# data analysis

descriptive descriptive and exploratory data analysis, hypothesis generating more than confirmatory analysis bayes Bayesian data analysis, modeling, inference model validation model validation and interpretation data problems statistical approaches dealing with missing data and measurement error uncertainty Quantifying uncertainty, displaying uncertainty, estimation of uncertainty, incorporation of uncertainty into decision making, etc. This includes but is not limited to confidence intervals, standard errors, Bayesian credible intervals, and sources of uncertainty. data reduction data reduction (principal components, etc.), clustering, unsupervised learning accuracy accuracy and information measures, discriminaton, calibration models Formulation, parameter estimation, and interpretation of specific statistical models formal statistical tests and inference machine learning machine learning, exclusive of traditional statistical models probability Probability theory, meaning, and application exclusive of statistical tests, etc. generalizability Generalizability of studies and statistical inferences, sample representativeness, target population comparative methods comparative performance of statistical analysis methods and predictive modeling approaches variable selection Selection of predictive features in multivariable modeling, one-at-a-time screening of variables, and the cost of feature selection compared to using fuller models, possibly with penalization (shrinkage; regularization). causal inference Methods and approaches to causal inference modeling strategy General model specification issues, nonlinearities, interactions and heterogeneity of treatment effect, avoiding categorization, how to sequence multiple steps (which may involve multiple imputation and data reduction)

Assess sensitivity of a binary screening result across other continuous covariate(s)
[modeling strategy]
(2)

Rebranding of "Statistics" as "Machine Learning" to Sound More Impactful & Negative Fallout
( 2 )
[machine learning]
(24)

Comparing 2 biomarkers using restricted cubic splines - how-to, interpretation, model building
( 2 3 4 )
[descriptive]
(64)

Inverse probability weighting for treatment selection and loss to follow up in observational studies
[data analysis]
(16)