Melissa Middleton

Melissa Middleton

Biostatistician | PhD Candidate

Murdoch Children's Research Institute

The University of Melbourne


Melissa Middleton is a PhD candidate based at the Murdoch Childrens Research Institute & the University of Melbourne. Her research focuses on the application of statistical methods to handle missing data in complex epidemiological studies, with a main focus on survey weights and multiple imputation.

Whilst her main research focus is on biostatistical methodology, she also has research interests in the areas of maternal mental health & substance use as an applied statistician.


  • Missing Data Methodology
  • Multiple Imputation
  • Longitudinal Studies
  • Maternal Mental Health


  • PhD in Biostatistical Methodology, 2023

    University of Melbourne

  • Master of Biostatistics, 2018

    University of Melbourne

  • Bachelor of Biomedical Science, 2017

    La Trobe University




Murdoch Children’s Research Institute

Apr 2019 – Apr 2020 Melbourne
Part of the Victorian Adolescent & Intergenerational Health Cohort Study team, contributed to research examining the affects of parental substance use on the next generation and mitigating factors of maternal mental health. She also had the opportunity to contribute to preliminary preparation of wave 11 data.

Data Analyst

Monash University

Oct 2018 – Jul 2019 Melbourne
Whilst based at the Monash Addiction Research Centre, she contributed to topical research in the area of opioid use. Highlights include being lead author on an investigation into opioid prescription trends following codeine rescheduling in Australia, which was featured on ABCs 7.30


University of Melbourne

Jul 2018 – Dec 2019 Melbourne
Tutored for the master level subject ‘Linear & Logistic Regression’ (POPH90144).

Recent Publications

Preventing postnatal depression: A causal mediation analysis of a 20-year preconception cohort

Postnatal depression (PND) is common and predicts a range of adverse maternal and offspring outcomes. PND rates are highest among women …


Evaluation and Development of Approaches for Handling Missing Data in Complex Longitudinal Studies

Handling missing data is an important process in the analysis of health research studies. For complex study designs, information on how the study was conducted needs to be incorporated into the process and it is currently unclear how best to do this. My project aims to evaluate and compare the various methods available, and produce guidance on the issue for use in the analysis of public health studies.

Recent & Upcoming Talks

Multiple Imputation in the context of the unequal sampling probabilities of case cohort studies

Multiple imputation (MI) is commonly used to address missing data in epidemiological studies, but valid use requires compatibility between the imputation and analysis models. How to achieve compatibility is unclear when missing data occur in the context of unequal sampling probabilities, such as in case cohort studies, where the exposure is collected only on cases and a random subcohort. The unequal sampling probabilities in this study design can be accounted for during analyses through inverse probability weighting (IPW). To ensure compatibility these weights also need to be incorporated into the imputation model. This study assessed the performance of various approaches to accommodate weighting during MI to handle missing covariate data in the context of a case-cohort study estimating either a risk ratio or odds ratio. The study was motivated by a case-cohort investigation within the Barwon Infant Study (BIS). A simulation study was conducted to mimic BIS and missingness was introduced into two covariates, varying the proportion of incomplete cases, probability of subcohort selection and strength of outcome-exposure relationships. Various methods to incorporate weighting in the imputation were applied to handle covariate missingness, while IPW was used to analyse the imputed datasets. IPW was also applied to complete cases for comparison. The MI methods were also applied to the BIS data. All MI methods performed similarly in terms of bias and efficiency, with marked improvement when compared to IPW applied to the complete cases. A similar pattern of results were seen in the case study. Our results suggest that MI increases the accuracy and efficiency in the analysis of case-cohort studies with missing covariate data compared to IPW applied to complete cases. How weighting is accounted for during MI makes little difference in the analysis of case-cohort studies.