Estimating relative risks for longitudinal and clustered binary data

估计纵向和聚类二进制数据的相对风险

• 批准号: 8554065
• 负责人: Binbing Yu
• 金额: $ 4.95万
• 依托单位: NATIONAL INSTITUTE ON AGING
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8554065
• 关键词: Address; Binomial Model; Case-Control Studies; Clinical Research; Clinical Trials; Cohort Effect; Cohort Studies; Control Groups; Cross-Sectional Studies; Data; Data Set; Epidemiology; Estimation Techniques; Failure; Inequality; Link; Literature; Logistic Models; Logistic Regressions; Longitudinal Studies; Markov Chains; Measurement; Measures; Medicine; Methods; Modeling; Monte Carlo Method; Odds Ratio; Outcome; Performance; Prevalence; Probability; Problem Solving; Public Health; Relative Risks; Research; Research Personnel; Retrospective Studies; Risk; Sampling; Social Sciences; Solutions; Statistical Methods; Techniques; Time; case control; design; interest; prospective; response; simulation; tool; treatment effect; trend; vector

Binary regression model is often used in situation where a dichotomous response variable and a vector of covariates are observed. The probability of a positive response, after suitable transformation, is assume to be linearly related to the covariates. The transformation, or link function, connect the probability to the linear predictors. The logistic regression with logit link is particularly popular because the regression parameters can be interpreted as log odds ratios (OR) and because the estimates of OR remain valid regardless retrospective or prospective sampling designs. There have been much discussions and interests in the literature concerning the appropriateness of using OR as the measure of exposure effect in epidemiological research. In case-control studies, the logistic model are preferable because of different sampling fractions in case and control groups. However, relative risk (RR) is more interpretable than OR, especially in prospective cohort studies. When the outcome is rare, the OR estimate from the logistic model is a good approximation to the RR, but it may substantially over-estimate the RR when outcome is common. Wacholder proposed to use a log-binomial model (relative risk regression) with binomial error and log link function to estimate the RR. One major problem of the log-binomial model is failure of convergence in computation. To solve this problem, many alternative methods of estimating RR have been proposed. So far, the log-binomial model is only proposed to data from cross-sectional studies. In recent years, longitudinal studies are increasingly used in public health, medicine and social sciences. A longitudinal study collects data over long periods of time. Measurements are taken on each variable over two or more distinct time periods. The longitudinal studies can separate the cohort effect from the treatment effect and, thus, also allow the researchers to measure change in variables over time. Two predominant regression approaches have been developed for longitudinal data. One is the marginal regression model (MRM) and the other is the generalized linear mixed (GLIMMIX) model. Logistic regression models for longitudinal binary response data have been widely discussed and used in various context. The major obstacle of estimating relative risks using log-binomial model is the inclusion of log link function. The proposed method will provide a solution to estimating the relative risks for longitudinal and clustered binary responses. This technique is expected to find applications in many epideimiological and clinical studies to assess the relative risks of treatments and exposures.

二元回归模型常用于观测到二分响应变量和协变量向量的情况。在适当的转换后，假定阳性响应的概率与协变量线性相关。转换或链接函数将概率与线性预测器连接起来。Logistic回归与logit链接是特别受欢迎的，因为回归参数可以解释为对数比值比（OR），因为OR的估计仍然有效，无论回顾性或前瞻性的抽样设计。关于在流行病学研究中使用OR作为暴露效应的度量是否合适，文献中有很多讨论和兴趣。在病例对照研究中，由于病例组和对照组的抽样分数不同，因此Logistic模型是优选的。然而，相对风险（RR）比OR更容易解释，特别是在前瞻性队列研究中。当结果罕见时，逻辑模型的OR估计值是RR的良好近似值，但当结果常见时，它可能会大大高估RR。Wacholder建议使用具有二项式误差和对数链接函数的对数二项式模型（相对风险回归）来估计RR。对数二项模型的一个主要问题是计算收敛失败。为了解决这个问题，已经提出了许多估计RR的替代方法。 到目前为止，对数二项模型仅适用于横断面研究的数据。近年来，纵向研究越来越多地应用于公共卫生、医学和社会科学领域。纵向研究收集长时间的数据。在两个或多个不同的时间段内对每个变量进行测量。纵向研究可以将队列效应与治疗效应分开，因此也允许研究人员测量变量随时间的变化。两个主要的回归方法已开发的纵向数据。一种是边际回归模型（MRM），另一种是广义线性混合模型（GLIMMIX）。纵向二元响应数据的Logistic回归模型已被广泛讨论和应用于各种场合。使用对数二项模型估计相对风险的主要障碍是包含对数链接函数。 所提出的方法将提供一个解决方案，以估计相对风险的纵向和集群的二进制响应。这项技术有望在许多人体免疫学和临床研究中找到应用，以评估治疗和暴露的相对风险。