Can supervise: YES
Nonresponses and missing data are common in observational studies. Ignoring or inadequately handling missing data may lead to biased parameter estimation, incorrect standard errors and, as a consequence, incorrect statistical inference and conclusions. We present a strategy for modelling non-ignorable missingness where the probability of nonresponse depends on the outcome. Using a simple case of logistic regression, we quantify the bias in regression estimates and show the observed likelihood is non-identifiable under non-ignorable missing data mechanism. We then adopt a selection model factorisation of the joint distribution as the basis for a sensitivity analysis to study changes in estimated parameters and the robustness of study conclusions against different assumptions. A Bayesian framework for model estimation is used as it provides a flexible approach for incorporating different missing data assumptions and conducting sensitivity analysis. Using simulated data, we explore the performance of the Bayesian selection model in correcting for bias in a logistic regression. We then implement our strategy using survey data from the 45 and Up Study to investigate factors associated with worsening health from the baseline to follow-up survey. Our findings have practical implications for the use of the 45 and Up Study data to answer important research questions relating to health and quality-of-life. Copyright © 2017 John Wiley & Sons, Ltd.
Wang, JJJ, Bartlett, M & Ryan, L 2017, 'On the impact of nonresponse in logistic regression: application to the 45 and Up study.', BMC Medical Research Methodology, vol. 17, no. 1, pp. 1-13.View/Download from: UTS OPUS or Publisher's site
In longitudinal studies, nonresponse to follow-up surveys poses a major threat to validity, interpretability and generalisation of results. The problem of nonresponse is further complicated by the possibility that nonresponse may depend on the outcome of interest. We identified sociodemographic, general health and wellbeing characteristics associated with nonresponse to the follow-up questionnaire and assessed the extent and effect of nonresponse on statistical inference in a large-scale population cohort study.We obtained the data from the baseline and first wave of the follow-up survey of the 45 and Up Study. Of those who were invited to participate in the follow-up survey, 65.2% responded. Logistic regression model was used to identify baseline characteristics associated with follow-up response. A Bayesian selection model approach with sensitivity analysis was implemented to model nonignorable nonresponse.Characteristics associated with a higher likelihood of responding to the follow-up survey include female gender, age categories 55-74, high educational qualification, married/de facto, worked part or partially or fully retired and higher household income. Parameter estimates and conclusions are generally consistent across different assumptions on the missing data mechanism. However, we observed some sensitivity for variables that are strong predictors for both the outcome and nonresponse.Results indicated in the context of the binary outcome under study, nonresponse did not result in substantial bias and did not alter the interpretation of results in general. Conclusions were still largely robust under nonignorable missing data mechanism. Use of a Bayesian selection model is recommended as a useful strategy for assessing potential sensitivity of results to missing data.
Wichitaksorn, N, Wang, JJJ, Choy, STB & Gerlach, R 2015, 'Analyzing return asymmetry and quantiles through stochastic volatility models using asymmetric Laplace error via uniform scale mixtures', APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, vol. 31, no. 5, pp. 584-608.View/Download from: UTS OPUS or Publisher's site
Fung, T, Wang, JJJ & Seneta, E 2014, 'The Deviance Information Criterion in Comparison of Normal Mixing Models', INTERNATIONAL STATISTICAL REVIEW, vol. 82, no. 3, pp. 411-421.View/Download from: UTS OPUS or Publisher's site
Olivier, J, Wang, J & Grzebieta, R 2014, 'A systematic review of methods used to assess mandatory bicycle helmet legislation in New Zealand', Journal of the Australasian College of Road Safety, vol. 25, no. 4, pp. 24-31.View/Download from: UTS OPUS
Background: Mandatory helmet legislation (MHL)
for cyclists became effective in New Zealand (NZ) on
1 January 1994. Assessments of the NZ MHL have led
to conflicting conclusions regarding its effectiveness at
reducing cycling head injury and risk of fatality. These
studies also differ in their use of analytic approaches and
Objectives: The aim of this paper is to systematically
review all studies that assess the NZ MHL in accordance
with quality criteria for assessing population-based
Data Sources: A search of Medline, Scopus and Web
of Science for peer-reviewed articles from 1994 to 9
September 2014 was conducted.
Study Selection: Documents were independently extracted
by two reviewers and limited to original articles in peerreviewed
journals that assessed the NZ MHL in terms of
cycling head injury.
Results: The results from three of the four included studies
indicated a positive effect of MHL for increasing helmet
wearing and reducing head injuries. However, the findings
of these studies must be interpreted within the context of
Conclusion: We believe more high quality evaluations are
needed to provide evidence for an objective assessment of
MHL in NZ.
Olivier, J, Wang, J, Walter, S & Grzebieta, R 2014, 'Anti-Helmet Arguments: Lies, Damned Lies and Flawed Statistics', Journal of the Australasian College of Road Safety, vol. 25, no. 4, pp. 10-23.View/Download from: UTS OPUS
Bicycle helmets are designed to mitigate head injury during
a collision. In the early 1990's, Australia and New Zealand
mandated helmet wearing for cyclists in an effort to
increase helmet usage. Since that time, helmets and helmet
laws have been portrayed as a failure in the peer-reviewed
literature, by the media and various advocacy groups. Many
of these criticisms claim helmets are ineffective, helmet
laws deter cycling, helmet wearing increases the risk of an
accident, no evidence helmet laws reduce head injuries at
a population level, and helmet laws result in a net health
reduction. This paper reviews the data and methods used
to support these arguments and shows they are statistically
flawed. When the majority of evidence against helmets or
mandatory helmet legislation (MHL) is carefully scrutinised
it appears overstated, misleading or invalid. Moreover,
much of the statistical analysis has been conducted by
people with known affiliations with anti-helmet or antiMHL
Wang, J, Olivier, J & Grzebieta, R 2014, 'Response to 'Evaluation of New Zealand's bicycle helmet law' article.', The New Zealand Medical Journal, vol. 127, no. 1389, pp. 106-108.
Wang, JJJ, Choy, STB & Chan, JSK 2013, 'Modelling stochastic volatility using generalized t distribution', JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, vol. 83, no. 2, pp. 340-354.View/Download from: UTS OPUS or Publisher's site
Wang, J, Bartlett, M & Ryan, L 2016, 'Housing transition and aging: 45 and Up Study', Joint Statistical Meetings, Chicago.
Wang, J, Olivier, J, Patummasut, M & Techakamolsuk, P 1970, 'The effect of the 100% Motorcycle Helmet Use Campaign on motorcyclist head injuries in Thailand', Australasian Road Safety Conference Proceedings, Australasian Road Safety Conference.
In 2011, the Thai government introduced the 100% Motorcycle Helmet Use campaign in an effort to increase helmet wearing among motorcyclists. A nationwide mass media campaign (television, radio, internet, and social media) provided information on the advantages of helmet wearing, disadvantages of non-helmet wearing, instructions for proper helmet usage and choosing a good quality helmet. The aim of this study was to assess the impact of this campaign on motorcycle head injury. Motorcycle injury data was extracted from twenty-seven hospitals that voluntarily participate in the Thai Injury Surveillance (IS) system for years 2009-2012 and helmet use estimates were taken from roadside surveys. Helmet use among motorcyclists changed very little with the onset of the helmet use campaign; however, motorcyclists wearing helmets were associated with a 52% reduction in the odds of a head injury (OR=0.48, 95% CI: 0.47-0.49). The immediate impact of the campaign on head injury rates was assessed for each hospital using an interrupted time series generalized linear autoregressive moving average (GLARMA) model. The results from each hospital were synthesized using a meta-analytic approach. We estimated no significant reduction in motorcycle head injuries (IRR=0.99, 95% CI: 0.92-1.06) following the onset of the helmet use campaign; however, the results by hospital were highly heterogeneous (I^2=65%). There is little evidence to suggest the helmet use campaign had any causal impact on motorcycle-related head injury during this period, although helmet use was associated with a significant reduction in head injury.
Wang, J, Grzebieta, R, Walter, S & Olivier, J 2013, 'An evaluation of the methods used to assess the effectiveness of mandatory bicycle helmet legislation in New Zealand', Australasian College of RoadSafety Conference, Adelaide.
Wang, J, Olivier, J, Grzebieta, R & Walter, S 2013, 'On The Use of Empirical Bayes for ComparativeInterrupted Time Series with an Application toMandatory Helmet Legislation', Road Safety Research, Policing and Education Conference, Brisbane.View/Download from: UTS OPUS
Wang, J, Olivier, J, Grzebieta, R & Walter, S 2013, 'Statistical errors in anti-helmet arguments', 2013 Australasian College of Road Safety Conference – 'A Safe System: The Road Safety Discussion', Australasian College of Road Safety Conference, Australasian College of Road Safety, Adelaide, Australia.View/Download from: UTS OPUS
Wang, J, Walter, S, Grzebieta, R & Olivier, J 2013, 'A Comparison of Statistical Methods in Interrupted Time Series Analysis to Estimate an Intervention Effect', 2013 Australasian Road Safety Research, Policing and Education Conference, Australasian Road Safety Research, Policing and Education Conference, Australasian College of Road Safety, Brisbane.View/Download from: UTS OPUS
Since the introduction of mandatory helmet legislation (MHL) in Australia, debate on the effect of MHL on cyclist head injuries has been ongoing. The debate sometimes revolves around the statistical methodology used to assess intervention effectiveness. Supporters of rescinding the MHL thereby encouraging cyclists to ride without helmets, regularly dismiss statistical evaluations as being flawed for various reasons. In a more general context, researchers want to estimate whether and how a policy intervention changed an outcome of interest. Quasi-experimental interrupted time series (ITS) is the most appropriate design to evaluate the longitudinal effects of policy interventions and segmented regression analysis is often used as a powerful statistical method for ITS. Recent research has employed a log-linear regression model for the hospital admission counts of head and limb injuries from New South Wales, Australia, from a 36 month period centred at the time of legislation. Estimation of the model was done using a frequentist approach. In this paper, we re-analyse this data using empirical Bayes and full Bayesian methods, since the use of these methods has become popular in road safety studies. In particular, we show how a full Bayesian method can be readily implemented in WinBUGS software. We discuss the advantages and disadvantages of each method and describe and compare the different estimation methods in terms of parameter estimates. The results show that all three estimation methods give consistent conclusions regarding the positive effect of compulsory helmet wearing on cyclist head injuries in New South Wales.