Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866929334154952704 |
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| author | Dashti, S. Ghazaleh Lee, Katherine J. Simpson, Julie A. White, Ian R. Carlin, John B. Moreno-Betancur, Margarita |
| author_facet | Dashti, S. Ghazaleh Lee, Katherine J. Simpson, Julie A. White, Ian R. Carlin, John B. Moreno-Betancur, Margarita |
| contents | Targeted Maximum Likelihood Estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate eight missing data methods in this context: complete-case analysis, extended TMLE incorporating outcome-missingness model, missing covariate missing indicator method, five multiple imputation (MI) approaches using parametric or machine-learning models. Six scenarios were considered, varying in exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/non-linear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a non-linear term. When choosing a method to handle missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and non-linearities is expected to perform well. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2112_05274 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation Dashti, S. Ghazaleh Lee, Katherine J. Simpson, Julie A. White, Ian R. Carlin, John B. Moreno-Betancur, Margarita Methodology Applications Targeted Maximum Likelihood Estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate eight missing data methods in this context: complete-case analysis, extended TMLE incorporating outcome-missingness model, missing covariate missing indicator method, five multiple imputation (MI) approaches using parametric or machine-learning models. Six scenarios were considered, varying in exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/non-linear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a non-linear term. When choosing a method to handle missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and non-linearities is expected to perform well. |
| title | Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2112.05274 |