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).
The overall effect of parental supply of alcohol across adolescence on alcohol-related harms in early adulthood—a prospective cohort study
Code for all analysis in the article by Clare et al 2020 published in Addiction: https://doi.org/10.1111/add.15005
| Description | R-code |
|---|---|
| 1 - Multiple imputation | Multiple imputation |
| 2 - Final data creation | Final data creation |
| 3 - LTMLE analysis of parental supply of alcohol on harms using the package ‘ltmle’ (1). | LTMLE analysis |
| 4 - LTMLE marginal structural model analysis of earlier initiation of supply. | LTMLE MSM analysis |
| 5 - Sensitivity analysis using naive analysis (GLMs) | Naive analysis |
| 6 - E-Value sensitivity analysis | E-value analysis |
| 7 - Secondary analysis of exposure (parental supply) beginning at age 15. | LTMLE - supply from age 15 |
| 8 - Sensitivity analysis with lagged predictors. | LTMLE - lagged predictors |
| 9 - Sensitivity analysis controlling for past obervations of outcome | LTMLE - control for past outcomes |
| 10 - Sensitivity analysis with continuous outcomes. | LTMLE - continuous outcomes |
Changes in mental health and help-seeking 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 paper, Upton et al 2021 published in Psychological Medicine: https://doi.org/10.1017/S0033291721001963
| Description | R-code |
|---|---|
| 1 - Missing data patterns | Missing data |
| 2 - Multiple imputation | Multiple imputation |
| 3 - Final data creation | Final data creation |
| 4 - Import MI data into Stata for analysis | Stata import |
| 5 - Primary analysis with data in long-form | Primary long-form analysis |
| 6 - Primary analysis with data in wide-form | Primary wide-form analysis |
| 7 - Additional analysis comparing COVID subsample to full APSALS cohort | Additional table |
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 paper, Clare et al 2021 published in Addiction: https://doi.org/10.1111/add.15599
| Description | R-code |
|---|---|
| 1 - Multiple imputation | Multiple imputation |
| 2 - Final data creation | Final data creation |
| 3 - Import MI data into Stata for analysis | Stata import |
| 4 - Cross-sectional descriptives in R | Cross-sectional descriptives |
| 5 - Longitudinal descriptives in Stata | Longitudinal descriptives |
| 6 - Primary analyses using mixed effects models with discrete time | Primary analyis |
| 7 - Sensitivity analysis using continuous time and ‘high risk’ consumption variable | Sensitivity analysis |
Tobacco and vaping characteristics over 5 years in the Australian Parental Supply of Alcohol Longitudinal Study (APSALS)
R and Stata code for all analysis of APSALS COVID-19 Tobacco paper, Boland et al 2024, in progress
| Description | R-code |
|---|---|
| 1 - Multiple imputation | Multiple imputation |
| 2 - Final data creation | Final data creation |
| 3 - Descriptive statistics | Descriptives |
| 4 - Analysis of raw trends | Trends |
| 5 - Multivariable regression models | Models |
| 6 - Correlation analysis | Correlation |
Does the trajectory of alcohol use and related harm differ based on the age of initiation to alcohol? Results from a prospective cohort study.
R and Stata code for all analysis of APSALS Initiation trajectories paper, Clare et al 2024, being presented at KBS2024.
| Description | R-code |
|---|---|
| 1 - Create data subset from APSALS cohort data | Data creation |
| 2 - Multiple imputation | Multiple imputation |
| 3 - Test model fit of nonlinear terms | Model fit |
| 4 - Primary analysis | Primary analysis |
| 5 - Secondary analysis | Secondary analysis |
| 6 - Pool MI results using Rubin’s rubles to get final estimates | Pool results |
| 7 - Create primary analysis figures using ggplot | Create primary figures |
| 8 - Create secondary analysis figures using ggplot | Create secondary figures |
| 9 - Descriptive statistics | Descriptives |
| 10 - Generate missing data summary for appendix | Missing data |
- 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.
Dr. Philip J Clare, PhD
Biostatistician at the Prevention Research Collaboration, University of Sydney.