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The 13th Oceania Stata Conference takes place 5 February 2026 virtually.
Share, learn, and engage with leading Stata users from around the world. Learn new methods and techniques and hear how leading researchers use Stata in their journey of discovery and exploration of data.
All times are AEST (UTC +10)
This year there will be two rooms: “The Stata Room” and “The Research Room”. Participants will be able to move between rooms.
| 9:00–9:10 | Welcome |
The Stata Room |
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| 9:10–10:10 | Some graphical tips for Stata users
Abstract:
This talk will cover a miscellany of graphical tips, some old, some new. I will discuss using both official commands and community-contributed commands. It will range from small stuff (often it is a matter of detail to change a good graph into a much better one), through various techniques and tricks, to broad strategy, both in learning and using Stata easily and effectively and in working with graphics for research, teaching, and service.
Nick Cox
Durham University
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| 10:10–10:40 | samregc: Stata module to perform sensitivity analysis of main regression coefficients
Abstract:
This presentation introduces samregc, a fast, flexible, and simple Stata command that systematizes specification-based sensitivity analysis. It evaluates the robustness of target coefficients by analyzing all—subsets (or user-defined subsets) regression results over alternative combinations of control variables.
Pablo Gluzmann
CEDLAS-UNLP and CONICET
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| 10:40–12:10 | Introduction to explainable machine learning using Stata
Abstract:
Machine learning (ML) has become a powerful tool for modeling complex data and providing accurate predictions. However, the “black-box” nature of many ML models often raises concerns about their explainability and trustworthiness. Explainable machine learning (XML) seeks to address these concerns by enhancing the transparency and understanding of ML predictions. This talk aims to provide a practical guide to XML techniques. It begins with an overview of ensemble decision tree models such as random forests and gradient boosting, which are widely used but often difficult to interpret. I then introduce methods for explaining predictions using both global and local XML techniques. These include state-of-the-art approaches such as SHAP values, individual conditional expectation (ICE) plots, variable importance measures, partial dependence plots, and global surrogate models.
Aramayis Dallakyan
StataCorp
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| 12:10–12:40 | xtvfreg: Stata module for estimating variance function panel regression
Abstract:
This presentation introduces xtvfreg, a new Stata module that implements an iterative mean variance panel regression estimator in which both the conditional mean and conditional variance of the dependent variable are modeled as functions of covariates. The estimator is designed for researchers working with panel data in which heteroskedasticity is substantively meaningful.
Irma Mooi-Reci
University of Melbourne
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| 12:40–1:25 | Open panel discussion with Stata developers
Contribute to the Stata community by sharing your feedback with StataCorp's developers. From feature improvements to bug fixes and new ways to analyze data, we want to hear how Stata can be made better for our users.
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| 1:25–1:55 | “Can I just use n=30 in each group?”: Using Stata for sample-size determination in an increasingly complex world
Abstract:
In this talk, I will outline some of the practical and technical challenges involving sample size that one faces as a biostatistician in the health sciences. I will describe how Stata helps to support a workflow, including simulations when needed, and use examples from recent research projects.
Andrew Gray
University of Otago
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| 1:55–2:25 | Swift Stata Stories |
| 2:25–2:55 | Healthcare quality control and improvement using Stata
Abstract:
Stata is widely used in healthcare to compare events such as falls, infections, and episodes of delirium over time using control charts across hospital wards or across hospitals. This presentation demonstrates methods including control charts, funnel plots, ANOM and contrast, and user-written CHAID with the goal of developing healthcare quality improvement techniques.
Dean McKenzie
Epworth HealthCare
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| 2:55–3:25 | gofbinreg: Goodness-of-fit statistics in binary regression models
Abstract:
When one reports the results of binary regression, it is crucial to evaluate the overall model adequacy using goodness-of-fit statistics. This presentation introduces a command gofbinreg, that assesses the performance of the Hosmer–Lemeshow normalized unweighted sum of squares and Hjort–Hosmer statistics to evaluate overall model adequacy.
Xuelu Sun
Swinburne University of Technology
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| 3:25–3:55 | The use of Stata putdocx for automating data safety monitoring committee reports
Abstract:
Data safety monitoring committee meetings for clinical trials necessitate the creation of a statistical report from which the trial's safety, progress, and data integrity can be assessed. This presentation shows how much of the repetitive process can be streamlined and automated using Stata’s putdocx commands via a do-file.
Alannah Rudkin
Murdoch Children's Research institute
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| 3:55–4:25 | Preliminary findings on advancing women's health in Singapore through AI acceptance
Abstract:
As artificial intelligence rapidly advances, it can integrate electronic health records, genetic profiles, and clinical information to enable individualized, female-specific prevention strategies. This presentation uses structural equation modeling in Stata to analyze survey data to examine factors influencing Singapore women’s adoption of AI-enabled healthcare.
Zixuan Cong
National University of Singapore
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| 4:25–4:55 | rdlasso: Regression discontinuity with high-dimensional data
Abstract:
This presentation discusses the rdlasso command, which allows the inclusion of high-dimensional covariates in regression discontinuity design (RDD) settings. The command allows for the inclusion of high-dimensional covariates in RDD for sharp and fuzzy cases, making the methodology accessible to Stata users and also automating the covariate selection procedure.
Marianna Nitti
Sapienza University of Rome
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The Research Room |
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| 1:25–1:50 | Limitations and comparison of the DFA PP and KPSS unit-root test: Evidence for labor variables of Mexico
Abstract:
Unit-root tests have represented a great contribution to time-series analysis by detecting variable stationarity. However, this presentation includes some of the criticisms that have been made about the unit-root tests by executing in Stata the three best-known unit-root tests for the main macroeconomic variables of Mexico, with the intention of analyzing, both graphically and technically, whether the series is stationary.
Ricardo Rodolfo Retamoza Yocupicio
National Autonomous University of Mexico (UNAM)
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| 1:50–2:15 | How cooking and eating at home shape emotional well-being: Insights from the Food & You survey
Abstract:
Using data from the Food & You survey, this study aims to evaluate the behavioral pathways linking emotional well-being, cooking behavior, and eating-at-home practices and to identify leverage points for public-health and behavioral interventions. Partial least-squares structural equation modeling was performed with Stata to assess these directional relationships.
Yuke Li
National University of Singapore
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| 2:15–2:40 | Multistate survival modeling of cardiovascular admission and mortality in a heart failure cohort in Singapore
Abstract:
Heart failure patients often experience complex clinical trajectories involving hospitalization and death. Conventional survival models that focus on a single endpoint may fail to capture these sequential outcomes adequately. This study applies a multistate survival framework to characterize transitions to cardiovascular admission and death.
Shufan Zhao
National University of Singapore
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| 2:40–3:05 | Young hearts at risk: Preliminary insights into detection and personalized management of acute myocardial infarction
Abstract:
This study investigates acute myocardial infarction among younger adults in Singapore and characterizes their clinical and risk profiles to guide early detection and targeted management. Using data from the national registry of diseases office, a retrospective cohort of patients was analyzed for one-year all-cause mortality using Bayesian proportional-hazards models in Stata.
Luyang Xiao
National University of Singapore
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| 3:05–3:30 | Fluid balance in postoperative patients and relationship to acute kidney injury
Abstract:
This talk presents an audit of 330 postoperative patients examining the relationship between postoperative cumulative fluid balance over 7 days and the incidence and rate of recovery of acute kidney injury. The data were analyzed with Stata using a zero-inflation Poisson regression model and compared with a GEE model and two models using H2O machine learning.
Mathew Piercy
Northwest Regional Hospital Burnie
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| 3:30–3:55 | Artificial intelligence in suicide prevention: Comparative evidence from a network meta-analysis
Abstract:
Artificial intelligence is emerging as a powerful tool in suicide prevention. Often outperforming traditional assessments, machine-learning models can analyze electronic health records and social-media language to identify subtle behavioral cues that precede suicidal thoughts or actions. This talk applies network meta-analysis to the systematic review by Lejeune et al. (2022), which highlights the potential of AI in improving suicide-risk detection, screening, and monitoring.
Hengni Yuan
NUS Yong Loo Lin School of Medicine
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| 3:55–4:25 | Household net wealth inequality in Indonesia: Evidence from a Dagum type III model
Abstract:
Investigating household net wealth inequality in Indonesia is important because it can worsen for low-class individuals or households that are unable to inherit sufficient capital for the next generations and maintain financial stability during a period of low or no income. This presentation applies the Dagum type III model to measure household net wealth inequality in Indonesia.
Thomas Soseco
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The conference is free, but you must register to attend.
Visit the official conference page for more information.
The logistics organizer for the 2026 Oceania Stata Conference is Survey Design and Analysis Services, the distributor of Stata in Australia, Indonesia, and New Zealand. The co-organizer is Columbia CP.
View the proceedings of previous Stata Conferences and international meetings.
