RESUMEN
This paper discusses regression analysis of current status data with dependent censoring, a problem that often occurs in many areas such as cross-sectional studies, epidemiological investigations and tumorigenicity experiments. Copula model-based methods are commonly employed to tackle this issue. However, these methods often face challenges in terms of model and parameter identification. The primary aim of this paper is to propose a copula-based analysis for dependent current status data, where the association parameter is left unspecified. Our method is based on a general class of semiparametric linear transformation models and parametric copulas. We demonstrate that the proposed semiparametric model is identifiable under certain regularity conditions from the distribution of the observed data. For inference, we develop a sieve maximum likelihood estimation method, using Bernstein polynomials to approximate the nonparametric functions involved. The asymptotic consistency and normality of the proposed estimators are established. Finally, to demonstrate the effectiveness and practical applicability of our method, we conduct an extensive simulation study and apply the proposed method to a real data example.
RESUMEN
OBJECTIVES: This study aims to study the accuracy of cone beam computed tomography (CBCT) for measuring peri-implant bone thickness in living patients via a novel visualization method (NVM). MATERIAL AND METHODS: The validity of the NVM was verified ex vivo by measuring the same peri-implant bone thicknesses in bovine ribs by using raw postoperative CBCT (clinical measurement, CM), the visualized fused images obtained using the NVM (visualized fused measurement, VF), and hard tissue sections (gold standard measurement, GS). The NVM was applied by deconstructing the postoperative CBCT model into the Modelpost-bone and Modelimplant and replacing it with bone from preoperative CBCT and standard implant models, respectively. In vivo, 52 implants were included, and the VF of each implant was obtained using data processing methods similar to those used ex vivo. Then, we compared the results of CM and VF. RESULTS: Ex vivo, the VF was similar to GS, while CM usually underestimated the peri-implant bone thickness, especially at the implant shoulder (P < 0.01). In vivo, on CBCT, areas with a peri-implant bone thickness of 0-0.50 mm were not visible, while those with a thickness of 0.50-1.00 mm were occasionally visible. There was less underestimation of bone along the implant long axis. CONCLUSIONS: Thin peri-implant bones could be completely underestimated on CBCT. CBCT scans alone are insufficient to warrant surgical intervention. Our NVM facilitates the accurate visual assessment of implant dimensions. CLINICAL RELEVANCE: The thickness of peri-implant bone could be completely underestimated when thinner than 1.0 mm in living patients. Familiarity with these confusing CBCT results may help clinicians and patients avoid further unnecessary evaluation, misdiagnosis, and invasive treatment.
