Pub Date : 2019-01-01DOI: 10.1093/med/9780198796619.003.0014
R. Riley, T. Debray, K. Moons
An alternative approach to meta-analysis of aggregate data from published prognosis research (as addressed in Chapter 9), with its challenges of heterogeneity and lack of information, is to conduct meta-analysis of individual participant data (IPD), that is, the original raw data of the individuals who are included in the primary prognosis studies. The approach is increasingly feasible as data sharing and open-access data become more popular, and the chapter highlights why they offer enormous advantages for a robust and meaningful evidence synthesis of prognosis studies. In particular, better prognostic models can be developed and directly validated across multiple settings, and power is increased to detect genuine predictors of treatment response. Key steps in such an IPD meta-analysis are explained, including practical guidance on how to obtain, handle, and synthesize data, and what potential challenges may be encountered.
{"title":"Individual participant data meta-analysis of prognosis studies","authors":"R. Riley, T. Debray, K. Moons","doi":"10.1093/med/9780198796619.003.0014","DOIUrl":"https://doi.org/10.1093/med/9780198796619.003.0014","url":null,"abstract":"An alternative approach to meta-analysis of aggregate data from published prognosis research (as addressed in Chapter 9), with its challenges of heterogeneity and lack of information, is to conduct meta-analysis of individual participant data (IPD), that is, the original raw data of the individuals who are included in the primary prognosis studies. The approach is increasingly feasible as data sharing and open-access data become more popular, and the chapter highlights why they offer enormous advantages for a robust and meaningful evidence synthesis of prognosis studies. In particular, better prognostic models can be developed and directly validated across multiple settings, and power is increased to detect genuine predictors of treatment response. Key steps in such an IPD meta-analysis are explained, including practical guidance on how to obtain, handle, and synthesize data, and what potential challenges may be encountered.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"4 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":"115470718","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}
Pub Date : 2019-01-01DOI: 10.1093/MED/9780198796619.003.0002
P. Croft, R. Riley, K. Moons
Predicting what might happen in the future to individuals, based on experience and available information, has always been a prominent part of medical practice and healthcare. This chapter describes the history of prognosis in healthcare. Prognosis had a central place in medical practice in times before scientific diagnosis and effective treatments, and predicting the likely course of an individual’s illness from experience and observation was a valued quality. As the science of diagnosis developed, prognosis lost its importance in medical education and practice. With the advent of effective treatments and with rapid acceleration of access to data—from genetics to physiology, psychology to social status—to inform outcome prediction in sick people and guide treatment decisions, prognosis is again at the centre of healthcare. Modern prognosis research provides an evidence base for prediction in practice.
{"title":"Prognosis in healthcare","authors":"P. Croft, R. Riley, K. Moons","doi":"10.1093/MED/9780198796619.003.0002","DOIUrl":"https://doi.org/10.1093/MED/9780198796619.003.0002","url":null,"abstract":"Predicting what might happen in the future to individuals, based on experience and available information, has always been a prominent part of medical practice and healthcare. This chapter describes the history of prognosis in healthcare. Prognosis had a central place in medical practice in times before scientific diagnosis and effective treatments, and predicting the likely course of an individual’s illness from experience and observation was a valued quality. As the science of diagnosis developed, prognosis lost its importance in medical education and practice. With the advent of effective treatments and with rapid acceleration of access to data—from genetics to physiology, psychology to social status—to inform outcome prediction in sick people and guide treatment decisions, prognosis is again at the centre of healthcare. Modern prognosis research provides an evidence base for prediction in practice.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"39 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":"124173675","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}
Pub Date : 2019-01-01DOI: 10.1093/MED/9780198796619.003.0007
R. Riley, K. Moons, J. Hayden, W. Sauerbrei, D. Altman
A prognostic factor is any variable associated with a subsequent outcome such as death or disability among people with a disease or health condition. Prognostic factors range from simple measures, such as age, gender, temperature, or pulse rate, to test results such as X-rays or psychological scores, whilst novel biomarkers and genetic information are increasingly studied. Different values of a prognostic factor are associated with a different prognosis and can be used to stratify overall prognosis estimates. This chapter details the potential use of prognostic factors (including disease definition, identifying new intervention targets, and providing building blocks for prognostic models); the design of exploratory and validation cohort studies to identify prognostic factors; the importance of examining the prognostic value of a new factor over and above existing factors; consideration of time-dependent prognostic effects; and the use of the REMARK reporting guideline.
{"title":"Prognostic factor research","authors":"R. Riley, K. Moons, J. Hayden, W. Sauerbrei, D. Altman","doi":"10.1093/MED/9780198796619.003.0007","DOIUrl":"https://doi.org/10.1093/MED/9780198796619.003.0007","url":null,"abstract":"A prognostic factor is any variable associated with a subsequent outcome such as death or disability among people with a disease or health condition. Prognostic factors range from simple measures, such as age, gender, temperature, or pulse rate, to test results such as X-rays or psychological scores, whilst novel biomarkers and genetic information are increasingly studied. Different values of a prognostic factor are associated with a different prognosis and can be used to stratify overall prognosis estimates. This chapter details the potential use of prognostic factors (including disease definition, identifying new intervention targets, and providing building blocks for prognostic models); the design of exploratory and validation cohort studies to identify prognostic factors; the importance of examining the prognostic value of a new factor over and above existing factors; consideration of time-dependent prognostic effects; and the use of the REMARK reporting guideline.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"25 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":"114973380","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}
Pub Date : 2019-01-01DOI: 10.1093/MED/9780198796619.003.0013
K. Morley, P. Perel
Prognosis research has played a major role in the development of approaches to the management of trauma. This is because of the need to identify those people who have a poor immediate prognosis if untreated and because of the many settings where choices have to be made on which patients to focus life-saving resources. This need for evidence-based triage based on prognostic information is particularly true for the problem of traumatic bleeding, and this chapter details the development and validation of a prognostic model and predictors of benefits or harms of treatment.
{"title":"Prognosis research in people with traumatic bleeding","authors":"K. Morley, P. Perel","doi":"10.1093/MED/9780198796619.003.0013","DOIUrl":"https://doi.org/10.1093/MED/9780198796619.003.0013","url":null,"abstract":"Prognosis research has played a major role in the development of approaches to the management of trauma. This is because of the need to identify those people who have a poor immediate prognosis if untreated and because of the many settings where choices have to be made on which patients to focus life-saving resources. This need for evidence-based triage based on prognostic information is particularly true for the problem of traumatic bleeding, and this chapter details the development and validation of a prognostic model and predictors of benefits or harms of treatment.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"46 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":"128432193","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}
Pub Date : 2019-01-01DOI: 10.1093/MED/9780198796619.003.0005
R. Riley, K. Snell, K. Moons, T. Debray
This chapter provides a set of ten principles for ensuring high-quality prognosis research. There are three general principles for strengthening prognosis research: the need for study registration and protocols, use of reporting guidelines, and importance of replication and validation studies. The seven other principles concern study analysis and presentation: use of estimation and confidence intervals rather than statistical hypothesis testing; use of interaction estimates when analysing subgroups; avoidance of categorization of continuous predictor and outcome variables; multiple imputation of missing values; adjustment of new prognostic factor estimates for established factors; avoidance of univariable estimates for predictor selection when developing prognostic models; use of penalization techniques within prognostic model development to reduce overfitting and overly extreme predictions for new individuals; and use of competing risk models in frail populations.
{"title":"Ten principles to strengthen prognosis research","authors":"R. Riley, K. Snell, K. Moons, T. Debray","doi":"10.1093/MED/9780198796619.003.0005","DOIUrl":"https://doi.org/10.1093/MED/9780198796619.003.0005","url":null,"abstract":"This chapter provides a set of ten principles for ensuring high-quality prognosis research. There are three general principles for strengthening prognosis research: the need for study registration and protocols, use of reporting guidelines, and importance of replication and validation studies. The seven other principles concern study analysis and presentation: use of estimation and confidence intervals rather than statistical hypothesis testing; use of interaction estimates when analysing subgroups; avoidance of categorization of continuous predictor and outcome variables; multiple imputation of missing values; adjustment of new prognostic factor estimates for established factors; avoidance of univariable estimates for predictor selection when developing prognostic models; use of penalization techniques within prognostic model development to reduce overfitting and overly extreme predictions for new individuals; and use of competing risk models in frail populations.","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":"130323536","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}
Pub Date : 2019-01-01DOI: 10.1093/MED/9780198796619.003.0006
H. Hemingway, P. Croft
Overall prognosis research concerns the description of average future outcomes of groups of people with a certain disease or health condition in the context, time, and setting of current healthcare. This chapter describes how overall prognosis is estimated among people with a defined health condition in relation to relevant health outcomes. Study design, from newly designed prospective cohorts to cohorts embedded in routine healthcare data, is discussed. The value of information derived from overall prognosis research for patients and for healthcare professionals, policymakers, and funders, is considered, particularly in relation to decision making in healthcare practice and to monitoring healthcare outcomes for policymaking. Wider roles of overall prognosis estimation in informing other types of prognosis research, the design and interpretation of treatment effectiveness studies, understanding the consequences of using new diagnostic tests, and identifying unintended benefits or harms of treatment, are described.
{"title":"Overall prognosis research","authors":"H. Hemingway, P. Croft","doi":"10.1093/MED/9780198796619.003.0006","DOIUrl":"https://doi.org/10.1093/MED/9780198796619.003.0006","url":null,"abstract":"Overall prognosis research concerns the description of average future outcomes of groups of people with a certain disease or health condition in the context, time, and setting of current healthcare. This chapter describes how overall prognosis is estimated among people with a defined health condition in relation to relevant health outcomes. Study design, from newly designed prospective cohorts to cohorts embedded in routine healthcare data, is discussed. The value of information derived from overall prognosis research for patients and for healthcare professionals, policymakers, and funders, is considered, particularly in relation to decision making in healthcare practice and to monitoring healthcare outcomes for policymaking. Wider roles of overall prognosis estimation in informing other types of prognosis research, the design and interpretation of treatment effectiveness studies, understanding the consequences of using new diagnostic tests, and identifying unintended benefits or harms of treatment, are described.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"177 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":"133303589","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}
Pub Date : 2019-01-01DOI: 10.1093/MED/9780198796619.003.0011
N. Foster, K. Dunn, P. Croft
Prognosis has dominated recent low back pain (LBP) research because of the lack of disease pathological explanations of LBP that lead to safe and effective treatments in many patients; the hazards of overdiagnosis and overtreatment; and the potential for beneficial outcomes in patients if treatment approaches are carefully matched to the likelihood of recovery, recurrence, or persistence, or the likely effect of specific treatments. This chapter uses examples from each of the four types of prognosis research to illustrate how prognosis research has contributed to understanding LBP and provided evidence to inform classification and treatment of patients with LBP.
{"title":"Prognosis research in people with low back pain","authors":"N. Foster, K. Dunn, P. Croft","doi":"10.1093/MED/9780198796619.003.0011","DOIUrl":"https://doi.org/10.1093/MED/9780198796619.003.0011","url":null,"abstract":"Prognosis has dominated recent low back pain (LBP) research because of the lack of disease pathological explanations of LBP that lead to safe and effective treatments in many patients; the hazards of overdiagnosis and overtreatment; and the potential for beneficial outcomes in patients if treatment approaches are carefully matched to the likelihood of recovery, recurrence, or persistence, or the likely effect of specific treatments. This chapter uses examples from each of the four types of prognosis research to illustrate how prognosis research has contributed to understanding LBP and provided evidence to inform classification and treatment of patients with LBP.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"4 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":"123883666","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}
Pub Date : 2019-01-01DOI: 10.1093/med/9780198796619.003.0015
K. Jordan, K. Moons
Electronic healthcare record (EHR) data, collected during the daily business of patient consultations and treatments, offer huge opportunities to expand the range and scale of prognosis research, in particular because of the real-time and continuous recording of potential prognostic factors and health-related events, and the amount of data and individuals involved. However, with these opportunities come challenges related to the size and complexity of EHR data. This chapter provides an overview of these issues.
{"title":"Electronic healthcare records and prognosis research","authors":"K. Jordan, K. Moons","doi":"10.1093/med/9780198796619.003.0015","DOIUrl":"https://doi.org/10.1093/med/9780198796619.003.0015","url":null,"abstract":"Electronic healthcare record (EHR) data, collected during the daily business of patient consultations and treatments, offer huge opportunities to expand the range and scale of prognosis research, in particular because of the real-time and continuous recording of potential prognostic factors and health-related events, and the amount of data and individuals involved. However, with these opportunities come challenges related to the size and complexity of EHR data. This chapter provides an overview of these issues.","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":"121514288","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}
Pub Date : 2019-01-01DOI: 10.1093/med/9780198796619.003.0008
R. Riley, K. Moons, T. Debray, K. Snell, E. Steyerberg, D. Altman, G. Collins
Prognostic models combine multiple prognostic factors to estimate the risk of future outcomes in individuals with a particular disease or health condition. A useful model provides accurate predictions to support decision making by individuals and caregivers. This chapter describes the three phases of prognostic model research development (including internal validation), external validation (including model updating), and impact on decision making and individual health outcomes. Methodology is detailed for each phase, including the need for large representative datasets, methods to avoid or reduce overfitting and optimism, and the use of both discrimination and calibration to assess a model’s predictive performance. TRIPOD reporting guidelines are introduced. Emphasis is also given to the application of models in practice, including linking the model to clinical decisions using risk thresholds, and evaluating this using measures of net benefit, decision curves, cost-effectiveness analyses, and impact studies (such as randomized trials) to evaluate the effectiveness of models in improving outcomes.
{"title":"Prognostic model research","authors":"R. Riley, K. Moons, T. Debray, K. Snell, E. Steyerberg, D. Altman, G. Collins","doi":"10.1093/med/9780198796619.003.0008","DOIUrl":"https://doi.org/10.1093/med/9780198796619.003.0008","url":null,"abstract":"Prognostic models combine multiple prognostic factors to estimate the risk of future outcomes in individuals with a particular disease or health condition. A useful model provides accurate predictions to support decision making by individuals and caregivers. This chapter describes the three phases of prognostic model research development (including internal validation), external validation (including model updating), and impact on decision making and individual health outcomes. Methodology is detailed for each phase, including the need for large representative datasets, methods to avoid or reduce overfitting and optimism, and the use of both discrimination and calibration to assess a model’s predictive performance. TRIPOD reporting guidelines are introduced. Emphasis is also given to the application of models in practice, including linking the model to clinical decisions using risk thresholds, and evaluating this using measures of net benefit, decision curves, cost-effectiveness analyses, and impact studies (such as randomized trials) to evaluate the effectiveness of models in improving outcomes.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"318 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":"124237209","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}
Pub Date : 2019-01-01DOI: 10.1093/MED/9780198796619.003.0004
R. Riley, K. Snell, K. Moons, T. Debray
This chapter introduces and describes the fundamental statistical measures, methods, and principles that form the bedrock of prognosis research. A major emphasis is given to linear regression for continuous outcomes, logistic regression for binary outcomes, and Cox regression and parametric survival models for time-to-event outcomes. It is shown how these models can be used to identify prognostic factors; obtain measures of prognostic value of such factors such as mean differences, odds ratios, and hazard ratios; and produce a model for predicting outcomes (and outcome risk) in new individuals. Details are provided on how the predictive performance of a prognostic model should be evaluated using a specific set of statistical techniques, including measuring and displaying overall fit, calibration, and discrimination. The importance of investigating non-linear prognostic associations (using methods such as fractional polynomials and cubic splines) are also covered. The chapter is designed to ensure that novice and experienced prognosis researchers have a firm grasp of the statistical principles underlying the four types of prognosis research discussed throughout the book.
{"title":"Fundamental statistical methods for prognosis research","authors":"R. Riley, K. Snell, K. Moons, T. Debray","doi":"10.1093/MED/9780198796619.003.0004","DOIUrl":"https://doi.org/10.1093/MED/9780198796619.003.0004","url":null,"abstract":"This chapter introduces and describes the fundamental statistical measures, methods, and principles that form the bedrock of prognosis research. A major emphasis is given to linear regression for continuous outcomes, logistic regression for binary outcomes, and Cox regression and parametric survival models for time-to-event outcomes. It is shown how these models can be used to identify prognostic factors; obtain measures of prognostic value of such factors such as mean differences, odds ratios, and hazard ratios; and produce a model for predicting outcomes (and outcome risk) in new individuals. Details are provided on how the predictive performance of a prognostic model should be evaluated using a specific set of statistical techniques, including measuring and displaying overall fit, calibration, and discrimination. The importance of investigating non-linear prognostic associations (using methods such as fractional polynomials and cubic splines) are also covered. The chapter is designed to ensure that novice and experienced prognosis researchers have a firm grasp of the statistical principles underlying the four types of prognosis research discussed throughout the book.","PeriodicalId":138014,"journal":{"name":"Prognosis Research in Health Care","volume":"15 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":"124019009","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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