一个带协变量调整多响应比较的高效方法及其在基因组数据中的应用

目前在文献中有很多关于多响应比较的研究方法,但是对带协变量调整的非参数检验的研究较少。一种直观的想法是将数据先投影到协变量的正交空间中,然后再利用秩和检验、调整的秩和检验或最大值检验方法。但是,功效普遍不高。在调整的秩和检验和伪F检验两种方法基础上,构建MIN2检验。大量模拟和实际数据表明,MIN2检验的效果优于现有的非参数检验方法。     

Testing Measurement Invariance Across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling

Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.     

Discrete Time Survival Analysis Via Latent Variable Modeling: A Note on Lagged Depression Links to Stroke in Middle and Late Life

This article is concerned with a latent variable modeling approach to discrete time survival analysis that includes both time-invariant and time-varying covariates. The approach is illustrated with data from the Health and Retirement Study, which are utilized to study further the relationship of depression to stroke in middle and late life. Employing lag-1 depression scores as time-varying covariates, in addition to a set of relevant medical and demographic variables as time-invariant covariates collected at baseline, the article addresses a particular aspect of the prominent vascular depression hypothesis representing an important area in aging research, gerontology, geriatrics, and medicine. The results indicate considerable links of immediately prior depression levels to subsequent occurrences of stroke in middle-aged and older adults. The findings complement those reported by Raykov, Gorelick, Zajacova, and Marcoulides (2017), and are consistent with that hypothesis implying depression as a potential warning sign of an impending stroke.     

有协变量的推广增长曲线模型回归参数的估计

考虑有协变量的推广增长曲线模型中参数的估计问题，本文利用矩阵理论和递归算法，给出了二重推广增长曲线模型中回归系数阵在trace意义下的最小二乘估计的具体表达式，其结果形式简洁，便于解释．     

Bias Reduction in Quasi-Experiments With Little Selection Theory but Many Covariates

Abstract: In observational studies, selection bias will be completely removed only if the selection mechanism is ignorable, namely, all confounders of treatment selection and potential outcomes are reliably measured. Ideally, well-grounded substantive theories about the selection process and outcome-generating model are used to generate the sample of covariates. However, covariate selection is more heuristic in actual practice. Using two empirical data sets in a simulation study, we investigate four research questions about bias reduction when the selection mechanism is not known but many covariates are measured: (1) How important is the conceptual heterogeneity of the covariate domains in the data set? (2) How important is the number of covariates assessing each domain? (3) What are the joint effects of this conceptual heterogeneity and of the number of covariates per domain? (4) What happens to bias reduction when the set of covariates is deliberately impoverished by removing the covariates most responsible for selection bias, thus ensuring a slightly smaller but still heterogeneous set of covariates? The results indicate: (1) increasingly more bias is reduced as the number of covariate domains and the number of covariates per domain increase, though the rate of bias reduction is diminishing in each case; (2) sampling covariates from multiple heterogeneous covariate domains is more important than choosing many measures from fewer domains; (3) the most heterogeneous set of covariate domains removes almost all of the selection bias when at least five covariates are assessed in each domain; and (4) omitting the most crucial covariates generally replicates the pattern of results due to the number of domains and the number of covariates per domain, but the amount of bias reduction is less than when all variables are included and will surely not satisfy all consumers of causal research.     

研究问题与统计方法的有机配合

在实证性研究的设计阶段．研究者应明确研究课题，界定自变量、因变量、控制变量和协变量的操作定义和相互关系，并根据研究课题和变量关系选择合适的统计技术。分别讨论了分析变量关系、探讨组间差异、预测组别隶属以及考察变量结构这四类统计技术解答的研究问题，并通过科研实例介绍了根据研究问题选择统计技术的思路。特别指出。一个研究问题通常可以由多个统计方法加以解决。     

Using Latent Variable Modeling for Discrete Time Survival Analysis: Examining the Links of Depression to Mortality

Using a latent variable modeling approach to discrete time survival analysis, the dynamics of the relationships of depression and body mass index to mortality are examined with data from the multiwave, nationally representative Health and Retirement Study. A set of medical and demographic variables are employed as time-invariant covariates along with lag-1 depression scores and body mass indexes as time-varying covariates for mortality within an up to 2-year follow-up interval. The results indicate marked links of immediately prior depression levels, as well as notable relations of the body mass indexes, to within-wave mortality in middle-aged and older adults. The approach highlights the benefits of using latent variable modeling for survival analysis, and its findings represent potentially important relationships of clinical and theoretical relevance.     

Validity of a Residualized Dependent Variable After Pretest Covariance Adjustments: Still the Same Variable?

The authors empirically examined whether the validity of a residualized dependent variable after covariance adjustment is comparable to that of the original variable of interest. When variance of a dependent variable is removed as a result of one or more covariates, the residual variance may not reflect the same meaning. Using the pretest–posttest design as a general framework, the authors compared the nomological validity network for the (a) original dependent variable scores and (b) residualized dependent variable scores after having covaried-out variance explainable by a pretest. Heuristic and empirical examples are provided that demonstrate potential variation in construct validity of residualized dependent variables is a function of correlations among dependent, covariate, and validity variables.     

Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

Abstract

Bayesian alternatives to frequentist propensity score approaches have recently been proposed. However, few studies have investigated their covariate balancing properties. This article compares a recently developed two-step Bayesian propensity score approach to the frequentist approach with respect to covariate balance. The effects of different priors on covariate balance are evaluated and the differences between frequentist and Bayesian covariate balance are discussed. Results of the case study reveal that both the Bayesian and frequentist propensity score approaches achieve good covariate balance. The frequentist propensity score approach performs slightly better on covariate balance for stratification and weighting methods, whereas the two-step Bayesian approach offers slightly better covariate balance in the optimal full matching method. Results of a comprehensive simulation study reveal that accuracy and precision of prior information on propensity score model parameters do not greatly influence balance performance. Results of the simulation study also show that overall, the optimal full matching method provides the best covariate balance and treatment effect estimates compared to the stratification and weighting methods. A unique feature of covariate balance within Bayesian propensity score analysis is that we can obtain a distribution of balance indices in addition to the point estimates so that the variation in balance indices can be naturally captured to assist in covariate balance checking.     

第一专题 研究问题与统计技术的有机配合

“体育科学研究方法”专栏的开设始自张力为博士的一个想法。当时我曾委托他为本刊主持一个专题，而对学问严肃又负责的他却迟迟没有组到自认为合格的稿子。交流中我们谈起目前体育学术界在研究方法上存在的问题种种，不久就有了他的12篇关于研究方法的文章寄来，看来他已是厚积薄发。于是，我们想索性办个专栏，引发更多学者的关注和参与。我们请姜达维教授审阅了张博士的文章，便有了网上精彩的对话和更多思想火花的进发。“体育科学研究方法”专栏就在这样一个充满激情与智慧的讨论中开场了。