Genetic matching for time-dependent treatments: a longitudinal extension and simulation study
Abstract Background Longitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a mach...
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-023-01995-5 |
| _version_ | 1797558785484521472 |
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| author | Deirdre Weymann Brandon Chan Dean A. Regier |
| author_facet | Deirdre Weymann Brandon Chan Dean A. Regier |
| author_sort | Deirdre Weymann |
| collection | DOAJ |
| description | Abstract Background Longitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching. Methods To evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models. Results In all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates. Conclusions While the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points. |
| first_indexed | 2024-03-10T17:36:18Z |
| format | Article |
| id | doaj.art-2c2060ced5e94b3e9f724620b9f108b4 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-10T17:36:18Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-2c2060ced5e94b3e9f724620b9f108b42023-11-20T09:49:46ZengBMCBMC Medical Research Methodology1471-22882023-08-0123111310.1186/s12874-023-01995-5Genetic matching for time-dependent treatments: a longitudinal extension and simulation studyDeirdre Weymann0Brandon Chan1Dean A. Regier2Cancer Control Research, BC CancerCancer Control Research, BC CancerCancer Control Research, BC CancerAbstract Background Longitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching. Methods To evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models. Results In all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates. Conclusions While the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.https://doi.org/10.1186/s12874-023-01995-5Longitudinal matchingTime-dependent treatmentPropensity scoreMonte Carlo simulationMachine learning |
| spellingShingle | Deirdre Weymann Brandon Chan Dean A. Regier Genetic matching for time-dependent treatments: a longitudinal extension and simulation study BMC Medical Research Methodology Longitudinal matching Time-dependent treatment Propensity score Monte Carlo simulation Machine learning |
| title | Genetic matching for time-dependent treatments: a longitudinal extension and simulation study |
| title_full | Genetic matching for time-dependent treatments: a longitudinal extension and simulation study |
| title_fullStr | Genetic matching for time-dependent treatments: a longitudinal extension and simulation study |
| title_full_unstemmed | Genetic matching for time-dependent treatments: a longitudinal extension and simulation study |
| title_short | Genetic matching for time-dependent treatments: a longitudinal extension and simulation study |
| title_sort | genetic matching for time dependent treatments a longitudinal extension and simulation study |
| topic | Longitudinal matching Time-dependent treatment Propensity score Monte Carlo simulation Machine learning |
| url | https://doi.org/10.1186/s12874-023-01995-5 |
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