Abstract
AbstractBackgroundPeople with comorbidities are under-represented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD).Methods and ResultsUsing 126 industry-sponsored phase 3/4 trials across 23 index conditions, we performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition), (ii) presence or absence of the six commonest comorbid diseases for each index condition, and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular function).Comorbidities were under-represented in trial participants and few had >2 comorbidities. We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for three conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., Sodium-glucose co-transporter inhibitors for type 2 diabetes – interaction term for comorbidity count 0.004, 95% CI - 0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma – interaction term -0.22, 95% CI -1.07 to 0.54).ConclusionFor trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. Our findings support the assumption that estimates of treatment efficacy are constant, at least across modest levels of comorbidity.
Publisher
Cold Spring Harbor Laboratory