1998
DOI: 10.1002/(sici)1097-0258(19980115)17:1<59::aid-sim733>3.0.co;2-7
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Choosing among generalized linear models applied to medical data

Abstract: When testing for a treatment effect or a difference among groups, the distributional assumptions made about the response variable can have a critical impact on the conclusions drawn. For example, controversy has arisen over transformations of the response (Keene). An alternative approach is to use some member of the family of generalized linear models. However, this raises the issue of selecting the appropriate member, a problem of testing non-nested hypotheses. Standard model selection criteria, such as the A… Show more

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Cited by 152 publications
(76 citation statements)
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“…For visualisation of PTHLH expression in osteochondromas of male patients, the curve and the approximate 95% confidence intervals were estimated using P-splines [12], to model the logit of the probability. The optimal weight of the penalty was determined by Akaike Information Criterion [22]. A P value <0.05 was considered significant.…”
Section: Discussionmentioning
confidence: 99%
“…For visualisation of PTHLH expression in osteochondromas of male patients, the curve and the approximate 95% confidence intervals were estimated using P-splines [12], to model the logit of the probability. The optimal weight of the penalty was determined by Akaike Information Criterion [22]. A P value <0.05 was considered significant.…”
Section: Discussionmentioning
confidence: 99%
“…Mixed model with hospitals as random effects were used to account for the presence of possible variability between hospitals and are designed to handle nested data (like patients in each hospital) and unequal group study. 21,22 The second method analyzed differences in total costs (primary admission and Q1) among 7 categories of complications by using multivariate regression models with hospitals as random effects. First, the analysis was performed without risk-adjustment; then with risk-adjustment for patient's characteristics illustrated in Table 1; in the last analysis risk-adjustment for patient's characteristics and all type of complications were included in the mixed model.…”
Section: Statistical Analysesmentioning
confidence: 99%
“…As backward elimination and forward selection procedures yielded different results, the best model was found by using the Akaike information criterion [18]. Patients entering the cancer journey with any of the following of the eight people variables were statistically more likely to have at least one significant unmet need: being younger; having a long-standing illness or disability; not owning/having use of a car; not having a faith.…”
Section: Managing Emotions and Self-identitymentioning
confidence: 99%