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Stephen J. Kuritz
Researcher at Celanese
Publications - 4
Citations - 302
Stephen J. Kuritz is an academic researcher from Celanese. The author has contributed to research in topics: Cochran–Mantel–Haenszel statistics & Categorical variable. The author has an hindex of 4, co-authored 4 publications receiving 275 citations.
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Journal ArticleDOI
A General Overview of Mantel-Haenszel Methods: Applications and Recent Developments
TL;DR: Many health research investigations are concerned with the relationship between a primary factor, such as a potentially harmful exposure, a new therapy, or an intervention, and a response variable such as disease status, level of functioning, or extent of improvement, and both of these variables are reported on categorical measurement scales.
Journal ArticleDOI
Summary attributable risk estimation from unmatched case‐control data
TL;DR: An alternative method to obtain summary estimators, variances and confidence intervals for attributable risk measures utilizes the Mantel-Haenszel estimate of an average odds ratio, and can be implemented using the matrix procedure in SAS.
Reference EntryDOI
Mantel–Haenszel Methods
TL;DR: The authors provided an overview of generalized Mantel-Haenszel (MH) methods for the analysis of categorical data from factor-response and repeated measures study designs, using data from two different clinical research studies, investigating treatment differences within several different sets of 2 × 2 tables in a clinical trial, and within-subject differences in an ordinal response across ordinal factor levels within a repeated measures design.
OtherDOI
Mantel–Haenszel Methods†
TL;DR: This paper provided an overview of generalized Mantel-Haenszel (MH) methods for the analysis of categorical data from factor-response and repeated measures study designs, using data from two different clinical research studies, investigating treatment differences within several different sets of 2 × 2 tables in a clinical trial, and within-subject differences in an ordinal response across ordinal factor levels within a repeated measures design.