Australian Parental Supply of Alcohol Longitudinal Study (APSALS) Analysis Code

R Analysis Code

This repository contains R code used in a number of articles using the Australian Parental Supply of Alcohol Longitudinal Study (APSALS).

Code for all analysis in the article by Clare et al published in Addiction, 2020: https://doi.org/10.1111/add.15005

DescriptionR-code
A1 - Multiple imputationMultiple imputation
A2 - Final data creationFinal data creation
A3 - LTMLE analysis of parental supply of alcohol on harms using the package ‘ltmle’ (1).LTMLE analysis
A4 - LTMLE marginal structural model analysis of earlier initiation of supply.LTMLE MSM analysis
A5 - Sensitivity analysis using naive analysis (GLMs)Naive analysis
A6 - E-Value sensitivity analysisE-value analysis
A7 - Secondary analysis of exposure (parental supply) beginning at age 15.LTMLE - supply from age 15
A8 - Sensitivity analysis with lagged predictors.LTMLE - lagged predictors
A9 - Sensitivity analysis controlling for past obervations of outcomeLTMLE - control for past outcomes
A10 - Sensitivity analysis with continuous outcomes.LTMLE - continuous outcomes

Alcohol use among young Australian adults during the COVID-19 pandemic: a prospective cohort study

R and Stata code for all analysis of APSALS COVID-19 alcohol data (in progress).

DescriptionR-code
S1 - Multiple imputation using UNSW HPC ‘Katana’Multiple imputation
S2 - Final data creationFinal data creation
S3 - Import MI data into Stata for analysisStata import
S4 - Cross-sectional descriptives in RCross-sectional descriptives
S5 - Longitudinal descriptives in StataLongitudinal descriptives
S6 - Primary analyses using mixed effects models with discrete timePrimary analyis
S7 - Sensitivity analysis using continuous time and ‘high risk’ consumption variableSensitivity analysis
  1. Lendle SD, Schwab J, Petersen ML, van der Laan MJ. ltmle: An R Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data. Journal of Statistical Software. 2017;81(1):1-21.
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Dr. Philip J Clare, PhD

Biostatistician at the Prevention Research Collaboration, University of Sydney.

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