Estimation of the treatment effect in the presence of non-compliance and missing data

Treatment non-compliance and missing data are common problems in clinical trials. Non-compliance is a broad term including any kind of deviation from the assigned treatment protocol, such as dose modification, treatment discontinuation or switch, often resulting in missing values. Missing values and treatment non-compliance may bias study results. Follow-up of all patients until the planned end of treatment period irrespective of their protocol adherence may provide useful information on the effectiveness of a study drug, taking the actual compliance into account. In this paper, we consider non-compliance as discontinuation of treatment and assume that the endpoint of interest is recorded for some non-complying patients after treatment discontinuation. As a result, the patient's longitudinal profile is dividable into on- and off-treatment observations. Within the framework of depression trials, which usually show a considerably high amount of dropouts, we compare different analysis strategies including both on- and off-treatment observations to gain insight into how the additional use of off-treatment data may affect the trial's outcome. We compare naïve strategies, which simply ignore off-treatment data or treat on- and off-treatment data in the same way, with more complex strategies based on piecewise linear mixed models, which assume different treatment effects for on- and off-treatment data. We show that naïve strategies may considerably overestimate treatment effects. Therefore, it is worthwhile to follow up as many patients as possible until the end of their planned treatment period irrespective of compliance, including all available data in an analysis that accounts for the different treatment conditions.