Constructing a T ‐Test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data
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2025
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| author | Minxin Lu Annie Green Howard Penny Gordon‐Larsen Katie A. Meyer Hsiao‐Chuan Tien Shufa Du Huijun Wang Bing Zhang Michael R. Kosorok |
| author_facet | Minxin Lu Annie Green Howard Penny Gordon‐Larsen Katie A. Meyer Hsiao‐Chuan Tien Shufa Du Huijun Wang Bing Zhang Michael R. Kosorok Minxin Lu Annie Green Howard Penny Gordon‐Larsen Katie A. Meyer Hsiao‐Chuan Tien Shufa Du Huijun Wang Bing Zhang Michael R. Kosorok |
| collection | Wiley Open Access |
| contents | Constructing a T ‐Test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data Minxin Lu Annie Green Howard Penny Gordon‐Larsen Katie A. Meyer Hsiao‐Chuan Tien Shufa Du Huijun Wang Bing Zhang Michael R. Kosorok Statistics in Medicine ABSTRACT Optimal individualized treatment decision‐making has improved health outcomes in recent years. The value function is commonly used to evaluate the goodness of an individualized treatment decision rule. Despite recent advances, comparing value functions between different treatment decision rules or constructing confidence intervals around value functions remains difficult. We propose a t ‐test based method applied to a test set that generates valid p ‐values to compare value functions between a given pair of treatment decision rules when some of the data are missing. We demonstrate the ease in use of this method and evaluate its performance via simulation studies and apply it to the China Health and Nutrition Survey data. 10.1002/sim.70210 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.70210 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70210 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Constructing a T ‐Test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data Minxin Lu Annie Green Howard Penny Gordon‐Larsen Katie A. Meyer Hsiao‐Chuan Tien Shufa Du Huijun Wang Bing Zhang Michael R. Kosorok Statistics in Medicine Constructing a T ‐Test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data Minxin Lu Annie Green Howard Penny Gordon‐Larsen Katie A. Meyer Hsiao‐Chuan Tien Shufa Du Huijun Wang Bing Zhang Michael R. Kosorok Statistics in Medicine ABSTRACT Optimal individualized treatment decision‐making has improved health outcomes in recent years. The value function is commonly used to evaluate the goodness of an individualized treatment decision rule. Despite recent advances, comparing value functions between different treatment decision rules or constructing confidence intervals around value functions remains difficult. We propose a t ‐test based method applied to a test set that generates valid p ‐values to compare value functions between a given pair of treatment decision rules when some of the data are missing. We demonstrate the ease in use of this method and evaluate its performance via simulation studies and apply it to the China Health and Nutrition Survey data. 10.1002/sim.70210 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Constructing a T ‐Test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70210 |