Instrumental Variable Methods for Longitudinal Discrete Data

描述（由申请人提供）：本申请提出了解决当前工具变量（IV）方法（例如，控制函数、广义矩法、线性IV模型），比较治疗，同时调整观察性和随机心理健康研究的横截面和纵向二分、计数和比率结局的未测量混杂因素。我们专注于“治疗的治疗”（TOT）的治疗效果，已被指定为至关重要的医疗保健政策和临床决策。在心理健康研究中，TOT效应通常是用“治疗后”效应来估计的，这种效应已被证明是有偏差的。目前的IV方法不能以临床可解释的比值或风险比的形式对TOT效应的二分、计数或发生率结局进行无偏估计。由于TOT效应涉及接受患者和提供者选择的治疗，因此在观察性和随机试验中，它们都容易受到选择偏倚的影响，TOT效应不涉及随机组之间的比较。该方法的创新源于解决基于横断面或纵向结局的TOT比值和风险比的当前IV方法的问题，在logistic和对数线性模型下评估IV假设，并评估治疗的时变效应调节因子，如特定目的：目的1：通过评估关键假设，将用于估计治疗间比值、风险和发生率比的非线性IV结构嵌套均值（SNM）模型扩展至复杂的观察和随机背景，这种TOT的估计是无法获得其他IV方法，包括控制功能和广义矩量法的方法。目标二：将目标1和2中的上述新方法扩展到纵向二元、计数和发生率结局，以解释治疗对结局的急性和滞后效应。目的3：扩展目的2中的纵向方法，以评估治疗对纵向结局的时变效应修饰因子。目的4：通过模拟和应用于几项随机和观察性研究，评估目的1、2和3中的方法。目标5：通过相应的软件、文档和网络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平均模型方法，用于评估IV假设、纵向结局和随时间变化的效应调节剂治疗方面的观察性和随机化试验的治疗优势风险和发生率。

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.