Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group
Abstract Background The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across st...
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-023-02020-5 |
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| author | Vivek A. Rudrapatna Vignesh G. Ravindranath Douglas V. Arneson Arman Mosenia Atul J. Butte Shan Wang |
| author_facet | Vivek A. Rudrapatna Vignesh G. Ravindranath Douglas V. Arneson Arman Mosenia Atul J. Butte Shan Wang |
| author_sort | Vivek A. Rudrapatna |
| collection | DOAJ |
| description | Abstract Background The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group. Methods This work was conducted within the context of a broader effort to study comparative efficacy in Crohn’s disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn’s Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE. Results Using our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses. Conclusions This new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care. |
| first_indexed | 2024-03-10T17:36:39Z |
| format | Article |
| id | doaj.art-8da8d83d495e41b6a439f4f4fd481d54 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-10T17:36:39Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-8da8d83d495e41b6a439f4f4fd481d542023-11-20T09:49:32ZengBMCBMC Medical Research Methodology1471-22882023-10-0123111210.1186/s12874-023-02020-5Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control groupVivek A. Rudrapatna0Vignesh G. Ravindranath1Douglas V. Arneson2Arman Mosenia3Atul J. Butte4Shan Wang5Division of Gastroenterology, Department of Medicine, University of CaliforniaBakar Computational Health Sciences Institute, University of CaliforniaBakar Computational Health Sciences Institute, University of CaliforniaSchool of Medicine, University of CaliforniaBakar Computational Health Sciences Institute, University of CaliforniaDepartment of Mathematics and Statistics, University of San FranciscoAbstract Background The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group. Methods This work was conducted within the context of a broader effort to study comparative efficacy in Crohn’s disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn’s Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE. Results Using our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses. Conclusions This new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care.https://doi.org/10.1186/s12874-023-02020-5Individual participant data meta-analysisRandomized clinical trialsCrohn’s diseaseComparative effectivenessComparative efficacyEvidence synthesis |
| spellingShingle | Vivek A. Rudrapatna Vignesh G. Ravindranath Douglas V. Arneson Arman Mosenia Atul J. Butte Shan Wang Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group BMC Medical Research Methodology Individual participant data meta-analysis Randomized clinical trials Crohn’s disease Comparative effectiveness Comparative efficacy Evidence synthesis |
| title | Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group |
| title_full | Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group |
| title_fullStr | Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group |
| title_full_unstemmed | Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group |
| title_short | Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group |
| title_sort | sequential regression and simulation a method for estimating causal effects from heterogeneous clinical trials without a common control group |
| topic | Individual participant data meta-analysis Randomized clinical trials Crohn’s disease Comparative effectiveness Comparative efficacy Evidence synthesis |
| url | https://doi.org/10.1186/s12874-023-02020-5 |
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