The need for innovative statistical methodologies that prioritise patients’ perspectives in cancer treatment decisions is becoming increasingly recognised. Traditional clinical trials, which focus predominantly on efficacy endpoints, often overlook safety and quality of life considerations. This narrow focus can misalign with patient experiences, limiting a comprehensive understanding of treatment benefits and risks.
The Generalised Pairwise Comparisons (GPC) method addresses these gaps by enabling a holistic analysis of treatment effects across multiple clinically relevant outcomes, facilitating patient-focused analyses. Additionally, GPC can significantly reduce sample size requirements in clinical trials. The SHAPERS trial exemplifies GPC’s potential by transforming its design from non-inferiority to superiority, incorporating both efficacy and safety outcomes into a single and unified benefit-risk assessment. Ultimately, the broader adoption of GPC can enhance clinical research, enabling patients and clinicians to express their treatment preferences before any treatment decision, ensuring that patients remain at the forefront of medical advancements.
The need for innovative statistical methodologies that ensure patients’ perspectives are central to treatment decisions is increasingly evident. Clinical trials are the cornerstone of medical advancements in cancer care, offering critical insights before the approval of new therapies. These trials typically focus on efficacy endpoints, which are designed to reflect the intended effects of a treatment and include a broad range of assessments, from survival time to tumour response, often relegating safety and quality of life outcomes to secondary considerations.
Treatments are often approved based on the results associated to a single dimension of the treatment effect. Although this primary endpoint is chosen for its clinical relevance by medical professionals, it may not always align with the daily experiences and preferences of patients, potentially overlooking broader effects critical to patient wellbeing. This narrow focus can limit the understanding of a treatment’s benefits and risks, potentially overlooking important factors that might impact patient quality of life and overall satisfaction with the therapy.
Patient needs often include multiple dimensions, especially when evaluating treatments for diseases with different symptomatic expressions or functional impacts. This problem is exacerbated by the intrinsic limitations of traditional statistical tests frequently employed in clinical trials. These traditional methods are limited to the analysis of a single variable at a time, preventing a comprehensive understanding of how different outcomes of interest collectively influence the efficacy and safety of a treatment.
