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Last updated: 1 December 2009

2009 Australian and New Zealand Users Group meeting

5 November 2009

Sydney, Australia

The Darlington Centre
The University of Sydney
NSW 2006


The impact of water supply and sanitation interventions on child health: Evidence from Demographic and Health Surveys

Ron Bose
International Initiative for Impact Evaluation (3ie)
In this presentation, I examine the impacts on child health, using diarrhea as the health outcome, (among children living in households) with access to different types of water and sanitation facilities, and from socioeconomic and child specific factors. Using multiyear cross-sectional health DHS data, I employ the quasi-experimental estimators (matching) to match children belonging to different treatment groups, defined by water types and sanitation facilities, with children in a control group. Quantile regression models are used to benchmark results and to check for their robustness. The empirical framework yields strong support that access to improved sanitation has had a substantial impact on reducing the (predicted) diarrhea outcomes. This is especially true among very young children defined as those below 24 months of age whose rates of diarrhea have shown the largest declines between 2001 and 2006. These estimates serve as an input into cost-effectiveness analysis that compares the provision of increased access to sanitation with other public health interventions in developing nations (especially sub-Saharan Africa and South Asia) to underline its importance in achieving Millennium Development Goals (MDGs).

The use of a quantile regression model in an occupational health and safety study

Maya Guest
University of Newcastle
May Boggess
Texas A&M University
The ISO-7029: statistical distribution of hearing thresholds as a function of age provides by gender the expected median value of hearing thresholds relative to the median threshold at the age of 18 years and the statistical distribution above and below the median value for the range of audiometric frequencies from 125 Hz to 8000 Hz for populations of otologically normal persons of given age between 18 and 70 years. Comparing hearing thresholds of a study population has been problematic. Published studies have used a series of t tests as the analysis method. In this paper, we aim to present the hearing threshold data collected for the SHOAMP study to compare the hearing thresholds with the reference population using quantile regression.

Using data collected as part of the SHOAMP study, hearing thresholds were assessed in both ears of 614 exposed personnel, 513 technical tradesmen, and 403 nontechnical tradesmen using pure-tone audiometry (air conduction) at the frequencies of 0.1, 1, 2, 3, 4, 6, and 8 kHz. The results were compared with the otologically normal population using a quantile regression model, controlling for possible confounding variables.

Model estimates median hearing thresholds significantly lower than normal. The extent of the hearing loss is substantial in that a 95% confidence band for the median lies below the 30th percentile of the normal population for most frequencies and ages. The largest loss occurs at 6 and 8 kHz for those under 30 years of age.

Additional information

Effects of lack of independence in meta-epidemiology

Peter Herbison
University of Otago
Meta-epidemiology is the use of the characteristics of individual reports of randomized trials to examine the effects on meta-analyses. Traditionally only one meta-analysis from a systematic review is included in these studies, due to a fear of what would happen because of the lack of independence if the same study was included in more than one meta-analysis.

We have some data on 64 meta-analyses but these come from only 18 systematic reviews. Papers submitted from this data have been heavily criticized because of the feared effects of this lack of independence. One suggestion for a sensitivity analysis is to randomly select one meta-analysis from each systematic review. As an extension to that I chose to bootstrap the results of interest to examine the effects of the lack of independence. Stata makes these analyses trivial. This talk will present the results of two such sensitivity analyses and show that the lack of independence appears to have little effect on the interpretation of the results.

Additional information

Meta-analysis in animal health and reproduction: Methods and applications using Stata

I.J. Lean
A.R. Rabiee
Meta-analysis is a rapidly expanding area of research that has been relatively underutilized in animal and veterinary science. It is a quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research. Outcomes from a meta-analysis may include a more precise estimate of the effect of treatment or risk factor for disease, or other outcomes, than any individual study contributing to the pooled analysis. The examination of variability or heterogeneity in study results is also a critical outcome. Examples where meta-analyses have been repeated in animal science or veterinary medicine show good consistency in estimates of effect. Rigorously conducted meta- analyses are useful tools to improve animal well-being and productivity. The need to integrate findings from many studies ensures that meta-analytic research is desirable and the large body of research now generated makes the conduct of this research feasible.

Many of the statistical methods to conduct meta-analysis are widely used. In this presentation, we will demonstrate how Stata can provide a comprehensive suite of programs that can be used in meta-analysis. Some detail on the common statistical methods used, such as metan and metareg, is presented and examples of when these have been used in studies using cattle are provided. The post-hoc methods used to evaluate heterogeneity and publication bias (metabias, confunnel>), which include the I2 statistic, L’Abb plots, Galbraith plots, Rosenthal’s N, and influential study analysis are exclusively used in meta-analysis.

Additional information

Generating RTF files in Stata to create tables for inclusion in Word documents

Kieran McCaul
University of Western Australia
When an analysis is completed, the final results need to be tabulated for inclusion into a manuscript, and this can be a very time-consuming task. Hence it is not surprising that in the past few years, a variety of user-written ador- files have appeared that generate tables of results for inclusion in Word, Excel, or LaTeX. While these may provide a solution for many users, they can be difficult to use at first. There is no standard format for a table of results, and consequently, any ado-file must provide many options to give the user the ability to control the arrangement of results in a table.

Perhaps an easier approach would be to simply generate the required table directly from within a program. Because most users will be writing manuscripts in Word, a table created as an RTF file can be included into a Word document quite easily, and generating such a table from within a Stata program requires knowledge of only a few RTF commands. While this is a “brute force” method for generating a table, it is particularly useful if many tables of the same layout have to be generated (in a thesis, for example), or where an annual report has to be produced and the number and layout of tables remains essentially the same from year to year.

In this talk, I will describe the RTF instructions required to produce a table, and I will outline the approach that I take when generating an RTF file from within a Stata program.

Additional information

Automating reports with Mata and mail merge

Karl Keesman
Survey Design and Analysis
Mail merge is a convenient way of getting data into a report. It allows data to be interspersed between normal report texts. Graphs and table data can also be input with mail merge.

Where standard reports are required on a regular basis or large numbers of reports are required, automating the reporting process saves time and reduces the chance of errors.

Stata and Mata can generate an output that mail merge can read. This will be demonstrated using a simple example. The commands and process of doing this with Mata will be explained.

Additional information

Complementing Stata with geovisualization

Philip S. Morrison
Victoria University of Wellington—New Zealand
Statistical agencies are increasingly recognizing the value of configuring their data in formats that facilitate geovisualization—the representation of data across the geographic domain. For Stata users this poses a challenge because the present geovisualization capacity within the conventional Stata product is quite limited.

This presentation reports on a project completed for Statistics New Zealand on Geovisualization where graphical tools from Stata were complemented by the geovisualization capacity afforded by GeoViz. The presentation illustrates the returns to geovisualization via a specific case study and considers the advantages that could potentially accrue to Stata users if such a capacity was provided within the Stata system itself.

Additional information

Working correlation structure and model selection in GEE analyses of longitudinal data

Jisheng Cui
Deakin University
The GEE method is one of the most commonly used statistical methods in the analysis of longitudinal data. A working correlation structure for the repeated measures of the outcome variable of a subject needs to be specified in this method. However, statistical criteria for selecting the best correlation structure and the best subset of explanatory variables in GEE are only available recently. Maximum likelihood–based model selection methods, such as AIC, are not applicable directly to GEE.

Based on the QIC method proposed by Pan (2001, Biometrics 57: 120–125), we systematically developed a general computing program to calculate the QIC value for a range of different distributions, link functions, and correlation structures. The QIC value can be used to select both the best correlation structure and the best subset of explanatory variables. The program was written in Stata software.

In this talk, I will introduce the QIC method and program, and I will demonstrate how to use it to select the most parsimonious model in GEE analyses through several representative examples.

Additional information

Competing risks regression

Roberto G. Gutierrez
No abstract.

Additional information

Scientific organizers

Demetris Christodoulou, University of Sydney

Vasilis Sarafidis, University of Sydney

Logistics organizers

Survey Design and Analysis Services Pty Ltd, the official distributor of Stata in Australia and New Zealand.