Comparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indication

For patients who were previously treated for prostate cancer, salvage hormone therapy is frequently given when the longitudinal marker prostate‐specific antigen begins to rise during follow‐up. Because the treatment is given by indication, estimating the effect of the hormone therapy is challenging. In a previous paper we described two methods for estimating the treatment effect, called two‐stage and sequential stratification. The two‐stage method involved modeling the longitudinal and survival data. The sequential stratification method involves contrasts within matched sets of people, where each matched set includes people who did and did not receive hormone therapy. In this paper, we evaluate the properties of these two methods and compare and contrast them with the marginal structural model methodology. The marginal structural model methodology involves a weighted survival analysis, where the weights are derived from models for the time of hormone therapy. We highlight the different conditional and marginal interpretations of the quantities being estimated by the three methods. Using simulations that mimic the prostate cancer setting, we evaluate bias, efficiency, and accuracy of estimated standard errors and robustness to modeling assumptions. The results show differences between the methods in terms of the quantities being estimated and in efficiency. We also demonstrate how the results of a randomized trial of salvage hormone therapy are strongly influenced by the design of the study and discuss how the findings from using the three methodologies can be used to infer the results of a trial. Copyright © 2013 John Wiley & Sons, Ltd.     

Inverse probability weighted estimating equations for randomized trials in transfusion medicine

Thrombocytopenia is a condition characterized by extremely low platelet counts, which puts patients at elevated risk of morbidity and mortality because of bleeding. Trials in transfusion medicine are routinely designed to assess the effect of experimental platelet products on patients’ platelet counts. In such trials, patients may receive multiple platelet transfusions over a predefined period of treatment, and a response is available from each such administration. The resulting data comprised multiple responses per patient, and although it is natural to want to use this data in testing for treatment effects, naive analyses of the multiple responses can yield biased estimates of the probability of response and associated treatment effects. These biases arise because only subsets of the patients randomized contribute response data on the second and subsequent administrations of therapy and the balance between treatment groups with respect to potential confounding factors is lost. We discuss the design and analysis issues involved in this setting and make recommendations for the design of future platelet transfusion trials. Copyright © 2013 John Wiley & Sons, Ltd.     

Causal inference,probability theory,and graphical insights

Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M‐bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK‐Plot for understanding Simpson's paradox with a binary confounder, the BK2‐Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD‐Plot for understanding the principal stratification component of the paired availability design. Published 2013. This article is a US Government work and is in the public domain in the USA.     

Early multidisciplinary evaluation and advice was ineffective for whiplash‐associated disorders

Background: Whiplash is the most common type of injury reported in traffic accidents, but the effectiveness of conservative treatment for whiplash lacks evidence. Aims: To assess the effect of early multidisciplinary evaluation and advice on the frequency of chronic neck pain three years post‐injury in persons with minor or moderate traffic injuries. Methods: In an insurance setting, we tested the effect by (1) a risk score matched prospective cohort design, (2) a propensity score matched nested case‐control design and, (3) a risk and propensity score adjusted multivariate analysis in an unmatched prospective cohort design. We simulated unobserved risk and propensity factors to evaluate the robustness of the results for hidden bias. Results: All three designs showed a significantly increased risk for chronic neck pain among those who received the intervention compared to those who did not. The relative risks ranged from 1.7 (95% CI: 1.0–2.6) to 2.6 (95% CI: 1.5–4.0). The results were robust to hidden bias. Conclusion: The consistency of the findings across the different designs strongly suggest that early multidisciplinary evaluation and advice increased the risk of having chronic neck pain three years following a minor or moderate traffic injury. Literally, the intervention may therefore have done more harm than good.     

如何控制观察性疗效比较研究中的混杂因素：（一）已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补充,其应用价值越来越受到关注,混杂偏倚是其重要偏倚来源。本文介绍观察性疗效比较研究中已测量的混杂因素控制的统计分析方法。对于已测量的混杂因素,可采用传统的分层分析、配对分析、协方差分析或多因素分析,也可采用倾向性评分、疾病风险评分等方法进行混杂因素匹配、分层和调整。良好的设计需从源头控制各种混杂,事后统计分析则应在理解各类方法的应用前提下,严格把握适用条件。     

Doubly robust estimation and causal inference for recurrent event data

Many longitudinal databases record the occurrence of recurrent events over time. In this article, we propose a new method to estimate the average causal effect of a binary treatment for recurrent event data in the presence of confounders. We propose a doubly robust semiparametric estimator based on a weighted version of the Nelson-Aalen estimator and a conditional regression estimator under an assumed semiparametric multiplicative rate model for recurrent event data. We show that the proposed doubly robust estimator is consistent and asymptotically normal. In addition, a model diagnostic plot of residuals is presented to assess the adequacy of our proposed semiparametric model. We then evaluate the finite sample behavior of the proposed estimators under a number of simulation scenarios. Finally, we illustrate the proposed methodology via a database of circus artist injuries.     

Sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival and competing risks outcomes

No unmeasured confounding is often assumed in estimating treatment effects in observational data, whether using classical regression models or approaches such as propensity scores and inverse probability weighting. However, in many such studies collection of confounders cannot possibly be exhaustive in practice, and it is crucial to examine the extent to which the resulting estimate is sensitive to the unmeasured confounders. We consider this problem for survival and competing risks data. Due to the complexity of models for such data, we adapt the simulated potential confounder approach of Carnegie et al (2016), which provides a general tool for sensitivity analysis due to unmeasured confounding. More specifically, we specify one sensitivity parameter to quantify the association between an unmeasured confounder and the exposure or treatment received, and another set of parameters to quantify the association between the confounder and the time-to-event outcomes. By varying the magnitudes of the sensitivity parameters, we estimate the treatment effect of interest using the stochastic expectation-maximization (EM) and the EM algorithms. We demonstrate the performance of our methods on simulated data, and apply them to a comparative effectiveness study in inflammatory bowel disease. An R package “survSens” is available on CRAN that implements the proposed methodology.     

Causal Diagrams and Multivariate Analysis I: A Quiver Full of Arrows

How do we know which variables we should include in our multivariate analyses? What role does each variable play in our understanding of the analysis? In this article I begin a discussion of these issues and describe 2 different types of studies for which this problem must be handled in different ways.