Seong-Hwa Jeong

Density of the alveolar and basal bones of the maxilla and the mandible.

INTRODUCTION The purpose of this investigation was to quantitatively evaluate density of the alveolar and basal bones of the maxilla and the mandible. METHODS Sixty-three sets of computed tomographic (CT) images were selected, and bone density was measured with V-Works imaging software (Cybermed, Seoul, Korea). The sample consisted of 23 men (ages, 29 +/- 10.9 years) and 40 women (ages, 25.6 +/- 7.6 years). Cortical and cancellous bone densities at the alveolar and basal bones at the incisor, canine, premolar, molar, and maxillary tuberosity/retromolar areas were measured. RESULTS The cortical bone density of the maxilla ranged approximately between 810 and 940 Hounsfield units (HU) at the alveolar bone except for the maxillary tuberosity (443 HU at the buccal and 615 HU at the palatal alveolar bone), and between 835 and 1113 HU at the basal cortical bone except for tuberosity (542 HU). The cortical bone density of the mandible ranged between 800 and 1580 HU at the alveolar bone and 1320 and 1560 HU at the basal bone. The highest bone density in the maxilla was observed in the canine and premolar areas, and maxillary tuberosity showed the lowest bone density. Density of the cortical bone was greater in the mandible than in the maxilla and showed a progressive increase from the incisor to the retromolar area. CONCLUSIONS These data might provide valuable information when selecting sites and placement methods for miniscrew or microscrew implants in the dental arch.

Confidence Intervals for the Median Survival Time under Proportional Censorship

In this paper, we demonstrate the more accurate confidence intervals for median survival time under the simple proportional hazard model of Koziol and Green (1976) via the Edgeworth expansion for the distribution of the studentized ACL estimator derived in Jeong (2000). The numerical results show that the intervals, so-called test-based and reflect intervals (Slud et al., 1984), outperform normal approximating method in the small sample sizes and/or heavy censoring.