Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>