Alternatives in modeling of body mass index as a continuous response variable and relevance of residual analysis.
This article presents alternatives for modeling body mass index (BMI) as a continuous variable and the role of residual analysis. We sought strategies for the application of generalized linear models with appropriate statistical adjustment and easy interpretation of results. The analysis included 2,060 participants in Phase 1 of a longitudinal study (Pró-Saúde Study) with complete data on weight, height, age, race, family income, and schooling. In our study, the residual analysis of models estimated by maximum likelihood methods yielded inadequate adjustment. The transformed response variable resulted in a good fit but did not lead to estimates with straightforward interpretation. The best alternative was to apply quasi-likelihood as the estimation method, presenting a better adjustment and constant variance. In epidemiological data modeling, researchers should always take trade-offs into account between adequate statistical techniques and interpretability of results.
Fonseca
Maria de Jesus Mendes da
Mde J
Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil. mariajmf@ensp.fiocruz.br
Andreozzi
Valeska Lima
VL
Faerstein
Eduardo
E
Chor
Dora
D
Carvalho
Marília Sá
MS
D015992:Body Mass Index; D004522:Educational Status; D005260:Female; D006801:Humans; D007182:Income; D008297:Male; D015233:Models, Statistical; D015203:Reproducibility of Results