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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.

Supplemental material, sj-docx-1-mdm-10.1177_0272989X231162069 for General Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations by Mathyn Vervaart, Eline Aas, Karl P. Claxton, Mark Strong, Nicky J. Welton, Torbjørn Wisløff and Anna Heath in Medical Decision Making

Supplemental material, sj-docx-1-mdm-10.1177_0272989X231165985 for Constructing Relative Effect Priors for Research Prioritization and Trial Design: A Meta-epidemiological Analysis by David Glynn, Georgios Nikolaidis, Dina Jankovic and Nicky J. Welton in Medical Decision Making

Background: Various treatments for acne vulgaris exist, but little is known about their comparative effectiveness in relation to acne severity. Objectives: To identify best treatments for mild-to-moderate and moderate-to-severe acne, as determined by clinician-assessed morphological features. Methods: We undertook a systematic review and network meta-analysis of randomized controlled trials (RCTs) assessing topical pharmacological, oral pharmacological, physical and combined treatments for mild-to-moderate and moderate-to-severe acne, published up to May 2020. Outcomes included percentage change in total lesion count from baseline, treatment discontinuation for any reason, and discontinuation owing to side-effects. Risk of bias was assessed using the Cochrane risk-of-bias tool and bias adjustment models. Effects for treatments with ≥ 50 observations each compared with placebo are reported below. Results: We included 179 RCTs with approximately 35 000 observations across 49 treatment classes. For mild-to-moderate acne, the most effective options for each treatment type were as follows: topical pharmacological – combined retinoid with benzoyl peroxide (BPO) [mean difference 26·16%, 95% credible interval (CrI) 16·75–35·36%]; physical – chemical peels, e.g. salicylic or mandelic acid (39·70%, 95% CrI 12·54–66·78%) and photochemical therapy (combined blue/red light) (35·36%, 95% CrI 17·75–53·08%). Oral pharmacological treatments (e.g. antibiotics, hormonal contraceptives) did not appear to be effective after bias adjustment. BPO and topical retinoids were less well tolerated than placebo. For moderate-to-severe acne, the most effective options for each treatment type were as follows: topical pharmacological – combined retinoid with lincosamide (clindamycin) (44·43%, 95% CrI 29·20–60·02%); oral pharmacological – isotretinoin of total cumulative dose ≥ 120 mg kg −1 per single course (58·09%, 95% CrI 36·99–79·29%); physical – photodynamic therapy (light therapy enhanced by a photosensitizing chemical) (40·45%, 95% CrI 26·17–54·11%); combined – BPO with topical retinoid and oral tetracycline (43·53%, 95% CrI 29·49–57·70%). Topical retinoids and oral tetracyclines were less well tolerated than placebo. The quality of included RCTs was moderate to very low, with evidence of inconsistency between direct and indirect evidence. Uncertainty in findings was high, in particular for chemical peels, photochemical therapy and photodynamic therapy. However, conclusions were robust to potential bias in the evidence. Conclusions: Topical pharmacological treatment combinations, chemical peels and photochemical therapy were most effective for mild-to-moderate acne. Topical pharmacological treatment combinations, oral antibiotics combined with topical pharmacological treatments, oral isotretinoin and photodynamic therapy were most effective for moderate-to-severe acne. Further research is warranted for chemical peels, photochemical therapy and photodynamic therapy for which evidence was more limited. What is already known about this topic? Acne vulgaris is the eighth most common disease globally. Several topical, oral, physical and combined treatments for acne vulgaris exist. Network meta-analysis (NMA) synthesizes direct and indirect evidence and allows simultaneous inference for all treatments forming an evidence network. Previous NMAs have assessed a limited range of treatments for acne vulgaris and have not evaluated effectiveness of treatments for moderate-to-severe acne. What does this study add? For mild-to-moderate acne, topical treatment combinations, chemical peels, and photochemical therapy (combined blue/red light; blue light) are most effective. For moderate-to-severe acne, topical treatment combinations, oral antibiotics combined with topical treatments, oral isotretinoin and photodynamic therapy (light therapy enhanced by a photosensitizing chemical) are most effective. Based on these findings, along with further clinical and cost-effectiveness considerations, National Institute for Health and Care Excellence (NICE) guidance recommends, as first-line treatments, fixed topical treatment combinations for mild-to-moderate acne and fixed topical treatment combinations, or oral tetracyclines combined with topical treatments, for moderate-to-severe acne.

This protocol describes the 2023 update of the original systematic review and network meta-analysis on treatments for patellofemoral pain. The first version of the protocol was published in 2018:( Winters M, Holden S, Vicenzino B, et al Which treatment is most effective for patients with patellofemoral pain? A protocol for a living systematic review including network meta-analysis BMJ Open 2018;8:e022920. doi: 10.1136/bmjopen-2018-022920) and the first report was published in 2021 (Winters M, Holden S, Lura CB, Welton NJ, Caldwell DM, Vicenzino BT, Weir A, Rathleff MS. Comparative effectiveness of treatments for patellofemoral pain: a living systematic review with network meta-analysis. Br J Sports Med. 2020 Oct 26;55(7))

ObjectivesTo estimate the association between individual participant characteristics and attrition from randomised controlled trials.DesignMeta-analysis of individual participant level data (IPD).Data sourcesClinical trial repositories (Clinical Study Data Request and Yale University Open Data Access).Eligibility criteria for selecting studiesEligible phase 3 or 4 trials identified according to prespecified criteria (PROSPERO CRD42018048202).Main outcome measuresAssociation between comorbidity count (identified using medical history or concomitant drug treatment data) and trial attrition (failure for any reason to complete the final trial visit), estimated in logistic regression models and adjusted for age and sex. Estimates were meta-analysed in bayesian linear models, with partial pooling across index conditions and drug classes.ResultsIn 92 trials across 20 index conditions and 17 drug classes, the mean comorbidity count ranged from 0.3 to 2.7. Neither age nor sex was clearly associated with attrition (odds ratio 1.04, 95% credible interval 0.98 to 1.11; and 0.99, 0.93 to 1.05, respectively). However, comorbidity count was associated with trial attrition (odds ratio per additional comorbidity 1.11, 95% credible interval 1.07 to 1.14). No evidence of non-linearity (assessed via a second order polynomial) was seen in the association between comorbidity count and trial attrition, with minimal variation across drug classes and index conditions. At a trial level, an increase in participant comorbidity count has a minor impact on attrition: for a notional trial with high level of attrition in individuals without comorbidity, doubling the mean comorbidity count from 1 to 2 translates to an increase in trial attrition from 29% to 31%.ConclusionsIncreased comorbidity count, irrespective of age and sex, is associated with a modest increased odds of participant attrition. The benefit of increased generalisability of including participants with multimorbidity seems likely to outweigh the disadvantages of increased attrition.

Background: \ud There is limited guidance for using common drug therapies in the context of multimorbidity. In part, this is because their effectiveness for patients with specific comorbidities cannot easily be established using subgroup analyses in clinical trials. Here, we use simulations to explore the feasibility and implications of concurrently estimating effects of related drug treatments in patients with multimorbidity by partially pooling subgroup efficacy estimates across trials.\ud \ud Methods: \ud We performed simulations based on the characteristics of 161 real clinical trials of noninsulin glucose-lowering drugs for diabetes, estimating subgroup effects for patients with a hypothetical comorbidity across related trials in different scenarios using Bayesian hierarchical generalized linear models. We structured models according to an established ontology—the World Health Organization Anatomic Chemical Therapeutic Classifications—allowing us to nest all trials within drugs and all drugs within anatomic chemical therapeutic classes, with effects partially pooled at each level of the hierarchy. In a range of scenarios, we compared the performance of this model to random effects meta-analyses of all drugs individually.\ud \ud Results: \ud Hierarchical, ontology-based Bayesian models were unbiased and accurately recovered simulated comorbidity-drug interactions. Compared with single-drug meta-analyses, they offered a relative increase in precision of up to 250% in some scenarios because of information sharing across the hierarchy. Because of the relative precision of the approaches, a large proportion of small subgroup effects was detectable only using the hierarchical model.\ud \ud Conclusions: \ud By assuming that similar drugs may have similar subgroup effects, Bayesian hierarchical models based on structures defined by existing ontologies can be used to improve the precision of treatment efficacy estimates in patients with multimorbidity, with potential implications for clinical decision making.

Additional file 3: Supplementary figures S24-S44. Observed/expected SAEs before and after standardization by multimorbidity count. Figure S24. Observed/expected SAEs before and after standardization by multimorbidity count: asthma trials. Figure S25. Observed/expected SAEs before and after standardization by multimorbidity count: atrial fibrillation trials. Figure S26. observed/expected SAEs before and after standardization by multimorbidity count: axial spondyloarthritis trials. Figure S27. Observed/expected SAEs before and after standardization by multimorbidity count: benign prostatic hyperplasia trials. Figure S28. Observed/expected SAEs before and after standardization by multimorbidity count: dementia trials. Figure S29. observed/expected SAEs before and after standardization by multimorbidity count: type 2 diabetes mellitus trials. Figure S30. Observed/expected SAEs before and after standardization by multimorbidity count: hypertension trials. Figure S31. Observed/expected SAEs before and after standardization by multimorbidity count: pulmonary hypertension trials. Figure S32. Observed/expected SAEs before and after standardization by multimorbidity count: inflammatory bowel disease trials. Figure S33. Observed/expected SAEs before and after standardization by multimorbidity count: migraine trials. Figure S34. Observed/expected SAEs before and after standardization by multimorbidity count: osteoarthritis trials. Figure S35. Observed/expected SAEs before and after standardization by multimorbidity count: osteoporosis trials. Figure S36. Observed/expected SAEs before and after standardization by multimorbidity count: Parkinson’s disease trials. Figure S37. Observed/expected SAEs before and after standardization by multimorbidity count: psoriasis trials. Figure S38. Observed/expected SAEs before and after standardization by multimorbidity count: psoriatic arthropathy trials. Figure S39. Observed/expected SAEs before and after standardization by multimorbidity count: chronic obstructive pulmonary disease trials. Figure S40. Observed/expected SAEs before and after standardization by multimorbidity count: pulmonary fibrosis trials. Figure S41. Observed/expected SAEs before and after standardization by multimorbidity count: restless legs syndrome trials. Figure S42. Observed/expected SAEs before and after standardization by multimorbidity count: rheumatoid arthritis trials. Figure S43. Observed/expected SAEs before and after standardization by multimorbidity count: systemic lupus erythematosus trials. Figure S44. Observed/expected SAEs before and after standardization by multimorbidity count: thromboembolism trials.

Supplemental material, sj-pdf-2-mdm-10.1177_0272989X221117162 for Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis by David M. Phillippo, Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Daniel Saure, Yves Schymura and Nicky J. Welton in Medical Decision Making

Additional file 1: Supplementary figures S1-S21. Trial level estimates of observed/expected SAEs. Figure S1. Observed/expected SAEs in asthma trials. Figure S2. Observed/expected SAEs in atrial fibrillation trials. Figure S3. Observed/expected SAEs in axial spondyloarthritis trials. Figure S4. Observed/expected SAEs in benign prostatic hyperplasia trials. Figure S5. Observed/expected SAEs in dementia trials. Figure S6. Observed/expected SAEs in type 2 diabetes mellitus trials. Figure S7. Observed/expected SAEs in epilepsy trials. Figure S8. Observed/expected SAEs in hypertension trials. Figure S9. Observed/expected SAEs in pulmonary hypertension trials. Figure S10. Observed/expected SAEs in inflammatory bowel disease trials. Figure S11. Observed/expected SAEs in myocardial infarction trials. Figure S12. Observed/expected SAEs in osteoarthritis trials. Figure S13. Observed/expected SAEs in osteoporosis trials. Figure S14. Observed/expected SAEs in Parkinson’s disease trials. Figure S15. Observed/expected SAEs in psoriasis trials. Figure S16. Observed/expected SAEs in psoriatic arthropathy trials. Figure S17. Observed/expected SAEs in chronic obstructive pulmonary disease trials. Figure S18. Observed/expected SAEs in pulmonary fibrosis trials. Figure S19. Observed/expected SAEs in rheumatoid arthritis trials. Figure S20. Observed/expected SAEs in systemic lupus erythematosus trials. Figure S21. Observed/expected SAEs in thromboembolism trials.

Supplemental material, sj-pdf-1-mdm-10.1177_0272989X221117162 for Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis by David M. Phillippo, Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Daniel Saure, Yves Schymura and Nicky J. Welton in Medical Decision Making

Supplemental material, sj-pdf-1-mdm-10.1177_0272989X211068019 for An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial by Mathyn Vervaart, Mark Strong, Karl P. Claxton, Nicky J. Welton, Torbj��rn Wisl��ff and Eline Aas in Medical Decision Making

Additional file 4. Statistical methods.

Supplemental material, sj-docx-3-mdm-10.1177_0272989X221117162 for Validating the Assumptions of Population Adjustment: Application of Multilevel Network Meta-regression to a Network of Treatments for Plaque Psoriasis by David M. Phillippo, Sofia Dias, A. E. Ades, Mark Belger, Alan Brnabic, Daniel Saure, Yves Schymura and Nicky J. Welton in Medical Decision Making