A Bayesian hierarchical model for correlation in microarray studies

Paper presented at Strathmore International Math Research Conference on July 23 - 27, 2012Microarrays are miniaturised biological devices consisting of molecules (e.g. DNA or protein), called \probes", that are orderly arranged at a microscopic scale onto a solid support such as a nylon membrane or a glass slide.The array elements (probes) bind specically to labeled molecules, called "targets", into complex molecular mixtures,thereby generating signals that reveal the identity and the concentration of the interacting labeled cells.Microarray analysis has a broad range of applications that involve dierent types of probes and/or targets (cDNA or oligos).Microarrays are miniaturised biological devices consisting of molecules (e.g. DNA or protein), called \probes", that are orderly arranged at a microscopic scale onto a solid support such as a nylon membrane or a glass slide.The array elements (probes) bind speci cally to labeled molecules, called "targets", into complex molecular mixtures,thereby generating signals that reveal the identity and the concentration of the interacting labeled cells.Microarray analysis has a broad range of applications that involve di erent types of probes and/or targets (cDNA or oligos

A Smooth Test of Goodness-of-Fit for the Weibull Distribution: An Application to an HIV Retention Data

In this study, we fit the two-parameter Weibull distribution to an HIV retention data and assess the fit using a smooth test of goodness-of-fit. The smooth test described here is a score test and is derived as an extension of the Neyman's smooth test. Simulations are conducted to compare the power of the smooth test with the power of each of three empirical goodness-of-fit tests for the Weibull distribution. Results show that the smooth tests of order three and four are more powerful than the three empirical goodness-of-fit tests. For validation, we used retention data from an HIV care setting in Kenya

A Smooth Test of Goodness-of-Fit for the Baseline Hazard Function for Time-to-First Occurrence in Recurrent Events: An Application to HIV Retention Data

Motivated by HIV retention, we present an application of the smooth test of goodness-of-fit under right-censoring to time to first occurrence of a recurrent event. The smooth test applied here is an extension of Neyman's smooth test to a class of hazard functions for the initial distribution of a recurrent failure-time event. We estimate the baseline hazard function of time-to-first loss to follow-up, using a Block, Borges and Savits (BBS) minimal repair model of the data (n = 2,987,72% censored). Simulations were conducted at various percentages of censoring to assess the performance of the smooth test. Results show that the smooth test performed well under right-censoring

Risk for Cardiovascular Disease in Blacks with HIV/AIDS in America: A Systematic Review and Meta-analysis

Cardiovascular disease (CVD) related to HIV infection is becoming a major public health concern in the United Stated. Epidemiologic studies show that prolonged use of Highly Active Antiretroviral Therapy, HIV/AIDS itself, and a combination of traditional vascular risk factors increase the risk for CVD among people with HIV/AIDS. However, little is known about any racial disparities in the risk for CVD in the HIV/AIDS population. We conducted a systematic review and meta-analysis of literature on HIV/AIDS and CVD (June 1, 2010-July 31, 2014) through MEDLINE to examine whether and how HIV-positive African Americans are disproportionately affected by CVD compared to their white counterparts. The corrected pooled effect from the eligible studies was 1.26 (95% confidence interval 1.22-1.30). Blacks living with HIV/AIDS have higher risk for CVD than non-Hispanic whites. The findings of this study provide an important basis for prevention efforts as well as recommendations for addressing the existing racial disparities in the risk for CVD among people living with HIV/AIDS

Validation of the Smooth Test of Goodness-of-Fit for Proportional Hazards in Cancer Survival Studies

In this study, we validate the smooth test of goodness-of-fit for the proportionality of the hazard function in the two-sample problem in cancer survival studies. The smooth test considered here is an extension of Neyman's smooth test for proportional hazard functions. Simulations are conducted to compare the performance of the smooth test, the data-driven smooth test, the Kolmogorov-Smirnov proportional hazards test and the global test, in terms of power. Eight real cancer datasets from different settings are assessed for the proportional hazard assumption in the Cox proportional hazard models, for validation. The smooth test performed best and is independent of the number of covariates in the Cox proportional hazard models

Cautions of Using Allele-Based Tests Under Heterosis

Abstract: In genetic studies, heterotic effects are commonly assessed as dominant, additive, or recessive effects for a given genetic marker. However, the distorting effect of heterosis on statistical tests is non-trivial. An inheritance model needs to be carefully chosen to achieve highest testing power. We assess this through simulations via allele- and genotype-based tests. Chi-square test statistics for different inheritance models are formulated as a function of relative risks and allele frequencies. The results indicate that testing power from the commonly used allele-based tests can be substantially diminished by heterosis. Assessing the existence of heterosis is thus recommended to avoid false negative findings

Evaluation of Methods for Gene Selection in Melanoma Cell Lines

A major objective in microarray experiments is to identify a panel of genes that are associated with a disease outcome or trait. Many statistical methods have been proposed for gene selection within the last fifteen years. While the comparison of some of these methods has been done, most of them concentrated on finding gene signatures based on two groups. This study evaluates four gene selection methods when the outcome of interested is continuous in nature. We provide a comparative review of four methods: the Statistical Analysis of Microarrays (SAM), the Linear Models for Microarray Analysis (LIMMA), the Lassoed Principal Components (LPC), and the Quantitative Trait Analysis (QTA). Comparison is based on the power to identify differentially expressed genes, the predictive ability of the genelists for a continuous outcome (G2 checkpoint function), and the prognostic properties of the genelists for distant metastasis-free survival. A simulated dataset and a publicly available melanoma cell lines dataset are used for simulations and validation, respectively. A primary melanoma dataset is used for assessment of prognosis. No common genes were found among the genelists from the four methods. While the SAM was generally the best in terms of power, the QTA genelist performed the best in the prediction of the G2 checkpoint function. Identification of genelists depends on the choice of the gene selection method. The QTA method would be preferred over the other approaches in predicting a quantitative outcome in melanoma research. We recommend the development of more robust statistical methods for differential gene expression analysis

Adverse Event Risk Assessment on Patients Receiving Combination Antiretroviral Therapy in South Africa

Purpose: To determine the risk factors for the development of serious adverse events (AEs) in black adult patients on combination antiretroviral therapy (cART). Methods: This prospective cohort study consisted of 368 adult black HIV positive patients receiving cART at the Grey's Hospital, KwaZulu-Natal, South Africa. Patients were intensively monitored for incidence of adverse events and the factors associated with their development, under the Antiretroviral Cohort Adverse Event Monitoring in KwaZulu-Natal (ACADEMIK). Multiple logistic regression models were used to identify the risk factors for AEs. Results: A total of 406 AEs were reported across the 13 patient hospital visits in the study. Peripheral neuropathy was the most prevalent adverse event (16%), followed by hypercholesterolaemia (14%), lipoatrophy/lipodystrophy (13%) and skin reaction (11%). Cluster differentiation (CD4) counts (p = 0.0280), age (p = 0.0227) and weight (p = 0.0017) were identified as the significant predictors for hypercholesterolaemia, while sex (p = 0.0309) was significant with respect to skin reaction. CD4 counts (p=0.0200) was also significant for lipoatrophy/lipodystrophy. Skin reaction (23%), diarrhea (18%), hypercholesterolaemia (15%), thrombocytopenia (15%) and peripheral neuropathy (13%) were the top five most incident AEs. Overall, about 46% of the regimens administered were tenofovir-based and 31% zidovudine-based. Conclusions: To enhance the prevention of hypercholesterolaemia, lipoatrophy/lipodystrophy and skin reaction among black adult HIV positive patients on cART, we recommend that CD4 counts and weight be closely monitored and documented during clinic visits

A smooth test of goodness-of-fit for the baseline hazard function in recurrent event models

Conference paperIn this paper, we formulate a smooth test of goodness-of-fit for a simple hypothesis about the baseline hazard function in recurrent-event models. The formulation is an extension of Neyman' s goodness-of-fit approach, whose score tests are obtained by embedding the null hypothesis in a larger class of hazard rate functions. Since the application is in recurrent event models , the data is dynamic.A useful feature about this test is the parametric approach that makes inference about the hazard function more efficient. To examine the finite-sample properties of this test, we used simulated data . For validation, we applied the test to a real-life recurrent event data. Results show that the test possesses better power over wide range of alternatives, when compared with similar tests of the chi-square type in the literature.In this paper, we formulate a smooth test of goodness-of-fit for a simple hypothesis about the baseline hazard function in recurrent-event models. The formulation is an extension of Neyman' s goodness-of-fit approach, whose score tests are obtained by embedding the null hypothesis in a larger class of hazard rate functions. Since the application is in recurrent event models , the data is dynamic.A useful feature about this test is the parametric approach that makes inference about the hazard function more efficient. To examine the finite-sample properties of this test, we used simulated data . For validation, we applied the test to a real-life recurrent event data. Results show that the test possesses better power over wide range of alternatives, when compared with similar tests of the chi-square type in the literature

A Generalized Class of Kumaraswamy Lindley Distribution with Applications to Lifetime Data

In this paper, we propose a new class of generalized distributions called the Exponentiated Kumaraswamy Lindley (EKL) distribution, as well as related sub-distributions. This class of distributions contains the Kumaraswamy Lindley (KL), generalized Lindley (GL), and Lindley (L) distributions as special cases. A series expansion of the density is obtained. Statistical properties of this class of distributions, including the hazard and reverse hazard functions, monotonicity property, shapes, moments, reliability, quantile function, mean deviations, Bonferroni and Lorenz curves, entropy and Fisher information are derived among others. The method of maximum likelihood is adopted for estimating the model parameters. Two applications to real data sets demonstrate the usefulness and importance of the proposed distribution