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Design aspects for prognostic factor studies.

作者信息

Sekula Peggy, Steinbrenner Inga, Schultheiss Ulla T, Valveny Neus, Rebora Paola, Halabi Susan, Cadarette Suzanne M, Riley Richard D, Collins Gary S, Sauerbrei Willi, Gail Mitchell H

机构信息

Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany

Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.

出版信息

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


DOI:10.1136/bmjopen-2024-095065
PMID:40887130
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12406931/
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.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/780e/12406931/66b66a06cd20/bmjopen-15-8-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/780e/12406931/a18325a06bf1/bmjopen-15-8-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/780e/12406931/385e7628b02a/bmjopen-15-8-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/780e/12406931/66b66a06cd20/bmjopen-15-8-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/780e/12406931/a18325a06bf1/bmjopen-15-8-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/780e/12406931/385e7628b02a/bmjopen-15-8-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/780e/12406931/66b66a06cd20/bmjopen-15-8-g003.jpg

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本文引用的文献

[1]
Accounting for Competing Risks in Clinical Research.

JAMA. 2024-6-25

[2]
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

BMJ. 2024-4-16

[3]
Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.

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[4]
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Value Health. 2022-10

[5]
Structured reporting to improve transparency of analyses in prognostic marker studies.

BMC Med. 2022-5-12

[6]
The Use of 'Omics for Diagnosing and Predicting Progression of Chronic Kidney Disease: A Scoping Review.

Front Genet. 2021-11-8

[7]
Methodology over metrics: current scientific standards are a disservice to patients and society.

J Clin Epidemiol. 2021-10

[8]
Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R.

BMC Med Res Methodol. 2021-4-2

[9]
Grading Evolution and Contemporary Prognostic Biomarkers of Clinically Significant Prostate Cancer.

Cancers (Basel). 2021-2-5

[10]
Reducing bias and improving transparency in medical research: a critical overview of the problems, progress and suggested next steps.

J R Soc Med. 2020-11

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