What is being explored by the simulation study?
As the aim of pragmatic trials is to mimic real-life situations, the comparator or control treatment used for pragmatic trials is ideally what is currently used in clinical practice. However, as there is often not just one treatment used in clinical practice, the ‘comparator’ arm in the trial is likely to include a number of treatments. As the choice of treatment in the patients in the control arm is also likely to be affected by some factors that also affect the binary outcome (confounders), it may introduce confounding bias. When a comparison of the new treatment with the control treatments as a whole is perfomed, confounding is not an issue. In the case, however, where one needs to compare the new treatment with a specific treatment from the comparator arm, confounding is a problem. This bias presents issues in conducting analyses for and interpreting results from pragmatic trials.
GetReal has examined different methods for adjusting for this type of confounding in pragmatic clinical trials using a simulation study.
What was examined in the simulation study?
The aim of this study was to evaluate the performance of different methodologies for controlling confounding with time-to-event endpoints using a simulation study over a wide range of parameter settings. The evaluation criteria include bias, square-root of mean-squared-error (MSE), Type-I error, power, and coverage of 95% confidence interval (CI).
Six methods were considered for controlling confounding with time-to-event outcome:
- Marginal effect models:
- Inverse probability weighting
- Inverse propensity score weighting
- Conditional effect models:
- Multivariable Cox model
- Stratified Cox model
- Propensity score as a covariate
- Doubly robust inverse probability weighting
(note: some of these methods are also used to adjust for confounding bias in non-randomised or observational studies – see here)
What were the findings and conclusions?
- Preliminary results were presented to GetReal members in September 2016 to positive feedback.
Hongwei Wang, Sanofi