Application of the disposition model to breast cancer data

In this paper, we have presented the second level nesting of Bonney's disposition model (Bonney, 1998) and examined the implications of higher level nesting of the disposition model in relation to the dimension of the parameter space. We have also compared the performance of the disposition model with Cox's regression model (Cox, 1972). It has been observed that the disposition model has a very large number of unknown parameters, and is therefore limited by the method of estimation used. In the case of the maximum likelihood method, reasonable estimates are obtained if the number of parameters in the model is at most nine. This corresponds to about four to seven covariates. Since each covariate in Cox's model provides a parameter, it is possible to include more covariates in the regression analysis. On the other hand, as opposed to Cox's model, the disposition model is fitted with parameters to capture aggregation in families, if there should be any. The choice of a particular model should therefore depend on the available data set and the purpose of the statistical analysis. --Second level nesting,Proportional hazards model,Quadratic exponential form,Partial likelihood,Familial aggregation,Second-order methods,Marginal models,Conditional models

Which strategy is better for linkage analysis: single-nucleotide polymorphisms or microsatellites? Evaluation by identity-by-state – identity-by-descent transformation affected sib-pair method on GAW14 data

The central issue for Genetic Analysis Workshop 14 (GAW14) is the question, which is the better strategy for linkage analysis, the use of single-nucleotide polymorphisms (SNPs) or microsatellite markers? To answer this question we analyzed the simulated data using Duffy's SIB-PAIR program, which can incorporate parental genotypes, and our identity-by-state – identity-by-descent (IBS-IBD) transformation method of affected sib-pair linkage analysis which uses the matrix transformation between IBS and IBD. The advantages of our method are as follows: the assumption of Hardy-Weinberg equilibrium is not necessary; the parental genotype information maybe all unknown; both IBS and its related IBD transformation can be used in the linkage analysis; the determinant of the IBS-IBD transformation matrix provides a quantitative measure of the quality of the marker in linkage analysis. With the originally distributed simulated data, we found that 1) for microsatellite markers there are virtually no differences in types I and II error rates when parental genotypes were or were not used; 2) on average, a microsatellite marker has more power than a SNP marker does in linkage detection; 3) if parental genotype information is used, SNP markers show lower type I error rates than microsatellite markers; and 4) if parental genotypes are not available, SNP markers show considerable variation in type I error rates for different methods

COVID-19 pandemic: ten research questions Africa must answer for itself

The COVID-19 pandemic is spreading through Africa and governments are making frantic efforts to control spread, hospitalizations and deaths. While control measures are being taken, research into the biomedical and socio-cultural aspects of the pandemic, relevant to the African population, should not be ignored. It should not be assumed that research performed in Asian, American and European populations will always be applicable to Africa. Rather, research should be done in Africa to answer questions peculiar to the epidemic on the continent and help inform international guidelines. National guidelines for treatment and prevention, patient recoveries and discharge, and public health control measures should be based on research performed in the appropriate context for them to be effective and robust. Urgent research is needed in viral immunology and shedding, treatment and prevention trials, protection of healthcare personnel, and antimicrobial use among others. In this article, we propose ten research questions that when answered in a timely manner by scientists in Africa, will enhance Africa’s response to the pandemic

Are Fc gamma receptor polymorphisms important in HIV-1 infection outcomes and latent reservoir size?

Fc gamma receptors (FcγR) are cell surface glycoproteins which trigger specific effector-cell responses when cross-linked with the Fc portions of immunoglobulin (IgG) antibodies. During HIV-1 infection, the course of disease progression, ART response, and viral reservoir size vary in different individuals. Several factors may account for these differences; however, Fc gamma receptor gene polymorphisms, which influence receptor binding to IgG antibodies, are likely to play a key role. FcγRIIa (CD32) was recently reported as a potential marker for latent HIV reservoir, however, this assertion is still inconclusive. Whether FcγR polymorphisms influence the size of the viral reservoir, remains an important question in HIV cure studies. In addition, potential cure or viral suppression methods such as broadly neutralizing antibody (bNAbs) may depend on FcγRs to control the virus. Here, we discuss the current evidence on the potential role played by FcγR polymorphisms in HIV-1 infection, treatment and vaccine trial outcomes. Importantly, we highlight contrasting findings that may be due to multiple factors and the relatively limited data from African populations. We recommend further studies especially in sub-Saharan Africa to confirm the role of FcγRIIa in the establishment of latent reservoir and to determine their influence in therapies involving bNAbs

Application of the Disposition Model to Breast Cancer Data

In this paper, we have presented the second level nesting of Bonney’s disposition model (Bonney, 1998) and examined the implications of higher level nesting of the disposition model in relation to the dimension of the parameter space. We have also compared the performance of the disposition model with Cox’s regression model (Cox, 1972). It has been observed that the disposition model has a very large number of unknown parameters, and is therefore limited by the method of estimation used. In the case of the maximum likelihood method, reasonable estimates are obtained if the number of parameters in the model is at most nine. This corresponds to about four to seven covariates. Since each covariate in Cox’s model provides a parameter, it is possible to include more covariates in the regression analysis. On the other hand, as opposed to Cox’s model, the disposition model is fitted with parameters to capture aggregation in families, if there should be any. The choice of a particular model should therefore depend on the available data set and the purpose of the statistical analysis

Gene Copy Number Analysis for Family Data Using Semiparametric Copula Model

Gene copy number changes are common characteristics of many genetic disorders. A new technology, array comparative genomic hybridization (a-CGH), is widely used today to screen for gains and losses in cancers and other genetic diseases with high resolution at the genome level or for specific chromosomal region. Statistical methods for analyzing such a-CGH data have been developed. However, most of the existing methods are for unrelated individual data and the results from them provide explanation for horizontal variations in copy number changes. It is potentially meaningful to develop a statistical method that will allow for the analysis of family data to investigate the vertical kinship effects as well. Here we consider a semiparametric model based on clustering method in which the marginal distributions are estimated nonparametrically, and the familial dependence structure is modeled by copula. The model is illustrated and evaluated using simulated data. Our results show that the proposed method is more robust than the commonly used multivariate normal model. Finally, we demonstrated the utility of our method using a real dataset

HIV cure strategies: Which ones are appropriate for Africa?

Although combination antiretroviral therapy (ART) has reduced mortality and improved lifespan for people living with HIV, it does not provide a cure. Patients must be on ART for the rest of their lives and contend with side effects, unsustainable costs, and the development of drug resistance. A cure for HIV is, therefore, warranted to avoid the limitations of the current therapy and restore full health. However, this cure is difficult to find due to the persistence of latently infected HIV cellular reservoirs during suppressive ART. Approaches to HIV cure being investigated include boosting the host immune system, genetic approaches to disable co-receptors and the viral genome, purging cells harboring latent HIV with latency-reversing latency agents (LRAs) (shock and kill), intensifying ART as a cure, preventing replication of latent proviruses (block and lock) and boosting T cell turnover to reduce HIV-1 reservoirs (rinse and replace). Since most people living with HIV are in Africa, methods being developed for a cure must be amenable to clinical trials and deployment on the continent. This review discusses the current approaches to HIV cure and comments on their appropriateness for Africa

Correlated Weibull regression model for multivariate binary data

The correlated Weibull regression model for the analysis of correlated binary data is presented. This regression model is based on Bonney’s disposition model for the regression analysis of correlated binary outcomes. Parameter estimation was done through the maximum likelihood method. The correlated Weibull regression model was contrasted with the correlated logistic regression model. The results showed that both regression models were useful in explaining the familial aggregation of oesophageal cancer. The correlated logistic regression model fitted the oesophageal cancer data better than the correlated Weibull regression model for both the non-nested and nested cases. Furthermore, the correlated logistic regression model was computationally more attractive than the correlated Weibull regression model