PhD Designing Smarter Trials Value in Healthcare

Welcome to Maastricht University! Are you interested in developing procedures for sample size calculation for cost-effectiveness trials? Join us to develop novel procedures for the design and sample size calculation of cluster-randomized trials that have cost-effectiveness as outcome.

• Our goal: To develop efficient and innovative statistical procedures for sample size calculation in cost effectiveness cluster randomized trials, with the goal to improve the quality and efficiency of healthcare research.
• Your colleagues: you will work in the FHML division of the interfaculty Department of Methodology and Statistics (M&S) at FHML and at the Faculty of Psychology and Neuroscience. With around 20 staff members, M&S provides statistics education across multiple Bachelor, Master, and PhD programs. The department also offers statistical support for researchers from both faculties. Initially focused on study design and analysis methods for nested and longitudinal data, M&S now includes Bayesian statistics, multilevel interrater agreement, missing data methods, latent two-mode interaction, structural equation modelling, and causal inference.

We are looking for a motivated PhD candidate for a statistical project on efficient designs and sample size calculation for cost-effectiveness cluster randomized trials. The project focuses on developing statistical methods that optimize trial efficiency: achieving a required statistical power level or a limited width of the confidence interval at the lowest possible research costs.

The research will specifically address cost-effectiveness outcomes in cluster randomized trials, where participants are naturally grouped within clusters such as schools, workplaces, or healthcare settings. You will develop and evaluate innovative approaches for determining optimal sample sizes and study designs, including precision-based approaches, robust design strategies (e.g. Bayesian and maximin approaches), and decision-based approaches to trial design. The project combines statistical theory, mathematical derivation, simulation studies, and applications in health economics and clinical research.

The methods developed in this PhD project will contribute to more efficient and informative healthcare studies and support better evidence-based decision-making.

In this project, your responsibilities will entail:

• Derive efficient and innovative procedures for sample size calculation in cluster randomized trials where a measure of cost-effectiveness is the primary outcome variable of the study.
• Conduct simulations to evaluate approximate sample size formulas and assess their performance across different scenarios.
• Develop user-friendly interactive apps/software packages/code that facilitate the implementation of sample size procedures for researchers and practitioners.
• Present research at conferences and publish results in international methodological journals.
• Contribute to teaching activities within the department (at most 0.1 fte).

Are you ready to set the course for the years ahead? Then we’d love to meet you.

We’re not looking for check boxes; we’re interested in who you are and what you bring. Do you recognize yourself in this?

You take initiative, are creative, and possess strong analytical skills. You work in an organized and focused manner. In addition, you are someone who can work independently while also collaborating effectively within a research team. Furthermore, you bring:

• Master’s degree in Statistics, Mathematics, Econometrics, Biostatistics, Psychometrics or a related field.
• Excellent mathematical and statistical programming skills, particularly in R, as demonstrated by your master's thesis or other scientific work.
• Excellent communication and academic writing skills in English (an official certificate of English proficiency (e.g., IELTS) is considered an asset).
• Interest in Bayesian methods, sample size methodology and simulation studies.
• Experience with Bayesian statistics and Bayesian computational tools is a plus.

• A 12-month contract (1.0 FTE) with the prospect of a 3-year…