![]() ![]() To address this, there has been a significant effort to improve the design, conduct and reporting of prognostic studies. Clearly, we need to do better because poorly conceived and reported research is wasteful, potentially misleading and arguably not ethical. Recent systematic reviews regarding prognosis in whiplash, cancer and cardiovascular disease also report methodological problems in many studies. ![]() A decade later, and despite calls to improve the methodological quality of prognostic research, the acceptance rate by the international systematic review group who updated these findings, remained similarly low at 34%. For example, in a large international systematic review published in 2004 to determine the prognosis after mild traumatic brain injury, only 28% of the studies were of sufficiently high quality (i.e., low risk of bias) to be included in a best-evidence synthesis. In our areas of expertise in neck and back pain, traffic injuries, and mild traumatic brain injury, there are currently few examples of the implementation of prognostic research resulting in improved patient care, and critical appraisal of prognostic studies in these areas has clearly demonstrated the need to improve the conduct, design, analysis and interpretation of prognosis research. Prognostic research spans different areas of inquiry from classical epidemiology and public health through to clinical practice and stratified care, each with its particular focus but also with considerable overlap of shared methods. It can also provide indications about which prognostic variables appear to be on the causal pathway of a health condition or outcome. 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. This paper uses a framework to clarify some concepts in prognostic research that remain poorly understood and implemented, to stimulate discussion about how prognostic studies can be strengthened and appropriately interpreted. 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. ![]()
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