Abstract
AimsVarious 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.MethodsData 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.ResultsLast 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.ConclusionsInferences 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.
Highlights
Treatment effects are often evaluated by comparing change over time in outcome measures vs. placebo or an active control
With the increased popularity of mixed model for repeated measures (MMRM), it is important to characterise results from MMRM, last observation carried forward (LOCF) and observed case (OC) in safety outcomes and in long-term studies. This investigation compared results from efficacy and safety outcomes in a long-term clinical trial in major depressive disorder, thereby illustrating how the benefits of more robust analyses such as MMRM can improve our understanding of the risks and benefits of drugs
Analyses of mean changes from baseline in clinical trials have traditionally relied on simple methods such as analysis of covariance with missing data imputed by carrying the last observation forward or by including only completers – those patients who had a 2008 Eli Lilly and Company Journal compilation a 2008 Blackwell Publishing Ltd Int J Clin Pract, August 2008, 62, 8, 1147–1158
Summary
Treatment effects are often evaluated by comparing change over time in outcome measures vs. placebo or an active control. Impact of Analytic Method on Interpretation of Outcomes an observation at the end-point visit (observed cases, OC). These approaches entail the restrictive assumption that there is no relationship between either the observed or unobserved outcomes for the variable being analysed and the probability of dropout. This assumption is referred to in the statistical literature as missing completely at random (MCAR). The LOCF approach further assumes that subjects’ responses would have been constant from the last observed value to the end-point of the trial
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
