STRETCHing HIV treatment: A Replication Study of Task Shifting in South Africa

The Streamlining Tasks & Roles to Expand Treatment and Care for HIV (STRETCH) program was developed to increase the reach of antiretroviral therapy (ART) for HIV/AIDS patients in Sub-Saharan Africa by training nurses to prescribe, initiate, and maintain ART. Fairall and colleagues conducted a cluster-randomized trial to determine the effects/impact of STRETCH on patient health outcomes in South Africa between 2008 and 2010. The purpose of our replication study is to evaluate Fairall and colleagues\u27 findings. We conducted push button and pure replication studies and measurement and estimation analyses (MEA). Our MEA validates the original findings: (1) overall, time to death did not differ between intervention (STRETCH) and control (ART) patients; (2) in a subgroup analysis of patients with CD4 counts of 201-350 cells per μL, the intervention group patients had a 30% lower risk of death than those in the control group, when controlling for baseline characteristics; (3) in a subgroup analysis of patients with CD4 counts of ≤200 cells per μL, time to death did not differ between the two groups; and (4) rates of viral suppression one year after enrollment did not differ between the intervention and control groups. This set of results have more caveats in the MEA. Although the intervention did not lead to improvements in the main outcomes, the effectiveness of STRETCH was proven to be similar to standard care while increasing the pool of prescribers, expanding their geographical range, and improving the quality of care for patients. Therefore, our analyses support the implementation of task shifting of antiretroviral therapy from doctors to trained nurses, which enhances confidence in the implementation of the intervention program and policymaking not only in South Africa but also in other developing countries that have similar circumstances

Non-Homogeneous Markov Process Models with Incomplete Observations: Application to a Dementia Disease Study

Identifying risk factors for transition rates among normal cognition, mildly cognitive impairment, dementia and death in an Alzheimer\u27s disease study is very important. It is known that transition rates among these states are strongly time dependent. While Markov process models are often used to describe these disease progressions, the literature mainly focuses on time homogeneous processes, and limited tools are available for dealing with non-homogeneity. Further, patients may choose when they want to visit the clinics, which creates informative observations. In this paper, we develop methods to deal with non-homogeneous Markov processes through time scale transformation when observation times are pre-planned with some observations missing. Maximum likelihood estimation via the EM algorithm is derived for parameter estimation. Simulation studies demonstrate that the proposed method works well under a variety of situations. An application to the AD study identifies that there is a significant increase in transition rates as a function of time. Furthermore, our models reveal that the nonignorable missing mechanism is perhaps reasonable

Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates

Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the EM algorithm give consistent estimators for model parameters when data are missing at random provided that the response model and the missing covariate model are correctly specified; but we do not need to specify the missing data mechanism. An alternative method is the weighted estimating equation which gives consistent estimators if the missing data and response models are correctly specified; but we do not need to specify the distribution of the covariates that have missing values. In this paper we develop a doubly robust estimation method for longitudinal data with missing response and missing covariate when data are missing at random. This method is appealing in that it can provide consistent estimators if either the missing data model or the missing covariate model is correctly specified. Simulation studies demonstrate that this method performs well in a variety of situations

Equity on compulsory education in China

Thesis(Master) --KDI School:Master of Public Policy,2004masterpublishedby Zhu Baojiang

Fuzzy Comprehensive Evaluation in Well Control Risk Assessment Based on AHP: A Case Study

To give a quantitative description of well control risk, a multi-layer fuzzy comprehensive evaluation based on AHP (analytic hierarchy process) is used. During the evaluation, risk factors and weight are given by Delphi method and AHP method. A multi-level and multi-factor evaluation system is built including four level-one factors of geologic uncertainty, well control equipments, techniques and crew quality, and fourteen level-two factors. Then a calculation is given with an oilfield in West China. The result shows geologic uncertainty is the primary factor leading to well control risks and the grade of well control risk is “higher risk”. The application result indicates that well control risk assessment by fuzzy comprehensive evaluation is feasible.Key words: Risk assessment; Fuzzy comprehensive evaluation; Analytic hierarchy process; Weight; Risk facto

Statistical Methods for Multi-State Analysis of Incomplete Longitudinal Data

Analyses of longitudinal categorical data are typically based on semiparametric models in which covariate effects are expressed on marginal probabilities and estimation is carried out based on generalized estimating equations (GEE). Methods based on GEE are motivated in part by the lack of tractable models for clustered categorical data. However such marginal methods may not yield fully efficient estimates, nor consistent estimates when missing data are present. In the first part of the thesis I develop a Markov model for the analysis of longitudinal categorical data which facilitates modeling marginal and conditional structures. A likelihood formulation is employed for inference, so the resulting estimators enjoy properties such as optimal efficiency and consistency, and remain consistent when data are missing at random. Simulation studies demonstrate that the proposed method performs well under a variety of situations. Application to data from a smoking prevention study illustrates the utility of the model and interpretation of covariate effects. Incomplete data often arise in many areas of research in practice. This phenomenon is common in longitudinal data on disease history of subjects. Progressive models provide a convenient framework for characterizing disease processes which arise, for example, when the state represents the degree of the irreversible damage incurred by the subject. Problems arise if the mechanism leading to the missing data is related to the response process. A naive analysis might lead to biased results and invalid inferences. The second part of this thesis begins with an investigation of progressive multi-state models for longitudinal studies with incomplete observations. Maximum likelihood estimation is carried out based on an EM algorithm, and variance estimation is provided using Louis method. In general, the maximum likelihood estimates are valid when the missing data mechanism is missing completely at random or missing at random. Here we provide likelihood based method in that the parameters are identifiable no matter what the missing data mechanism. Simulation studies demonstrate that the proposed method works well under a variety of situations. In practice, we often face data with missing values in both the response and the covariates, and sometimes there is some association between the missingness of the response and the covariate. The proper analysis of this type of data requires taking this correlation into consideration. The impact of attrition in longitudinal studies depends on the correlation between the missing response and missing covariate. Ignoring such correlation can bias the statistical inference. We have studied the proper method that incorporates the association between the missingness of the response and missing covariate through the use of inverse probability weighted generalized estimating equations. The simulation illustrates that the proposed method yields a consistent estimator, while the method that ignores the association yields an inconsistent estimator. Many analyses for longitudinal incomplete data focus on studying the impact of covariates on the mean responses. However, little attention has been directed to address the impact of missing covariates on the association parameters in clustered longitudinal studies. The last part of this thesis mainly addresses this problem. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association parameters

Study of the slippage of particle / supercritical CO2 two-phase flow

In this paper, the slippage velocity and displacement between particles and supercritical CO2 (SC-CO2) were studied to reveal the particle-SC-CO2 two-phase flow behavior. Visualization experiments were performed to directly measure the slippage velocity and displacement. Eight groups of experiments involving various pressures (7.89–10.96 MPa), temperatures (38.6–47.5 °C), particle diameters (0.3–0.85 mm), particle densities (2630 and 3120 kg/m3) and SC-CO2 flow rates (0.920–1.284 m/s) were conducted. The measured particle slippage velocities in the flowing direction were approximately 10.3% of the SC-CO2 flow rate. The measured particle slippage displacements were all at the centimeter level, which indicated that SC-CO2 had a superior particle transporting capability that was similar to those of liquids even if it had a low viscosity that was similar to those of gases. A numerical model was built, and analytic slippage calculations were performed for SC-CO2 for additional analyses. The density of SC-CO2 was found to have a greater influence on the slippage than the viscosity. Moreover, a comparison of the slippage between SC-CO2 and water showed that the particle slippage in water was constant, while the particle slippage in SC-CO2 continually accumulated at an extremely slow rate

The effect of shortening lock-in periods in telecommunication services

In this research note, we study the welfare implications of shortening the length of the lock-in period associated with triple play contracts using household level data, from a large telecommunications provider, for a period of 6 months. Using a multinomial logit model to explain consumer behavior we show that, in our setting, shortening the length of the lock-in period decreases the aggregated profit of the firms in the market more than it increases consumer surplus. This result arises because shortening the length of the lock-in period increases churn, and the costs to set up service for the consumers that churn and join a new carrier supersede the increase in the consumers' willingness to pay for service when the length of the lock-in period shortens.info:eu-repo/semantics/publishedVersio

Study on the Imprinting Status of Insulin-Like Growth Factor II (IGF-II) Gene in Villus during 6–10 Gestational Weeks

Objective. To compare the difference of imprinting status of insulin-like growth factor II (IGF-II) gene in villus between normal embryo development group and abnormal embryo development group and to investigate the relationship between karyotype and the imprinting status of IGF-II gene. Methods. A total of 85 pregnant women with singleton pregnancy were divided into two groups: one with abnormal embryo development (n = 38) and the other with normal embryo development (n = 47). Apa I polymorphism of IGF-II gene in chorionic villus was assayed with reverse transcriptase polymerase chain reaction (RT-PCR) and restriction fragment length polymorphism (RFLP). The relationship between chromosomal abnormal karyotype and IGF-II gene imprinting status was analyzed by primary cell culture and G-banding chromosomal karyotype analysis. Results. IGF-II imprinting loss rate was higher in the abnormal embryo development group than the normal embryo development group (44.7% versus 31.6%), but without significant difference (P > .05). The percentage of abnormal chromosomes of chorionic villus in the abnormal embryo development group was 42.5%, in which IGF-II imprinting loss rate reached 64.7%. No abnormal karyotypes were found in the normal embryo development group. However, there was significant difference in IGF-II imprinting loss rate between two groups (P > .05). Conclusion. During weeks 6–10 of gestation, abnormal embryonic development is correlated with chromosomal abnormalities. The imprinting status of IGF-II gene played important roles in embryonic development, and imprinting loss might be related to chromosomal abnormalities