Steyerberg EW. Clinical prediction models. Cham: Springer; 2019. https://doi.org/10.1007/978-3-030-16399-0.
Book
Google Scholar
Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999;130(6):515–24. https://doi.org/10.7326/0003-4819-130-6-199903160-00016.
Article
CAS
PubMed
Google Scholar
Shmueli G, Koppius OR. Predictive analytics in information systems research. MIS Q. 2011;35(3):553–72. https://doi.org/10.2307/23042796.
Article
Google Scholar
Hemingway H, Croft P, Perel P, Hayden JA, Abrams K, Timmis A, et al. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. Bmj. 2013;346:e5595. https://doi.org/10.1136/bmj.e5595.
Article
PubMed
PubMed Central
Google Scholar
Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381. https://doi.org/10.1371/journal.pmed.1001381.
Article
PubMed
PubMed Central
Google Scholar
Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11(10):e1001744. https://doi.org/10.1371/journal.pmed.1001744.
Article
PubMed
PubMed Central
Google Scholar
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(1):W1–W73. https://doi.org/10.7326/M14-0698.
Article
PubMed
Google Scholar
Debray TP, Moons KG, Ahmed I, Koffijberg H, Riley RD. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med. 2013;32(18):3158–80. https://doi.org/10.1002/sim.5732.
Article
PubMed
Google Scholar
Steyerberg EW, Uno H, Ioannidis JP, et al. Poor performance of clinical prediction models: the harm of commonly applied methods. J Clin Epidemiol. 2018;98:133–43. https://doi.org/10.1016/j.jclinepi.2017.11.013.
Article
PubMed
Google Scholar
Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016;35(2):214–26. https://doi.org/10.1002/sim.6787.
Article
PubMed
Google Scholar
Pajouheshnia R, Van Smeden M, Peelen L, Groenwold R. How variation in predictor measurement affects the discriminative ability and transportability of a prediction model. J Clin Epidemiol. 2019;105:136–41. https://doi.org/10.1016/j.jclinepi.2018.09.001.
Article
CAS
PubMed
Google Scholar
Luijken K, Groenwold RH, Van Calster B, Steyerberg EW, van Smeden M. Impact of predictor measurement heterogeneity across settings on the performance of prediction models: a measurement error perspective. Stat Med. 2019;38(18):3444–59. https://doi.org/10.1002/sim.8183.
Article
CAS
PubMed
PubMed Central
Google Scholar
Khudyakov P, Gorfine M, Zucker D, Spiegelman D. The impact of covariate measurement error on risk prediction. Stat Med. 2015;34(15):2353–67. https://doi.org/10.1002/sim.6498.
Article
PubMed
PubMed Central
Google Scholar
Rosella LC, Corey P, Stukel TA, Mustard C, Hux J, Manuel DG. The influence of measurement error on calibration, discrimination, and overall estimation of a risk prediction model. Popul Health Metrics. 2012;10(1):1–11. https://doi.org/10.1186/1478-7954-10-20.
Article
Google Scholar
Luijken K, Wynants L, van Smeden M, van Calster B, Steyerberg EW, Groenwold RHH, et al. Changing predictor measurement procedures affected the performance of prediction models in clinical examples. J Clin Epidemiol. 2020;119:7–18. https://doi.org/10.1016/j.jclinepi.2019.11.001.
Article
PubMed
Google Scholar
Keogh RH, White IR. A toolkit for measurement error correction, with a focus on nutritional epidemiology. Stat Med. 2014;33(12):2137–55. https://doi.org/10.1002/sim.6095.
Article
PubMed
PubMed Central
Google Scholar
Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement error in nonlinear models: a modern perspective: Chapman and Hall/CRC; 2006. https://doi.org/10.1201/9781420010138.
Book
Google Scholar
Fuller WA. Measurement error models, vol. 305: Wiley; 2009.
Keogh RH, Shaw PA, Gustafson P, Carroll RJ, Deffner V, Dodd KW, et al. STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: part 1—basic theory and simple methods of adjustment. Stat Med. 2020;39(16):2197–231. https://doi.org/10.1002/sim.8532.
Article
PubMed
PubMed Central
Google Scholar
Shaw PA, Gustafson P, Carroll RJ, Deffner V, Dodd KW, Keogh RH, et al. STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2—More complex methods of adjustment and advanced topics. Stat Med. 2020;39(16):2232–63. https://doi.org/10.1002/sim.8531.
Article
PubMed
PubMed Central
Google Scholar
Whittle R, Royle K-L, Jordan KP, Riley RD, Mallen CD, Peat G. Prognosis research ideally should measure time-varying predictors at their intended moment of use. Diagn Prognostic Res. 2017;1(1):1–9. https://doi.org/10.1186/s41512-016-0006-6.
Article
Google Scholar
Wynants L, Collins GS, Van Calster B. Key steps and common pitfalls in developing and validating risk models. BJOG Int J Obstet Gynaecol. 2017;124(3):423–32. https://doi.org/10.1111/1471-0528.14170.
Article
CAS
Google Scholar
Cowley LE, Farewell DM, Maguire S, Kemp AM. Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature. Diagn Prognostic Res. 2019;3(1):1–23. https://doi.org/10.1186/s41512-019-0060-y.
Article
Google Scholar
Toll D, Janssen K, Vergouwe Y, Moons K. Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol. 2008;61(11):1085–94. https://doi.org/10.1016/j.jclinepi.2008.04.008.
Article
CAS
PubMed
Google Scholar
Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. Bmj. 2009;338:b604. https://doi.org/10.1136/bmj.b604.
Article
PubMed
Google Scholar
Riley RD, Ensor J, Snell KI, et al. Calculating the sample size required for developing a clinical prediction model. Bmj. 2020;368:m441. https://doi.org/10.1136/bmj.m441.
Article
PubMed
Google Scholar
Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87. https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4.
Article
PubMed
Google Scholar
Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans M, Vergouwe Y, Habbema JDF. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774–81. https://doi.org/10.1016/S0895-4356(01)00341-9.
Article
CAS
PubMed
Google Scholar
Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. Bmj. 2009;338:b605. https://doi.org/10.1136/bmj.b605.
Article
PubMed
Google Scholar
Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med. 2000;19(4):453–73. https://doi.org/10.1002/(SICI)1097-0258(20000229)19:4<453::AID-SIM350>3.0.CO;2-5.
Article
CAS
PubMed
Google Scholar
Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. Bmj. 2009;338:b606. https://doi.org/10.1136/bmj.b606.
Article
PubMed
Google Scholar
Vergouwe Y, Nieboer D, Oostenbrink R, Debray TPA, Murray GD, Kattan MW, et al. A closed testing procedure to select an appropriate method for updating prediction models. Stat Med. 2017;36(28):4529–39. https://doi.org/10.1002/sim.7179.
Article
PubMed
Google Scholar
Ensor J, Snell KI, Debray TP, et al. Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model. Stat Med. 2021;40(13):3066–84. https://doi.org/10.1002/sim.8959.
Article
PubMed
Google Scholar
Adams ST, Leveson SH. Clinical prediction rules. Bmj. 2012;344:d8312. https://doi.org/10.1136/bmj.d8312.
Article
PubMed
Google Scholar
Reilly BM, Evans AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med. 2006;144(3):201–9. https://doi.org/10.7326/0003-4819-144-3-200602070-00009.
Article
PubMed
Google Scholar
Morris TP, White IR, Crowther MJ. Using simulation studies to evaluate statistical methods. Stat Med. 2019;38(11):2074–102. https://doi.org/10.1002/sim.8086.
Article
PubMed
PubMed Central
Google Scholar
Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Stat Med. 2005;24(11):1713–23. https://doi.org/10.1002/sim.2059.
Article
PubMed
Google Scholar
Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW. A calibration hierarchy for risk models was defined: from utopia to empirical data. J Clin Epidemiol. 2016;74:167–76. https://doi.org/10.1016/j.jclinepi.2015.12.005.
Article
PubMed
Google Scholar
Van Calster B, McLernon DJ, Van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17(1):1–7. https://doi.org/10.1186/s12916-019-1466-7.
Article
Google Scholar
Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics. 2005;61(1):92–105. https://doi.org/10.1111/j.0006-341X.2005.030814.x.
Article
PubMed
Google Scholar
Uno H, Cai T, Tian L, Wei L-J. Evaluating prediction rules for t-year survivors with censored regression models. J Am Stat Assoc. 2007;102(478):527–37. https://doi.org/10.1198/016214507000000149.
Article
CAS
Google Scholar
Blanche P, Kattan MW, Gerds TA. The c-index is not proper for the evaluation of-year predicted risks. Biostatistics. 2019;20(2):347–57. https://doi.org/10.1093/biostatistics/kxy006.
Article
PubMed
Google Scholar
Brier GW. Verification of forecasts expressed in terms of probability. Mon Weather Rev. 1950;78(1):1–3. https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.
Article
Google Scholar
Kattan MW, Gerds TA. The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models. Diagn Prognostic Res. 2018;2(1):1–7.
Article
Google Scholar
R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2020. http://www.R-project.org/
Zhang M, Zhang H, Wang C, Ren Y, Wang B, Zhang L, et al. Development and validation of a risk-score model for type 2 diabetes: a cohort study of a rural adult Chinese population. Plos One. 2016;11(4):e0152054. https://doi.org/10.1371/journal.pone.0152054.
Article
CAS
PubMed
PubMed Central
Google Scholar
Okamura T, Hashimoto Y, Hamaguchi M, Obora A, Kojima T, Fukui M. Ectopic fat obesity presents the greatest risk for incident type 2 diabetes: a population-based longitudinal study. Int J Obes. 2019;43(1):139–48. https://doi.org/10.1038/s41366-018-0076-3.
Article
Google Scholar
Booth S, Riley RD, Ensor J, Lambert PC, Rutherford MJ. Temporal recalibration for improving prognostic model development and risk predictions in settings where survival is improving over time. Int J Epidemiol. 2020;49(4):1316–25. https://doi.org/10.1093/ije/dyaa030.
Article
PubMed
PubMed Central
Google Scholar
American Diabetes Association. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2021;44(Supplement 1):S15–33.
Article
Google Scholar
Warnick GR, Kimberly MM, Waymack PP, Leary ET, Myers GL. Standardization of measurements for cholesterol, triglycerides, and major lipoproteins. Lab Med. 2008;39(8):481–90. https://doi.org/10.1309/6UL9RHJH1JFFU4PY.
Article
Google Scholar
World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: report of a WHO/IDF consultation. 2006.
Google Scholar
D'Orazio P, Burnett RW, Fogh-Andersen N, Jacobs E, Kuwa K, Külpmann WR, et al. Approved IFCC recommendation on reporting results for blood glucose: International Federation of Clinical Chemistry and Laboratory Medicine Scientific Division, Working group on selective electrodes and point-of-care testing (IFCC-SD-WG-SEPOCT). Clin Chem Lab Med. 2006;44(12):1486–90. https://doi.org/10.1515/CCLM.2006.275.
Article
CAS
PubMed
Google Scholar
van Geloven N, Swanson SA, Ramspek CL, Luijken K, van Diepen M, Morris TP, et al. Prediction meets causal inference: the role of treatment in clinical prediction models. Eur J Epidemiol. 2020;35(7):619–30. https://doi.org/10.1007/s10654-020-00636-1.
Article
PubMed
PubMed Central
Google Scholar
Nawaz H, Chan W, Abdulrahman M, Larson D, Katz DL. Self-reported weight and height: implications for obesity research. Am J Prev Med. 2001;20(4):294–8. https://doi.org/10.1016/S0749-3797(01)00293-8.
Article
CAS
PubMed
Google Scholar
Allison C, Colby S, Opoku-Acheampong A, Kidd T, Kattelmann K, Olfert MD, et al. Accuracy of self-reported BMI using objective measurement in high school students. J Nutr Sci. 2020;9:e35. https://doi.org/10.1017/jns.2020.28.
Article
PubMed
PubMed Central
Google Scholar
Dekkers JC, van Wier MF, Hendriksen IJ, Twisk JW, van Mechelen W. Accuracy of self-reported body weight, height and waist circumference in a Dutch overweight working population. BMC Med Res Methodol. 2008;8(1):1–13. https://doi.org/10.1186/1471-2288-8-69.
Article
Google Scholar
Villarini M, Acito M, Gianfredi V, Berrino F, Gargano G, Somaini M, et al. Validation of self-reported anthropometric measures and body mass index in a subcohort of the dianaweb population study. Clin Breast Cancer. 2019;19(4):e511–8. https://doi.org/10.1016/j.clbc.2019.04.008.
Article
PubMed
Google Scholar
Ortiz-Panozo E, Yunes-Díaz E, Lajous M, Romieu I, Monge A, López-Ridaura R. Validity of self-reported anthropometry in adult Mexican women. Salud Publica Mex. 2017;59:266–75. https://doi.org/10.21149/7860.
Article
PubMed
Google Scholar
Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S. Good practices for quantitative bias analysis. Int J Epidemiol. 2014;43(6):1969–85. https://doi.org/10.1093/ije/dyu149.
Article
PubMed
Google Scholar
Lash TL, Fox MP, Fink AK. Applying quantitative bias analysis to epidemiologic data: Springer Science & Business Media; 2011.
Google Scholar
Cook JR, Stefanski LA. Simulation-extrapolation estimation in parametric measurement error models. J Am Stat Assoc. 1994;89(428):1314–28. https://doi.org/10.1080/01621459.1994.10476871.
Article
Google Scholar
Stefanski LA, Cook JR. Simulation-extrapolation: the measurement error jackknife. J Am Stat Assoc. 1995;90(432):1247–56. https://doi.org/10.1080/01621459.1995.10476629.
Article
Google Scholar