New SAS, R, and Stata Software for Prevention Scientists: Tools for LCA, Missing Data, Adaptive Interventions, and EMA Data

This technology demonstration presents recent advances in SAS, R, and Stata software developed at The Methodology Center at Penn State. New and updated SAS procedures, R packages, Stata plug-ins, and free-standing applets for latent class analysis (LCA), missing data, adaptive interventions, and ecological momentary assessments (EMA, also called intensive longitudinal data) will be showcased. All tools and corresponding users’ guides are available free-of-charge at methodology.psu.edu, and will be distributed on USB drives at the annual meeting.

Specifically, the following tools will be demonstrated: (1) a SAS macro (%LCA_Distal) for use with PROC LCA that estimates the association between latent class membership and a distal outcome; (2) an R package (lcca.R) that estimates the causal effect of a latent, categorical exposure (i.e., latent class membership) on a distal outcome; (3) a Stata plug-in to conduct LCA; (4) two free-standing applets (MI Automate, Aux Simulate) that help with multiple imputation in SPSS software and HLM analyses; (5) a SAS procedure (PROC QLEARN) to analyze data from a sequential, multiple assignment, randomized trial (SMART) also known as an adaptive intervention; (6) an expanded suite of SAS macros (%TVEM_normal, %TVEM_logistic, %TVEM_poisson, %TVEM_zip) to analyze EMA data using the time-varying effects model that allows the estimation of both time-invariant and time-varying effects of predictors on an intensively measured outcome.    

In addition, information will be available about other software created and maintained by The Methodology Center at Penn State. For example, this includes a SAS macro (%RelativeCosts1) to calculate the costs of different multiple-factor experimental designs (for use with the Multiphase Optimization Strategy [MOST]), as well as SAS example code for conducting multiple imputation with PROC LCA. All demonstrations and information will utilize real-world empirical data relevant to prevention scientists; data will stem from studies focusing on drug use etiology and prevention. Attention will be given to practical implementation of the statistical methods using the demonstrated software, as well as new features that have been incorporated into the software recently.