# data analysis

models Formulation, parameter estimation, and interpretation of specific statistical models generalizability Generalizability of studies and statistical inferences, sample representativeness, target population bayes Bayesian data analysis, modeling, 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) data reduction data reduction (principal components, etc.), clustering, unsupervised learning probability Probability theory, meaning, and application exclusive of statistical tests, etc. model validation model validation and interpretation formal statistical tests and inference descriptive descriptive and exploratory data analysis, hypothesis generating more than confirmatory analysis accuracy accuracy and information measures, discriminaton, calibration 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 problems statistical approaches dealing with missing data and measurement error machine learning machine learning, exclusive of traditional statistical models reporting This subcategory relates to how results of data analyses should be reported, for example which summary statistics should be reported for a logistic regression model. comparative methods comparative performance of statistical analysis methods and predictive modeling approaches causal inference Methods and approaches to causal inference 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).