Abstract. Researchers faced with incomplete data are encouraged to consider whether their data are ‘missing completely at random’ (MCAR), ‘missing at rando

Paul Allison, Ph.D., teaches this self-paced course on missing data using R, covering multiple imputation and maximum likelihood for accurate, unbiased results.

Join Prof. Frank E. Harrell Jr. (Vanderbilt University), a leading authority in applied biostatistics and regression methodology, and Drew G. Levy, PhD, for an online seminar designed for PhD students and professional researchers seeking to strengthen their quantitative research practice. Drawing on Regression Modeling Strategies (Harrell, 2015) and the continuously updated online materials, this workshop covers contemporary approaches to specifying, fitting, validating, and interpreting multivariable regression models for prediction and inference. Participants will explore key challenges in empirical research, including nonlinearity and interactions, overfitting, missing data, model validation, causal inference-based model specification, comparisons between regression and machine-learning approaches, and modeling continuous, binary, ordinal, longitudinal, and survival outcomes. With a strong emphasis on principled model formulation, graphical interpretation, reproducible workflows, and applied R examples using the rms package, the seminar equips participants to produce more robust, transparent, and publishable analyses. The seminar is available via live Zoom sessions or asynchronously with recordings and materials, includes access to an expert-moderated Q&A forum, and provides an official Instats certificate of completion. #BayesianStatistics #Biostatistics #CausalInference #ComputationalStatistics #DataScience #Epidemiology #HealthSciences #LongitudinalDataAnalysis #MachineLearning #MissingData #RegressionModelingStrategies #Statisticalmodellingandregression #Statistics #PublicHealth #R #Research #ResearchTraining #Instats