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Targeted maximum likelihood estimation of treatment effects in randomized controlled trials and drug safety analysis.

机译：在随机对照试验和药物安全性分析中针对治疗效果的目标最大似然估计。

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摘要

In most randomized controlled trials (RCTs), investigators typically rely on estimators of causal effects that do not exploit the information in the many baseline covariates that are routinely collected in addition to treatment and the outcome. Ignoring these covariates can lead to a significant loss is estimation efficiency and thus power. Statisticians have underscored the gain in efficiency that can be achieved from covariate adjustment in RCTs with a focus on problems involving linear models. Despite recent theoretical advances, there has been a reluctance to adjust for covariates based on two primary reasons; (1) covariate-adjusted estimates based on non-linear regression models have been shown to be less precise than unadjusted methods, and, (2) concern over the opportunity to manipulate the model selection process for covariate adjustment in order to obtain favorable results. This dissertation describes statistical approaches for covariate adjustment in RCTs using targeted maximum likelihood methodology for estimation of causal effects with binary and right-censored survival outcomes.;Chapter 2 provides the targeted maximum likelihood approach to covariate adjustment in RCTs with binary outcomes, focusing on the estimation of the risk difference, relative risk and odds ratio. In such trials, investigators generally rely on the unadjusted estimate as the literature indicates that covariate-adjusted estimates based on logistic regression models are less efficient. The crucial step that has been missing when adjusting for covariates is that one must integrate/average the adjusted estimate over those covariates in order to obtain the population-level effect. Chapter 2 shows that covariate adjustment in RCTs using logistic regression models can be mapped, by averaging over the covariate(s), to obtain a fully robust and efficient estimator of the marginal effect, which equals a targeted maximum likelihood estimator. Simulation studies are provided that demonstrate that this targeted maximum likelihood method increases efficiency and power over the unadjusted method, particularly for smaller sample sizes, even when the regression model is misspecified.;Chapter 3 applies the methodology presented in Chapter 2 to a sampled RCT dataset with a binary outcome to further explore the origin of the gains in efficiency and provide a criterion for determining whether a gain in efficiency can be achieved with covariate adjustment over the unadjusted method. This chapter demonstrates through simulation studies and the data analysis that not only is the relation between R2 and efficiency gain important, but also the presence of empirical confounding. Based on the results of these studies, a complete strategy for analyzing these type of data is formalized that provides a robust method for covariate adjustment while protecting investigators from misuse of these methods for obtaining favorable inference.;Chapters 4 and 5 focus on estimation of causal effects with right-censored survival outcomes. Time-to-event outcomes are naturally subject to right-censoring due to early patient withdrawals. In chapter 4, the targeted maximum likelihood methodology is applied to the estimation of treatment specific survival at a fixed end-point in time. In chapter 5, the same methodology is applied to provide a competitor to the logrank test. The proposed covariate adjusted estimators, under no or uninformative censoring, do not require any additional parametric modeling assumptions, and under informative censoring, are consistent under consistent estimation of the censoring mechanism or the conditional hazard for survival. These targeted maximum likelihood estimators have two important advantages over the Kaplan-Meier and logrank approaches; (1) they exploit covariates to improve efficiency, and (2) they are consistent in the presence of informative censoring. These properties are demonstrated through simulation studies.;Chapter 6 concludes with a summary of the preceding chapters and a discussion of future research directions.
机译：在大多数随机对照试验（RCT）中，研究人员通常依赖因果效应的估计值，这些因果关系并未利用除治疗和结果外常规收集的许多基线协变量中的信息。忽略这些协变量会导致明显的损失，即估计效率，从而降低功耗。统计学家强调了RCT中协变量调整可以提高效率，重点是解决涉及线性模型的问题。尽管最近在理论上取得了进步，但仍然不愿根据两个主要原因来调整协变量。 （1）已经证明，基于非线性回归模型的经协变量调整的估计比未经调整的方法更不准确；（2）关注为协变量调整而选择模型选择过程以获得良好结果的机会。本论文介绍了使用目标最大似然方法估计具有二元和右删失生存结果的因果效应的随机对照试验协变量调整的统计方法。第二章提供了针对具有二进制结果的随机对照试验协变量调整的目标最大似然方法，重点是估算风险差异，相对风险和优势比。在此类试验中，研究人员通常依赖于未经调整的估计，因为文献表明基于逻辑回归模型的经协变量调整的估计效率较低。调整协变量时缺少的关键步骤是，必须对这些协变量进行调整后的估计值的积分/平均，才能获得总体水平的影响。第2章显示，可以通过对协变量进行平均，来映射使用逻辑回归模型对RCT中的协变量调整进行映射，以获得边际效应的充分鲁棒且有效的估计量，该估计量等于目标最大似然估计量。仿真研究表明，该目标最大似然方法相对于未经调整的方法提高了效率和功效，特别是对于较小的样本量，即使回归模型指定不当也是如此;第3章将第2章介绍的方法应用于采样的RCT数据集通过二元结果进一步探索效率增益的来源，并提供一个标准来确定是否可以通过对未调整方法进行协变量调整来实现效率增益。本章通过仿真研究和数据分析表明，R2和效率增益之间的关系很重要，而且经验混杂的存在也很重要。根据这些研究的结果，正式确定了用于分析此类数据的完整策略，该策略为协变量调整提供了一种可靠的方法，同时可以防止调查人员滥用这些方法来获得有利的推论。第4章和第5章着重于因果关系的估计删节生存结果的效果。由于患者提早退役，因此事件发生时间的结果自然会受到权利审查。在第4章中，将有针对性的最大似然方法应用于在固定的时间点估计治疗的特定生存期。在第5章中，使用相同的方法为对数秩检验提供了竞争者。拟议的协变量调整估计量在无检查或无信息检查的情况下，不需要任何其他参数建模假设，并且在信息检查下，在检查机制的一致估计或生存的条件危害下是一致的。与Kaplan-Meier和logrank方法相比，这些目标最大似然估计器具有两个重要优点； （1）他们利用协变量提高效率，（2）在提供信息审查的情况下保持一致。通过仿真研究证明了这些特性。第6章最后总结了前几章，并讨论了未来的研究方向。

著录项

  • 作者

    Moore, Kelly L.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Biology Biostatistics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 119 p.
  • 总页数 119
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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