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|Title: ||Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data|
|Other Titles: ||BMC Medical Research Methodology|
|Authors: ||Santos, Carlos Antonio de Souza Teles|
Fiaccone, Rosemeire Leovigildo
Oliveira, Nelson Fernandes de
Barreto, Mauricio Lima
Carmo, Maria Beatriz Barreto do
Moncayo, Ana Lucia
Rodrigues, Laura C.
Cooper, Philip J.
Amorim, Leila Denise Alves Ferreira
|Issue Date: ||2008|
|Abstract: ||Background: Many epidemiologic studies report the odds ratio as a measure of association for cross-sectional studies with common outcomes. In such cases, the prevalence ratios may not be
inferred from the estimated odds ratios. This paper overviews the most commonly used
procedures to obtain adjusted prevalence ratios and extends the discussion to the analysis of clustered cross-sectional studies. Methods: Prevalence ratios(PR) were estimated using logistic models with random effects. Their 95% confidence intervals were obtained using delta method and clustered bootstrap. The performance of these approaches was evaluated through simulation studies. Using data from two
studies with health-related outcomes in children, we discuss the interpretation of the measures of association and their implications. Results: The results from data analysis highlighted major differences between estimated OR and PR. Results from simulation studies indicate an improved performance of delta method compared
to bootstrap when there are small number of clusters.Conclusion: We recommend the use of logistic model with random effects for analysis of clustered data. The choice of method to estimate confidence intervals for PR (delta or bootstrap method) should be based on study design.|
|Description: ||p. 1-10|
|Appears in Collections:||Artigos Publicados em Periódicos Estrangeiros (ISC)|
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