如何控制观察性疗效比较研究中的混杂因素：（三）混杂因素控制的敏感性分析方法

观察性疗效比较研究中混杂在所难免,在利用一些统计分析方法对已测量或未测量混杂因素加以控制后,是否消除了混杂的影响不得而知,此时需进行敏感性分析。本文介绍混杂因素处理中的敏感性分析方法。基于不同的研究,敏感性分析思路各不相同,对于已测量混杂因素可采用传统的敏感性分析方法,对于未测量混杂因素目前理论相对系统的方法主要有混杂函数法、边界因子法和倾向性评分校正法,另外Monte Carlo敏感性分析和Bayes敏感性分析也是近年来备受热议的方法。当敏感性分析结果与主要分析结果一致时,无疑提高了研究结论的可靠性。     

如何控制观察性疗效比较研究中的混杂因素：（二）未知或未测量混杂因素的统计学分析方法

观察性疗效比较研究作为随机对照研究的证据补充,其应用价值越来越受到关注。未测量混杂因素的统计学分析方法是观察性疗效比较研究中的重大挑战,本文对观察性疗效比较研究中未知或未测量的混杂因素控制的统计分析方法进行述评。未测量混杂因素的统计学方法包括工具变量法、本底事件率比校正法和双重差分模型及其衍生方法。工具变量法模型构造巧妙,但满足条件的工具变量在实际研究中并不易得;本底事件率比校正法和双重差分模型均要求研究数据有干预前信息,有些实际研究中往往无法满足。未测量混杂因素对统计学方法提出了新的要求、新的挑战,有待国内外统计学者的进一步完善和研究。     

医院感染危险因素研究中的混杂偏倚

在医院感染危险因素的研究中，有些研究者往往忽视了混杂偏倚的存在，本文选出两篇有关论文就其混杂偏倚问题进行分析，并列出了控制混杂偏倚的几种主要方法，提醒研究者注意预防和控制混杂偏倚     

观察性研究中校正连续型混杂因素的非线性方法

<正>混杂偏倚(confounding bias)是观察性研究中的一类重要偏倚,它是指由于混杂因素既与暴露因素又与结局存在相关关系,导致暴露与结局之间的真实关系受到了干扰而产生的偏倚[1]。因此,观察性研究中如何控制混杂一直是研究人员所关注的重要问题。在统计分析阶段一种常用的处理办法是将混杂因素纳入回归模型中进行校正。实际问题中常常遇到这样的情况,即混杂变量为连续型指标,该变量与结局变量间的     

观察性研究方法与医院资料的统计分析

目的通过个案分析,说明如何采用观察性研究的方法对医院资料进行统计分析.方法正确的研究设计与搜集客观准确的基础数据,明确观察对象所能推论的总体,设立对比组,通过分层分析及率的标准化等方法进行组间差别比较.结果医院资料不同于试验数据.由于医院资料通常不能随机分组,并存在混杂,不能简单地用假设检验方法推论因果关系.结论在对医院资料进行统计分析时,应遵循观察性研究的原则和方法,以避免可能存在的信息偏倚、选择偏倚和混杂偏倚.     

研究设计时混杂控制策略的结构分类

混杂影响着人群因果关系的发生。依据混杂因素是否已知、可测量及已测量,可将其分为4类情形。基于有向无环图,对混杂的控制策略分为两类：①混杂路径打断法,又可分为单路径和双路径打断法,分别对应于暴露完全干预法、限制法和分层法;②混杂路径保留法,分别对应于暴露不完全干预法（工具变量设计或不完美的随机对照试验）、中间变量法和匹配法。其中,随机对照试验、工具变量设计或孟德尔随机化设计、中间变量分析可满足4类混杂的控制,而限制法、分层法和匹配法仅适用于已知、可测量并已测量的混杂。识别不同类型混杂的控制机制,有助于在研究设计阶段提出应对措施,是获得正确因果效应估计的前提。     

“探针变量”：观察性研究中混杂因素识别的工具

观察性研究中往往存在未知或未测量的混杂因素，是流行病学因果关联研究中的重大挑战。本文介绍一种可以应用在观察性研究中的一种对未知/未测量混杂因素进行识别和效应评估的工具——“探针变量”。其主要可以分为暴露探针变量、结局探针变量以及中介探针3种形式，前2种不仅可以对未知/未测量混杂因素进行识别，也可以对其效应量进行估计，从而揭示真实的暴露与结局之间的关联。而中介探针则是针对“中介因子”进行控制，从而识别暴露和结局之间是否存在未测量混杂因素。该理论实践过程中最大的困难在于“探针变量”的选择和确定，不恰当的“探针变量”可能引入新的混杂，导致未测量混杂因素识别不准确。“探针变量”可以推荐作为观察性研究报告中的一项敏感性分析内容，有助于读者真实理解暴露与结局之间的关联，增加观察性流行病学研究中的证据力度。     

队列研究中的依时混杂偏倚和随访时间偏倚

【导读】未加识别或控制的混杂因素和偏倚会影响流行病学研究中因果关系的准确判断。目前已熟知队列研究中可能出现的选择偏倚及失访偏倚,为此笔者复习了队列研究中可能存在的依时混杂偏倚及其控制方法,并通过实例提出随访时间偏倚的概念及其控制方法。     

倾向评分分层和回归分析

在流行病学研究中,分层分析和回归分析是资料分析阶段控制混杂偏倚的重要手段。将倾向评分法与传统的分层和回归结合,则可更有效地控制混杂偏倚,同时可以克服传统方法的一些局限性。     

针对连续型处理因素的广义倾向性评分估计方法简介

在观察性研究中进行因果推断的众多方法中,用于控制已测量混杂的倾向性评分方法应用越来越广泛。该类方法主要分为两步：首先估计倾向性评分,然后采取回归、加权、匹配和分层等手段进一步估计感兴趣的因果参数。不同于传统的二分类处理情况,近年来针对连续型处理因素的广义倾向性评分方法被提出。目前已发展出了许多估计广义倾向性评分和直接估...     

基于阴性对照理论的未测量混杂因素控制方法

非随机对照研究中未测量混杂因素的控制极具挑战。阴性对照理论基于“阴性对照的检测结果必须阴性”的思想，在进行人群研究时，额外设置合适的阴性对照，将关联特异度的思想融入人群研究中进行未测量混杂因素的识别和控制。本文从统计学角度解析阴性对照理论控制未测量混杂因素的基本原理，详细介绍基于阴性对照理论的系列衍生方法：死亡率标准化校正法、校正P值法、广义双重差分模型以及双向阴性对照法，并结合代表性案例对其合理应用进行述评。阴性对照是识别、校正和控制未测量混杂因素的重要统计设计思想，是基于现实世界数据开展实效比较研究的重要方法。     

On variance estimate for covariate adjustment by propensity score analysis

Propensity score (PS) methods have been used extensively to adjust for confounding factors in the statistical analysis of observational data in comparative effectiveness research. There are four major PS‐based adjustment approaches: PS matching, PS stratification, covariate adjustment by PS, and PS‐based inverse probability weighting. Though covariate adjustment by PS is one of the most frequently used PS‐based methods in clinical research, the conventional variance estimation of the treatment effects estimate under covariate adjustment by PS is biased. As Stampf et al. have shown, this bias in variance estimation is likely to lead to invalid statistical inference and could result in erroneous public health conclusions (e.g., food and drug safety and adverse events surveillance). To address this issue, we propose a two‐stage analytic procedure to develop a valid variance estimator for the covariate adjustment by PS analysis strategy. We also carry out a simple empirical bootstrap resampling scheme. Both proposed procedures are implemented in an R function for public use. Extensive simulation results demonstrate the bias in the conventional variance estimator and show that both proposed variance estimators offer valid estimates for the true variance, and they are robust to complex confounding structures. The proposed methods are illustrated for a post‐surgery pain study. Copyright © 2016 John Wiley & Sons, Ltd.     

On regression adjustment for the propensity score

Propensity scores are widely adopted in observational research because they enable adjustment for high‐dimensional confounders without requiring models for their association with the outcome of interest. The results of statistical analyses based on stratification, matching or inverse weighting by the propensity score are therefore less susceptible to model extrapolation than those based solely on outcome regression models. This is attractive because extrapolation in outcome regression models may be alarming, yet difficult to diagnose, when the exposed and unexposed individuals have very different covariate distributions. Standard regression adjustment for the propensity score forms an alternative to the aforementioned propensity score methods, but the benefits of this are less clear because it still involves modelling the outcome in addition to the propensity score. In this article, we develop novel insights into the properties of this adjustment method. We demonstrate that standard tests of the null hypothesis of no exposure effect (based on robust variance estimators), as well as particular standardised effects obtained from such adjusted regression models, are robust against misspecification of the outcome model when a propensity score model is correctly specified; they are thus not vulnerable to the aforementioned problem of extrapolation. We moreover propose efficient estimators for these standardised effects, which retain a useful causal interpretation even when the propensity score model is misspecified, provided the outcome regression model is correctly specified. Copyright © 2014 John Wiley & Sons, Ltd.     

Comparing the performance of propensity score methods in healthcare database studies with rare outcomes

Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, studies constructed from electronic healthcare databases, for example, administrative claims data, often adjust for many, possibly hundreds, of potential confounders. Despite the importance of maximizing efficiency when there are many confounders and few observed outcome events, there has been relatively little research on the relative performance of different propensity score methods in this context. In this paper, we compare a wide variety of propensity‐based estimators of the marginal relative risk. In contrast to prior research that has focused on specific statistical methods in isolation of other analytic choices, we instead consider a method to be defined by the complete multistep process from propensity score modeling to final treatment effect estimation. Propensity score model estimation methods considered include ordinary logistic regression, Bayesian logistic regression, lasso, and boosted regression trees. Methods for utilizing the propensity score include pair matching, full matching, decile strata, fine strata, regression adjustment using one or two nonlinear splines, inverse propensity weighting, and matching weights. We evaluate methods via a ‘plasmode’ simulation study, which creates simulated datasets on the basis of a real cohort study of two treatments constructed from administrative claims data. Our results suggest that regression adjustment and matching weights, regardless of the propensity score model estimation method, provide lower bias and mean squared error in the context of rare binary outcomes. Copyright © 2017 John Wiley & Sons, Ltd.     

Safety surveillance and the estimation of risk in select populations: Flexible methods to control for confounding while targeting marginal comparisons via standardization

We consider the critical problem of pharmacosurveillance for adverse events once a drug or medical product is incorporated into routine clinical care. When making inference on comparative safety using large-scale electronic health records, we often encounter an extremely rare binary adverse outcome with a large number of potential confounders. In this context, it is challenging to offer flexible methods to adjust for high-dimensional confounders, whereas use of the propensity score (PS) can help address this challenge by providing both confounding control and dimension reduction. Among PS methods, regression adjustment using the PS as a covariate in an outcome model has been incompletely studied and potentially misused. Previous studies have suggested that simple linear adjustment may not provide sufficient control of confounding. Moreover, no formal representation of the statistical procedure and associated inference has been detailed. In this paper, we characterize a three-step procedure, which performs flexible regression adjustment of the estimated PS followed by standardization to estimate the causal effect in a select population. We also propose a simple variance estimation method for performing inference. Through a realistic simulation mimicking data from the Food and Drugs Administration's Sentinel Initiative comparing the effect of angiotensin-converting enzyme inhibitors and beta blockers on incidence of angioedema, we show that flexible regression on the PS resulted in less bias without loss of efficiency, and can outperform other methods when the PS model is correctly specified. In addition, the direct variance estimation method is a computationally fast and reliable approach for inference.     

Matching with time-dependent treatments: A review and look forward

Observational studies of treatment effects attempt to mimic a randomized experiment by balancing the covariate distribution in treated and control groups, thus removing biases related to measured confounders. Methods such as weighting, matching, and stratification, with or without a propensity score, are common in cross-sectional data. When treatments are initiated over longitudinal follow-up, a target pragmatic trial can be emulated using appropriate matching methods. The ideal experiment of interest is simple; patients would be enrolled sequentially, randomized to one or more treatments and followed subsequently. This tutorial defines a class of longitudinal matching methods that emulate this experiment and provides a review of existing variations, with guidance regarding study design, execution, and analysis. These principles are illustrated in application to the study of statins on cardiovascular outcomes in the Framingham Offspring cohort. We identify avenues for future research and highlight the relevance of this methodology to high-quality comparative effectiveness studies in the era of big data.     

New strategies are needed to improve the accuracy of influenza vaccine effectiveness estimates among seniors

ObjectiveThe magnitude of the benefit of influenza vaccine among elderly individuals has been recently debated. Existing vaccine effectiveness estimates derive primarily from observational studies, which may be biased. In this paper, we provide a methodological examination of the potential sources of bias in observational studies of influenza vaccine effectiveness in seniors and propose design and analysis strategies to reduce bias in future studies.Study Design and SettingWe draw parallels to bias documented in observational studies of therapies in other areas of medical research including pharmacoepidemiology, discuss reasons why existing adjustment methods in influenza studies may not adequately control for the bias, and evaluate statistical approaches that may yield more accurate estimation of influenza vaccine effectiveness.ResultsThere is strong evidence for the presence of bias in existing observational estimates of influenza vaccine effectiveness in the elderly and the failure of current adjustment methods to reduce bias.ConclusionPromising approaches for reducing bias include obtaining more accurate information on confounders, such as functional status, avoiding all-cause death in favor of outcomes, such as pneumonia or influenza-related pneumonia, and evaluating the extent to which bias is reduced by these and other methods using the ‘control’ period before influenza season.     

A dynamic adaptation of the propensity score adjustment for effectiveness analyses of ordinal doses of treatment

The propensity score adjustment is a method to reduce bias in observational studies. We propose a strategy that involves a novel combination of three data analytic techniques, which adapts the propensity adjustment for additional perturbations of longitudinal, observational studies. First, ordinal logistic regression examines propensity for ordinal doses of treatment. Second, a mixed-model approach incorporates the multiple treatment trials and multiple episodes that are characteristic of chronically ill subjects. Finally, a mixed-effects grouped-time survival model incorporates the propensity score in treatment effectiveness analyses. The strategy that is applied here to an observational study of affective illness can also be used to evaluate the effectiveness of treatments for other chronic illnesses.