Deanna M Hoelscher, Nancy F Butte, Sarah Barlow, Elizabeth A Vandewater, Shreela V Sharma, Terry Huang, Eric Finkelstein, Stephen Pont, Paul Sacher, Courtney Byrd-Williams, Abiodun O Oluyomi, Casey Durand, Linlin Li, Steven H Kelder
Incorporating primary and secondary prevention approaches to address childhood obesity prevention and treatment in a low-income, ethnically diverse population: study design and demographic data from the Texas Childhood Obesity Research Demonstration (TX CORD) study
Childhood obesity (Print)
study parametric inference rich class hazard regression models presence rightcensoring previous literature reported inferential challenges multimodal flat likelihood surfaces class models particular data sets formalize study inferential problems linking concepts nearredundancy practical nonidentifiability parameters show maximum likelihood estimators parameters class models consistent asymptotically normal thus inferential problems class models related finitesample scenario difficult distinguish fitted model nested nonidentifiable ie parameterredundant model propose method detecting nearredundancy based distances probability distributions also employ methods used areas detecting practical nonidentifiability nearredundancy including inspection profile likelihood function hessian method cases inferential problems detected discuss alternatives using model selection tools identify simpler models exhibit inferential problems increasing sample size extending followup time illustrate performance proposed methods simulation study simulation study reveals link presence nearredundancy practical nonidentifiability two illustrative applications using real data without inferential problems presented