Case-control studies = odds ratios: blame the retrospective model.
Case-control studies = odds ratios: blame the retrospective model.
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病例对照研究=比值比：归咎于回顾性模型。
DOI：
10.1097/ede.0b013e3181c308f5
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发表时间：
2010
期刊：
影响因子：
5.4
通讯作者：
B. Langholz
中科院分区：
文献类型：
--
作者：
B. Langholz
许多流行病学家和统计学家认为，优势比是唯一可以从病例对照研究中可靠估计的指标。我收到了许多病例对照研究论文的评论，指导我们将“比率比”改为“赔率比”，而事实上，这是我们估计的比率比率。此外，在讨论我们开发的从嵌套病例对照研究中估计绝对风险度量的方法时，我发现许多人惊讶地得知，在病例对照环境中，绝对风险可以被系统和可靠地估计。虽然病例对照研究最适合于估计相关措施，我不希望从病例对照数据总体上减少在其他规模上估计的挑战，但有可靠的方法可以做到这一点。报告绝对风险估计似乎是可取的，以补充通常的病例对照相对测量分析，1但即使在可行的情况下，也很少这样做。那么，为什么会有赔率比的固定呢？我认为核心问题是流行病学家经常认为病例对照研究是一种“追溯性模式”。根据这一观点，我们从一组病例和控制措施开始。然后，协变量(暴露和其他因素)作为独立的实现出现，其分布取决于疾病状态。这与队列数据的“前瞻性模型”不同，在队列数据中，我们从一组具有给定协变量的受试者开始，疾病状态是独立实现的结果，概率依赖于协变量的值。学生学习回溯性逻辑模型中的比数比参数与相应的前瞻性逻辑模型中的比数比参数相同，但其他测量方法不能转换。这一问题更令人困惑，因为可以使用相应的前瞻性队列数据Logistic回归获得追溯模型病例控制数据中的优势比参数的有效估计，而估计的“基线优势”是可以忽略的滋扰参数。2、3但病例对照研究不是后向队列(“特罗西”)研究。4表示病例对照设计的一种更现实的方法是从前瞻性队列中抽样，这取决于队列受试者的疾病结局和其他可用信息--我们称之为嵌套病例对照模型。5虽然这种表述肯定不是什么新鲜事，在流行病学教科书中经常被用作思考基本抽样和偏差问题的“概念框架”，但几乎总是用回溯模型的方法来发展分析方法。我和我的同事采取的另一种方法是完全基于嵌套式病例对照模型开发病例对照研究方法，并提供一个统一的框架，跨越队列和病例对照分析方法，以及跨越个别匹配和不匹配的病例对照研究设计。虽然仍有一些重要的空白需要填补，但我们已经取得了进展。之后
Many epidemiologists and statisticians believe that the odds ratio is the only measure that can be reliably estimated from case-control studies. I have received many reviews of case-control study papers instructing us to change “rate ratio” to “odds ratio” when, in fact, it was the rate ratio that we had estimated. Further, in discussions about methods we have developed to estimate absolute risk measures from nested case-control studies, I have found that many are surprised to learn that absolute risk can be estimated systematically and reliably in the case-control setting. While case-control studies are best suited for estimation of relative measures, and I do not wish to minimize the challenges of estimation on other scales from case-control data generally, there are reliable methods for doing so. Reporting of absolute risk estimates seems desirable to supplement the usual case-control relative measure analyses, 1 but this is only rarely done even when it is feasible. So why is there an odds-ratio fixation? I believe the core problem is that epidemiologists often think of case-control studies as a “retrospective model.” According to this view, we start with a set of cases and controls. Then the covariates (exposures and other factors) occur as independent realizations with distribution dependent on disease status. This is in contrast to the “prospective model” of cohort data, in which we start with a group of subjects with given covariates, and disease status is the result of independent realizations with probability dependent on the covariate values. Students learn that the odds ratio parameters in the retrospective logistic model are same as the odds ratio parameters in the corresponding prospective logistic model, but that other measures do not translate. The matter is further confused because valid estimation of odds ratio parameters from retrospective model casecontrol data may be obtained using the corresponding prospective cohort data logistic regression, with the estimated “baseline odds” a nuisance parameter to be ignored. 2,3 But case-control studies are not backwards cohort (“trohoc”) studies. 4 A more realistic way to represent case-control designs is as sampling from a prospective cohort that depends on disease outcomes and other information available on cohort subjects— what we have called the nested case-control model. 5 While this representation is certainly not new, and is often used in epidemiology textbooks as a “conceptual framework” to think about basic sampling and bias issues, almost invariably the retrospective model approach is used to develop the analysis methods. The alternative approach my colleagues and I have taken is to develop case-control study methods based completely on the nested case-control model, and to provide a unifying framework across cohort and case-control analysis methods, as well as across individually matched and unmatched case-control study designs. While there are still some important gaps to be filled, we have made progress. After