Lead Investigator: Camila Olarte Parra, London School of Hygiene and Tropical Medicine
Title of Proposal Research: Hypothetical estimand in clinical trials: an application of causal inference and missing data methods
Vivli Data Request: 6764
Funding Source: This research is funded by the UK Medical Research Council, grant MR/T023953/1.
Potential Conflicts of Interest: Dr. Bartlett has received consultancy fees from Bayer for advice on statistical methods in clinical trials. The University of Bath has received consultancy fees for Bartlett’s advice on statistical issues in clinical trials from Bayer, AstraZeneca, Novartis and Roche.
Summary of the Proposed Research:
To compare the benefits of two or more treatments for a given condition, we conduct experiments in a group of subjects with the condition, assign one of the treatments at random and follow them up to assess their outcome. These experiments are known as randomised trials. The analysis and interpretation of randomised trials is often complicated by the occurrence of non-planned events such as patients dropping out of the trial, patients requiring additional medication (i.e. rescue medication), or dying before their outcome is measured.
There are different statistical methods to handle these non-planned events. The chosen method for each non-planned event should be pre-specified in the statistical analysis plan when a trial is designed because different methods result in different estimates of the effectiveness and/or safety on the drug. This target effect to be measured in a trial is known as the estimand.
One of the possible estimands is the hypothetical estimand, where the treatment effect is estimated under the hypothetical scenario in which these non-planned events were prevented from occurring. For instance, the envisaged scenario could be one where patients continue taking their assigned drug as indicated and do not require additional medication.
In this project, we focus on different ways to estimate the hypothetical estimand. We consider statistical methods from causal inference, which are methods developed to evaluate the effects of treatments in settings where the treatment is not assigned at random (i.e. outside of a randomised trial). We will also apply missing data methods that were developed to deal with missing values in a given data set and have been more often used in the context of randomised trials. Establishing links between ‘causal inference estimators’ and ‘missing data estimators’ may help those familiar with one set of methods but not the other.
To compare the performance of these different statistical methods, we will apply them to the present diabetes clinical trial where rescue medication was allowed to be given in addition to the patient’s randomised treatment in case of inadequate blood sugar control with the study. Different rates of rescue medication used between treatment groups can potentially dilute or mask the treatment effect. By targeting the hypothetical estimand, the treatment effect can be adjusted for rescue medication use and other non-planned events including treatment discontinuation. We hope that by illustrating how the different statistical methods can be applied to a real clinical trial dataset we will demonstrate to other researchers how these methods can be usefully applied to their trials.
A 52-Week, Multi-Centre, Randomised, Parallel-Group, Double-Blind, Active Controlled, Phase IV Study to Evaluate the Safety and Efficacy of Dapagliflozin or Dapagliflozin Plus Saxagliptin Compared With Sulphonylurea All Given as Add-on Therapy to Metformin in Adult Patients With Type 2 Diabetes Who Have Inadequate Glycaemic Control on Metformin Monotherapy
Data Contributor: AstraZeneca
Study ID: NCT02471404
Sponsor ID: D1689C00014