Gary S. Collins, PhD; Johannes B. Reitsma, MD, PhD; Douglas G. Altman, DSc; Karel G.M. Moons, PhD
Grant Support: There was no explicit funding for the development of this checklist and guidance document. The consensus meeting in June 2011 was partially funded by a National Institute for Health Research Senior Investigator Award held by Dr. Altman, Cancer Research UK (grant C5529), and the Netherlands Organization for Scientific Research (ZONMW 918.10.615 and 91208004). Drs. Collins and Altman are funded in part by the Medical Research Council (grant G1100513). Dr. Altman is a member of the Medical Research Council Prognosis Research Strategy (PROGRESS) Partnership (G0902393/99558).
Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M14-0697.
Requests for Single Reprints: Gary S. Collins, PhD, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford OX3 7LD, United Kingdom; e-mail, firstname.lastname@example.org.
Current Author Addresses: Drs. Collins and Altman: Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford OX3 7LD, United Kingdom.
Drs. Reitsma and Moons: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, the Netherlands.
Author Contributions: Conception and design: G.S. Collins, J.B. Reitsma, D.G. Altman, K.G.M. Moons.
Analysis and interpretation of the data: G.S. Collins, D.G. Altman, K.G.M. Moons.
Drafting of the article: G.S. Collins, J.B. Reitsma, D.G. Altman, K.G.M. Moons.
Critical revision of the article for important intellectual content: G.S. Collins, J.B. Reitsma, D.G. Altman, K.G.M. Moons.
Final approval of the article: G.S. Collins, J.B. Reitsma, D.G. Altman, K.G.M. Moons.
Provision of study materials or patients: G.S. Collins, K.G.M. Moons.
Statistical expertise: G.S. Collins, J.B. Reitsma, D.G. Altman, K.G.M. Moons.
Obtaining of funding: G.S. Collins, D.G. Altman, K.G.M. Moons.
Administrative, technical, or logistic support: G.S. Collins, K.G.M. Moons.
Collection and assembly of data: G.S. Collins, D.G. Altman, K.G.M. Moons.
This article has been corrected. The original version (PDF) is appended to this article as a Supplement.
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 3-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).
Schematic representation of diagnostic and prognostic prediction modeling studies.
The nature of the prediction in diagnosis is estimating the probability that a specific outcome or disease is present (or absent) within an individual, at this point in time—that is, the moment of prediction (T = 0). In prognosis, the prediction is about whether an individual will experience a specific event or outcome within a certain time period. In other words, in diagnostic prediction the interest is in principle a cross-sectional relationship, whereas prognostic prediction involves a longitudinal relationship. Nevertheless, in diagnostic modeling studies, for logistical reasons, a time window between predictor (index test) measurement and the reference standard is often necessary. Ideally, this interval should be as short as possible and without starting any treatment within this period.
Similarities and differences between diagnostic and prognostic prediction models.
Types of prediction model studies covered by the TRIPOD Statement.
D = development data; V = validation data.
Table. Checklist of Items to Include When Reporting a Study Developing or Validating a Multivariable Prediction Model for Diagnosis or Prognosis*
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Harry B. Burke, MD, PhD
Uniformed Services University of the Health Sciences
January 12, 2015
Reporting time-to-event models
The article by Collins et al. (1) is very important because prediction is becoming an integral part of clinical medicine. I was gratified to note their statement that all predictions must be time denominated. (2) The authors suggest that there are only two types of predictions, namely, diagnostic and prognostic. They subsume risk predictions within diagnostic predictions. I have suggested that there are three types of predictions, namely, risk, diagnostic, and prognostic. (3) The differences between risk and diagnostic are their targets, degree of predictive accuracy, and time interval. In diagnostic predictions we wish to predict whether the person either does or does not have detectable disease, its time interval is instantaneous, and its accuracy must be close to 100%. In risk predictions we wish to predict the probability that the person will have detectable disease over a specified time interval, and its accuracy must be less than 100%. These are very different types of predictions and the distinction between risk and diagnosis is important for reporting prediction studies. Three additional points: (i) The authors did not mention the problem of “lifetime” predictions. (ii) They stated that, “In the case of poor performance, the model can be updated or adjusted on the basis of the validation data set” (p. 56) but they did not go on to say that the updating or adjustment means that the investigators have looked at their results, which means that they must perform another, independent external validation study. (iii) Since most of the medical prediction literature currently consists of bivariate studies, (4) I am not sure why only multivariate studies were included in the author’s prescriptive reporting requirements.1. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD Statement. Ann Intern Med. 2015 Jan 6;162(1):55-63.2. Burke HB. Power of prediction. Cancer 2008;113:890-2.3. Burke HB. Increasing the power of surrogate endpoint biomarkers: the aggregation of predictive factors. J Cell Biochem 1994;19S:278-82.4. Burke HB, Grizzle WE. Clinical validation of molecular biomarkers in translational medicine. In Sudhir Srivastava (Ed.), Biomarkers in Cancer Screening and Early Detection. Wiley: Oxford, UK, in press.
Gary Collins, Johannes Reitsma, Douglas Altman, Karel Moons
University of Oxford (UK) and UMC Utrecht (The Netherlands)
February 20, 2015
Response to Dr Burke
We thank Dr Burke for his positive comments regarding the TRIPOD Statement for clinical prediction models (1, 2). Dr Burke raised a number of issues that require clarification. The TRIPOD Statement concerns prediction models that are developed for diagnostic or prognostic purposes (1). The additional type of prediction Dr Burke refers to as risk prediction is in our view subsumed within the prognostic framework. Prognostic models as referred to in TRIPOD are models, which predict the development of a certain health condition (e.g. death, certain disease, complication, recurrent event or any other outcome) over a specified time period, in subjects at risk of this health condition. As such prognostic models may address either ill or healthy individuals. For example predicting the 1-year probability of dying for a patient with lung cancer or predicting long term (e.g. 10-year) probability of developing cardiovascular disease for a healthy individual.
As Dr Burke correctly points out, we made no mention on the issues of concerning models for predicting lifetime risk. However, whilst we made no explicit mention of life-time risk, these types of prediction model studies fit entirely within the remit of TRIPOD. Our decision not to explicitly discuss these is purely due to relative rarity of model being developed for predicting lifetime risk. If interest in these models increase, there is no doubt that whenever TRIPOD is revised and updated that a more explicit mention will be made. But, as discussed above, these models are in our view just examples of models predicting long term outcomes in (non-ill) general populations.
We completely agree with Dr Burke’s comment that any updated model should also undergo further evaluation in a separate dataset. In the accompanying Explanation and Elaboration document (page W38), we indeed stress that ‘The updated model is in essence a new model. Updated models, certainly when based on relatively small validation sets, still need to be validated before application in routine practice‘ (2).
Dr Burke’s final comment concerns single marker studies (biomarkers, prognostic factors). Whilst there are clear similarities between multivariable prediction model studies and single marker studies that apply some form of multivariable analysis, there are noticeable differences. The fact that such multivariable analysis is being applied does not necessarily make it a prediction model study. The delineating factor is that one develops, validates or updates a multivariable prediction model that as such can be used to produce a probability (or risk) estimate for an individual. In other words, TRIPOD addresses models that allow for individualised predictions. The word individualised can be considered the most important word in the TRIPOD acronym. For studies of single markers, authors should ensure complete and accurate reporting following the REMARK guideline (3).
1. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Annals of Internal Medicine. 2015;162(1):55-63.
2. Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Annals of Internal Medicine. 2015;162(1):W1-W73.
3. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst. 2005;97(16):1180-4.
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Ann Intern Med. 2015;162:55–63. doi: 10.7326/M14-0697
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Published: Ann Intern Med. 2015;162(1):55-63.
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