An accurate prognostic model is of no benefit if it is not generalisable or doesn’t change behaviour. In the last article in their series Karel Moons and colleagues discuss how to determine the ...practical value of models
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will ...occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, The BMJ, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying explanation and elaboration article is freely available only on www.annals.org; Annals of Internal Medicine holds copyright for that article.
Background
Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific ...event will occur in the future (prognostic models), to inform their decision‐making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed.
Methods
An extensive list of items based on a review of the literature was created, which was reduced after a web‐based survey and revised during a 3‐day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e‐mail discussions with the wider group of TRIPOD contributors.
Results
The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study.
Conclusion
The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. A complete checklist is available at http://www.tripod‐statement.org.
Various performance measures related to calibration and discrimination are available for the assessment of risk models. When the validity of a risk model is assessed in a new population, estimates of ...the model's performance can be influenced in several ways. The regression coefficients can be incorrect, which indeed results in an invalid model. However, the distribution of patient characteristics (case mix) may also influence the performance of the model. Here the authors consider a number of typical situations that can be encountered in external validation studies. Theoretical relations between differences in development and validation samples and performance measures are studied by simulation. Benchmark values for the performance measures are proposed to disentangle a case-mix effect from incorrect regression coefficients, when interpreting the model's estimated performance in validation samples. The authors demonstrate the use of the benchmark values using data on traumatic brain injury obtained from the International Tirilazad Trial and the North American Tirilazad Trial (1991–1994).
Abstract Objectives This study aims to investigate the influence of the amount of clustering intraclass correlation (ICC) = 0%, 5%, or 20%, the number of events per variable (EPV) or candidate ...predictor (EPV = 5, 10, 20, or 50), and backward variable selection on the performance of prediction models. Study Design and Setting Researchers frequently combine data from several centers to develop clinical prediction models. In our simulation study, we developed models from clustered training data using multilevel logistic regression and validated them in external data. Results The amount of clustering was not meaningfully associated with the models' predictive performance. The median calibration slope of models built in samples with EPV = 5 and strong clustering (ICC = 20%) was 0.71. With EPV = 5 and ICC = 0%, it was 0.72. A higher EPV related to an increased performance: the calibration slope was 0.85 at EPV = 10 and ICC = 20% and 0.96 at EPV = 50 and ICC = 20%. Variable selection sometimes led to a substantial relative bias in the estimated predictor effects (up to 118% at EPV = 5), but this had little influence on the model's performance in our simulations. Conclusion We recommend at least 10 EPV to fit prediction models in clustered data using logistic regression. Up to 50 EPV may be needed when variable selection is performed.
Abstract Objective To provide an overview of the research steps that need to follow the development of diagnostic or prognostic prediction rules. These steps include validity assessment, updating (if ...necessary), and impact assessment of clinical prediction rules. Study Design and Setting Narrative review covering methodological and empirical prediction studies from primary and secondary care. Results In general, three types of validation of previously developed prediction rules can be distinguished: temporal, geographical, and domain validations. In case of poor validation, the validation data can be used to update or adjust the previously developed prediction rule to the new circumstances. These update methods differ in extensiveness, with the easiest method a change in model intercept to the outcome occurrence at hand. Prediction rules—with or without updating—showing good performance in (various) validation studies may subsequently be subjected to an impact study, to demonstrate whether they change physicians' decisions, improve clinically relevant process parameters, patient outcome, or reduce costs. Finally, whether a prediction rule is implemented successfully in clinical practice depends on several potential barriers to the use of the rule. Conclusion The development of a diagnostic or prognostic prediction rule is just a first step. We reviewed important aspects of the subsequent steps in prediction research.
Latent class models (LCMs) combine the results of multiple diagnostic tests through a statistical model to obtain estimates of disease prevalence and diagnostic test accuracy in situations where ...there is no single, accurate reference standard. We performed a systematic review of the methodology and reporting of LCMs in diagnostic accuracy studies. This review shows that the use of LCMs in such studies increased sharply in the past decade, notably in the domain of infectious diseases (overall contribution: 59%). The 64 reviewed studies used a range of differently specified parametric latent variable models, applying Bayesian and frequentist methods. The critical assumption underlying the majority of LCM applications (61%) is that the test observations must be independent within 2 classes. Because violations of this assumption can lead to biased estimates of accuracy and prevalence, performing and reporting checks of whether assumptions are met is essential. Unfortunately, our review shows that 28% of the included studies failed to report any information that enables verification of model assumptions or performance. Because of the lack of information on model fit and adequate evidence "external" to the LCMs, it is often difficult for readers to judge the validity of LCM-based inferences and conclusions reached.
Summary Background Although colonoscopy is the accepted standard for detection of colorectal adenomas and cancers, many adenomas and some cancers are missed. To avoid interval colorectal cancer, the ...adenoma miss rate of colonoscopy needs to be reduced by improvement of colonoscopy technique and imaging capability. We aimed to compare the adenoma miss rates of full-spectrum endoscopy colonoscopy with those of standard forward-viewing colonoscopy. Methods We did an international, multicentre, randomised trial at three sites in Israel, one site in the Netherlands, and two sites in the USA between Feb 1, 2012, and March 31, 2013. Patients aged 18–70 years referred for colorectal cancer screening, polyp surveillance, or diagnostic assessment underwent same-day, back-to-back tandem colonoscopy with standard forward-viewing colonoscope and the full-spectrum endoscopy colonoscope. The patients were randomly assigned (1:1), via computer-generated randomisation with block size of 20, to which procedure was done first. The endoscopist was masked to group allocation until immediately before the start of colonoscopy examinations; patients were not masked. The primary endpoint was adenoma miss rates. We did per-protocol analyses. This trial is registered with ClinicalTrials.gov , number NCT01549535. Findings 197 participants were enrolled. 185 participants were included in the per-protocol analyses: 88 (48%) were randomly assigned to receive standard forward-viewing colonoscopy first, and 97 (52%) to receive full-spectrum endoscopy colonoscopy first. By per-lesion analysis, the adenoma miss rate was significantly lower in patients in the full-spectrum endoscopy group than in those in the standard forward-viewing procedure group: five (7%) of 67 vs 20 (41%) of 49 adenomas were missed (p<0·0001). Standard forward-viewing colonoscopy missed 20 adenomas in 15 patients; of those, three (15%) were advanced adenomas. Full-spectrum endoscopy missed five adenomas in five patients in whom an adenoma had already been detected with first-pass standard forward-viewing colonoscopy; none of these missed adenomas were advanced. One patient was admitted to hospital for colitis detected at colonoscopy, whereas five minor adverse events were reported including vomiting, diarrhoea, cystitis, gastroenteritis, and bleeding. Interpretation Full-spectrum endoscopy represents a technology advancement for colonoscopy and could improve the efficacy of colorectal cancer screening and surveillance. Funding EndoChoice.
Diagnostic and prognostic prediction models Hendriksen, J. M. T.; Geersing, G. J.; Moons, K. G. M. ...
Journal of thrombosis and haemostasis,
June 2013, 2013-Jun, 2013-06-00, 20130601, Volume:
11
Journal Article
Peer reviewed
Open access
Summary
Risk prediction models can be used to estimate the probability of either having (diagnostic model) or developing a particular disease or outcome (prognostic model). In clinical practice, ...these models are used to inform patients and guide therapeutic management. Examples from the field of venous thrombo‐embolism (VTE) include the Wells rule for patients suspected of deep venous thrombosis and pulmonary embolism, and more recently prediction rules to estimate the risk of recurrence after a first episode of unprovoked VTE. In this paper, the three phases that are recommended before a prediction model may be used in daily practice are described: development, validation, and impact assessment. In the development phase, the focus is on model development commonly using a multivariable logistic (diagnostic) or survival (prognostic) regression analysis. The performance of the developed model is expressed by discrimination, calibration and (re‐) classification. In the validation phase, the developed model is tested in a new set of patients using these same performance measures. This is important, as model performance is commonly poorer in a new set of patients, e.g. due to case‐mix or domain differences. Finally, in the impact phase the ability of a prediction model to actually guide patient management is evaluated. Whereas in the development and validation phase single cohort designs are preferred, this last phase asks for comparative designs, ideally randomized designs; therapeutic management and outcomes after using the prediction model is compared to a control group not using the model (e.g. usual care).
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