Jack C. Yue

A similarity measure based on species proportions

ABSTRACT There are several indices for measuring the similarity of two populations, including the ratio of the number of shared species to the number of distinct species (Jaccards index) and the conditional probability of observing a shared species (Smith et al., 1996). However, these indices only take into account the number of species and species proportions of shared species. In this article, we propose a new similarity index which includes the species proportions of both the shared and non shared species in each population, and also propose a Nonparametric Maximum Likelihood Estimator (NPMLE) for this index. Bootstrap and delta methods are used to evaluate the standard errors of the NPMLE. Based on a loss function, we also compare a class of nonparametric estimators for the proposed index in various situations.

Predicting Anesthesia Times for Diagnostic and Interventional Radiological Procedures

We studied anesthesia times for diagnostic and interventional radiology using anesthesia billing data and paper radiology logbooks. For computerized tomography and magnetic resonance imaging procedures, we tried to predict future anesthesia times by using historical anesthesia times classified by Current Procedural Terminology (CPT) codes. By this method, anesthesia times were estimated even less accurately than operating room cases. Computerized tomography and magnetic resonance imaging had many different CPT codes, most rare, and CPT codes reflected organs imaged, not scanning times. However, when, anesthesia times were estimated by expert judgment, face validity and accuracy were good. Lower and upper prediction bounds were also estimated from the expert estimates. For interventional radiology, predicting anesthesia times was challenging because few CPT codes accounted for most cases. Because interventional radiologists scheduled their elective cases into allocated time, the necessary goal was not to estimate the time to complete each case but rather the time to complete each day’s entire series of elective cases including turnover times. We determined the time of day (e.g., 4 pm) up to when interventional radiology could schedule so that on 80% of days the anesthesia team finishes no later than a specified time (e.g., 6 pm). Both diagnostic and interventional radiology results were similarly less accurate when Version 9 of the International Classifications of Diseases’ procedure codes was used instead of CPT.

Use of discharge abstract databases to differentiate among pediatric hospitals based on operative procedures: surgery in infants and young children in the state of Iowa.

Introduction A pediatric hospital may aim to show governmental agencies, charitable organizations, and philanthropic individuals how its clinical services differ from those of nonpediatric surgical facilities and of other pediatric hospitals. Yet, it is unknown how to use existing databases to quantify where infants and young children undergo surgery, and to use that information to differentiate among facilities. Methods Discharge abstracts were used to study inpatient and outpatient operative procedures performed between January and June 2001 in children 0–2 yr old at hospitals or hospital-affiliated outpatient surgery centers in Iowa. Results Of the 93 facilities performing at least one procedure, the 90 performing 15 or fewer different types of procedures provided surgical care for 80% of procedures. Among procedures performed at these 90 facilities, less than 0.15% were physiologically complex (more than seven American Society of Anesthesiologists’ basic units). In contrast, at the larger and smaller pediatric hospitals, the percentages were 26% and 7%, respectively. These pediatric hospitals performed 181 and 73 different types of procedures, respectively; 64% of the physiologically complex procedures performed statewide were performed at the larger pediatric hospital. The smaller pediatric hospital was no more similar to the larger pediatric hospital in its relative volumes of each type of procedure than it was to the other 91 facilities. Conclusions Statewide discharge abstract data can be used by a hospital to quantify how its surgical practice differs from that of other hospitals (e.g., to show that it provides a more diverse, comprehensive, and physiologically complex selection of procedures in younger patients).

A modification to geographically weighted regression

BackgroundGeographically weighted regression (GWR) is a modelling technique designed to deal with spatial non-stationarity, e.g., the mean values vary by locations. It has been widely used as a visualization tool to explore the patterns of spatial data. However, the GWR tends to produce unsmooth surfaces when the mean parameters have considerable variations, partly due to that all parameter estimates are derived from a fixed- range (bandwidth) of observations. In order to deal with the varying bandwidth problem, this paper proposes an alternative approach, namely Conditional geographically weighted regression (CGWR).MethodsThe estimation of CGWR is based on an iterative procedure, analogy to the numerical optimization problem. Computer simulation, under realistic settings, is used to compare the performance between the traditional GWR, CGWR, and a local linear modification of GWR. Furthermore, this study also applies the CGWR to two empirical datasets for evaluating the model performance. The first dataset consists of disability status of Taiwan’s elderly, along with some social-economic variables and the other is Ohio’s crime dataset.ResultsUnder the positively correlated scenario, we found that the CGWR produces a better fit for the response surface. Both the computer simulation and empirical analysis support the proposed approach since it significantly reduces the bias and variance of data fitting. In addition, the response surface from the CGWR reviews local spatial characteristics according to the corresponded variables.ConclusionsAs an explanatory tool for spatial data, producing accurate surface is essential in order to provide a first look at the data. Any distorted outcomes would likely mislead the following analysis. Since the CGWR can generate more accurate surface, it is more appropriate to use it exploring data that contain suspicious variables with varying characteristics.

Mortality Compression and Longevity Risk

Abstract Mortality improvements, especially of the elderly, have been a common phenomenon since the end of World War II. The longevity risk becomes a major concern in many countries because of underestimating the scale and speed of prolonged life. In this study we explore the increasing life expectancy by examining the basic properties of survival curves. Specifically, we check if there are signs of mortality compression (i.e., rectangularization of the survival curve) and evaluate what it means to designing annuity products. Based on the raw mortality rates, we propose an approach to verify if there is mortality compression. We then apply the proposed method to the mortality rates of Japan, Sweden, and the United States, using the Human Mortality Database. Unlike previous results using the graduated mortality rates, we found no obvious signs that mortality improvements are slowing down. This indicates that human longevity is likely to increase, and longevity risk should be seriously considered in pricing annuity products.

Mortality, Health, and Marriage: A Study Based on Taiwan's Population Data

Life expectancy has been increasing significantly since the start of the 20th century, and mortality improvement trends are likely to continue in the 21st century. Stochastic mortality models are used frequently to predict the expansion in life expectancy. In addition to gender, age, period, and cohort are the three main risk factors considered in constructing mortality models. Other than these factors, it is also believed that marital status is related to health and longevity, and many studies have found that married persons have a lower mortality rate than the unmarried. In this study, we have used Taiwans marital data for the whole population (married, unmarried, divorced/widowed) to evaluate if the marital status can be a preferred criteria. Furthermore, we also want to know whether the preferred criteria will be valid in the future. We chose two popular mortality models, the Lee-Carter and age-period-cohort, to model the mortality improvements for various marital statuses. Because of a linear dependence in the parameters of the age-period-cohort model, we used a computer simulation to choose the appropriate estimation method. Based on Taiwans marital data, we found that married persons have significantly lower mortality rates than the single, and if converting the difference into a life insurance policy, the discount amount is even larger than that for smokers/nonsmokers.

An Empirical Study of Mortality Models in Taiwan

There has been a significant increase in the life expectancies of the Taiwanese population after the end of Second World War. Like in many developed countries, due to the prolonging life expectancy and lower fertility rates, the aging population has now become a major policy concern in Taiwan. The search for feasible methods for modeling the future mortality changes has become a popular issue in Taiwan. The Lee-Carter (LC) model, the reduction factor (RF) model and the age-period-cohort (APC) model are three frequently used methods for modeling future mortality dynamics. In this paper, we introduce these three models and discuss their respective pros and cons. We carry out an empirical study using these models based on Taiwan mortality experience. In addition, we make a comparison analysis of different models with different mortality experience in Japan, England and Wales, and the US.

Bayesian sequential tests for comparing the number of species in two populations

We study a problem of scquentially comparing the number of species in two populations. We consider a sequential Bayesian approach which incorporates a sampling cost and a misclassification loss, and examine optimal and sub-optimal stopping rules. The optimal stopping rule is shown to be truncated when the prior distribution of the number of species is bounded. or when the prior is unbounded and satisfies certain conditions.

The Effect of Migration on a Similarity Index

Fewster and Buckland (2001) defined a similarity index between two communities by allowing changes between sites to reduce the influence of local discrepancies. The similarity index of Fewster and Buckland is calculated to attain the maximum similarity between two communities in the presence of migration. Instead of maximizing similarity, we propose random migration to measure the similarity of two communities with two types of stochastic migration. The similarity values based on the proposed methods can be treated as the expected value of similarity under migration. We use computer simulation and empirical examples to demonstrate our approach.

Generalized two-stage bandit problem

Two treatments which yield dichotomous outcomes are available for use in a clinical trial with a two-stage setting. Treatments are chosen sequentially in the first stage, and a single treatment (usually the better one) must be used in the second stage. The goal is to find the optimal strategy which maximizes the expected number of successes. The optimal strategy is to choose the unknown treatment when one treatment is known and when the number of patients is known, or unknown but satisfies certain regular conditions. In this paper, we extend the previous study by assuming that both treatments are unknown but that one treatment is better known (i.e. with smaller prior variance), and explore the conditions in which the better known treatment can be omitted in the first stage.