Maximum likelihood estimation of the structural nested mean model using SAS PROC NLP

Introduction to observational studies — Propensity score stratification and regression — Propensity score matching for estimating treatment effects — Doubly robust estimation of treatment effects — Propensity scoring with missing values — Instrumental variable method for addressing selection bias — Local control approach using JMP — A two-stage longitudinal propensity adjustment for analysis of observational data — Analysis of longitudinal observational data using marginal structural models — Structural nested models — Regression models on longitudinal propensity scores — Good research practices for the conduct of observational database studies — Dose-response safety analyses using large health care databases — Costs and cost-effectiveness analysis using propensity score bin bootstrapping — Incremental net benefit — Cost and cost-effectiveness analysis with censored data — Addressing measurement and sponsor biases in observational research — Sample size calculation for observational studies.