Treatment effect modification due to comorbidity: individual participant data meta-analyses of 120 randomised controlled trials

Hanlon, P. et al. (2023) Treatment effect modification due to comorbidity: individual participant data meta-analyses of 120 randomised controlled trials. PLoS Medicine, 20(6), e1004176. (doi: 10.1371/journal.pmed.1004176) (PMID:37279199) (PMCID:PMC10243630)

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Abstract

Background: People with comorbidities are underrepresented 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 findings: We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity–treatment interaction terms from each trial. 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 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). 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 3 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-2 (SGLT2) 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). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. Conclusions: Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Item Type:Articles
Additional Information:This work was funded by the Wellcome Trust (grant number 201492/Z/16/Z, grant recipients DMA, SD) and the Medical Research Council (grant number MR/S021949/1, grant recipient PH).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:McAllister, Professor David and Butterly, Dr Elaine and Hanlon, Dr Peter and Lewsey, Professor Jim and Hannigan, Dr Laurie and Mair, Professor Frances
Authors: Hanlon, P., Butterly, E., Shah, A. S.V., Hannigan, L. J., Lewsey, J., Mair, F. S., Kent, D., Guthrie, B., Wild, S. H., Welton, N. J., Dias, S., and McAllister, D. A.
College/School:College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > General Practice and Primary Care
College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment
College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Public Health
Journal Name:PLoS Medicine
Publisher:Public Library of Science
ISSN:1549-1277
ISSN (Online):1549-1676
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in PLoS Medicine 20(6): e1004176
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
173492Combining efficacy estimates from clinical trials with the natural history obtained from large routine healthcare databases to determine net overall treatment benefitsDavid McAllisterWellcome Trust (WELLCOTR)201492/Z/16/ZInstitute of Health & Wellbeing
305232Understanding prevalence and impact of frailty in chronic illness and implications for clinical managementFrances MairMedical Research Council (MRC)MR/S021949/1HW - General Practice and Primary Care