Estimation Efficiency With Omitted Covariates in Generalized Linear Models
广义线性模型中遗漏协变量与估计效率AI 解读 ↓
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广义线性模型中遗漏协变量与估计效率
- 研究问题
- 在遗漏协变量不导致目标系数偏差的情形下，遗漏与响应相关的变量何时降低或提高估计效率，随机试验和观察研究有何差异？
- 方法与思路
- 作者区分遗漏变量与已纳入变量独立，以及相关但不构成混杂的两类情况，推导表达式量化广义线性模型中的效率变化。
- 材料陈述的贡献
- 摘要称遗漏独立且与响应相关的协变量可能严重损失效率，而遗漏某些非混杂协变量可能提高效率，并据此讨论调整策略。
- 阅读关注点
- 应核对无偏情形的模型与链接函数条件、非混杂的具体含义，以及效率比较对应的参数和随机试验、观察研究中的适用范围。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；结论针对特定无偏情形，不能把观察研究中的调整建议推广为普遍因果识别规则。
原文摘要
Although omitting covariates associated with the response in generalized linear models can yield seriously biased estimates of the effects of the included covariates, in certain settings there is no bias. This article considers the effect of omitted covariates on the efficiency of the estimated effects of the included covariates in these cases. I consider the case where the omitted covariate is independent of the included covariates, as in studies with randomized treatment assignment, as well as the case where the omitted covariate is correlated with the included but not a confounder. I present expressions to show that omitting covariates associated with the response and independent of the included covariate can result in serious efficiency losses for estimates of the effects of the included covariates. I also present expressions to show that omitting nonconfounding covariates can lead to efficiency gains. These expressions also quantify the magnitude of estimation efficiency with omitted covariates. The findings suggest that to improve the efficiency of estimated covariate effects of interest, analysts of randomized clinical trial data should adjust for covariates that are strongly associated with the outcome, and that analysts of observational data should not adjust for covariates that do not confound the association of interest.