Title: A Programme of Applied Statistical Methodology Research (2019-2024)
Funder: Cancer Research UK

Abstract: Sound methodology underpins medical research and improvements in methodology have wide potential benefits. There is considerable evidence of poor methodology and reporting across the medical research literature. Many reviews have shown that these serious problems afflict the oncology literature (including diagnosis, prognosis, studies of therapy). This situation is unacceptable. Furthermore, with rapid developments in machine learning (for diagnosis, prognosis, personalised medicine), it is imperative that these new methods are robustly evaluated, and their strengths and limitations clearly elucidated before they are adopted. The philosophy underlying our programme of research is that it should have practical relevance, directly or indirectly, to clinicians and medical researchers, and hence should have the potential ultimately to benefit patients. The planned research falls under three main headings and we will carry out applied statistical research of relevance to cancer, in particular on topics relating to (1) studies of diagnosis and prognosis, (2) studies of therapy (randomised and non-randomised), and (3) a dissemination initiative relating to the conduct, reporting and reproducibility of oncology research (EQUATOR Network). The scope of the planned research is broad, but with increasing interest in using big data, routinely collected data, machine learning methods and reproducibility, we shall pay particular attention to how these affect studies of relevance to cancer.

Lay abstract: Sound methodology underpins medical research and improvements in methodology have wide potential benefits. There is considerable evidence of poor methodology and reporting across the medical research literature. Many reviews have shown that these serious problems afflict the oncology literature (including diagnosis, prognosis, studies of therapy). This situation is unacceptable. Furthermore, with rapid developments in machine learning (for diagnosis, prognosis, personalised medicine), it is imperative that these new methods are robustly evaluated, and their strengths and limitations clearly elucidated before they are adopted. The philosophy underlying our programme of research is that it should have practical relevance, directly or indirectly, to clinicians and medical researchers, and hence should have the potential ultimately to benefit patients. The planned research falls under three main headings and we will carry out applied statistical research of relevance to cancer, in particular on topics relating to (1) studies of diagnosis and prognosis, (2) studies of therapy (randomised and non-randomised), and (3) a dissemination initiative relating to the conduct, reporting and reproducibility of oncology research (EQUATOR Network). The scope of the planned research is broad, but with increasing interest in using big data, routinely collected data, machine learning methods and reproducibility, we shall pay particular attention to how these affect studies of relevance to cancer.

Research activity codes: 4.5 RESOURCES AND INFRASTRUCTURE (DETECTION)
Health categories: CANCER AND NEOPLASMS