You are here
Professor John Carlin
Professor John Carlin has a national and international reputation in biostatistics, the science of developing and applying statistical methods to problems in health and medical research. This field is increasingly recognised as fundamental to modern research because of rapidly increasing technological capacity to accumulate and manipulate complex numerical data, in the face of which deep understanding of the theory and application of statistical methods has become ever more important.
After completing a PhD in Statistics at Harvard University, John began a career in medical and public health research. As Director of the Clinical Epidemiology and Biostatistics Unit at Murdoch Childrens, he has played a leading role developing one of Australia's leading biostatistical centres. This unit has developed a broad program of work encompassing collaborative contributions to a wide range of clinical and population health research, including both clinical trials and observational studies, augmented by training in research methods and data management, and underpinned by an internationally recognised methodological research program.
In addition to his role within Murdoch Childrens, Professor Carlin has a professorial appointment in the Centre for Epidemiology and Biostatistics, Melbourne School of Population & Global Health, University of Melbourne. His international standing in the core discipline of statistics is attested by co-authorship of an influential graduate-level textbook with colleagues from Harvard University (Bayesian Data Analysis, 3rd edition 2014). He has over 350 scientific publications across a wide range of topics in statistical methodology and in numerous substantive clinical and public health areas.
- Honorary Professorial Fellow, Department of Paediatrics, University of Melbourne
- Professor, Melbourne School of Population and Global Health, University of Melbourne
2018: Elected Fellow of the Australian Academy of Health & Medical Sciences
Professor Carlin has maintained an ongoing program of research on statistical methods for the analysis of complex longitudinal data, including a particular focus on methods for dealing with missing data using the method of multiple imputation and more recently on causal inference. This work has been funded by the National Health and Medical Research Council (NHMRC) since the mid-1990's, and as well as being published widely (journals such as Statistics in Medicine , Biostatistics, American Journal of Epidemiology) has aided the effective analysis of many important epidemiological studies. Other areas of methodological research interest include methods for cluster-randomised and "stepped wedge" trials.
Complementing his methodological work, Professor Carlin has made fundamental contributions as a collaborator in numerous areas of child and adolescent health, including:
- rotavirus disease and vaccines
- natural history and clinical management of cystic fibrosis
- neonatal intensive care
- value of clinical signs for predicting severe illness in young infants in resource-poor settings
- epidemiology of childhood cardiomyopathy
- vaccines and vaccine-preventable childhood diseases
- epidemiology of asthma and other allergic diseases
- behavioural and mental health problems of adolescence and young adulthood
- childhood obesity: risk factors, natural history, comorbidities
- early life influences on health and development
- The Victorian Centre for Biostatistics: building capacity in a core discipline of public health
- Approaches to improving statistical practice via causal thinking and avoidance of false dichotomies
- Statistical methods for handling missing data in large epidemiological studies
- Design and analysis of complex randomised trials
- Child Health CheckPoint
- Victorian Adolescent Health Cohort Study
- Barwon Infant Study
Moreno-Betancur M, Lee KJ, Leacy FP, White IR, Simpson JA, Carlin JB. Canonical causal diagrams to guide the treatment of missing data in epidemiological studies, American Journal of Epidemiology, 2018, doi.org/10.1093/aje/kwy173.
Downes M, Gurrin L, English DR, Pirkis J, Currier D, Spittal MJ, Carlin JB. Multilevel Regression and Poststratification: A Modelling Approach to Estimating Population Quantities From Highly Selected Survey Samples. Am J Epidemiol, 2018; 187(8), 1780–1790.
Moreno-Betancur M, Koplin J, Ponsonby A-L, Lynch J, Carlin JB. Measuring the impact of differences in risk factor distributions on cross-population differences in disease occurrence: a causal approach. International Journal of Epidemiology, 2018 Feb 1;47(1):217-225.
Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN, Altman DG. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016; 31:337–350.
Lawrie J, Carlin JB, Forbes A. Optimal stepped wedge designs. Statistics and Probability Letters. 2015; 99:210-214.
Gelman A, Carlin JB. Beyond power calculations: Assessing type S (sign) and type M (magnitude) errors. Psychological Science. 2014; 9:641.651.
Patton GC, Coffey C, Romaniuk H, Mackinnon A, Carlin JB, Degenhardt L, Olsson CA, Moran P. The prognosis of common mental disorders in adolescents: a 14-year prospective cohort study. Lancet. 2014; doi:10.1016/S0140-6736(13)62116-9 .
Seaman S, Galati JC, Jackson D, Carlin JB. What is meant by 'Missing at Random'? Statistical Science. 2013; 28:257-268.
Carlin JB, Macartney K, Lee KJ, Quinn HE, Buttery J, Lopert R, Bines J, McIntyre P. Intussusception risk and disease prevention associated with rotavirus vaccines in Australia's national immunisation program. Clinical Infectious Diseases. 2013; 57:1427-1434.
Moran P, Coffey C, Romaniuk H, Olsson C, Borschmann R, Carlin JB, Patton GC. The natural history of self-harm from adolescence to young adulthood: A population-based cohort study. Lancet. 2012; 379:236-243.
Wainwright CE, Vidmar S, Armstrong DS, Byrnes CA, Carlin JB, Cheney J, Cooper PJ, Grimwood K, Moodie M, Robertson CF, Tiddens HA. Effect of Bronchoalveolar Lavage–Directed Therapy on Pseudomonas aeruginosa Infection and Structural Lung Injury in Children With Cystic Fibrosis: A Randomized Trial. JAMA. 2011; 306:163-171.
Lee KJ, Galati JC, Simpson JA, Carlin JB. Comparison of methods for imputing ordinal data using multivariate normal imputation: a case study of non-linear effects in a large cohort study. Stat Med. 2012; 31:4164-4174.
Lee KJ, Carlin JB. Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation. Am J Epidemiol. 2010; 171:624-632.
White IR, Carlin JB. Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values. Stat Med. 2010; 29:2920-2931.