Modelling of coping strategies as a missed opportunity in drug development

In randomized controlled clinical trials, the confounding of the treatment with other factors (sex, age, other patient characteristics) at baseline is of crucial importance as well as maintaining the integrity of the treatment during the trial. However, an important uncontrolled factor violating the integrity of the treatment is patient behaviour, especially in response to his changing health state (ie coping behavior).

For example, in rheumatic diseases such as┬áSystemic Lupus Erythematosus (SLE) a primary endpoint is the patient’s fatigue as measured by patient-reported outcomes. However, a natural reaction to the patient’s fatigue, ie the patient being tired, is to sleep more, ask other family members or friends to help with the daily duties, reduce workload, drink more coffee, or any other behaviour the patient considers to be helpful.

Therefore, it seems of great importance to measure these kind of coping behaviours and include them in the statistical model to reduce measurement error by transforming unexplained variation in the endpoint to explained variation. Measurement of coping behaviours may be performed by patient-reported outcomes, but also non-reactive measures based on sensor measures (cf internet of things), eg to measure sleep duration, may applied.