CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding
and use of data. Without an understanding of cause–effect relationships, we cannot use data …

An important goal of the health system is to identify new adverse drug events (ADEs) in the
postapproval period. Data‐mining methods that can transform data into meaningful …

Study Design Systematic review. Background While most people with acute low back pain
(LBP) recover quickly, recurrences are believed to be common. To our knowledge, no …

INDICE: Generalidades. Variable epidemiológica. Medidas de frecuencia. Indicadores de
frecuencia de la morbimortalidad. Estrategia de la epidemiología y proceso de causalidad …

Cross-sectional study design is a type of observational study design. In a cross-sectional
study, the investigator measures the outcome and the exposures in the study participants at …

BACKGROUND: Annually, millions of children are exposed to anesthetic agents that cause
apoptotic neurodegeneration in immature animals. To explore the possible significance of …

This new book provides a unified, in-depth, readable introduction to the multipredictor
regression methods most widely used in biostatistics: linear models for continuous …

Context Indicated prevention is currently regarded as the most promising strategy to
attenuate, delay, or even avert psychosis. Existing criteria need improvement in terms of …

This is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its
practical and accessible approach, with excellent examples and good guidance on …

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are
growing in popularity. Their versatility and modelling power is now employed across a …