consistently underestimated within-group mean changes in efficacy (benefit) and safety (risk) for both drugs compared with MMRM, whereas OC tended to overestimate within-group changes. Inferences are based on between-group comparisons. Therefore, whether or not underestimating (overestimating) within-group changes was conservative or anticonservative depended on the relative magnitude of the bias in each treatment and on whether within-group changes represented improvement or worsening. Preference should be given in analytic plans to methods whose assumptions are more likely to be valid rather than relying on a method based on the hope that its results, if biased, will be conservative.