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This textbook and guide focuses on methodologies for bias analysis
in epidemiology and public health, not only providing updates to
the first edition but also further developing methods and adding
new advanced methods. As computational power available to analysts
has improved and epidemiologic problems have become more advanced,
missing data, Bayes, and empirical methods have become more
commonly used. This new edition features updated examples
throughout and adds coverage addressing: Measurement error
pertaining to continuous and polytomous variables Methods
surrounding person-time (rate) data Bias analysis using missing
data, empirical (likelihood), and Bayes methods A unique feature of
this revision is its section on best practices for implementing,
presenting, and interpreting bias analyses. Pedagogically, the text
guides students and professionals through the planning stages of
bias analysis, including the design of validation studies and the
collection of validity data from other sources. Three chapters
present methods for corrections to address selection bias,
uncontrolled confounding, and measurement errors, and subsequent
sections extend these methods to probabilistic bias analysis,
missing data methods, likelihood-based approaches, Bayesian
methods, and best practices.
Bias analysis quantifies the influence of systematic error on an
epidemiology study's estimate of association. The fundamental
methods of bias analysis in epi- miology have been well described
for decades, yet are seldom applied in published presentations of
epidemiologic research. More recent advances in bias analysis, such
as probabilistic bias analysis, appear even more rarely. We suspect
that there are both supply-side and demand-side explanations for
the scarcity of bias analysis. On the demand side, journal
reviewers and editors seldom request that authors address
systematic error aside from listing them as limitations of their
particular study. This listing is often accompanied by explanations
for why the limitations should not pose much concern. On the supply
side, methods for bias analysis receive little attention in most
epidemiology curriculums, are often scattered throughout textbooks
or absent from them altogether, and cannot be implemented easily
using standard statistical computing software. Our objective in
this text is to reduce these supply-side barriers, with the hope
that demand for quantitative bias analysis will follow.
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