Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials.
<h4>Background</h4>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 co...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-06-01
|
| Series: | PLoS Medicine |
| Online Access: | https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1004176&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850129610485792768 |
|---|---|
| author | Peter Hanlon Elaine W Butterly Anoop Sv Shah Laurie J Hannigan Jim Lewsey Frances S Mair David M Kent Bruce Guthrie Sarah H Wild Nicky J Welton Sofia Dias David A McAllister |
| author_facet | Peter Hanlon Elaine W Butterly Anoop Sv Shah Laurie J Hannigan Jim Lewsey Frances S Mair David M Kent Bruce Guthrie Sarah H Wild Nicky J Welton Sofia Dias David A McAllister |
| author_sort | Peter Hanlon |
| collection | DOAJ |
| description | <h4>Background</h4>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).<h4>Methods and findings</h4>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.<h4>Conclusions</h4>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. |
| format | Article |
| id | doaj-art-fa16bc9ce7af4bb2b3525ec362ce6eb3 |
| institution | OA Journals |
| issn | 1549-1277 1549-1676 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Medicine |
| spelling | doaj-art-fa16bc9ce7af4bb2b3525ec362ce6eb32025-08-20T02:32:55ZengPublic Library of Science (PLoS)PLoS Medicine1549-12771549-16762023-06-01206e100417610.1371/journal.pmed.1004176Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials.Peter HanlonElaine W ButterlyAnoop Sv ShahLaurie J HanniganJim LewseyFrances S MairDavid M KentBruce GuthrieSarah H WildNicky J WeltonSofia DiasDavid A McAllister<h4>Background</h4>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).<h4>Methods and findings</h4>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.<h4>Conclusions</h4>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.https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1004176&type=printable |
| spellingShingle | Peter Hanlon Elaine W Butterly Anoop Sv Shah Laurie J Hannigan Jim Lewsey Frances S Mair David M Kent Bruce Guthrie Sarah H Wild Nicky J Welton Sofia Dias David A McAllister Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials. PLoS Medicine |
| title | Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials. |
| title_full | Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials. |
| title_fullStr | Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials. |
| title_full_unstemmed | Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials. |
| title_short | Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials. |
| title_sort | treatment effect modification due to comorbidity individual participant data meta analyses of 120 randomised controlled trials |
| url | https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1004176&type=printable |
| work_keys_str_mv | AT peterhanlon treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT elainewbutterly treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT anoopsvshah treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT lauriejhannigan treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT jimlewsey treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT francessmair treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT davidmkent treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT bruceguthrie treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT sarahhwild treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT nickyjwelton treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT sofiadias treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials AT davidamcallister treatmenteffectmodificationduetocomorbidityindividualparticipantdatametaanalysesof120randomisedcontrolledtrials |