Extending the Mann–Whitney–Wilcoxon rank sum test to longitudinal regression analysis

A semiparametric marginalized zero-inflated model for analyzing healthcare utilization panel data with missingness

Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. However, interpretations of those models focus on the at-risk subpopulation of a two-component population mixture and fail to provide direct inference about marginal effects for the overall population. Recently, new approaches have been proposed to facilitate such marginal inferences for count responses with excess zeros. However, they are likelihood based and impose strong assumptions on data distributions. In this paper, we propose a new distribution-free, or semiparametric, alternative to provide robust inference for marginal effects when population mixtures are defined by zero-inflated count outcomes. The proposed method also applies to longitudinal studies with missing data following the general missing at random mechanism. The proposed approach is illustrated with both simulated and real study data.

Distribution-free models for latent mixed population responses in a longitudinal setting with missing data

Many biomedical and psychosocial studies involve population mixtures, which consist of multiple latent subpopulations. Because group membership cannot be observed, standard methods do not apply when differential treatment effects need to be studied across subgroups. We consider a two-group mixture in which membership of latent subgroups is determined by structural zeroes of a zero-inflated count variable and propose a new approach to model treatment differences between latent subgroups in a longitudinal setting. It has also been incorporated with the inverse probability weighted method to address data missingness. As the approach builds on the distribution-free functional response models, it requires no parametric distribution model and thereby provides a robust inference. We illustrate the approach with both real and simulated data.

The effect of contingencies on mutual influence among quality awards and quality initiatives

Purpose

The purpose of this paper is to examine the interactive nature/mutual influence (MI) among quality initiatives (QI) and quality awards (QA) in Indian organizations subject to three contingencies, namely, QA won, QI adopted before winning a QA and QI adopted after winning a QA.

Design/methodology/approach

Administration of survey to collect the data were followed by validity and reliability analyses of the instrument. Hypotheses were tested by parametric/non-parametric one-sample and independent-samples tests.

Findings

The inferences on the effect of contingencies on the MI were inconclusive. Eight QI adopted before winning the QA, have influenced four such Indian QA. Three Indian QA have influenced four QI that were adopted after winning these QA. However, this MI is independent of specific QI adopted/QA won.

Research limitations/implications

The approach to test the hypotheses, small sample size and generic research questions have led to “preliminary” recommendations/inferences. Further research with larger data and advanced methods for analysis of interaction is suggested.

Practical implications

Based on clarity of MI, preliminary recommendations for adopting some QI before/after winning a QA were made. The way in which these recommendations can be used by experienced and fresh adopters of QI/QA and givers of QA has been outlined.

Originality/value

This study attempts to fill the gap of scarce holistic studies (that evaluate numerous QI and QA models) on the interactive nature of QI and the dissemination of QI into different periods of Continuous Improvement journey.

Variable selection for distribution-free models for longitudinal zero-inflated count responses

Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. Like most parametric models, they are quite sensitive to departures from assumed distributions. Recently, new approaches have been proposed to provide distribution-free, or semi-parametric, alternatives. These methods extend the generalized estimating equations to provide robust inference for population mixtures defined by zero-inflated count outcomes. In this paper, we propose methods to extend smoothly clipped absolute deviation (SCAD)-based variable selection methods to these new models. Variable selection has been gaining popularity in modern clinical research studies, as determining differential treatment effects of interventions for different subgroups has become the norm, rather the exception, in the era of patent-centered outcome research. Such moderation analysis in general creates many explanatory variables in regression analysis, and the advantages of SCAD-based methods over their traditional counterparts render them a great choice for addressing this important and timely issues in clinical research. We illustrate the proposed approach with both simulated and real study data. Copyright © 2016 John Wiley & Sons, Ltd.