Instrumental Variable Methods for Longitudinal Discrete Data

描述(由申请人提供)：本申请提出了新的方法来解决当前工具变量(IV)方法(例如，控制函数、广义矩方法、线性IV模型)的问题，这些方法比较治疗方法，同时针对来自观察性和随机化的心理健康研究的横断面和纵向二分法、计数和比率结果进行未测量的混杂调整。我们专注于“治疗后治疗”(TOT)的治疗效果，它已被指定为卫生保健政策和临床决策的关键。在心理健康研究中，TOT效应通常被估计为“治疗后”效应，但已证明是有偏见的。目前的静脉注射方法不能以临床可解释的优势比或风险比的形式对二分、计数或发病率结果的TOT效应进行无偏见的估计。由于TOT效应涉及患者和提供者选择的治疗方案的接受，因此在观察性和随机化试验中都容易受到选择偏差的影响，因此TOT效应不涉及随机组之间的比较。这一方法的创新之处在于解决了目前基于横断面或纵向结果的TOT赔率和风险比的IV方法的问题，评估了Logistic模型和对数线性模型下的IV假设，并评估了具体目标中概述的治疗的时变效应修正因素：目标1：将用于估计治疗后治疗的赔率、风险和发病率的非线性IV结构嵌套平均(SNM)模型推广到复杂的观察和随机情况，对于这些情况，这种TOT估计在其他IV方法下是无法获得的，包括控制函数和广义矩方法。目的2：将目标1和目标2中的上述新方法扩展到纵向二元、计数和发病率结果，以说明治疗对结果的急性和滞后影响。目的3：扩展目标2中的纵向方法学，以评估治疗的时变效应修饰物对纵向结果的影响。目的4：通过模拟和在几个随机观察性研究中的应用，对目标1、2和3中的方法进行评估。目标5：通过相应的软件、文件和基于网络的教程传播所产生的方法。 公共卫生相关性：扩展了非线性工具变量结构嵌套均值模型方法，用于评估IV假设、纵向结果和时变效应修饰物治疗方面的治疗优势风险、观察性试验和随机试验的发生率。

DESCRIPTION (provided by applicant): This application proposes new methodology to resolve problems with current instrumental variable (IV) methods (e.g., control function, generalized method of moments, linear IV models) that compare treatments while adjusting for unmeasured confounding with respect to cross-sectional and longitudinal dichotomous, count, and rate outcomes from observational and randomized mental health studies. We focus on the "treated-on-treated" (TOT) treatment effect that has been designated as crucial for health care policy and clinical decisions. The TOT effect is commonly estimated in mental health studies with the "as-treated" effect that has shown to be biased. Current IV methods do not yield unbiased estimates of TOT effects for dichotomous, count, or incidence outcomes in the form of clinically interpretable odds or risk ratios. Because TOT effects involve the receipt of treatments selected by patients and providers, they are vulnerable to selection bias in both observational and randomized trials, for which the TOT effect does not involve comparisons between randomized groups. The innovation of this approach arises from resolving the problems of current IV approaches for the TOT odds and risk ratio based on either cross- sectional or longitudinal outcomes, evaluating IV assumptions under the logistic and log-linear models, and assessing time-varying effect modifiers of treatment as outlined in the Specific Aims: Aim 1: To extend, with assessments of crucial assumptions, non-linear IV structural nested mean (SNM) models for estimating treated-on-treated odds, risk, and incidence ratios to complex observational and randomized contexts, for which such TOT estimates are not obtainable under other IV methods, including the control function and generalized method of moments approaches. Aim 2: To extend the above new methodology in Aim 1 and 2 to longitudinal binary, count, and incidence outcomes in accounting for acute and lagged effects of treatment on outcomes. Aim 3: To extend the longitudinal methodology in Aim 2 to assess time-varying effect modifiers of treatment on longitudinal outcomes. Aim 4: To assess the methods in Aims 1, 2 and 3 with simulations and applications to several randomized and observational studies. Aim 5: To disseminate the resulting methodology with corresponding software, documentation, and web-based tutorials. PUBLIC HEALTH RELEVANCE: The nonlinear instrumental variable structural nested mean model method is extended for estimating treatment on treated odds risk, and incidence ratios for observational and randomized trials with respect to assessing IV assumptions, longitudinal outcomes, and time-varying effect modifiers treatment.