Yang-Jin Kim

Analysis of panel count data with measurement errors in the covariates

Panel count data often occurs in clinical, industrial, and demographic studies where the subjects may experience multiple recurrences of the event of interest over time. This paper considers the regression analysis of panel count data when covariates are measured with error. The simplest method to solve this problem is the complete case method, which only analyzes subjects with complete covariates. In the context of right-censored data, Zhou and Pepe [Zhou, H. and Pepe, M.S., 1995, Auxiliary covariate data in failure time regression analysis. Biometrika, 82, 139–149] and Zhou and Wang [Zhou, H. and Wang, C.-Y., 2000, Failure time regression with continuous covariates measured with error. Journal of Royal Statistical Society Series B, 62, 657–665] proposed the estimated partial likelihood methods using discrete auxiliary covariates and continuous auxiliary covariates, respectively. In this paper, these methods are extended to panel count data and an iterative algorithm is developed, in order to estimate the baseline mean function and regression parameters. In addition, simulation studies are conducted to evaluate the proposed method.

Evaluation of Traffic Injury Prevention Programs Using Counting Process Approaches

Traumatic brain injury (TBI) is among the most devastating of injuries leading to death and disability among young people today. The major cause of TBI is motor vehicle crashes. One way to reduce the rates of such crashes and thus TBI is through prevention programs. This article analyzes a study conducted for assessing a 1-day educational traffic injury prevention program for young traffic offenders with speeding violations. The obtained data include information about traffic convictions for speeding violations on a group of 16- to 23-year-old drivers. A common method for analyzing such studies is to use simple two-sample rank tests on summary statistics. But this approach ignores the detailed conviction process information and can assess only the long-term overall effect of the program. In this article, we treat the data as recurrent event data and apply a novel approach based on counting processes to evaluate the program. Our approach makes use of the information ignored by the rank tests and allows the assessment of both short- and long-term effects of the program. The analysis results indicate that the prevention program has an effect for a short period and suggest that a long-term effect could be gained if the program is repeated.

Regression analysis of clustered interval-censored data with informative cluster size.

Interval-censored data are commonly found in studies of diseases that progress without symptoms, which require clinical evaluation for detection. Several techniques have been suggested with independent assumption. However, the assumption will not be valid if observations come from clusters. Furthermore, when the cluster size relates to response variables, commonly used methods can bring biased results. For example, in a study on lymphatic filariasis, a parasitic disease where worms make several nests in the infected persons lymphatic vessels and reside until adulthood, the response variable of interest is the nest-extinction times. As the extinction times of nests are checked by repeated ultrasound examinations, exact extinction times are not observed. Instead, data are composed of two examination points: the last examination time with living worms and the first examination time with dead worms. Furthermore, as Williamson et al. (Statistics in Medicine 2008; 27:543-555) pointed out, larger nests show a tendency for low clearance rates. This association has been denoted as an informative cluster size. To analyze the relationship between the numbers of nests and interval-censored nest-extinction times, this study proposes a joint model for the relationship between cluster size and clustered interval-censored failure data. A proportional hazard model with random effect and a mixed ordinal regression model are applied to failure times and cluster size, respectively. The joint model approach addresses both the association among failure times from the same cluster and the dependency of failure times on cluster size. Simulation studies are performed to assess the finite sample properties of the estimators and lymphatic filariasis data are analyzed as an illustration.

Regression Analysis of Bivariate Current Status Data Using a Multistate Model

We consider bivariate current status data with death which often occur in animal tumorigenicity experiments. Instead of observing exact tumor onset time, the existence of tumor is known at death time or sacrifice time. Such an incomplete data structure makes it difficult to investigate the effect of treatment on tumor onset times. Furthermore, when tumor onsets occur at two sites, information for the order of their onsets is unknown. A multistate model is applied to incorporate the sequential occurrence of events. For the inference of parameters, an EM algorithm is applied and a real NTP (National Toxicology Program) dataset is analyzed as an illustrative example.

Regression analysis of recurrent events data with incomplete observation gaps

For analyzing recurrent event data, either total time scale or gap time scale is adopted according to research interest. In particular, gap time scale is known to be more appropriate for modeling a renewal process. In this paper, we adopt gap time scale to analyze recurrent event data with repeated observation gaps which cannot be observed completely because of unknown termination times of observation gaps. In order to estimate termination times, interval-censored mechanism is applied. Simulation studies are done to compare the suggested methods with the unadjusted method ignoring incomplete observation gaps. As a real example, conviction data set with suspensions is analyzed with suggested methods.

Cure rate model with bivariate interval censored data

ABSTRACT A mixture model is proposed to analyze a bivariate interval censored data with cure rates. There exist two types of association related with bivariate failure times and bivariate cure rates, respectively. A correlation coefficient is adopted for the association of bivariate cure rates and a copula function is applied for bivariate survival times. The conditional expectation of unknown quantities attributable to interval censored data and cure rates are calculated in the E-step in ES (Expectation-Solving algorithm) and the marginal estimates and the association measures are estimated in the S-step through a two-stage procedure. A simulation study is performed to evaluate the suggested method and a real data from HIV patients is analyzed as a real data example.

Analysis of interval censored competing risk data with missing causes of failure using pseudo values approach

ABSTRACT Competing risks often occur when subjects may fail from one of several mutually exclusive causes. For example, when a patient suffering a cancer may die from other cause, we are interested in the effect of a certain covariate on the probability of dying of cancer at a certain time. Several approaches have been suggested to analyse competing risk data in the presence of complete information of failure cause. In this paper, our interest is to consider the occurrence of missing causes as well as interval censored failure time. There exist no method to discuss this problem. We applied a Klein–Andersens pseudo-value approach [Klein, JP Andersen PK. Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function. Biometrics. 2005;61:223–229] based on the estimated cumulative incidence function and a regression coefficient is estimated through a multiple imputation. We evaluate the suggested method by comparing with a complete case analysis in several simulation settings.

Analysis of interval censored failure time data with competing risk

ABSTRACT In this article, we analyze interval censored failure time data with competing risks. A new estimator for the cumulative incidence function is derived using an approximate likelihood and a test statistic to compare two samples is then obtained by extending Suns test statistic. Small sample properties of the proposed methods are examined by conducting simulations and a cohort dataset from AIDS patients is analyzed as a real example.

Regression analysis of interval censored competing risk data using a pseudo-value approach

Interval censored data often occur in an observational study where the subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are available. There are several methods to analyze interval censored failure time data (Sun, 2006). However, in the presence of competing risks, few methods have been suggested to estimate covariate effect on interval censored competing risk data. A subdistribution hazard model is a commonly used regression model because it has one-to-one correspondence with a cumulative incidence function. Alternatively, Klein and Andersen (2005) proposed a pseudo-value approach that directly uses the cumulative incidence function. In this paper, we consider an extension of the pseudo-value approach into the interval censored data to estimate regression coefficients. The pseudo-values generated from the estimated cumulative incidence function then become response variables in a generalized estimating equation. Simulation studies show that the suggested method performs well in several situations and an HIV-AIDS cohort study is analyzed as a real data example.

Nonparametric Two-Sample Tests of Longitudinal Data with Termination Events

In longitudinal data, observations of response variables occur at fixed or random time points, and can be stopped by a termination event. When comparing longitudinal data for two groups, such irregular observation behavior must be considered to yield suitable results. In this article, we propose the use of nonparametric tests based on the difference between weighted cumulative mean functions for comparing two mean functions with an adjustment for difference in the timing of termination events. We also derive the asymptotic null distributions of the test statistics and examine their small sample properties through simulations. We apply our method to data from a study of liver cirrhosis.