A weighted casebase framework for outcome-dependent two-phase sampling designs.
8 affiliations
- 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
- 2Lady Davis Institute for Medical Research, Montreal, QC, Canada.
- 3Computational Biology Program, Ontario Institute for Cancer Research, Toronto, ON, Canada.
- 4Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- 5Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK.
- 6Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.
- 7Computational Biology Program, Ontario Institute for Cancer Research, Toronto, ON, Canada. [email protected].
- 8Department of Mathematics, Wilfrid Laurier University, Waterloo, ON, N2L 3C5, Canada. [email protected].
Abstract
Background
Outcome-dependent two-phase sampling designs permit cost-effective measurement of expensive covariates in large prospective cohorts. Weighted Cox regression is commonly used for these designs, but applications requiring smooth hazard estimation or direct absolute risk prediction motivate a complementary parametric approach.Methods
We extend the casebase framework, which represents hazard regression as logistic regression on sampled person-moments, by combining the casebase sampling offset with inverse-probability weights for phase-two inclusion. We formulate the estimator as a participant-level M-estimator, establish consistency and asymptotic normality under standard conditions, and develop a computationally efficient dfbeta-based jackknife/sandwich variance estimator. Performance was evaluated against weighted Cox regression in simulations, and implementation was illustrated using a frequency-matched biomarker substudy within the Ontario Health Study.Results
Weighted casebase and weighted Cox regression had broadly similar coefficient performance across the scenarios studied. Both methods showed small bias and approximately nominal coverage in the binary-covariate and targeted sensitivity scenarios, whereas continuous or highly skewed covariates combined with fixed phase-two sample sizes and large weights produced finite-sample instability for both approaches. In the illustrative application, the two methods showed similar discrimination and weighted Brier score patterns, while the casebase model additionally produced smooth absolute-risk curves.Conclusions
Weighted casebase regression provides a practical GLM-based approach to smooth parametric or flexible-parametric hazard estimation and direct absolute risk prediction under outcome-dependent two-phase sampling. It is complementary to weighted Cox regression rather than a replacement for it.
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BibTeX
@article{kwan2026weighted,
title = {{A weighted casebase framework for outcome-dependent two-phase sampling designs.}},
author = {Karina Kwan and Nicholas Cheng and Philip Awadalla and Celia M T Greenwood and David Soave},
journal = {BMC medical research methodology},
year = {2026},
month = jul,
doi = {10.1186/s12874-026-02957-3},
url = {https://europepmc.org/article/MED/42928138},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A weighted casebase framework for outcome-dependent two-phase sampling designs.
AU - Karina Kwan
AU - Nicholas Cheng
AU - Philip Awadalla
AU - Celia M T Greenwood
AU - David Soave
PY - 2026
DA - 2026/07/29
JO - BMC medical research methodology
DO - 10.1186/s12874-026-02957-3
UR - https://europepmc.org/article/MED/42928138
AB - Background Outcome-dependent two-phase sampling designs permit cost-effective measurement of expensive covariates in large prospective cohorts. Weighted Cox regression is commonly used for these designs, but applications requiring smooth hazard estimation or direct absolute risk prediction motivate a complementary parametric approach.Methods We extend the casebase framework, which represents hazard regression as logistic regression on sampled person-moments, by combining the casebase sampling offset with inverse-probability weights for phase-two inclusion. We formulate the estimator as a participant-level M-estimator, establish consistency and asymptotic normality under standard conditions, and develop a computationally efficient dfbeta-based jackknife/sandwich variance estimator. Performance was evaluated against weighted Cox regression in simulations, and implementation was illustrated using a frequency-matched biomarker substudy within the Ontario Health Study.Results Weighted casebase and weighted Cox regression had broadly similar coefficient performance across the scenarios studied. Both methods showed small bias and approximately nominal coverage in the binary-covariate and targeted sensitivity scenarios, whereas continuous or highly skewed covariates combined with fixed phase-two sample sizes and large weights produced finite-sample instability for both approaches. In the illustrative application, the two methods showed similar discrimination and weighted Brier score patterns, while the casebase model additionally produced smooth absolute-risk curves.Conclusions Weighted casebase regression provides a practical GLM-based approach to smooth parametric or flexible-parametric hazard estimation and direct absolute risk prediction under outcome-dependent two-phase sampling. It is complementary to weighted Cox regression rather than a replacement for it.
SN - 1471-2288
ER -