Prognosis and prognostic research: validating a prognostic model

By Douglas G. Altman, Y. Vergouwe, Patrick Royston, Karel G. M. Moons. British medical journal, 2009

1,429 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/65a6af8318b9776fb0104f290fdb4d0e34182b0d

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In Cardiology and Cardiovascular Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, methodology): “…example, by plotting observed versus predicted risks across tenths of predicted risk, 10 using a flexible calibration plot with a smoothed non-linear curve generated using a loess smoother or splines, 10 37 or displaying observed and predicted survival curves over time for different risk groups.”
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies (2019, neutral): “Round 6 refined the domains and introduced further optional guidance for the use of PROBAST.”
Meta-analysis of individual participant data: rationale, conduct, and reporting (2010, background): “Individual participant data also enable the statistical modelling of complex relationships, such as non-linear trends and time dependent treatment effects. They even facilitate the development of prognostic models (or risk scores),”
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, methodology): “…of the AIPM, external AIPM model validation can be based on discrimination (area under the receiver operating characteristic curve, sensitivity, speci ﬁ city, positive and negative predictive values), calibration (calibration plot) 5,6,13,17,49,56,78 , and Decision Curve Analysis 5,6,17,49,68,72 .”
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research (2013, methodology): “Published models have often been developed using inferior statistical methods [8,15,46,61,66].”
Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension (2013, neutral): “Third, without comparing the performance of our model against APACHE IV in an external cohort, we cannot accurately say that our model outperforms APACHE IV under all circumstances, since the ability of a model to discriminate and calibrate decreases when applied to new populations (42).”
Correcting for Optimistic Prediction in Small Data Sets (2014, background): “Moreover, methodological reviews about the development and validation of diagnostic tests did not address this issue in detail (11, 12).”
A new framework to enhance the interpretation of external validation studies of clinical prediction models. (2015, neutral): “These so-called (external) validation studies may range from temporal (eg, sample from the same hospital or primary care practice only later in time), to geographical (eg, sample from different hospital, region, or even country), to validations across different medical settings (eg, from secondary t”
Risk prediction models: II. External validation, model updating, and impact assessment (2012, background): “Therefore, performance of developed and internally validated prediction models should still be tested or validated in new individuals before they are implemented in guidelines or applied in practice.(10) When a validation study shows disappointing results, researchers often reject the original predi”
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration (2012, background): “’ [143] Explanation Invariably, the strongest evidence for the validity of results is confirmation of the findings on data not involved in the original analysis [144,145].”