横断面研究能否进行因果推断

基于变量调查(或测量)的共时性、统计学关联及幸存者偏倚等原因,横断面研究被认为不能进行因果推断,这是当前的"共识"。本文基于因果思维,借助因果图,首先明确定义真实截面和测量截面,并识别截面概念仅存在于理论的特性。实际横断面研究中,测量变量的共时性并不存在,而是无一例外地表现为非共时性时序,其实质上相当于测量变量间互为独立性假设,或不存在有差别错分偏倚。类似于累积病例对照研究和历史性队列研究,横断面研究均为暴露和结局已存在或发生后进行的测量,这种测量相当于对变量值的历史重建或"考古"。这类研究进行因果推断的共性前提条件之一是,测量变量与其历史变量间必须存在着因果律。测量变量均为真实变量的替代者,测量变量间的时序在因果推断上并不重要。应加强对横断面研究分析性角色的认识。     

Causal Diagram Techniques for Urologic Oncology Research

Large quantities of data are now available to medical researchers; however, observational studies are plagued by bias and confounding. Additionally, much of this research only speculates on variable associations, leaving prospective randomized clinical trials as the sole purveyors of claims about causal relations between variables. There has been a growing movement of causal inference that uses new techniques to investigate causality using observational data. These techniques include the implementation of directed acyclic graphs, which allow researchers to explicitly and reproducibly define the causal relationships between study variables, thus making statistical analysis more robust. Directed acyclic graphs further allow researchers to identify confounding and other sources of bias and to discover causal effects among complex networks of variables. This review aims to introduce these techniques to the general urology and urologic oncology research communities in order to provide a basic understanding of causal inference and analysis and call for integration of these practices more generally in research methodology.     

Estimating returns to hospital volume: Evidence from advanced cancer surgery

High-volume hospitals typically perform better than low-volume hospitals. In this paper, we study whether such patterns reflect a causal effect of case volume on patient outcomes. To this end, we exploit closures and openings of entire cancer clinics in Swedish hospitals which provides sharp and arguably exogenous variation in case volumes. Using detailed register data on more than 100,000 treatment episodes of advanced cancer surgery, our results suggest substantial positive effects of operation volume on survival. Complementary analyses point to learning-by-doing as an important explanation.     

基于大自然时间轴的测量时序分类法

因果推断中，时序（或方向性）的概念尚未完全明确。本文从因果思维出发，以真实因和真实果的发生时间将大自然时间轴划分为3个时域和2个时点，从而锚定了因果推断只能实现于第3时域。测量时序可分为5种类型：跨第1和3时域纵向时序（实验性时序）、跨第2和3时域纵向时序、同时域纵向时序、同时域逆纵向时序和同时域横向时序（观察性时序）。这种分类法适用于首次或多次测量、及时和延后测量等所有测量策略。除了实验中真实因的测量（或干预措施）在其发生之前（第1时域）或观察和实验中真实因的测量在真实果发生之前（第2时域）的情形外，所有其他测量策略类似于历史重建或"考古"，测量时序的重要性次于测量的准确性。从研究设计应整合偏倚设计的观点来看，本文提出基于大自然时间轴的测量时序五分类法，概念清楚并将有助于判断研究过程中可能出现的偏倚，为正确进行因果推断研究奠定基础。     

Activity spaces in studies of the environment and physical activity: A review and synthesis of implications for causality

Activity spaces are increasingly used to understand how people interact with their environment and engage in activity but their use may raise challenges regarding causal inference. We conducted a systematic review of findings and the methodological, analytical and conceptual issues relevant to causal inference. Studies were included if they comprised a spatial summary of locations visited, assessed any part of the causal pathway between the environment, physical activity and health, and used quantitative or qualitative methods. We searched seven electronic databases in January 2018 and screened 11910 articles for eligibility. Forty-seven studies were included for review. Studies answered research questions about features of or environmental features within activity spaces using a range of spatial and temporal summary techniques. The conceptual challenge of using activity spaces to strengthen causal inference was rarely considered, although some studies discussed circularity, temporality, and plausibility. Future studies should use longitudinal and experimental designs and consider the potential and actual use of spaces for physical activity, and their relationship with total levels of activity.     

Causal Inference and Estimands in Clinical Trials

Abstract

The National Research Council’s report on the prevention and treatment of missing data highlighted the need to clearly specify causal estimands. This focus fundamentally changed how the missing data problem was perceived and addressed in clinical trials. The recent ICH E9(R1) addendum is another major step in promoting the use of the causal estimands framework that should further influence how clinical trial protocols and statistical analysis plans are written and implemented. The language of potential outcomes that is widely accepted in the causal inference literature is not widely recognized in the clinical trialists community and was not used in defining causal estimands in the NRC report or the ICH E9(R1). In this article, we attempt to bridge the gap between the causal inference community and clinical trialists to further advance the use of causal estimands in clinical trial settings. We illustrate how concepts from causal literature, such as potential outcomes and dynamic treatment regimens, can facilitate defining and implementing causal estimands and may provide a unifying language to describing the targets for both observational and randomized clinical trials.     

Association of heart rate and diabetes among 0.5 million adults in the China Kadoorie biobank: Results from observational and Mendelian randomization analyses

Background and aimsObservational studies have associated resting heart rate with incident diabetes. Whether the associations are causal remains unclear. We aimed to examine the shape and strength of the associations and assessed the causal relevance of such associations in Chinese adults.Methods and resultsThe China Kadoorie Biobank enrolled 512,891 adults in China. Cox proportional hazard regression models was conducted to estimate hazard ratios (HRs) for the associations of resting heart rate with type 2 diabetes and total diabetes. Among 92,724 participants, 36 single-nucleotide polymorphisms (SNPs) related to resting heart rate were used to construct genetic risk score. We used Mendelian randomization analyses to make the causal inferences. During a median follow-up of 9 years, 7872 incident type 2 diabetes and 13,349 incident total diabetes were documented. After regression dilution bias adjustment, each 10 bpm higher heart rate was associated with about a 26% higher risk of type 2 diabetes (HR, 1.26 [95% CI, 1.23, 1.29]) and 23% higher risk of total diabetes (HR, 1.23 [95% CI, 1.20, 1.26]). Instrumental variable analyses showed participants at top quintile compared with those at bottom quintile had 30% higher risk for type 2 diabetes (HR, 1.30 [95% CI, 1.17, 1.43]), and 10% higher risk for total diabetes (HR, 1.10 [95% CI, 1.02, 1.20]).ConclusionsThis study provides evidence that resting heart rate is an important risk factor for diabetes risk. The results suggest that novel treatment approaches targeting reduction of high heart rate for incidence of diabetes may be worth further investigation.     

Approaches for drawing causal inferences from epidemiological birth cohorts: A review

Large-scale population-based birth cohorts, which recruit women during pregnancy or at birth and follow up their offspring through infancy and into childhood and adolescence, provide the opportunity to monitor and model early life exposures in relation to developmental characteristics and later life outcomes. However, due to confounding and other limitations, identification of causal risk factors has proved challenging and published findings are often not reproducible. A suite of methods has been developed in recent years to minimise problems afflicting observational epidemiology, to strengthen causal inference and to provide greater insights into modifiable intra-uterine and early life risk factors. The aim of this review is to describe these causal inference methods and to suggest how they may be applied in the context of birth cohorts and extended along with the development of birth cohort consortia and expansion of “omic” technologies.     

医疗保险对健康状况的影响研究综述

本文回顾了国内外学者对医疗保险和健康状况关系的研究。首先综述了关于医疗保险和健康状况关系的三种观点;其次依据研究的数据来源,将国外文献分为三个层次进行综述;最后,总结了近年来国内学者的相关研究。研究发现:(1)大多数基于观察数据的研究只能证明医疗保险与健康之间存在相关关系,要建立二者之间的因果关系,必须找到克服医疗保险内生性的方法。(2)不同研究的结论不同,但这些结论并不必然矛盾,其原因在于研究对象和研究方法存在差异。(3)研究结论并不是普遍适用的,未来的研究需要关注医疗保险对不同人群健康的长期影响。     

Causal attribution in individuals with subclinical and clinical autism spectrum disorder: An fMRI study

This neuroimaging study compares brain activation during causal attribution to three different attribution loci (i.e., self, another person, and situation) across a typical population without (N = 20) or with subclinical autism spectrum symptoms (N = 18) and a clinical population with autism spectrum disorder (ASD; N = 11). While they underwent fMRI, all participants read short sentences describing positive and negative behaviors and thoughts of another person directed toward the participant (i.e., “you”). Participants were then asked to attribute these behaviors to themselves, the other person, or the situation. Behavioral measures revealed self-serving attributions (i.e., attributing positive events to the self, while attributing negative events externally from the self) in all three participant groups. Neural measures revealed a great deal of shared activation across the three attribution loci and across the three participant groups in the temporo-parietal junction, the posterior superior sulcus, and the precuneus. Comparison between groups revealed more widespread activation in both subclinical and clinical ASD participants, which may be indicative of the extraneural resources these participants invest to compensate their impairments.