Longitudinal categorical data analysis: A continuous -time Markov chain approach

In this dissertation, we propose a continuous-time Markov chain model to examine the longitudinal data that have three categories in the outcome variable. The advantage of this model is that it permits a different number of measurements for each subject and the duration between two consecutive time points of measurements can be irregular. Using the maximum likelihood principle, we can estimate the transition probability between two time points. By using the information provided by the independent variables, this model can also estimate the transition probability for each subject. The Monte Carlo simulation method will be used to investigate the goodness of model fitting compared with that obtained from other models. A public health example will be used to demonstrate the application of this method. ^

Hybrid stochastic simulations of intracellular reaction-diffusion systems.

With the observation that stochasticity is important in biological systems, chemical kinetics have begun to receive wider interest. While the use of Monte Carlo discrete event simulations most accurately capture the variability of molecular species, they become computationally costly for complex reaction-diffusion systems with large populations of molecules. On the other hand, continuous time models are computationally efficient but they fail to capture any variability in the molecular species. In this study a hybrid stochastic approach is introduced for simulating reaction-diffusion systems. We developed an adaptive partitioning strategy in which processes with high frequency are simulated with deterministic rate-based equations, and those with low frequency using the exact stochastic algorithm of Gillespie. Therefore the stochastic behavior of cellular pathways is preserved while being able to apply it to large populations of molecules. We describe our method and demonstrate its accuracy and efficiency compared with the Gillespie algorithm for two different systems. First, a model of intracellular viral kinetics with two steady states and second, a compartmental model of the postsynaptic spine head for studying the dynamics of Ca+2 and NMDA receptors.

A Time-Motion Study of Registered Nurses’ Workflow in Intensive Care Unit Remote Monitoring

Utilizing advanced information technology, Intensive Care Unit (ICU) remote monitoring allows highly trained specialists to oversee a large number of patients at multiple sites on a continuous basis. In the current research, we conducted a time-motion study of registered nurses’ work in an ICU remote monitoring facility. Data were collected on seven nurses through 40 hours of observation. The results showed that nurses’ essential tasks were centered on three themes: monitoring patients, maintaining patients’ health records, and managing technology use. In monitoring patients, nurses spent 52% of the time assimilating information embedded in a clinical information system and 15% on monitoring live vitals. System-generated alerts frequently interrupted nurses in their task performance and redirected them to manage suddenly appearing events. These findings provide insight into nurses’ workflow in a new, technology-driven critical care setting and have important implications for system design, work engineering, and personnel selection and training.

Effect of continuous gastric enteral feeding on tonometric measurement of gastric intramucosal carbon dioxide levels in healthy volunteers

Purpose. The aim of this research was to evaluate the effect of enteral feeding on tonometric measurement of gastric regional carbon dioxide levels (PrCO2) in normal healthy volunteers. Design and methods. The sample included 12 healthy volunteers recruited by the University Clinical Research Center (UCRC). An air tonometry system monitored PrCO2 levels using a tonometer placed in the lumen of the stomach via orogastric intubation. PrCO2 was automatically measured and recorded every 10 minutes throughout the five hour study period. An oral dose of famotidine 40 mg was self-administered the evening prior to and the morning of the study. Instillation of Isocal® High Nitrogen (HN) was used for enteral feeding in hourly escalating doses of 0, 40, 60, and 80 ml/hr with no feeding during the fifth hour. Results . PrCO2 measurements at time 0 and 10 minutes (41.4 ± 6.5 and 41.8 ± 5.7, respectively) demonstrated biologic precision (Levene's Test statistic = 0.085, p-value 0.774). Biologic precision was lost between T130 and T140 40 when compared to baseline TO (Levene's Test statistic = 1.70, p-value 0.205; and 3.205, p-value 0.042, respectively) and returned to non-significant levels between T270 and T280 (Levene's Test statistic = 3.083, p-value 0.043; and 2.307, p-value 0.143, respectively). Isocal® HN significantly affected the biologic accuracy of PrCO2 measurements (repeated measures ANOVA F 4.91, p-value <0.001). After 20 minutes of enteral feeding at 40 ml/hr, PrCO2 significantly increased (41.4 ± 6.5 to 46.6 ± 4.25, F = 5.4, p-value 0.029). Maximum variance from baseline (41.4 ± 6.5 to 61.3 ± 15.2, F = 17.22, p-value <0.001) was noted after 30 minutes of Isocal® HN at 80 ml/hr or 210 minutes from baseline. The significant elevations in PrCO2 continued throughout the study. Sixty minutes after discontinuation of enteral feeding, PrCO2 remained significantly elevated from baseline (41.4 ± 6.5 to 51.8 ± 9.2, F = 10.15, p-value 0.004). Conclusion. Enteral feeding with Isocal® HN significantly affects the precision and accuracy of PrCO2 measurements in healthy volunteers. ^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Age -period -cohort analysis: An extension of Cox's lifetable regression with time -dependent covariates

It is well known that an identification problem exists in the analysis of age-period-cohort data because of the relationship among the three factors (date of birth + age at death = date of death). There are numerous suggestions about how to analyze the data. No one solution has been satisfactory. The purpose of this study is to provide another analytic method by extending the Cox's lifetable regression model with time-dependent covariates. The new approach contains the following features: (1) It is based on the conditional maximum likelihood procedure using a proportional hazard function described by Cox (1972), treating the age factor as the underlying hazard to estimate the parameters for the cohort and period factors. (2) The model is flexible so that both the cohort and period factors can be treated as dummy or continuous variables, and the parameter estimations can be obtained for numerous combinations of variables as in a regression analysis. (3) The model is applicable even when the time period is unequally spaced.^ Two specific models are considered to illustrate the new approach and applied to the U.S. prostate cancer data. We find that there are significant differences between all cohorts and there is a significant period effect for both whites and nonwhites. The underlying hazard increases exponentially with age indicating that old people have much higher risk than young people. A log transformation of relative risk shows that the prostate cancer risk declined in recent cohorts for both models. However, prostate cancer risk declined 5 cohorts (25 years) earlier for whites than for nonwhites under the period factor model (0 0 0 1 1 1 1). These latter results are similar to the previous study by Holford (1983).^ The new approach offers a general method to analyze the age-period-cohort data without using any arbitrary constraint in the model. ^