| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29358 |
| 来源ID | Working Paper 29358 |
| Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine | |
| Charles F. Manski | |
| 发表日期 | 2021-10-11 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | This paper extends my research applying statistical decision theory to treatment choice with sample data, using maximum regret to evaluate the performance of treatment rules. The specific new contribution is to study as-if optimization using estimates of illness probabilities in clinical choice between surveillance and aggressive treatment. Beyond its specifics, the paper sends a broad message. Statisticians and computer scientists have addressed conditional prediction for decision making in indirect ways, the former applying classical statistical theory and the latter measuring prediction accuracy in test samples. Neither approach is satisfactory. Statistical decision theory provides a coherent, generally applicable methodology. |
| 主题 | Econometrics ; Estimation Methods ; Health, Education, and Welfare ; Health |
| URL | https://www.nber.org/papers/w29358 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587032 |
| 推荐引用方式 GB/T 7714 | Charles F. Manski. Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w29358.pdf（396KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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