{"title":"Machine learning in prognosis research","authors":"M. Schaar, H. Hemingway","doi":"10.1093/MED/9780198796619.003.0017","DOIUrl":"https://doi.org/10.1093/MED/9780198796619.003.0017","url":null,"abstract":"Machine learning offers an alternative to the methods for prognosis research in large and complex datasets and for delivering dynamic models of prognosis. Machine learning foregrounds the capacity to learn from large and complex data about the pathways, predictors, and trajectories of health outcomes in individuals. This reflects wider societal drives for data-driven modelling embedded and automated within powerful computers to analyse large amounts of data. Machine learning derives algorithms that can learn from data and can allow the data full freedom, for example, to follow a pragmatic approach in developing a prognostic model. Rather than choosing factors for model development in advance, machine learning allows the data to reveal which features are important for which predictions. This chapter introduces key machine learning concepts relevant to each of the four prognosis research types, explains where it may enhance prognosis research, and highlights challenges.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126144247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prognosis research in people with coronary heart disease","authors":"A. Timmis, P. Perel, P. Croft","doi":"10.1093/med/9780198796619.003.0012","DOIUrl":"https://doi.org/10.1093/med/9780198796619.003.0012","url":null,"abstract":"Coronary heart disease (CHD) outcomes have improved in recent decades because of better treatment, improved investigations, and better secondary prevention. The results of prognosis research have contributed to the development and evaluation of these new components of healthcare for CHD, but have also critically questioned traditional classifications of CHD, emphasized the importance of long-term outcomes in judging the success of healthcare in CHD patients, and highlighted the potential of risk stratification to guide better treatment decisions for individual patients with CHD. This chapter uses example studies to illustrate this story.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127894147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Predictors of treatment effect","authors":"R. Riley, A. Hingorani, K. Moons","doi":"10.1093/med/9780198796619.003.0009","DOIUrl":"https://doi.org/10.1093/med/9780198796619.003.0009","url":null,"abstract":"A predictor of treatment effect is any factor or combination of factors (such as a patient characteristic, symptom, sign, test, or biomarker result) associated with the effect (benefit or harm) of a specific treatment in persons with a particular disease or health condition. Various terms are used across disciplines to refer to prediction of treatment effect, including treatment-predictor (treatment-covariate) interaction, effect modification, predictive (as opposed to prognostic) factors (in oncology), or moderation analysis. This chapter reviews principles of the design of studies of treatment effect predictors, such as exploration of treatment-predictor interactions in randomized trials and the importance of replication of such estimates using data from multiple trials. The application of predictors of treatment effect in practice for matching individuals or subgroups to specific treatments is introduced as one type of stratified care, and the need for impact studies to investigate whether stratified care leads to better outcomes and improved efficiency of healthcare is highlighted.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115296486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Novel statistical methods for prognosis research","authors":"M. Crowther, M. Rutherford","doi":"10.1093/med/9780198796619.003.0016","DOIUrl":"https://doi.org/10.1093/med/9780198796619.003.0016","url":null,"abstract":"This chapter introduces some advanced statistical methods that are growing in their application to address more complex data arising from prognosis research studies. Three major topics are covered: competing risks, multi-state models, and joint modelling of longitudinal and survival data. The advances in such statistical methods allow complex relationships and intricate prognosis pathways to be modelled, including multi-morbidities over time. They are needed to help identify prognostic factors at different parts of an individual’s time course, and to develop more dynamic prognostic models where outcome risk can be updated over time. Practical clinical examples are used throughout the chapter to illustrate the approaches.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128722047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A framework for prognosis research","authors":"P. Croft, R. Riley, K. Moons, H. Hemingway","doi":"10.1093/MED/9780198796619.003.0003","DOIUrl":"https://doi.org/10.1093/MED/9780198796619.003.0003","url":null,"abstract":"This chapter introduces the PROGRESS framework, which describes four types of prognosis research, each addressing different questions. The four types concern: studies of overall prognosis (the average outcome, or outcome risk, in people with a particular health condition, in the context of the nature and quality of current care); prognostic factors (characteristics associated with changes in the average outcome, or outcome risk, across individuals); prognostic models (development, validation, and impact evaluation of statistical models, incorporating multiple prognostic factors for use in clinical practice to predict an individual’s outcome value or to estimate their outcome risk); and predictors of treatment effect (characteristics that predict whether an individual responds to a particular treatment or not). Examples of each type are given to illustrate the framework.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127812775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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