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Review
. 2025 Aug 31;15(8):e095065.
doi: 10.1136/bmjopen-2024-095065.

Design aspects for prognostic factor studies

Peggy Sekula  1 , Inga Steinbrenner  2 , Ulla T Schultheiss  2   3   4 , Neus Valveny  5 , Paola Rebora  6   7 , Susan Halabi  8 , Suzanne M Cadarette  9 , Richard D Riley  10   11 , Gary S Collins  12 , Willi Sauerbrei  13 , Mitchell H Gail  14 ; for topic group 5 of the STRATOS initiative
Collaborators, Affiliations
Review

Design aspects for prognostic factor studies

Peggy Sekula et al. BMJ Open. .

Abstract

Prognostic research is clinically relevant and ultimately facilitates stratified medicine. However, its quality and output are limited. More guidance is needed to improve understanding and thus quality. On behalf of the topic group 'TG5: study design' of the STRATOS initiative and for the general readership, this article describes key concepts and issues for prognostic factor studies, a sub-area of prognosis research. After providing a general overview on prognosis research, the article covers aspects such as aims, estimands and designs of prognostic factor studies, highlighting standards and current practice. Focusing on prognostic factor studies that assess a single factor at a time and a binary outcome, this article is complemented by a glossary of terms and a list of general aspects to consider in prognostic factor studies.

Keywords: EPIDEMIOLOGIC STUDIES; EPIDEMIOLOGY; Prognosis; Research Design; STATISTICS & RESEARCH METHODS.

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Conflict of interest statement

Competing interests: UTS is working for SYNLAB MVZ Humangenetik Freiburg GmbH. NV is employee of TFS HealthScience. SH serves on several data monitoring committees (CG Oncology, BMS, Janssen, Beigene, Sanofi). RDR is affiliated with an institute that is funded by a grant by NIHR. The funder did not influence the results/outcomes of the study despite author affiliations with the funder. RDR also receives royalties for textbooks on Prognosis Research in Healthcare & Individual Participant Data Meta-Analysis and consultant fees (BMJ Statistics Editor). All other authors have nothing to disclose.

Figures

Figure 1
Figure 1. Prognostic factor: illustration of stages in research. See box 1 for more details.
Figure 2
Figure 2. Conceptual illustration of a cohort study for use in prognostic factor research. In the case of a prospective cohort study, the study entry lies in the present, whereas in a retrospective cohort study, the cohort is assembled retrospectively from information collected in the past. Of note, the calendar date of study entry of the participants does not necessarily have to be the same. Nevertheless, participants in both types of studies are followed as time goes on starting at the time of inclusion. For retrospective cohort studies, follow-up data may still be available from the past, but data collection may also continue in the future. An event of interest may be any prespecified outcome such as kidney failure in patients with chronic kidney disease. End of observation may include censoring due to administrative closure of the study or the loss of contact with some participants (lost to follow-up) or the occurrence of a competing event such as death from causes other than kidney failure.
Figure 3
Figure 3. Conceptual illustration of the inter-related aspects in designing a prognostic factor study. The figure attempts to illustrate aspects involved and their dependencies when designing a prognostic factor study. See table 2 for more details on single aspects.
See this image and copyright information in PMC

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