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Estimating parsimonious models of longitudinal causal effects using regressions on propensity scores
Parsimony is important for the interpretation of causal effect estimates of longitudinal treatments on subsequent outcomes. One method for parsimonious estimates fits marginal structural models by using inverse propensity scores as weights. This method leads to generally large variability that is un...
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| 主要な著者: | , , , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2013
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3910397/ https://ncbi.nlm.nih.gov/pubmed/23533091 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/sim.5801 |
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