Nonparametric and Semiparametric Analysis of Current Status Data Subject to Outcome Misclassification

In this article, we present nonparametric and semiparametric methods to analyze current status data subject to outcome misclassification. Our methods use nonparametric maximum likelihood estimation (NPMLE) to estimate the distribution function of the failure time when sensitivity and specificity may vary among subgroups. A nonparametric test is proposed for the two sample hypothesis testing. In regression analysis, we apply the Cox proportional hazard model and likelihood ratio based confidence intervals for the regression coefficients are proposed. Our methods are motivated and demonstrated by data collected from an infectious disease study in Seattle, WA

The Abandoned Radical Hysterectomy for Cervical Cancer: Clinical Predictors and Outcomes

Objective. Cervical cancer patients who had an abandoned radical hysterectomy were evaluated for preoperative clinical predictors, complication rates, and outcomes. Study Design. IRB approval was obtained for this retrospective analysis and chart review was performed. Results. From 268 women with early-stage (IA2 to IIA) cervical cancer, 19 (7%) had an abandoned hysterectomy for finding grossly positive lymph nodes (84%) or pelvic spread of tumor (16%). No clinical characteristics clearly identified women preoperatively at risk of having an abandoned hysterectomy. In the abandoned group, 26% suffered major morbidities, compared to 34% in the completed group (OR 0.69, [CI 0.16–2.57], P = .789). Thirty-seven percent recurred in the abandoned group, compared to 18% in the completed group (P = .168). Overall survival in the abandoned group was 73% versus 80% in the completed group (P = .772). Conclusion. The practice of abandoning a planned radical hysterectomy for unexpected metastatic disease may not worsen the outcome