Prognosis research strategy (PROGRESS) 1: A framework for researching clinical outcomes (original) (raw)
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Prognosis research strategy (PROGRESS) 4: stratified medicine research
BMJ (Clinical research ed.), 2013
In patients with a particular disease or health condition, stratified medicine seeks to identify those who will have the most clinical benefit or least harm from a specific treatment. In this article, the fourth in the PROGRESS series, the authors discuss why prognosis research should form a cornerstone of stratified medicine, especially in regard to the identification of factors that predict individual treatment response Aroon D Hingorani professor of genetic epidemiology 1 , Daniëlle A van der Windt professor in primary care epidemiology 2 , Richard D Riley senior lecturer in medical statistics 3 , Keith Abrams professor of medical statistics 4 , Karel G M Moons professor of clinical epidemiology 5 , Ewout W Steyerberg professor of medical decision making 6 , Sara Schroter senior researcher 7 , Willi Sauerbrei professor of medical biometry 8 , Douglas G Altman professor of statistics in medicine 9 , Harry Hemingway professor of clinical epidemiology 1 , for the PROGRESS Group
Journal of Clinical Epidemiology, 2008
Objective: To present an explanatory framework for understanding prognosis and illustrate it using data from a systematic review. Study Design and Setting: A framework including three phases of explanatory prognosis investigation was adapted from earlier work and a discussion of causal understanding was integrated. For illustration, prognosis studies were identified from electronic and supplemental searches of literature between 1966 and December 2006. We extracted characteristics of the populations, exposures, and outcomes and identified three phases of explanatory prognosis investigation: Phase 1, identifying associations; Phase 2, testing independent associations; and Phase 3, understanding prognostic pathways. The purpose of each phase is exploration, confirmation, and development of understanding, respectively.
Prognosis Research Strategy (PROGRESS) 3: prognostic model research
PLoS medicine, 2013
Prognostic models are abundant in the medical literature yet their use in practice seems limited. In this article, the third in the PROGRESS series, the authors review how such models are developed and validated, and then address how prognostic models are assessed for their impact on practice and patient outcomes, illustrating these ideas with examples.
Predicting survival in patients with advanced disease
European Journal of Cancer, 2008
Prognostication is an important clinical skill for all clinicians, particularly those clinicians working with patients with advanced cancer. However, doctors can be hesitant about prognosticating without a fundamental understanding of how to formulate a prognosis more accurately and how to communicate the information with honesty and compassion. Irrespective of the underlying type of malignancy, most patients with advanced cancer experience a prolonged period of gradual decline (months/years) before a short phase of accelerated decline in the last month or two. The main indicators of this final phase are poor performance status, weight loss, symptoms such as anorexia, breathlessness or confusion and abnormalities on laboratory parameters (e.g. high white cell count, lymphopaenia, hyopalbuminaemia, elevated lactate dehydrogenase or C-reactive protein). The clinical estimate of survival remains a powerful independent prognostic indicator, often enhanced by experience, but research has only begun to understand the different biases affecting clinicians' estimates. More recent research has shown probabilistic predictions to be more accurate than temporal predictions. Simple, reliable and valid prognostic tools have been developed in recent years that can be used readily at the bedside of terminally ill cancer patients. The greatest accuracy occurs with the use of a combination of subjective prognostic judgements and objective validated tools.
Diagnostic and Prognostic Research Evidence synthesis in prognosis research
Diagnostic and Prognostic Research, 2019
Over the past few years, evidence synthesis has become essential to investigate and improve the generalizability of medical research findings. This strategy often involves a meta-analysis to formally summarize quantities of interest, such as relative treatment effect estimates. The use of meta-analysis methods is, however, less straightforward in prognosis research because substantial variation exists in research objectives, analysis methods and the level of reported evidence. We present a gentle overview of statistical methods that can be used to summarize data of prognostic factor and prognostic model studies. We discuss how aggregate data, individual participant data, or a combination thereof can be combined through meta-analysis methods. Recent examples are provided throughout to illustrate the various methods.
The Risky Business of Studying Prognosis
The Journal of Rheumatology, 2013
Prognosis studies provide important healthcare information. Clinicians use prognostic factors to predict disease progress, thus allowing individualization of disease management. Prognosis is the issue in many translational studies that aim to identify biomarkers to predict outcomes. In a clinical trial, researchers may use prognostic factors to sort patients into risk groups, to clarify the effects of a new therapeutic agent. Prognosis studies can have significant effects on clinical practice.
Systematic reviews, 2017
Prognosis research is on the rise, its importance recognised because chronic health conditions and diseases are increasingly common and costly. Prognosis systematic reviews are needed to collate and synthesise these research findings, especially to help inform effective clinical decision-making and healthcare policy. A detailed, comprehensive search strategy is central to any systematic review. However, within prognosis research, this is challenging due to poor reporting and inconsistent use of available indexing terms in electronic databases. Whilst many published search filters exist for finding clinical trials, this is not the case for prognosis studies. This systematic review aims to identify and compare existing methodological filters developed and evaluated to identify prognosis studies of any of the three main types: overall prognosis, prognostic factors, and prognostic [risk prediction] models. Primary studies reporting the development and/or evaluation of methodological sea...