Showing papers by "Joseph W. McKean published in 2016"

Intimate Partner Violence Associated with Postpartum Depression, Regardless of Socioeconomic Status.

Abstract: Objective This study examined whether socioeconomic status moderated the association between intimate partner violence (IPV) and postpartum depression among a community-based sample of women. Defining the role of poverty in the risk of postpartum depression for IPV victims enables prioritization of health promotion efforts to maximize the effectiveness of existing maternal-infant resources. Methods This cross-sectional telephone-survey study interviewed 301 postpartum women 2 months after delivery, screening them for IPV and depression [using Edinburgh Postnatal Depression Scale (EPDS)]. Socioeconomic status was defined by insurance (Medicaid-paid-delivery or not). This analysis controlled for the following covariates, collected through interview and medical-record review: demographics, obstetric history, prenatal health and additional psychosocial risk factors. After adjusting for significant covariates, multiple linear regression was conducted to test whether socioeconomic status confounded or moderated IPV's relationship with EPDS-score. Results Ten percent of participants screened positive for postpartum depression, 21.3 % screened positive for current or previous adult emotional or physical abuse by a partner, and 32.2 % met poverty criteria. IPV and poverty were positively associated with each other (χ(2) (1) = 11.76, p < .001) and with EPDS score (IPV: beta 3.2 (CI 2.0, 4.5) p < .001, poverty: beta 1.3 (CI 0.2, 2.4) p = .017). In the multiple linear regression, IPV remained significantly associated, but poverty did not (IPV: adjusted beta 3.1 (CI 1.8, 4.3) p < .001, poverty: adjusted beta 0.8 (CI -0.3, 1.9) p = .141), and no statistically significant interaction between IPV and poverty was found. Conclusions Study findings illustrated that IPV was strongly associated with postpartum depression, outweighing the influence of socioeconomic status upon depression for postpartum women.

Rank-Based Analysis of Linear Models and Beyond: A Review

Abstract: In the 1940s Wilcoxon, Mann and Whitney, and others began the development of rank based methods for basic one and two sample models. Over the years a multitude of papers have been written extending the use of ranks to more and more complex models. In the late 60s and early 70s Jureckova and Jaeckel along with others provided the necessary asymptotic machinery to develop rank based estimates in the linear model. Geometrically Jaeckel’s fit of linear model is the minimization of the distance between the vector of responses and the column space of the design matrix where the norm is not the squared-Euclidean norm but a norm that leads to robust fitting. Beginning with his 1975 thesis, Joe McKean has worked with many students and coauthors to develop a unified approach to data analysis (model fitting, inference, diagnostics, and computing) based on ranks. This approach includes the linear model and various extensions, for example multivariate models and models with dependent error structure such as mixed models, time series models, and longitudinal data models. Moreover, McKean and Kloke have developed R libraries to implement this methodology. This paper reviews the development of this methodology. Along the way we will illustrate the surprising ubiquity of ranks throughout statistics.

Iterated reweighted rank-based estimates for GEE models

Abstract: Repeated measurement designs occur in many areas of statistical research. In 1986, Liang and Zeger offered an elegant analysis of these problems based on a set of generalized estimating equations (GEEs) for regression parameters, that specify only the relationship between the marginal mean of the response variable and covariates. Their solution is based on iterated reweighted least squares fitting. In this paper, we propose a rank-based fitting procedure that only involves substituting a norm based on a score function for the Euclidean norm used by Liang and Zeger. Our subsequent fitting, while also an iterated reweighted least squares solution to GEEs, is robust to outliers in response space and the weights can easily be adapted for robustness in factor space. As with the fitting of Liang and Zeger, our rank-based fitting utilizes a working covariance matrix. We prove that our estimators of the regression coefficients are asymptotically normal. The results of a simulation study show that the our proposed estimators are empirically efficient and valid. We illustrate our analysis on a real data set drawn from a hierarchical (three-way nested) design.