Assessing treatment effect modification due to comorbidity using individual participant data from industry-sponsored drug trials

Background People 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 Results Using 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). Conclusion For 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement David McAllister is funded via an Intermediate Clinical Fellowship and Beit Fellowship from the Wellcome Trust, who also supported other costs related to this project such as data access costs and database licences (“Treatment effectiveness in multimorbidity: Combining efficacy estimates from clinical trials with the natural history obtained from large routine healthcare databases to determine net overall treatment Benefits.” - 201492/Z/16/Z). Peter Hanlon is funded through a Clinical Research Training Fellowship from the Medical Research Council (Grant reference: MR/S021949/1). None of the funders had any influence over the study design, analysis or decision to submit for publication. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This project had approval from the University of Glasgow, College of Medicine, Veterinary and Life Sciences ethics committee (200160070). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Aggregated data and code required to run these models, along with full model descriptions, are available at [https://github.com/ChronicDiseaseEpi/csdr\_effect\_estimates][1] [https://github.com/ChronicDiseaseEpi/csdr\_effect\_estimates][1] [1]: https://github.com/ChronicDiseaseEpi/csdr_effect_estimates