Karla Diaz-Ordaz
UK Medical Research Council
Career Development Award in Biostatistics
Title
Multiple imputation methods for valid causal treatment effects estimation after departures from protocol
Duration
4 years (April 2014- March 2018)
Summary
Randomised controlled trials are considered the gold standard in determining the effect of an intervention. Two issues that often undermine the credibility of effectiveness measures are non-compliance with treatment allocation (participants who do not adhere to the protocol, e.g. by not receiving the intended treatment) and missing data (e.g. loss to follow-up).
Both these issues are relevant to the adoption of the Intention-to-treat (ITT) analysis, which aims to measure treatment effectiveness. The ITT principle states that all individuals randomised in a clinical trial should be included in the analysis, in the groups to which they were randomised, regardless of any departures from the randomised treatment.
Following this principle preserves the benefits of randomisation, i.e. having treatment groups that do not differ systematically on any factors except those assigned in the trial. But such pragmatic estimates may not be the only estimates of interest, and given the considerable costs and high number of patients involved in a typical confirmatory clinical trial, efforts should be made to obtain valid explanatory treatment effects, i.e. the “real” treatment effects that a patient taking the treatment 100% as prescribed can expect on average.
Ad hoc methods which ignore treatment allocation may lead to incorrect estimates of treatment effect. Existing statistical methods can handle some complex longitudinal settings, e.g. when compliance with treatment varies with time and is associated with the clinical outcome of interest (assumed to be a continuous measure). However, they rely on assumptions which are difficult to understand and involve sophisticated numerical iterative procedures to obtain the estimates of interest. It is therefore important that practical and efficient methods are available for handling non-compliance and missing data, especially when the data structure is complex, in a unified, transparent, and systematic manner.
The proposed research aims to develop new statistical methods to deal with these two issues within a single unifying framework and under transparent assumptions. This method is based on multiple imputation, which is a practical and flexible method already widely used to address missing data problems.