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Statistical Analysis System (SAS)

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...

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SAS

Matthew J Valente¹, David P MacKinnon¹

¹Arizona State University.

SAS Global Forum

|September 18, 2018

Summary

This summary is machine-generated.

This study updates SAS macros for mediation analysis, incorporating advanced causal inference methods like inverse-propensity weighting and sequential G-estimation with three waves of data to better understand how mediated effects develop over time.

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Area of Science:

Statistics
Causal Inference
Longitudinal Data Analysis

Background:

Mediation analysis quantifies how independent variables affect dependent variables through mediators.
Advancements in statistical mediation focus on the causal interpretation of mediated effects.
Causal inference in mediation is complex due to challenges in randomizing mediator levels.

Purpose of the Study:

To update existing SAS macros (%TWOWAVEMED, %TWOWAVEMONTECARLO, %TWOWAVEPOSTPOWER) for advanced mediation analysis.
To incorporate novel causal inference methods for estimating longitudinal mediated effects.
To enable the analysis of mediated effects across three waves of data.

Main Methods:

Updated SAS macros to include inverse-propensity weighting (propensity scores) for causal mediation.
Integrated sequential G-estimation, a causal inference method, into the macros.
Extended the analysis framework to accommodate three waves of longitudinal data.

Main Results:

The updated macros facilitate causal interpretation of mediated effects using potential outcomes.
New methods allow for the estimation of longitudinal mediated effects.
Analysis across three waves enables the study of temporal dynamics in causal mediation.

Conclusions:

The updated SAS macros provide powerful tools for causal mediation analysis with longitudinal data.
Researchers can now investigate the development and maintenance of causal mediated effects over time.
The integration of causal inference frameworks enhances the understanding of complex variable relationships.