Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement von Collins, G. S., Reitsma, J. B., Altman, D. G., Moons, K. G. M.

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Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review von Nijman, SWJ, Leeuwenberg, AM, Beekers, I, Verkouter, I, Jacobs, JJL, Bots, ML, Asselbergs, FW, Moons, KGM, Debray, TPA

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A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data von Wynants, L, Bouwmeester, W, Moons, K.G.M, Moerbeek, M, Timmerman, D, Van Huffel, S, Van Calster, B, Vergouwe, Y

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Validation, updating and impact of clinical prediction rules: A review von Toll, D.B, Janssen, K.J.M, Vergouwe, Y, Moons, K.G.M

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Diagnostic and prognostic prediction models von Hendriksen, J. M. T., Geersing, G. J., Moons, K. G. M., Groot, J. A. H.

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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement von Collins, G S, Reitsma, J B, Altman, D G, Moons, K G M

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Updating methods improved the performance of a clinical prediction model in new patients von Janssen, K.J.M, Moons, K.G.M, Kalkman, C.J, Grobbee, D.E, Vergouwe, Y

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Effectiveness of progestogens to improve perinatal outcome in twin pregnancies: an individual participant data meta‐analysis von Schuit, E, Stock, S, Rode, L, Rouse, DJ, Lim, AC, Norman, JE, Nassar, AH, Serra, V, Combs, CA, Vayssiere, C, Aboulghar, MM, Wood, S, Çetingöz, E, Briery, CM, Fonseca, EB, Worda, K, Tabor, A, Thom, EA, Caritis, SN, Awwad, J, Usta, IM, Perales, A, Meseguer, J, Maurel, K, Garite, T, Aboulghar, MA, Amin, YM, Ross, S, Cam, C, Karateke, A, Morrison, JC, Magann, EF, Nicolaides, KH, Zuithoff, NPA, Groenwold, RHH, Moons, KGM, Kwee, A, Mol, BWJ

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Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review von van Dieren, S, Beulens, J W J, Kengne, A P, Peelen, L M, Rutten, G E H M, Woodward, M, van der Schouw, Y T, Moons, K G M

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Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review von Nijman, S. W.J, Leeuwenberg, A. M, Beekers, I, Verkouter, I, Jacobs, J.J.L, Bots, M. L, Asselbergs, F. W, Moons, K. G.M, Debray, T. P.A

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Interpretation of CVD risk predictions in clinical practice: Mission impossible? von Lagerweij, G. R, Moons, K. G.M, De Wit, G. A, Koffijberg, H

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Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement von Collins, GS, Reitsma, JB, Altman, DG, Moons, KGM

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Impact of adding therapeutic recommendations to risk assessments from a prediction model for postoperative nausea and vomiting von Kappen, T.H., Vergouwe, Y., van Wolfswinkel, L., Kalkman, C.J., Moons, K.G.M., van Klei, W.A.

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Preoperative prediction of severe postoperative pain von Kalkman, C.J, Visser, K, Moen, J, Bonsel, G.J, Grobbee, D.E, Moons, K.G.M

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Predictive performance of the CHA2DS2‐VASc rule in atrial fibrillation: a systematic review and meta‐analysis von Doorn, S., Debray, T. P. A., Kaasenbrood, F., Hoes, A. W., Rutten, F. H., Moons, K. G. M., Geersing, G.J.

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Interpretation of CVD risk predictions in clinical practice: Mission impossible? von Lagerweij, G R, Moons, K G M, de Wit, G A, Koffijberg, H

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Effectiveness of CHA 2 DS 2 -VASc based decision support on stroke prevention in atrial fibrillation: A cluster randomised trial in general practice von van Doorn, S, Rutten, F H, O'Flynn, C M, Oudega, R, Hoes, A W, Moons, K G M, Geersing, G J

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Prediction models in obstetrics: understanding the treatment paradox and potential solutions to the threat it poses von Cheong‐See, F, Allotey, J, Marlin, N, Mol, BW, Schuit, E, Riet, G, Riley, RD, Moons, KGM, Khan, KS, Thangaratinam, S

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External validation is necessary in prediction research:: A clinical example von Bleeker, S.E, Moll, H.A, Steyerberg, E.W, Donders, A.R.T, Derksen-Lubsen, G, Grobbee, D.E, Moons, K.G.M

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Exclusion of deep vein thrombosis using the Wells rule in clinically important subgroups: individual patient data meta-analysis von Geersing, G J, Zuithoff, N P A, Kearon, C, Anderson, D R, ten Cate-Hoek, A J, Elf, J L, Bates, S M, Hoes, A W, Kraaijenhagen, R A, Oudega, R, Schutgens, R E G, Stevens, S M, Woller, S C, Wells, P S, Moons, K G M

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