Open AccessBook
Statistical Models in Epidemiology
David Clayton,Michael Hills +1 more
TLDR
This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level.Abstract:
Statistical models in epidemiology , Statistical models in epidemiology , کتابخانه مرکزی دانشگاه علوم پزشکی تهرانread more
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Journal ArticleDOI
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration
Jan P. Vandenbroucke,Erik von Elm,Douglas G. Altman,Peter C Gøtzsche,Cynthia D. Mulrow,Stuart J. Pocock,Charles Poole,James J. Schlesselman,Matthias Egger,Matthias Egger +9 more
TL;DR: The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers.
Journal ArticleDOI
Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio
TL;DR: Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio.
Journal ArticleDOI
Causal diagrams for epidemiologic research.
TL;DR: Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates and provide a method for critical evaluation of traditional epidemiologic criteria for confounding.