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Article

Snell, KIE, Levis, B, Damen, JAA, Dhiman, P, Debray, TPA, Hooft, L, Reitsma, JB, Moons, KGM, Collins, GS and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2023) Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 381. e073538 - ?.

Debray, TPA, Collins, GS, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Van Calster, B, Reitsma, JB and Moons, KGM (2023) Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ, 380. e071058 - ?.

Debray, TPA, Collins, GS, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Van Calster, B, Reitsma, JB and Moons, KGM (2023) Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist. BMJ, 380.

Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2022) Risk of bias of prognostic models developed using machine learning: a systematic review in oncology. Diagnostic and Prognostic Research, 6 (1). 13 - ?.

Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2022) Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Medical Research Methodology, 22 (1). 101 - ?.

Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R ORCID: https://orcid.org/0000-0002-1227-2703, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KGM and Hooft, L (2022) Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Medical Research Methodology, 22 (1). 12 - ?.

Hoogland, J, IntHout, J, Belias, M, Rovers, MM, Riley, RD, E Harrell, F, Moons, KGM, Debray, TPA and Reitsma, JB (2021) A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint. Stat Med, 20 (26). pp. 5961-5981.

Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KGM and Hooft, L (2021) Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ, 375. n2281 - ?.

Dhiman, P, Ma, J, Navarro, CA, Speich, B, Bullock, G, Damen, JA, Kirtley, S, Hooft, L, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2021) Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. Journal of Clinical Epidemiology, 138. pp. 60-72.

Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R ORCID: https://orcid.org/0000-0002-1227-2703, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KGM and Hooft, L (2021) Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review. arXiv.org. (Unpublished)

de Jong, VMT, Moons, KGM, Eijkemans, MJC, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 and Debray, TPA (2021) Developing more generalizable prediction models from pooled studies and large clustered data sets. Statistics in Medicine, 40 (15). pp. 3533-3559.

Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Debray, TPA, Lambert, PC, Look, MP, Mamas, MA ORCID: https://orcid.org/0000-0001-9241-8890, Moons, KGM and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2021) Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model. Statistics in Medicine.

Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, LC, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, KS, Mol, BW, Myers, J, Poston, L, Thilaganathan, B, Staff, AC, Smith, GCS, Ganzevoort, W, Laivuori, H, Odibo, AO, Arenas Ramírez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, AA, Seed, PT, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skråstad, RB, Salvesen, KÅ, Haavaldsen, C, Nagata, C, Rumbold, AR, Heinonen, S, Askie, LM, Smits, LJM, Vinter, CA, Magnus, P, Eero, K, Villa, PM, Jenum, AK, Andersen, LB, Norman, JE, Ohkuchi, A, Eskild, A, Bhattacharya, S, McAuliffe, FM, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, SA, Browne, JL, Moons, KGM, Riley, RD and Thangaratinam, S (2020) External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis. BMC Medicine, 18 (1).

Wynants, L, Van Calster, B, Bonten, MMJ, Collins, GS, Debray, TPA, De Vos, M, Haller, MC, Heinze, G, Moons, KGM, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Schuit, E, Smits, LJM, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Steyerberg, EW, Wallisch, C and van Smeden, M (2020) Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ, 369.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Harrell, FE, Martin, GP, Reitsma, JB, Moons, KGM, Collins, G and van Smeden, M (2020) Calculating the sample size required for developing a clinical prediction model. BMJ, 368. -.

de Jong, VMT, Moons, KGM, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Smith, CT, Marson, AG, Eijkemans, MJC and Debray, TPA (2019) Individual participant data meta-analysis of intervention studies with time-to-event outcomes: A review of the methodology and an applied example. Research Synthesis Methods.

Debray, TPA, de Jong, VMT, Moons, KGM and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2019) Evidence synthesis in prognosis research. Diagnostic and Prognostic Research, 3.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KI, Ensor, J, Burke, DL, Harrell, FE, Moons, KGM and Collins, GS (2019) Minimum sample size for developing a multivariable prediction model: Part II-binary and time-to-event outcomes. Statistics in Medicine, 38 (7). pp. 1276-1296.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KIE, Ensor, J, Burke, DL, Harrell, FE, Moons, KGM and Collins, GS (2019) Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Statistics in Medicine, 38 (7). 1262 - 1275.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KGM, Snell, KIE, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Hooft, L, Altman, DG, Hayden, J, Collins, GS and Debray, TPA (2019) A guide to systematic review and meta-analysis of prognostic factor studies. BMJ, 364.

Wolff, RF, Moons, KGM, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Whiting, PF, Westwood, M, Collins, GS, Reitsma, JB, Kleijnen, J, Mallett, S and PROBAST Group†, . (2019) PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Annals of Internal Medicine, 170 (1). 51 - 58.

Debray, TPA, Moons, KGM and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2018) Detecting small-study effects and funnel plot asymmetry in meta-analysis of survival data: A comparison of new and existing tests. Research Synthesis Methods, 9 (1). 41 - 50.

Allotey, J, Snell, KIE, Chan, C, Hooper, R, Dodds, J, Rogozinska, E, Khan, KS, Poston, L, Kenny, L, Myers, J, Thilaganathan, B, Chappell, L, Mol, BW, Von Dadelszen, P, Ahmed, A, Green, M, Poon, L, Khalil, A, Moons, KGM, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Thangaratinam, S and Collaborative Network, IPPIC (2017) External validation, update and development of prediction models for pre-eclampsia using an Individual Participant Data (IPD) meta-analysis: the International Prediction of Pregnancy Complication Network (IPPIC pre-eclampsia) protocol. Diagnostic and Prognostic Research, 1. 16 - ?.

Thangaratinam, S, Allotey, J, Marlin, N, Dodds, J, Cheong-See, F, von Dadelszen, P, Ganzevoort, W, Akkermans, J, Kerry, S, Mol, BW, Moons, KGM, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 and Khan, KS (2017) Prediction of complications in early-onset pre-eclampsia (PREP): development and external multinational validation of prognostic models. BMC Medicine, 15 (1). 68 -?.

Debray, TPA, Damen, JAAG, Snell, KIE, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Hooft, L, Reitsma, JB, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 and Moons, KGM (2017) A guide to systematic review and meta-analysis of prediction model performance. British Medical Journal, 356.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Snell, KIE, Debray, TPA, Altman, DG, Moons, KGM and Collins, GS (2016) External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ, 353.

Peat, G ORCID: https://orcid.org/0000-0002-9008-0184, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Croft, P, Morley, KI, Kyzas, PA, Moons, KGM, Perel, P, Steyerberg, EW, Schroter, S, Altman, DG and Hemingway, H (2014) Improving the transparency of prognosis research: the role of reporting, data sharing, registration, and protocols. PLoS Medicine, 11 (7). e1001671 -?.

This list was generated on Wed Nov 1 01:09:36 2023 UTC.