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Multivariate graphical methods provide an insightful way to formulate explanatory hypotheses from limited categorical data.

Abstract：

OBJECTIVE:Graphical methods for generating explanatory hypotheses from limited categorical data are described and illustrated. STUDY DESIGN AND SETTING:Univariate, bivariate, multivariate, and multiplicative graphical methods were applied to clinical data regarding very ill older persons. The data to which these methods were applied were limited as to their nature (e.g., nominal categorical data) or quality (e.g., data subject to measurement error and missing values). Such limitations make confirmatory inference problematic but might still allow for meaningful generation of new explanatory hypotheses in some cases. RESULTS:A striking feature of the graphical results from this study's major illustrative application was that posttraumatic stress disorder (PTSD) after intensive care unit discharge occurred rarely and nearly always co-occurred with two or more other mental health conditions. These results suggest the explanatory hypothesis that PTSD in this context is less attributable to single traumatic causes than to acute illnesses contributing to a cascade of mental health decrements. CONCLUSION:Illustrative applications of a sequence of graphical procedures yield more informative and less abstract representations of limited data than do descriptive statistics alone, and by doing so, they aid in the formulation of explanatory hypotheses.

journal_name

J Clin Epidemiol

journal_title

Journal of clinical epidemiology

authors

Van Ness PH,Murphy TE,Araujo KL,Pisani MA

doi

10.1016/j.jclinepi.2011.06.007

subject

Has Abstract

pub_date

2012-02-01 00:00:00

pages

179-88

issue

2

eissn

0895-4356

issn

1878-5921

pii

S0895-4356(11)00187-9

journal_volume

65

pub_type

杂志文章

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