Comparison of Joint Modelling and Landmarking Approaches for Dynamic Prediction Using Bootstrap Simulation
Notice bibliographique
Résumé
Abstract Prediction models for clinical outcomes can greatly help clinicians with early diagnosis, cost-effective management and primary prevention of many medical conditions. In conventional prediction models, predictors are typically measured at a fixed time point, either at baseline or at other time point of interest such as biomarker values measured at the most recent follow-up. Dynamic prediction has emerged as a more appealing prediction technique that takes account of longitudinal history of biomarkers for making predictions. We compared prediction performance of two well-known approaches for dynamic prediction, namely joint modelling and landmarking, using bootstrap simulation based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data with repeat Mini-Mental State Examination (MMSE) scores as the longitudinal biomarker and time-to-Alzheimer’s disease (AD) as the survival outcome. We assessed the performance of both approaches in terms of extended definitions of discrimination and calibration , namely dynamic area under the receiver operating characteristic curve (dynAUC) and expected prediction error (PE). We focused on real data-based bootstrap simulation in an attempt to be as impartial as possible to both methods as landmarking is a pragmatic approach which does not specify a statistical model for the longitudinal markers, and therefore any comparison based on model based data simulation may potentially be more advantageous to joint modelling approach. The dynAUC and PE were compared at landmarks $$ t_{s}=1.0, 1.5, 2.0\,\text{ and }\,2.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>t</mml:mi> <mml:mi>s</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>1.0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1.5</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2.0</mml:mn> <mml:mspace/> <mml:mspace/> <mml:mtext>and</mml:mtext> <mml:mspace/> <mml:mspace/> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:math> years and within a 2-year window from the landmark time points. The optimism corrected estimates of dynAUC for joint modelling were slightly higher (1.26, 3.22, 2.76 and 0.12% higher at the four landmark time points) than that of landmarking approach. Apart from the final landmark point (at 2.5 years), dynamic prediction based on joint models has also performed slightly better in terms of calibration. The expected prediction errors (PE) for joint models were 0.70, 2.56 and 2.04% lower at the first three landmark time points, respectively, compared to the landmarking approach. In general, joint modelling approach has performed better than the landmarking approach in terms of both discrimination (dynAUC) and calibration (PE), although the margin of gain in performance by using joint models over landmarking was relatively small indicating that landmarking approach was close enough, despite not having a precise statistical model characterising the evolution of the longitudinal markers. Future comparative studies should consider extended versions of joint modelling and landmarking approaches which may overcome some of the limitations of the standard methods.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».