Epiphyllous liverworts on rosette leaves of Ardisia species (Myrsinaceae) in China

Four species of Ardisia (Myrsinaceae, Magnoliopsida) with rosette or low-lying leaves in China (including Hong Kong) have been found to be the hosts for 12 species of epiphyllous liverworts which belong to 4 families and 9 genera. However, no obvious species-specific hostepiphyte relationship could be recognized

RECURRENT EVENT MODELS IN THE PRESENCE OF A TERMINAL EVENT: COMPARISON, INFERENCE AND DATA ANALYSIS

This article focuses on statistical implications of proportional rate models for recurrent event data in the presence of a terminal event. In such circumstances, various definitions of the recurrent rate function have been adopted in the proportional rate models. Although these rate functions have quite different interpretations, recognition of the differences has been lacking theoretically and practically. We compare three types of rate functions from both conceptual and quantitative perspectives; conclude that the inappropriate choice of a rate function may lead to misleading scientific conclusions. Simulations are conducted for comparisons of the focused models. Analysis of data from an AIDS clinical trial is presented to illustrate the analytical results

Estimation and Projection of Indicence and Prevalence Based on Doubly Truncated Data with Application to Pharmacoepidemiological Databases

Incidences of disease are of primary interest in any epidemiological analysis of disease spread in general populations. Ordinary estimates obtained from follow-up of an initially non-diseased cohort are costly, and so such estimates are not routinely available. In contrast, routine registers exist for many diseases with data on all detected cases within a given calendar time period, but lacking information on non-diseased. In the present work we show how this type of data supplemented with data on the past birth process can be analyzed to yield age specific incidence estimates as well as lifetime prevalence. A non-parametric model is studied with emphasis on the required assumptions, and a brief outlook on the analysis of the non-stationary case with calendar trends in age-specific incidence is given. The developed methods are applied to case cohort data on treatment with anti-diabetic medications and projections are provided for both diabetes incidence and prevalence. As projection of diabetes prevalence requires estimation of the distribution of disease durations, two novel approaches for this estimation is studied, a parametric and a non-parametric, respectively

Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To analyze this type of bivariate failure time data, it is important to recognize the presence of bias arising due to interval sampling. In this paper, nonparametric and semiparametric methods are developed to analyze the bivariate survival data with interval sampling under stationary and semi-stationary conditions. Numerical studies demonstrate the proposed estimating approaches perform well with practical sample sizes in different simulated models. We apply the proposed methods to SEER ovarian cancer registry data for illustration of the methods and theory