Separate Estimation of Primary and Secondary Cancer Preventive Impact: Analysis of a Case-Control Study of Skin Self-Examination and Melanoma
癌症一级与二级预防效果的分别估计:皮肤自检与黑色素瘤病例对照研究AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要明确关注筛查对癌症发生与死亡的预防影响及偏倚处理,因此归入因果推断,但未提供因果识别条件。
癌症一级与二级预防效果的分别估计:皮肤自检与黑色素瘤病例对照研究AI 解读 ↓
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Case-control studies increasingly have been used to evaluate the impact of cancer screening strategies. In this context the intent of the screening test has been to reduce cancer mortality by early detection of cancers, permitting curative therapy in some patients who would die of the disease if diagnosis were delayed until the disease was detected clinically. This phenomenon is known as secondary prevention. In an analysis of a case-control study of skin self-examination (SSE) in reducing mortality from melanoma, it was recognized that the exposure (SSE) may encourage the removal of precancerous nevi (moles), thereby reducing cancer incidence (primary prevention). This article describes an analytic strategy for obtaining separate estimates of the primary and secondary preventive impact of the screening practice. The method is focused primarily on resolving the problem of lead-time bias, caused by the artifactual advancement of the time of diagnosis in cases detected by screening.
Two frequent approaches for identifying structural VARs are external instruments, which carry economic content but are often weak, and non-Gaussianity of the shocks, which provides statistical identification but carries no economic meaning. We combine the two strategies in a single generalized method of moments framework. This hybrid approach point-identifies the target shocks while also identifying the non-target shocks up to sign and ordering. Under suitable rank…
The conditional average treatment effect (CATE) is frequently estimated in clinical studies to refute a homogeneous treatment effect hypothesis. Under this regime, all patients making up the population experience identical benefit from a given treatment relative to a comparator. Uncovering heterogeneous treatment effects through inference about the CATE, however, requires that covariates truly modifying the treatment effect be reliably collected at baseline. CATE-based…
内生性检验:一种基于矩的贝叶斯方法
A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this article, using the exponentially tilted empirical likelihood (ETEL) framework, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity…
We study difference-in-differences designs in which all units are initially untreated and later receive heterogeneous, strictly positive treatment doses. When quasi-untreated units receive doses close to zero, a difference-in-difference estimand using those units as the control group identifies a weighted average treatment effect per unit of treatment, under a parallel-trends assumption. We develop a nonparametric estimator and bias-corrected confidence intervals for this…
伪面板事件研究:可信应用的计量经济学基础
Event studies with panel data are widely used to estimate treatment effects. With repeated cross-sections, future treatment dates are unobserved for untreated individuals. Pseudo-panel event studies impute pre-treatment outcomes of the treated using matched untreated individuals. This paper establishes identification in such designs and characterizes bias when identification conditions fail. I show that the pseudo-panel regression is equivalent to weighted least squares with…
自适应实验中策略改进价值的推断
In this paper, we develop an asymptotically normal estimator for the value of policy improvement (VPI) under adaptively collected data. This value, which is defined as the gap between the value of the best treatment policy in a class and that of a baseline policy, gauges whether or not it is worth deploying a personalized treatment policy. Building an estimator for the VPI is naturally more difficult than estimating the value of a fixed policy. This is because, in addition…