Variance identification and efficiency analysis in randomized experiments under the matched-pair design

In his 1923 landmark article, Neyman introduced randomization-based inference to estimate average treatment effects from experiments under the completely randomized design. Under this framework, Neyman considered the statistical estimation of the sample average treatment effect and derived the variance of the standard estimator using the treatment assignment mechanism as the sole basis of inference. In this paper, I extend Neyman's analysis to randomized experiments under the matched-pair design where experimental units are paired based on their pre-treatment characteristics and the randomization of treatment is subsequently conducted within each matched pair. I study the variance identification for the standard estimator of average treatment effects and analyze the relative efficiency of the matched-pair design over the completely randomized design. I also show how to empirically evaluate the relative efficiency of the two designs using experimental data obtained under the matched-pair design. My randomization-based analysis differs from previous studies in that it avoids modeling and other assumptions as much as possible. Finally, the analytical results are illustrated with numerical and empirical examples.     

A simple,flexible, and effective covariate‐adaptive treatment allocation procedure

We present a method for allocating treatment when subjects arrive in sequence. Based on the theory of propensity scores more commonly used in observational studies, the method balances both discrete and continuous covariates without assuming a model for the outcome. Although we allow for a number of possible specifications, we explore some specific instances in depth. The proposed method is compared with previously suggested sequential randomization and allocation procedures with relationships to some well‐known methods highlighted. Through simulations, the deterministic version is shown to achieve both covariate balance and near optimum efficiency with minimal assumptions. We also investigate the properties of selected randomized versions with respect to both optimality and selection bias. We conclude with an application to a pilot study on weight loss. The proposed method is shown to be robust to the number of covariates balanced and the marginal and joint distributions of those covariates. Copyright © 2013 John Wiley & Sons, Ltd.     

Severity of bias of a simple estimator of the causal odds ratio in Mendelian randomization studies

Mendelian randomization studies estimate causal effects using genetic variants as instruments. Instrumental variable methods are straightforward for linear models, but epidemiologists often use odds ratios to quantify effects. Also, odds ratios are often the quantities reported in meta‐analyses. Many applications of Mendelian randomization dichotomize genotype and estimate the population causal log odds ratio for unit increase in exposure by dividing the genotype‐disease log odds ratio by the difference in mean exposure between genotypes. This ‘Wald‐type’ estimator is biased even in large samples, but whether the magnitude of bias is of practical importance is unclear. We study the large‐sample bias of this estimator in a simple model with a continuous normally distributed exposure, a single unobserved confounder that is not an effect modifier, and interpretable parameters. We focus on parameter values that reflect scenarios in which we apply Mendelian randomization, including realistic values for the degree of confounding and strength of the causal effect. We evaluate this estimator and the causal odds ratio using numerical integration and obtain approximate analytic expressions to check results and gain insight. A small simulation study examines finite sample bias and mild violations of the normality assumption. For our simple data‐generating model, we find that the Wald estimator is asymptotically biased with a bias of around 10% in fairly typical Mendelian randomization scenarios but which can be larger in more extreme situations. Recently developed methods such as structural mean models require fewer untestable assumptions and we recommend their use when the individual‐level data they require are available. The Wald‐type estimator may retain a role as an approximate method for meta‐analysis based on summary data. Copyright © 2012 John Wiley & Sons, Ltd.     

Step‐forward Randomization in Multicenter Emergency Treatment Clinical Trials

The authors present a new centralized randomization method for multicenter emergency treatment clinical trials. With this step‐forward method, treatment randomization for the next subject is performed immediately after the enrollment of the current subject. This design ensures the readiness of the treatment assignment for each subject at the point of study enrollment, and it simultaneously provides effective control on treatment assignments balance and distributions of covariates. The authors also discuss procedures of the step‐forward randomization method along with its implementation for two National Institute of Neurological Disorders and Stroke–funded multicenter acute stroke trials, one double‐blinded and one open‐labeled. Advantages and limitations are presented based on experience gained in these two trials. ACADEMIC EMERGENCY MEDICINE 2010; 17:659–665 © 2010 by the Society for Academic Emergency Medicine     

Interval estimation for treatment effects using propensity score matching

In causal studies without random assignment of treatment, causal effects can be estimated using matched treated and control samples, where matches are obtained using estimated propensity scores. Propensity score matching can reduce bias in treatment effect estimators in cases where the matched samples have overlapping covariate distributions. Despite its application in many applied problems, there is no universally employed approach to interval estimation when using propensity score matching. In this article, we present and evaluate approaches to interval estimation when using propensity score matching.     

A causal relationship between childhood obesity and risk of osteoarthritis: results from a two-sample Mendelian randomization analysis

PurposeIt has been found that childhood obesity (CO) may play an important role in the onset and progression of osteoarthritis (OA). Thus we conducted this mendelian randomisation analysis (MR) to evaluate the causal association between childhood obesity and osteoarthritis.MethodsInstrumental variables (IVs) were obtained from publicly available genome-wide association study datasets. The leave-one-out sensitivity test, MR Pleiotropy RESidual Sum and Outlier test (MR-PRESSO), and Cochran’s Q test were used to confirm the heterogeneity and pleiotropy of identified IVs, then five different models, including the inverse variance weighted model (IVW), weighted median estimator model (WME), weighted model-based method (WM), MR-Egger regression model (MER), and MR-Robust Adjusted Profile Score (MRAPS) were applied in this MR analysis.ResultsAfter excluding all outliers identified by the MR-PRESSO test, no evident directional pleiotropy was found. Significant heterogeneity was found in the secondary MR and as a result, the multiplicative random-effect model was used. Significant causal association between CO and OA (OR 1.0075, 95% CI [1.0054, 1.0010], p = 8.12 × 10⁻¹³). The secondary MR also revealed that CO was causally associated with knee OA (OR 1.1067, 95% CI [1.0769, 1.1373], p = 3.30 × 10⁻¹³) and hip OA (OR 1.1272, 95% CI [1.0610, 1.1976], p = 1.07 × 10⁻⁴). The accuracy and robustness of these findings were confirmed by sensitivity tests.ConclusionThere appears to be a causal relationship between childhood obesity and OA. Our results indicate that individuals with a history of childhood obesity require specific clinical attention to prevent the development of knee and hip OA.     

最小随机化在国内针灸临床研究中的应用与思考

目的:通过文献检索,研究最小随机化在国内针灸领域临床研究中的应用现状.方法:检索中国知网、万方数据平台、维普网、中国生物医学文献服务系统(SionMed)和PubMed数据库,整理、归纳2009年1月至2018年12月间有国内针灸领域应用最小随机化的临床研究,统计并分析总样本量,组间基线水平等信息.结果:共纳入文献14...