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A Simulation Method for Sample Size Determination in Calibration Studies of Prognostic Biomarkers

Abstract Number:

2360 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Alexander Piehler (1), Yi Ren (2), Huei-Chung Huang (3)

Institutions:

(1) ArteraAI, N/A, (2) N/A, N/A, (3) ArteraAI, Canada

Co-Author(s):

Yi Ren  
N/A
Huei-Chung Huang  
ArteraAI

First Author:

Alexander Piehler  
ArteraAI

Presenting Author:

Alexander Piehler  
ArteraAI

Abstract Text:

Evaluating the calibration of prognostic biomarkers is crucial for assessing how well predicted risks match observed event rates. A common approach bins predicted risks and compares the observed event rates in each bin against the predicted rates. However, small sample sizes can yield wide confidence intervals (CIs), which can obscure whether observed deviations reflect actual miscalibration or random variation.

A simulation framework was developed to determine the necessary sample size for reliable estimation of bin-specific event rates. We leverage assumptions on the marginal distribution of biomarker risk groups (RGs), the conditional bin distribution per RG, and event rates within RGs. Using a Dirichlet‐multinomial process, individuals are assigned to a RG, then randomly allocated to bins conditional on the RG assignment. Using an exponential survival model, event times are generated to calculate the bin‐level survival estimates and CIs at a fixed time point.

The method provides practical guidance for choosing sample size to ensure robust calibration assessments using an adaptable approach towards different bin schemes and biomarker assumptions.

Keywords:

Calibration|Sample Size|Biomarker|Survival Analysis|Simulation|Confidence Interval

Sponsors:

Biometrics Section

Tracks:

Risk Prediction

Can this be considered for alternate subtype?

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I have read and understand that JSM participants must abide by the Participant Guidelines.

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I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.

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