8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Two-Part and Related Regression Models for Longitudinal Data

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular interest in the use of these models for the analysis of repeated measures of an outcome variable over time. The aim of this review is to consider motivations for the use of such models in this context and to highlight the central issues that arise with their use. We examine two-part models for semicontinuous and zero-heavy count data, and we also consider models for count data with a two-part random effects distribution.

          Related collections

          Most cited references69

          • Record: found
          • Abstract: not found
          • Article: not found

          Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Approximate Inference in Generalized Linear Mixed Models

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The Stanford Health Assessment Questionnaire: Dimensions and Practical Applications

              The ability to effectively measure health-related quality-of-life longitudinally is central to describing the impacts of disease, treatment, or other insults, including normal aging, upon the patient. Over the last two decades, assessment of patient health status has undergone a dramatic paradigm shift, evolving from a predominant reliance on biochemical and physical measurements, such as erythrocyte sedimentation rate, lipid profiles, or radiographs, to an emphasis upon health outcomes based on the patient's personal appreciation of their illness. The Health Assessment Questionnaire (HAQ), published in 1980, was among the first instruments based on generic, patient-centered dimensions. The HAQ was designed to represent a model of patient-oriented outcome assessment and has played a major role in many diverse areas such as prediction of successful aging, inversion of the therapeutic pyramid in rheumatoid arthritis (RA), quantification of NSAID gastropathy, development of risk factor models for osteoarthrosis, and examination of mortality risks in RA. Evidenced by its use over the past two decades in diverse settings, the HAQ has established itself as a valuable, effective, and sensitive tool for measurement of health status. It is available in more than 60 languages and is supported by a bibliography of more than 500 references. It has increased the credibility and use of validated self-report measurement techniques as a quantifiable set of hard data endpoints and has contributed to a new appreciation of outcome assessment. In this article, information regarding the HAQ's development, content, dissemination and reference sources for its uses, translations, and validations are provided.
                Bookmark

                Author and article information

                Journal
                101622422
                42125
                Annu Rev Stat Appl
                Annu Rev Stat Appl
                Annual review of statistics and its application
                2326-8298
                2326-831X
                3 August 2017
                March 2017
                08 September 2017
                : 4
                : 283-315
                Affiliations
                [1 ]Medical Research Council Biostatistics Unit, Institute of Public Health, University of Cambridge, Cambridge CB2 0SR, United Kingdom
                [2 ]Department of Biostatistics, West Virginia University, Morgantown, West Virginia 26506
                Author notes
                Article
                EMS73565
                10.1146/annurev-statistics-060116-054131
                5590716
                28890906
                43456f4d-29d0-4d73-abf4-80c29b7bcf59

                This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third party material in this article for license information.

                History
                Categories
                Article

                longitudinal data,marginal covariate effects,mixture distributions,random effects,two-part models

                Comments

                Comment on this article