Shen-Ming Lee

Joint Modeling of Survival Time and Longitudinal Data with Subject-specific Changepoints in the Covariates

Joint models are frequently used in survival analysis to assess the relationship between time-to-event data and time-dependent covariates, which are measured longitudinally but often with errors. Routinely, a linear mixed-effects model is used to describe the longitudinal data process, while the survival times are assumed to follow the proportional hazards model. However, in some practical situations, individual covariate profiles may contain changepoints. In this article, we assume a two-phase polynomial random effects with subject-specific changepoint model for the longitudinal data process and the proportional hazards model for the survival times. Our main interest is in the estimation of the parameter in the hazards model. We incorporate a smooth transition function into the changepoint model for the longitudinal data and develop the corrected score and conditional score estimators, which do not require any assumption regarding the underlying distribution of the random effects or that of the changepoints. The estimators are shown to be asymptotically equivalent and their finite-sample performance is examined via simulations. The methods are applied to AIDS clinical trial data.

Semiparametric analysis of randomized response data with missing covariates in logistic regression

In this article, two semiparametric approaches are developed for analyzing randomized response data with missing covariates in logistic regression model. One of the two proposed estimators is an extension of the validation likelihood estimator of Breslow and Cain [Breslow, N.E., and Cain, K.C. 1988. Logistic regression for two-stage case-control data. Biometrika. 75, 11-20]. The other is a joint conditional likelihood estimator based on both validation and non-validation data sets. We present a large sample theory for the proposed estimators. Simulation results show that the joint conditional likelihood estimator is more efficient than the validation likelihood estimator, weighted estimator, complete-case estimator and partial likelihood estimator. We also illustrate the methods using data from a cable TV study.

A unified approach to estimating population size for a births only model

Abstract The primary goal of this paper is to estimate the population size for a births only model when the capture probabilities vary with behavior response and time (or sampling occasion). A Bayesian framework is developed from the births only models for the capture–recapture experiment. We propose a unified approach for estimating the population size on each sampling occasion for four specified models using the Gibbs sampler, a Markov chain Monte Carlo method. The proposed methodology is illustrated with a simulation study and HIV serosurveillance data of Thailand. The results show that Gibbs sampler provides a reasonable estimate of population size when compared with the classical technique.

Approximate Nonparametric Corrected-score Method for Joint Modeling of Survival and Longitudinal Data Measured with Error

We consider the problem of jointly modeling survival time and longitudinal data subject to measurement error. The survival times are modeled through the proportional hazards model and a random effects model is assumed for the longitudinal covariate process. Under this framework, we propose an approximate nonparametric corrected-score estimator for the parameter, which describes the association between the time-to-event and the longitudinal covariate. The term nonparametric refers to the fact that assumptions regarding the distribution of the random effects and that of the measurement error are unnecessary. The finite sample size performance of the approximate nonparametric corrected-score estimator is examined through simulation studies and its asymptotic properties are also developed. Furthermore, the proposed estimator and some existing estimators are applied to real data from an AIDS clinical trial.

Improved Estimation Methods for Unrelated Question Randomized Response Techniques

ABSTRACT The randomized response technique (RRT) is an important tool, commonly used to avoid biased answers in survey on sensitive issues by preserving the respondents’ privacy. In this paper, we introduce a data collection method for survey on sensitive issues combining both the unrelated-question RRT and the direct question design. The direct questioning method is utilized to obtain responses to a non sensitive question that is related to the innocuous question from the unrelated-question RRT. These responses serve as additional information that can be used to improve the estimation of the prevalence of the sensitive behavior. Furthermore, we propose two new methods for the estimation of the proportion of respondents possessing the sensitive attribute under a missing data setup. More specifically, we develop the weighted estimator and the weighted conditional likelihood estimator. The performances of our estimators are studied numerically and compared with that of an existing one. Both proposed estimators are more efficient than the Greenbergs estimator. We illustrate our methods using real data from a survey study on illegal use of cable TV service in Taiwan.

Estimation in closed capture-recapture models when covariates are missing at random.

Individual covariates are commonly used in capture-recapture models as they can provide important information for population size estimation. However, in practice, one or more covariates may be missing at random for some individuals, which can lead to unreliable inference if records with missing data are treated as missing completely at random. We show that, in general, such a naive complete-case analysis in closed capture-recapture models with some covariates missing at random underestimates the population size. We develop methods for estimating regression parameters and population size using regression calibration, inverse probability weighting, and multiple imputation without any distributional assumptions about the covariates. We show that the inverse probability weighting and multiple imputation approaches are asymptotically equivalent. We present a simulation study to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. We also illustrate an analysis using data on the bird species yellow-bellied prinia collected in Hong Kong.

Estimating survival rates using an extended Ricker's two-release method

Rickers two-release method is an easy sampling method to estimate the survival rate of animals. However, the time interval between the second release and the recapture is so short that the animals just released may exhibit different behavioral responses. A model to extend Rickers method is proposed. This model incorporates recapture information just before the second release; moreover, it shows the behavioral responses of recaptured animals. The maximum likelihood estimator is derived for the extended Rickers two-release model. The proposed method is illustrated with a simulation study as well as a real data set. The results show that the proposed method gives a more reasonable inference for the survival rate compared with the traditional Rickers estimator.

Intent to abuse addictive substances in regions with serious drug abuse among early adolescents

Abstract Background: The intentions to use addictive substances among early adolescents residing in geographic areas characterised by severe drug abuse remain poorly understood due to a lack of sufficient research attention. Methods: We assessed participants (N = 217) in grades 5 and 6 in Yunlin County, where severe drug abuse is common. Self-reported data were acquired via questionnaires. Results: Only perceived behavioural control was found to have an effect on addictive substance use intentions. The subject norms and attitude behaviours concerning addictive substances had small, but positive effects, on perceived behavioural control. Conclusion: Our findings suggest that, in high-drug abuse areas, prevention programmes aimed at early adolescents should focus on improving perceived behavioural control. Such interventions may increase adolescents’ likelihood of refraining from drug use while decreasing drug exposure channels.

Semiparametric Methods in the Proportional Odds Model for Ordinal Response Data with Missing Covariates

We consider the estimation problem of a proportional odds model with missing covariates. Based on the validation and nonvalidation data sets, we propose a joint conditional method that is an extension of Wang et al. (2002, Statistica Sinica 12, 555-574). The proposed method is semiparametric since it requires neither an additional model for the missingness mechanism, nor the specification of the conditional distribution of missing covariates given observed variables. Under the assumption that the observed covariates and the surrogate variable are categorical, we derived the large sample property. The simulation studies show that in various situations, the joint conditional method is more efficient than the conditional estimation method and weighted method. We also use a real data set that came from a survey of cable TV satisfaction to illustrate the approaches.

Modeling and analysis of different control strategies for fogging system in a subtropical greenhouse

A dynamic environment model was established on the effects of greenhouse cooling on different control strategies in a fogging system, which simulations were then conducted by Matlab. Simulation results showed that by applying a suitable set of control strategy, the indoor temperature dropped and was less than the outdoor temperature of 34.4°C The temperature reduction degree changed within limits and delayed time. Inversely, when relative humidity and time delay were used as the control factors, the indoor temperature rose under high solar irradiation. The above phenomenon can be avoided when temperature control was applied. When operations of the fogging system used the dead zone as the setting control, variations of the indoor temperature was mainly relevant to the upper and lower limits of the zone. In situations where the spraying can reach the shutoff condition, the temperature variance increased as the range was larger, and the actuation frequency fogging system also decreased. When the time delay factor was employed in the fogging system, the indoor temperature varied in terms of fogging time and stop time. In order to narrow down the variation of indoor temperature, the operation and stop time in a cycle must be shortened. In this study, simulations with the cooling effects in a greenhouse were performed by using different control strategies for a fogging system. The results can serve as reference for planning future cooling control strategies for a fogging system. Key words : Greenhouse, fogging system, control strategy.