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💡专注R语言在🩺生物医学中的使用
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最近在整理临床预测模型方面的经典文献,我精挑细选了16篇文献,每一篇都是经典,涉及:
模型建立的步骤、
模型评价、
缺失值处理、
样本量计算、
数据划分(内外部验证、bootstrap等)
数据预处理、
论文写作等多个方面。
16篇文献的详细信息如下所示,为了方便大家学习,我已经把这16篇文献整理好了,公众号后台回复20240608即可获取全部文献。Collins G S, Moons K G M, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods[J]. BMJ (Clinical research ed.), 2024, 385: e078378. DOI:10.1136/bmj-2023-078378.
Collins G S, Reitsma J B, Altman D G, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement[J]. British Medical Journal, 2015, 350(jan07 4): g7594–g7594. DOI:10.1136/bmj.g7594.
Steyerberg E W, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation[J]. European Heart Journal, 2014, 35(29): 1925–1931. DOI:10.1093/eurheartj/ehu207.
Van Calster B, Wynants L, Verbeek J F M, et al. Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators[J]. European Urology, 2018, 74(6): 796–804. DOI:10.1016/j.eururo.2018.08.038.
Balachandran V P, Gonen M, Smith J J, et al. Nomograms in Oncology – More than Meets the Eye[J]. The Lancet. Oncology, 2015, 16(4): e173–e180. DOI:10.1016/S1470-2045(14)71116-7.
Sterne J A C, White I R, Carlin J B, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls[J]. BMJ (Clinical research ed.), 2009, 338: b2393. DOI:10.1136/bmj.b2393.
Archer L, Snell K I E, Ensor J, et al. Minimum sample size for external validation of a clinical prediction model with a continuous outcome[J]. Statistics in Medicine, 2021, 40(1): 133–146. DOI:10.1002/sim.8766.
Riley R D, Debray T P A, Collins G S, et al. Minimum sample size for external validation of a clinical prediction model with a binary outcome[J]. Statistics in Medicine, 2021, 40(19): 4230–4251. DOI:10.1002/sim.9025.
Riley R D, Collins G S, Ensor J, et al. Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome[J]. Statistics in Medicine, 2022, 41(7): 1280–1295. DOI:10.1002/sim.9275.
Riley R D, Snell K I E, Archer L, et al. Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study[J]. British Medical Journal, 2024, 384: e074821. DOI:10.1136/bmj-2023-074821.
Riley R D, Archer L, Snell K I E, et al. Evaluation of clinical prediction models (part 2): how to undertake an external validation study[J]. British Medical Journal, 2024, 384: e074820. DOI:10.1136/bmj-2023-074820.
Collins G S, Dhiman P, Ma J, et al. Evaluation of clinical prediction models (part 1): from development to external validation[J]. British Medical Journal, 2024, 384: e074819. DOI:10.1136/bmj-2023-074819.
Alba A C, Agoritsas T, Walsh M, et al. Discrimination and Calibration of Clinical Prediction Models: Users’ Guides to the Medical Literature[J]. JAMA, 2017, 318(14): 1377–1384. DOI:10.1001/jama.2017.12126.
Strandberg R, Jepsen P, Hagström H. Developing and validating clinical prediction models in hepatology—An overview for clinicians[J]. Journal of Hepatology, 2024: S0168-8278(24)00213–7. DOI:10.1016/j.jhep.2024.03.030.
Fitzgerald M, Saville B R, Lewis R J. Decision curve analysis[J]. JAMA, 2015, 313(4): 409–410. DOI:10.1001/jama.2015.37.
Rd R, J E, Kie S, et al. Calculating the sample size required for developing a clinical prediction model[J]. British Medical Journal, BMJ, 2020, 368. DOI:10.1136/bmj.m441.
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