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Modeling flexibility is attained through using monotone splines to approximate the baseline cumulative hazard function, as well as B-splines to accommodate nonlinear covariate effects. To facilitate model fitting, a computationally efficient and easy to implement expectation-maximization algorithm is developed through a two-stage data augmentation process involving carefully structured latent Poisson random variables. Asymptotic normality and the efficiency of the spline estimator of the regression coefficients are established, and the spline estimators of the nonparametric components are shown to possess the optimal rate of convergence under suitable regularity conditions. The finite-sample performance of the proposed approach is evaluated through Monte Carlo simulation and it is further illustrated using uterine fibroid data arising from a prospective cohort study on early pregnancy.<\/jats:p>","DOI":"10.1111\/biom.12914","type":"journal-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T19:36:10Z","timestamp":1530819370000},"page":"1240-1249","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Partially Linear Proportional Hazards Model for Current Status Data"],"prefix":"10.1093","volume":"74","author":[{"given":"Minggen","family":"Lu","sequence":"first","affiliation":[{"name":"School of Community Health Sciences, University of Nevada-Reno , Reno, Nevada 89557 , U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5056-9615","authenticated-orcid":false,"given":"Christopher 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