2003
DOI: 10.3386/t0293
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Generalized Modeling Approaches to Risk Adjustment of Skewed Outcomes Data

Abstract: There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., OLS on ln(y)); and (2) alternative weighting approaches based on exponential conditional models (ECM) and generalized linear model (GLM) approaches. In this paper, we encompass these two classes of models using the three parameter generalized gamma (GGM) distribution, which includes several of the standard alt… Show more

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Cited by 242 publications
(365 citation statements)
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References 24 publications
(28 reference statements)
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“…A g distribution was used to model cost, whereas LOS was modeled using a negative binomial distribution. 22,28 The VRE indicator was used as a fixed effect along with other covariates such as age, gender, race/ethnicity, insurance, year, and propensity score (to account for variability across the matched cohorts). The matching of case and controls was taken into account by introducing a random effect for the matching identifier.…”
Section: Discussionmentioning
confidence: 99%
“…A g distribution was used to model cost, whereas LOS was modeled using a negative binomial distribution. 22,28 The VRE indicator was used as a fixed effect along with other covariates such as age, gender, race/ethnicity, insurance, year, and propensity score (to account for variability across the matched cohorts). The matching of case and controls was taken into account by introducing a random effect for the matching identifier.…”
Section: Discussionmentioning
confidence: 99%
“…Due to the skewed nature of LOS and cost data, LOS was analyzed using multivariate negative binomial regression and cost was analyzed using multivariate gamma regression. 21 Binary outcomes (ICU admission and 30-day readmission) were analyzed using multivariate logistic regression. The analysis accounted for potential confounding factors by inclusion of the following covariates: age group, gender, race, admission source, and DeyoCharlson Comorbidity Index score.…”
Section: Outcome Measures and Statistical Analysesmentioning
confidence: 99%
“…An assessment of nationally representative hospital discharge data estimated that 7% of inpatient admissions for AHT among patients 0 to 4 years old ended in death during 2000-2009. 20 The data used for this study did not permit investigation of AHT victims' relationships to perpetrators, which might have been associated with patients' insurance coverage after AHT diagnosis. This study examined only the direct medical cost of AHT and did not examine other, probably substantial long-term costs attributable to AHT, such as developmental services, special education, and lifelong medical care and support, such as assistive eating devices, that some AHT victims need.…”
Section: Discussionmentioning
confidence: 99%
“…Research has indicated that gender, age, race or ethnicity, having a young mother, and low socioeconomic status are among the risk factors for AHT. 20 We did not match patients based on non-AHT health conditions, including comorbidities; for any factor not included in the matching algorithm we implicitly assumed an even distribution of that factor among AHT and non-AHT patients, which is a limitation. Our objective was to determine how much medical care for a child with AHT costs above and beyond medical care for a typical child.…”
Section: Discussionmentioning
confidence: 99%