TY - JOUR
T1 - On Estimation of the Hazard Function From Population-Based Case–Control Studies
AU - Hsu, Li
AU - Gorfine, Malka
AU - Zucker, David
N1 - Publisher Copyright:
© 2018, © 2018 American Statistical Association.
PY - 2018/4/3
Y1 - 2018/4/3
N2 - The population-based case–control study design has been widely used for studying the etiology of chronic diseases. It is well established that the Cox proportional hazards model can be adapted to the case–control study and hazard ratios can be estimated by (conditional) logistic regression model with time as either a matched set or a covariate. However, the baseline hazard function, a critical component in absolute risk assessment, is unidentifiable, because the ratio of cases and controls is controlled by the investigators and does not reflect the true disease incidence rate in the population. In this article, we propose a simple and innovative approach, which makes use of routinely collected family history information, to estimate the baseline hazard function for any logistic regression model that is fit to the risk factor data collected on cases and controls. We establish that the proposed baseline hazard function estimator is consistent and asymptotically normal and show via simulation that it performs well in finite samples. We illustrate the proposed method by a population-based case–control study of prostate cancer where the association of various risk factors is assessed and the family history information is used to estimate the baseline hazard function. Supplementary materials for this article are available online.
AB - The population-based case–control study design has been widely used for studying the etiology of chronic diseases. It is well established that the Cox proportional hazards model can be adapted to the case–control study and hazard ratios can be estimated by (conditional) logistic regression model with time as either a matched set or a covariate. However, the baseline hazard function, a critical component in absolute risk assessment, is unidentifiable, because the ratio of cases and controls is controlled by the investigators and does not reflect the true disease incidence rate in the population. In this article, we propose a simple and innovative approach, which makes use of routinely collected family history information, to estimate the baseline hazard function for any logistic regression model that is fit to the risk factor data collected on cases and controls. We establish that the proposed baseline hazard function estimator is consistent and asymptotically normal and show via simulation that it performs well in finite samples. We illustrate the proposed method by a population-based case–control study of prostate cancer where the association of various risk factors is assessed and the family history information is used to estimate the baseline hazard function. Supplementary materials for this article are available online.
KW - Copula model
KW - Family history
KW - Marginal hazard function
KW - Multivariate survival analysis
UR - http://www.scopus.com/inward/record.url?scp=85048503570&partnerID=8YFLogxK
U2 - 10.1080/01621459.2017.1356315
DO - 10.1080/01621459.2017.1356315
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AN - SCOPUS:85048503570
SN - 0162-1459
VL - 113
SP - 560
EP - 570
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 522
ER -