Prognostic research also can establish an evidence-based understanding of an individual’s probability of developing different outcomes and can inform the development of interventions and policies to improve the diagnosis of health conditions and management of patients. It is also a method to investigate variables associated with health outcomes of interest. It aims to describe the natural history and clinical course of health conditions, and it provides evidence about the burden of disease. Prognostic research serves many purposes. Questions of prognosis are among the most important for patient care. Furthermore, we address common conceptual misunderstandings related to study design, analysis, and interpretation of multivariable models from the perspectives of association, prediction and causation. We also propose definitions of ‘candidate prognostic factors’, ‘prognostic factors’, ‘prognostic determinants (causal)’ and ‘prognostic markers (non-causal)’. Including considerations of causation within a prognostic framework provides a more comprehensive roadmap of how different types of studies conceptually relate to each other, and better clarity about appropriate model performance measures and the inferences that can be drawn from different types of prognostic studies. These either fall under the umbrella of exploratory (description, association, and prediction model development) or confirmatory (prediction model external validation and investigation of causation). By causation, we mean the effect of prediction and decision rules on outcomes as determined by intervention studies and the investigation of whether a prognostic factor is a determinant of outcome (on the causal pathway). We propose that there are four main objectives of prognostic studies – description, association, prediction and causation. In this paper, we aim to show how different categories of prognostic research relate to each other, to differentiate exploratory and confirmatory studies, discuss moderators and mediators, and to show how important it is to understand study designs and the differences between prediction and causation. Recent initiatives to improve this include the Prognosis Research Strategy (PROGRESS) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Statement. But much prognostic research has been poorly conducted and interpreted, indicating that a number of conceptual areas are often misunderstood. Prognostic research has many important purposes, including (i) describing the natural history and clinical course of health conditions, (ii) investigating variables associated with health outcomes of interest, (iii) estimating an individual’s probability of developing different outcomes, (iv) investigating the clinical application of prediction models, and (v) investigating determinants of recovery that can inform the development of interventions to improve patient outcomes. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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