000307383 001__ 307383
000307383 005__ 20251223120207.0
000307383 0247_ $$2doi$$a10.1016/j.jclinepi.2025.112117
000307383 0247_ $$2pmid$$apmid:41423140
000307383 0247_ $$2ISSN$$a0895-4356
000307383 0247_ $$2ISSN$$a1878-5921
000307383 037__ $$aDKFZ-2025-03027
000307383 041__ $$aEnglish
000307383 082__ $$a610
000307383 1001_ $$aLegha, Amardeep$$b0
000307383 245__ $$aSequential sample size calculations and learning curves safeguard the robust development of a clinical prediction model for individuals.
000307383 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2025
000307383 3367_ $$2DRIVER$$aarticle
000307383 3367_ $$2DataCite$$aOutput Types/Journal article
000307383 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1766414821_3634864
000307383 3367_ $$2BibTeX$$aARTICLE
000307383 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000307383 3367_ $$00$$2EndNote$$aJournal Article
000307383 500__ $$aepub
000307383 520__ $$aWhen recruiting participants to a new study developing a clinical prediction model (CPM), sample size calculations are typically conducted before data collection based on sensible assumptions. This leads to a fixed sample size, but if the assumptions are inaccurate, the actual sample size required to develop a reliable model may be higher or even lower. To safeguard against this, adaptive sample size approaches have been proposed, based on sequential evaluation of (changes in) a model's predictive performance.To illustrate and extend sequential sample size calculations for CPM development by (i) proposing stopping rules for prospective data collection based on minimising uncertainty (instability) and misclassification of individual-level predictions, and (ii) showcasing how it safeguards against inaccurate fixed sample size calculations.Using the sequential approach repeats the pre-defined model development strategy every time a chosen number (e.g., 100) of participants are recruited and adequately followed up. At each stage, CPM performance is evaluated using bootstrapping, leading to prediction and classification stability statistics and plots, alongside optimism-adjusted measures of calibration and discrimination. Learning curves display the trend of results against sample size and recruitment is stopped when a chosen stopping rule is met.Our approach is illustrated for model development of acute kidney injury using (penalised) logistic regression CPMs. Prior to recruitment based on perceived sensible assumptions, the fixed sample size calculation suggests recruiting 342 patients to minimise overfitting; however, during data collection the sequential approach reveals that a much larger sample size of 1100 is required to minimise overfitting (targeting a bootstrap-corrected calibration slope ≥0.9). If the stopping rule criteria also target small uncertainty and misclassification probability of individual predictions, the sequential approach suggests an even larger sample size of about n=1800.For CPM development studies involving prospective data collection, a sequential sample size approach allows users to dynamically monitor individual-level prediction and classification instability. This helps determine when enough participants have been recruited and safeguards against using inaccurate assumptions in a sample size calculation prior to data recruitment. Engagement with patients and other stakeholders is crucial to identify sensible context-specific stopping rules for robust individual predictions.
000307383 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
000307383 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
000307383 650_7 $$2Other$$aClinical Prediction Models
000307383 650_7 $$2Other$$aInstability
000307383 650_7 $$2Other$$aLearning Curves
000307383 650_7 $$2Other$$aModel Development
000307383 650_7 $$2Other$$aSample Size
000307383 650_7 $$2Other$$aSequential
000307383 650_7 $$2Other$$aUncertainty
000307383 7001_ $$aEnsor, Joie$$b1
000307383 7001_ $$aWhittle, Rebecca$$b2
000307383 7001_ $$aArcher, Lucinda$$b3
000307383 7001_ $$aVan Calster, Ben$$b4
000307383 7001_ $$0P:(DE-He78)8da2eca0bc6341c8681c317fe2b8e27b$$aChristodoulou, Evangelia$$b5$$udkfz
000307383 7001_ $$aSnell, Kym I E$$b6
000307383 7001_ $$aSadatsafavi, Mohsen$$b7
000307383 7001_ $$aCollins, Gary S$$b8
000307383 7001_ $$aRiley, Richard D$$b9
000307383 773__ $$0PERI:(DE-600)1500490-9$$a10.1016/j.jclinepi.2025.112117$$gp. 112117 -$$pnn$$tJournal of clinical epidemiology$$vnn$$x0895-4356$$y2025
000307383 909CO $$ooai:inrepo02.dkfz.de:307383$$pVDB
000307383 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)8da2eca0bc6341c8681c317fe2b8e27b$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ
000307383 9131_ $$0G:(DE-HGF)POF4-315$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vBildgebung und Radioonkologie$$x0
000307383 9141_ $$y2025
000307383 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2024-12-11$$wger
000307383 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ CLIN EPIDEMIOL : 2022$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2024-12-11
000307383 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bJ CLIN EPIDEMIOL : 2022$$d2024-12-11
000307383 9201_ $$0I:(DE-He78)E130-20160331$$kE130$$lE130 Intelligente Medizinische Systeme$$x0
000307383 980__ $$ajournal
000307383 980__ $$aVDB
000307383 980__ $$aI:(DE-He78)E130-20160331
000307383 980__ $$aUNRESTRICTED