PhD Studentship: Developing Practical Method Based Guidance for Trial Statisticians for the Design a

Supervisors: Professor Victoria Cornelius, Dr Hadith Rastad, Dr Tim Rawson

About the role:

Sequential Multiple Assignment Randomised Trials (SMARTs) are increasingly used to evaluate multistage treatment strategies that adapt to patient response. They are now common in mental health, substance use, chronic disease management and other areas. A recent review by Freeman et al. (2009–2024) provides the most comprehensive overview of SMARTs to date and highlights several important common problems. First, sample size calculations rarely reflect the multistage structure and the intended treatment‑sequence comparisons. Second, reporting of initial and subsequent randomisation procedures is often incomplete. Third, primary aims and estimands are frequently unclear. Finally, embedded‑regime and tailored‑regime analyses are under‑used:  Only a minority of trials analyse prespecified treatment sequences, and none in the review implemented deeply tailored Dynamic Treatment Regimens methods in primary reports.

There are several complex aspects to SMARTs and these require specialised attention. Specific supportive guidance is lacking and there is a lack of consideration regarding reporting, interpretability, reproducibility and clinical impact of SMARTs. Existing resources tend to present methods at a high level and are not tailored to the practical needs of applied trial statisticians. This aim of this PhD will be to understand and develop a practical, method‑by‑method guideline that support statisticians in the design and analysis choices of SMART trial. This includes:

• Approaches to sample size calculation and design considerations
• Selection of suitable analysis models for SMART designs, aligned with chosen estimands and assumptions.  Comparison of model performance (under violated assumptions, with respect to bias and loss of efficiency)
• Assessment of ease of implementation and interpretation in standard statistical software.
• Identification of minimum reporting items needed to make SMART methods transparent and reproducible.

Importantly, the project will be anchored in a clinical case study: the PATH sepsis trial, a stratified, sequentially randomised trial of precision antimicrobial prescribing with a nested feasibility assessment of second‑stage randomisation. PATH provides a rich, realistic setting in which to:

• Apply and compare candidate SMART analysis strategies to actual two‑stage treatment sequences;
• Explore sample‑size and design issues; and
• Translate methodological recommendations into concrete reporting guidance for multi‑stage trials.

Overall aim:

To develop a practical, method focused guideline for the design, analysis and reporting of SMART trials.

Methods overview:

• Evidence synthesis: extended scoping/systematic review of SMARTs, structured data extraction and descriptive analyses of current practice.
• Methodological work: focused simulations using realistic but generic SMART scenarios; comparison of methods on bias, efficiency and coverage.
• Guideline development: synthesis of review and simulation results into decision tables, worked examples and reporting items, with iterative refinement based on feedback from biostatisticians experienced in SMARTs.

The student will:

• Undertake a structured review of current practice, including design, analysis, sample‑size methods and reporting of SMARTs.
• Carry out an analytical review of existing sample‑size approaches for SMARTs and evaluate their performance through targeted simulation studies.
• Conduct methodological and simulation work comparing key analysis strategies for treatment sequences (e.g. weighting/replication, g‑methods,), focusing on bias, efficiency and robustness.
• Apply candidate methods to PATH trial data to illustrate their implementation, assess feasibility in a real setting and generate design inputs for a future SMART.
• Synthesise findings into explicit recommendations on method choice, assumptions, complexity, potential biases and minimum reporting requirements, then refine these via feedback from methodologists and applied trial statisticians (e.g. survey or workshop).

Application process