Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals

Critical Care Medicine

Volumn 48, Issue 5, 2020, Pages 623-633

  Leisman, Daniel E ^a   Harhay, Michael O ^b   Lederer, David J ^c,d   Abramson, Michael ^e   Adjei, Alex A ^f   Bakker, Jan ^g   Ballas, Zuhair K ^h   Barreiro, Esther ⁱ   Bell, Scott C ^j   Bellomo, Rinaldo ^k   Bernstein, Jonathan A ^l   Branson, Richard D ^m   Brusasco, Vito ⁿ   Chalmers, James D ^o   Chokroverty, Sudhansu ^p   Citerio, Giuseppe ^q   Collop, Nancy A ^r   Cooke, Colin R ^s   Crapo, James D ^t   Donaldson, Gavin ^u   Fitzgerald, Dominic A ^v   Grainger, Emma ^w   Hale, Lauren ^x   Herth, Felix J ^y   Kochanek, Patrick M ^z   Marks, Guy ^v   Moorman, J Randall ^aa   Ost, David E ^ab   Schatz, Michael ^ac   Sheikh, Aziz ^ad   Smyth, Alan R ^ae   Stewart, Iain ^ae   Stewart, Paul W ^af   Swenson, Erik R ^ag   Szymusiak, Ronald ^ah   Teboul, Jean Louis ^ai   Vincent, Jean Louis ^aj   Wedzicha, Jadwiga A ^u   Maslove, David M ^ak   more..

^a MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c REGENERON PHARMACEUTICALS INC   (United States)

^d NewYork Presbyterian Hospital   (United States)

^e MONASH UNIVERSITY   (Australia)

^f MAYO CLINIC   (United States)

^g NEW YORK UNIVERSITY   (United States)

^h UNIVERSITY OF IOWA   (United States)

ⁱ HOSPITAL DEL MAR   (Spain)

^j THE PRINCE CHARLES HOSPITAL   (Australia)

^k UNIVERSITY OF MELBOURNE   (Australia)

^l UNIVERSITY OF CINCINNATI COLLEGE OF MEDICINE   (United States)

^m UNIVERSITY OF CINCINNATI   (United States)

ⁿ UNIVERSITY OF GENOA   (Italy)

^o UNIVERSITY OF DUNDEE   (United Kingdom)

^p NEW JERSEY NEUROSCIENCE INSTITUTE   (United States)

^q UNIVERSITY OF MILANO BICOCCA   (Italy)

^r EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

^s UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^t NATIONAL JEWISH MEDICAL AND RESEARCH CENTER   (United States)

^u NATIONAL HEART AND LUNG INSTITUTE   (United Kingdom)

^v UNIVERSITY OF SYDNEY   (Australia)

^w THE LANCET   (United Kingdom)

^x Stony Brook Medicine   (United States)

^y UNIVERSITY OF HEIDELBERG   (Germany)

^z UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^aa UNIVERSITY OF VIRGINIA   (United States)

^ab UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^ac KAISER PERMANENTE MEDICAL CENTER   (United States)

^ad UNIVERSITY OF EDINBURGH   (United Kingdom)

^ae UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^af IL   (United States)

^ag UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

^ah UNIVERSITY OF CALIFORNIA   (United States)

^ai BICÊTRE HOSPITAL   (France)

^aj UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

^ak QUEEN S UNIVERSITY   (Canada)

more..

Author keywords

critical care; prediction models; pulmonary medicine; sleep medicine

Indexed keywords

ARTICLE; EDITOR; HUMAN; INTENSIVE CARE; MEASUREMENT ERROR; PRACTICE GUIDELINE; PREDICTION; PREDICTOR VARIABLE; PROGNOSIS; PULMONOLOGY; RISK FACTOR; SLEEP MEDICINE; DECISION SUPPORT SYSTEM; ORGANIZATION AND MANAGEMENT; PUBLICATION; REPRODUCIBILITY; RESPIRATORY TRACT DISEASE; SLEEP DISORDER; STATISTICAL BIAS; STATISTICAL MODEL;

BIAS; CRITICAL CARE; DECISION SUPPORT TECHNIQUES; HUMANS; MODELS, STATISTICAL; PERIODICALS AS TOPIC; PROGNOSIS; REPRODUCIBILITY OF RESULTS; RESPIRATORY TRACT DISEASES; SLEEP WAKE DISORDERS;

EID: 85083903238     PISSN: 00903493     EISSN: 15300293     Source Type: Journal    
DOI: 10.1097/CCM.0000000000004246     Document Type: Article

References (41)

1. Altman DG, Vergouwe Y, Royston P, et al: Prognosis and prognostic research: Validating a prognostic model. BMJ 2009; 338:b605
2. Moons KG, Altman DG, Vergouwe Y, et al: Prognosis and prognostic research: Application and impact of prognostic models in clinical practice. BMJ 2009; 338:b606
3. Moons KG, Royston P, Vergouwe Y, et al: Prognosis and prognostic research: What, why, and how? BMJ 2009; 338:b375
4. Royston P, Moons KG, Altman DG, et al: Prognosis and prognostic research: Developing a prognostic model. BMJ 2009; 338:b604
5. Steyerberg E: Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Second Edition. New York, Springer International Publishing, 2019
6. Collins GS, Reitsma JB, Altman DG, et al: Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). Ann Intern Med 2015; 162:735-736
7. Moons KG, Altman DG, Reitsma JB, et al: Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162:W1-73
8. Lederer DJ, Bell SC, Branson RD, et al: Control of confounding and reporting of results in causal inference studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals. Ann Am Thorac Soc 2019; 16:22-28
9. Shmueli G: To explain or to predict? Statist Sci 2010; 25:289-310
10. Zimmerman JE, Kramer AA, McNair DS, et al: Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today's critically ill patients. Crit Care Med 2006; 34:1297-1310
11. Goodman SN, Goel S, Cullen MR: Machine learning, health disparities, and causal reasoning. Ann Intern Med 2018; 169:883-884
12. Braun L: Race, ethnicity and lung function: A brief history. Can J Respir Ther 2015; 51:99-101
13. Eneanya ND, Yang W, Reese PP: Reconsidering the consequences of using race to estimate kidney function. JAMA 2019; 322:113-114
14. Gianfrancesco MA, Tamang S, Yazdany J, et al: Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 2018; 178:1544-1547
15. Rajkomar A, Hardt M, Howell MD, et al: Ensuring fairness in machine learning to advance health equity. Ann Intern Med 2018; 169:866-872
16. Sun GW, Shook TL, Kay GL: Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 1996; 49:907-916
17. Smith G: Step away from stepwise. J Big Data 2018; 5:32
18. Hawkins DM: The problem of overftting. J Chem Inf Comput Sci 2004; 44:1-12
19. Mundry R, Nunn CL: Stepwise model ftting and statistical inference: Turning noise into signal pollution. Am Nat 2009; 173:119-123
20. Walter S, Tiemeier H: Variable selection: current practice in epidemi-ological studies. Eur J Epidemiol 2009; 24:733-736
21. Wasserstein RL, Lazar NA: The ASA's statement on p-Values: Context, process, and purpose. Am Stat 2016; 70:129-133
22. Steyerberg EW, Eijkemans MJC, Habbema JDF: Application of shrinkage techniques in logistic regression analysis: A case study. Stat Neerl 2001; 55:76-88
23. Riley RD, Snell KIE, Ensor J, et al: Minimum sample size for developing a multivariable prediction model: Part I-Continuous outcomes. Stat Med 2019; 38:1262-1275
24. Moons KGM, Wolff RF, Riley RD, et al: PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med 2019; 170:W1-W33
25. Riley RD, Snell KI, Ensor J, et al: Minimum sample size for developing a multivariable prediction model: PART II-Binary and time-to-event outcomes. Stat Med 2019; 38:1276-1296
26. van Smeden M, Moons KG, de Groot JA, et al: Sample size for binary logistic prediction models: Beyond events per variable criteria. Stat Methods Med Res. 2019; 28:2455-2474
27. Senn S: Dichotomania: An obsessive compulsive disorder that is badly affecting the quality of analysis of pharmaceutical trials. In: Proceedings of the International Statistical Institute, 55th Session, 2005, Sydney, Australia, April 6-12, 2005
28. Royston P, Altman DG, Sauerbrei W: Dichotomizing continuous predictors in multiple regression: A bad idea. Stat Med 2006; 25:127-141
29. Sharafoddini A, Dubin JA, Maslove DM, et al: A new insight into missing data in intensive care unit patient profles: Observational study. JMIR Med Inform 2019; 7:e11605
30. Ware JH, Harrington D, Hunter DJ, et al: Missing data. N Engl J Med 2012; 367:1353-1354
31. Newgard CD, Haukoos JS: Advanced statistics: Missing data in clinical research-Part 2: Multiple imputation. Acad Emerg Med 2007; 14:669-678
32. Altman DG, Royston P: What do we mean by validating a prognostic model? Stat Med 2000; 19:453-473
33. Steyerberg EW, Vergouwe Y: Towards better clinical prediction models: Seven steps for development and an ABCD for validation. Eur Heart J 2014; 35:1925-1931
34. van Klaveren D, Gönen M, Steyerberg EW, et al: A new concordance measure for risk prediction models in external validation settings. Stat Med 2016; 35:4136-4152
35. Debray TP, Vergouwe Y, Koffjberg H, et al: A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol 2015; 68:279-289
36. Vergouwe Y, Moons KG, Steyerberg EW: External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coeffcients. Am J Epidemiol 2010; 172:971-980
37. Steyerberg EW, Harrell FE Jr, Borsboom GJ, et al: Internal validation of predictive models: Effciency of some procedures for logistic regression analysis. J Clin Epidemiol 2001; 54:774-781
38. Lee J, Maslove DM: Customization of a severity of illness score using local electronic medical record data. J Intensive Care Med 2017; 32:38-47
39. Steyerberg EW, Vickers AJ, Cook NR, et al: Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology 2010; 21:128-138
40. Alba AC, Agoritsas T, Walsh M, et al: Discrimination and calibration of clinical prediction models: Users' guides to the medical literature. JAMA 2017; 318:1377-1384
41. Leisman DE: Rare events in the ICU: An emerging challenge in clas-sifcation and prediction. Crit Care Med 2018; 46:418-424