Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patientdata meta-analysis
Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and d...
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| Veröffentlicht in: | Statistical methods in medical research Jg. 31; H. 4; S. 689 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.04.2022
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| ISSN: | 1477-0334, 1477-0334 |
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| Abstract | Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis, where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model for effect modification by different patient characteristics and co-medications in a meta-analysis of observational individual patient data. We develop, evaluate, and apply a targeted maximum likelihood estimator for the doubly robust estimation of the parameters of the proposed marginal structural model in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study. |
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| AbstractList | Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis, where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model for effect modification by different patient characteristics and co-medications in a meta-analysis of observational individual patient data. We develop, evaluate, and apply a targeted maximum likelihood estimator for the doubly robust estimation of the parameters of the proposed marginal structural model in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study. Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis, where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model for effect modification by different patient characteristics and co-medications in a meta-analysis of observational individual patient data. We develop, evaluate, and apply a targeted maximum likelihood estimator for the doubly robust estimation of the parameters of the proposed marginal structural model in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study.Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis, where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model for effect modification by different patient characteristics and co-medications in a meta-analysis of observational individual patient data. We develop, evaluate, and apply a targeted maximum likelihood estimator for the doubly robust estimation of the parameters of the proposed marginal structural model in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study. |
| Author | Liu, Yan Wang, Guanbo Kennedy, Edward Menzies, Dick Benedetti, Andrea Vargas, Mario H Viiklepp, Piret Schnitzer, Mireille E Sotgiu, Giovanni |
| Author_xml | – sequence: 1 givenname: Yan orcidid: 0000-0001-8835-0467 surname: Liu fullname: Liu, Yan organization: Department of Epidemiology, Biostatistics and Occupational Health, 5620McGill University, Canada – sequence: 2 givenname: Mireille E orcidid: 0000-0001-8049-9646 surname: Schnitzer fullname: Schnitzer, Mireille E organization: Department of Social and Preventive Medicine, 5622Université de Montréal, Canada – sequence: 3 givenname: Guanbo surname: Wang fullname: Wang, Guanbo organization: Department of Epidemiology, Biostatistics and Occupational Health, 5620McGill University, Canada – sequence: 4 givenname: Edward surname: Kennedy fullname: Kennedy, Edward organization: Department of Statistics & Data Science, 6612Carnegie Mellon University, USA – sequence: 5 givenname: Piret surname: Viiklepp fullname: Viiklepp, Piret organization: National Institute for Health Development, Estonia – sequence: 6 givenname: Mario H surname: Vargas fullname: Vargas, Mario H organization: 42635Instituto Nacional de Enfermedades Respiratorias, Mexico – sequence: 7 givenname: Giovanni surname: Sotgiu fullname: Sotgiu, Giovanni organization: Clinical Epidemiology and Medical Statistics Unit, Department of Medical, Surgical and Experimental Sciences, University of Sassari, Italy – sequence: 8 givenname: Dick surname: Menzies fullname: Menzies, Dick organization: Montréal Chest Institute & McGill International TB Centre, Research Institute of the McGill University Health Centre, Montréal, Canada – sequence: 9 givenname: Andrea orcidid: 0000-0002-8314-9497 surname: Benedetti fullname: Benedetti, Andrea organization: Department of Medicine, McGill University, Canada |
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| CitedBy_id | crossref_primary_10_1002_sim_70121 crossref_primary_10_1017_rsm_2025_5 crossref_primary_10_1289_EHP13961 crossref_primary_10_1371_journal_pone_0279976 crossref_primary_10_1002_sim_10003 crossref_primary_10_1016_j_annepidem_2023_06_004 crossref_primary_10_1002_sim_10116 |
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| Keywords | Conditional average treatment effect individual patient data targeted maximum likelihood estimation meta-analysis multidrug-resistant tuberculosis double robustness marginal structural model |
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| Title | Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patientdata meta-analysis |
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