Empirical Bayes Approach to Controlling Familywise Error: An Application to HIV Resistance Data

Statistical challenges arise in identifying meaningful patterns and structures from high dimensional genomic data sets. Relating HIV genotype (sequence of amino acids) to phenotypic resistance presents a typical problem. When the HIV virus is under antiretroviral drug pressure, unfavorable mutations of the target genes often lead to greatly increased resistance of the virus to drugs, including drugs the virus has not been exposed to. Identification of mutation combinations and their correlation to drug resistance is critical in guiding efficient prescription of HIV drugs. The identification of a subset of codons associated with drug resistance from a set of several hundreds of codons presents a multiple testing problem. Statistical issues arising from genomic data multiple testing procedures include the choice of the null test-statistic distribution used to define cut-offs. Controlling familywise error rate implies controlling the number of false positives among true nulls. Given the large number of hypotheses to be tested, the number of true nulls is unknown. We apply two multiple testing procedures (MTPs) controlling familywise error rate: an adhoc augmented-Bonferroni method and a Empirical Bayes procedure originally proposed in van der Laan, Birkner and Hubbard(2005). Using simulations, we demonstrate that the proposed MTPs are less conservative than the traditional methods such as Bonferroni and Holm\u27s procedures. We apply the methods to HIV resistance data where we wish to identify mutations in the protease gene associated with Amprenavir resistance

Efficacy Studies of Malaria Treatments in Africa: Efficient Estimation with Missing Indicators of Failure

Efficacy studies of malaria treatments can be plagued by indeterminate outcomes for some patients. The study motivating this paper defines the outcome of interest (treatment failure) as recrudescence and for some subjects, it is unclear whether a recurrence of malaria is due to that or new infection. This results in a specific kind of missing data. The effect of missing data in causal inference problems is widely recognized. Methods that adjust for possible bias from missing data include a variety of imputation procedures (extreme case analysis, hot-deck, single and multiple imputation), inverse weighting methods, and likelihood based methods (data augmentation, EM procedures and their extensions). In this article, we focus on multiple imputation, two inverse weighting procedures (the inverse probability of censoring weighted (IPCW) and the doubly robust (DR) estimators), and a likelihood based methodology (G-computation), comparing the methods\u27 applicability to the efficient estimation of malaria treatments effects. We present results from a simulation study as well as results from a data analysis of malaria efficacy studies from Uganda

Short-Term Exposure to Tobacco Toxins Alters Expression of Multiple Proliferation Gene Markers in Primary Human Bronchial Epithelial Cell Cultures

The biological effects of only a finite number of tobacco toxins have been studied. Here, we describe exposure of cultures of human bronchial epithelial cells to low concentrations of tobacco carcinogens: nickel sulphate, benzo(b)fluoranthene, N-nitrosodiethylamine, and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). After a 24-hour exposure, EGFR was expressed in cell membrane and cytoplasm, BCL-2 was expressed only in the irregular nuclei of large atypical cells, MKI67 was expressed in nuclei with no staining in larger cells, cytoplasmic BIRC5 with stronger nuclear staining was seen in large atypical cells, and nuclear TP53 was strongly expressed in all cells. After only a 24-hour exposure, cells exhibited atypical nuclear and cytoplasmic features. After a 48-hour exposure, EGFR staining was localized to the nucleus, BCL-2 was slightly decreased in intensity, BIRC5 was localized to the cytoplasm, and TP53 staining was increased in small and large cells. BCL2L1 was expressed in both the cytoplasm and nuclei of cells at 24- and 48-hour exposures. We illustrate that short-termexposure of a bronchial epithelial cell line to smoking-equivalent concentrations of tobacco carcinogens alters the expression of key proliferation regulatory genes, EGFR, BCL-2, BCL2L1, BIRC5, TP53, and MKI67, similar to that reported in biopsy specimens of pulmonary epithelium described to be preneoplastic lesions

The critical need for pooled data on coronavirus disease 2019 in African children : an AFREhealth call for action through multicountry research collaboration

Globally, there are prevailing knowledge gaps in the epidemiology, clinical manifestations, and outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among children and adolescents; and these gaps are especially wide in African countries. The availability of robust age-disaggregated data is a critical first step in improving knowledge on disease burden and manifestations of coronavirus disease 2019 (COVID-19) among children. Furthermore, it is essential to improve understanding of SARS-CoV-2 interactions with comorbidities and coinfections such as human immunodeficiency virus (HIV), tuberculosis, malaria, sickle cell disease, and malnutrition, which are highly prevalent among children in sub-Saharan Africa. The African Forum for Research and Education in Health (AFREhealth) COVID-19 Research Collaboration on Children and Adolescents is conducting studies across Western, Central, Eastern, and Southern Africa to address existing knowledge gaps. This consortium is expected to generate key evidence to inform clinical practice and public health policy-making for COVID-19 while concurrently addressing other major diseases affecting children in African countries.The US National Institutes of Health (NIH)/ Fogarty International Centre (FIC) to the African Forum for Research and Education in Health (AFREhealth).https://academic.oup.com/cidam2022Paediatrics and Child Healt