Getachew A. Dagne

Adaptive Designs for Randomized Trials in Public Health

In this article, we present a discussion of two general ways in which the traditional randomized trial can be modified or adapted in response to the data being collected. We use the term adaptive design to refer to a trial in which characteristics of the study itself, such as the proportion assigned to active intervention versus control, change during the trial in response to data being collected. The term adaptive sequence of trials refers to a decision-making process that fundamentally informs the conceptualization and conduct of each new trial with the results of previous trials. Our discussion below investigates the utility of these two types of adaptations for public health evaluations. Examples are provided to illustrate how adaptation can be used in practice. From these case studies, we discuss whether such evaluations can or should be analyzed as if they were formal randomized trials, and we discuss practical as well as ethical issues arising in the conduct of these new-generation trials.

LIN28B Polymorphisms Influence Susceptibility to Epithelial Ovarian Cancer

Defective microRNA (miRNA) biogenesis contributes to the development and progression of epithelial ovarian cancer (EOC). In this study, we examined the hypothesis that single nucleotide polymorphisms (SNP) in miRNA biogenesis genes may influence EOC risk. In an initial investigation, 318 SNPs in 18 genes were evaluated among 1,815 EOC cases and 1,900 controls, followed up by a replicative joint meta-analysis of data from an additional 2,172 cases and 3,052 controls. Of 23 SNPs from 9 genes associated with risk (empirical P < 0.05) in the initial investigation, the meta-analysis replicated 6 SNPs from the DROSHA, FMR1, LIN28, and LIN28B genes, including rs12194974 (G>A), an SNP in a putative transcription factor binding site in the LIN28B promoter region (summary OR = 0.90, 95% CI: 0.82-0.98; P = 0.015) which has been recently implicated in age of menarche and other phenotypes. Consistent with reports that LIN28B overexpression in EOC contributes to tumorigenesis by repressing tumor suppressor let-7 expression, we provide data from luciferase reporter assays and quantitative RT-PCR to suggest that the inverse association among rs12194974 A allele carriers may be because of reduced LIN28B expression. Our findings suggest that variants in LIN28B and possibly other miRNA biogenesis genes may influence EOC susceptibility.

The Role of Behavior Observation in Measurement Systems for Randomized Prevention Trials

The role of behavior observation in theory-driven prevention intervention trials is examined. A model is presented to guide choice of strategies for the measurement of five core elements in theoretically informed, randomized prevention trials: (1) training intervention agents, (2) delivery of key intervention conditions by intervention agents, (3) responses of clients to intervention conditions, (4) short-term risk reduction in targeted client behaviors, and (5) long-term change in client adjustment. It is argued that the social processes typically thought to mediate interventionist training (Element 1) and the efficacy of psychosocial interventions (Elements 2 and 3) may be powerfully captured by behavior observation. It is also argued that behavior observation has advantages in the measurement of short-term change (Element 4) engendered by intervention, including sensitivity to behavior change and blinding to intervention status.

A Bayesian Approach to Joint Mixed-Effects Models with a Skew-Normal Distribution and Measurement Errors in Covariates

In recent years, nonlinear mixed-effects (NLME) models have been proposed for modeling complex longitudinal data. Covariates are usually introduced in the models to partially explain intersubject variations. However, one often assumes that both model random error and random effects are normally distributed, which may not always give reliable results if the data exhibit skewness. Moreover, some covariates such as CD4 cell count may be often measured with substantial errors. In this article, we address these issues simultaneously by jointly modeling the response and covariate processes using a Bayesian approach to NLME models with covariate measurement errors and a skew-normal distribution. A real data example is offered to illustrate the methodologies by comparing various potential models with different distribution specifications. It is showed that the models with skew-normality assumption may provide more reasonable results if the data exhibit skewness and the results may be important for HIV/AIDS studies in providing quantitative guidance to better understand the virologic responses to antiretroviral treatment.

Prognostic Performance of Metabolic Indexes in Predicting Onset of Type 1 Diabetes

OBJECTIVE In this investigation we evaluated nine metabolic indexes from intravenous glucose tolerance tests (IVGTTs) and oral glucose tolerance tests (OGTTs) in an effort to determine their prognostic performance in predicting the development of type 1 diabetes in those with moderate risk, as defined by familial relation to a type 1 diabetic individual, a positive test for islet cell antibodies and insulin autoantibody, but normal glucose tolerance. RESEARCH DESIGN AND METHODS Subjects (n = 186) who had a projected risk of 25–50% for developing type 1 diabetes within 5 years were followed until clinical diabetes onset or the end of the study as part of the Diabetes Prevention Trial–Type 1. Prognostic performance of the metabolic indexes was determined using receiver operating characteristic (ROC) curve and survival analyses. RESULTS Two-hour glucose from an OGTT most accurately predicted progression to disease compared with all other metabolic indicators with an area under the ROC curve of 0.67 (95% CI 0.59–0.76), closely followed by the ratio of first-phase insulin response (FPIR) to homeostasis model assessment of insulin resistance (HOMA-IR) with an area under the curve value of 0.66. The optimal cutoff value for 2-h glucose (114 mg/dl) maintained sensitivity and specificity values >0.60. The hazard ratio for those with 2-h glucose ≥114 mg/dl compared with those with 2-h glucose <114 mg/dl was 2.96 (1.67–5.22). CONCLUSIONS The ratio of FPIR to HOMA-IR from an IVGTT provided accuracy in predicting the development of type 1 diabetes similar to that of 2-h glucose from an OGTT, which, because of its lower cost, is preferred. The optimal cutoff value determined for 2-h glucose provides additional guidance for clinicians to identify subjects for potential prevention treatments before the onset of impaired glucose tolerance.

Inherited Variants in Mitochondrial Biogenesis Genes May Influence Epithelial Ovarian Cancer Risk

Background: Mitochondria contribute to oxidative stress, a phenomenon implicated in ovarian carcinogenesis. We hypothesized that inherited variants in mitochondrial-related genes influence epithelial ovarian cancer (EOC) susceptibility. Methods: Through a multicenter study of 1,815 Caucasian EOC cases and 1,900 controls, we investigated associations between EOC risk and 128 single nucleotide polymorphisms (SNPs) from 22 genes/regions within the mitochondrial genome (mtDNA) and 2,839 nuclear-encoded SNPs localized to 138 genes involved in mitochondrial biogenesis (BIO, n = 35), steroid hormone metabolism (HOR, n = 13), and oxidative phosphorylation (OXP, n = 90) pathways. Unconditional logistic regression was used to estimate OR and 95% CI between genotype and case status. Overall significance of each gene and pathway was evaluated by using Fishers method to combine SNP-level evidence. At the SNP level, we investigated whether lifetime ovulation, hormone replacement therapy (HRT), and cigarette smoking were confounders or modifiers of associations. Results: Interindividual variation involving BIO was most strongly associated with EOC risk (empirical P = 0.050), especially for NRF1, MTERF, PPARGC1A, ESRRA, and CAMK2D. Several SNP-level associations strengthened after adjustment for nongenetic factors, particularly for MTERF. Statistical interactions with cigarette smoking and HRT use were observed with MTERF and CAMK2D SNPs, respectively. Overall variation within mtDNA, HOR, and OXP was not statistically significant (empirical P > 0.10). Conclusion: We provide novel evidence to suggest that variants in mitochondrial biogenesis genes may influence EOC susceptibility. Impact: A deeper understanding of the complex mechanisms implicated in mitochondrial biogenesis and oxidative stress may aid in developing strategies to reduce morbidity and mortality from EOC. Cancer Epidemiol Biomarkers Prev; 20(6); 1131–45. ©2011 AACR.

Effect of combining mosquito repellent and insecticide treated net on malaria prevalence in Southern Ethiopia: a cluster-randomised trial

BackgroundA mosquito repellent has the potential to prevent malaria infection, but there has been few studies demonstrating the effectiveness of combining this strategy with the highly effective long-lasting insecticidal nets (LLINs). This study aimed to determine the effect of combining community-based mosquito repellent with LLINs in the reduction of malaria.MethodsA community-based clustered-randomised trial was conducted in 16 rural villages with 1,235 households in southern Ethiopia between September and December of 2008. The villages were randomly assigned to intervention (mosquito repellent and LLINs, eight villages) and control (LLINs alone, eight villages) groups. Households in the intervention villages received mosquito repellent (i.e., Buzz-Off® petroleum jelly, essential oil blend) applied every evening. The baseline survey was followed by two follow-up surveys, at one month interval. The primary outcome was detection of Plasmodium falciparum, Plasmodium vivax, or both parasites, through microscopic examination of blood slides. Analysis was by intention to treat. Baseline imbalances and clustering at individual, household and village levels were adjusted using a generalized linear mixed model.Results3,078 individuals in intervention and 3,004 in control group were enrolled into the study. Compared with the control arm, the combined use of mosquito repellent and LLINs significantly reduced malaria infection of all types over time [adjusted Odds Ratio (aOR) = 0.66; 95% CI = 0.45-0.97]. Similarly, a substantial reduction in P. falciparum malaria infection during the follow-up surveys was observed in the intervention group (aOR = 0.53, 95% CI = 0.31-0.89). The protective efficacy of using mosquito repellent and LLINs against malaria infection of both P. falciparum/P. vivax and P. falciparum was 34% and 47%, respectively.ConclusionsDaily application of mosquito repellent during the evening followed by the use of LLINs during bedtime at community level has significantly reduced malaria infection. The finding has strong implication particularly in areas where malaria vectors feed mainly in the evening before bedtime.Trial registrationClinicalTrials.gov identifier: NCT01160809.

Bayesian inference on joint models of HIV dynamics for time‐to‐event and longitudinal data with skewness and covariate measurement errors

Normality (symmetry) of the model random errors is a routine assumption for mixed-effects models in many longitudinal studies, but it may be unrealistically obscuring important features of subject variations. Covariates are usually introduced in the models to partially explain inter-subject variations, but some covariates such as CD4 cell count may be often measured with substantial errors. This paper formulates a class of models in general forms that considers model errors to have skew-normal distributions for a joint behavior of longitudinal dynamic processes and time-to-event process of interest. For estimating model parameters, we propose a Bayesian approach to jointly model three components (response, covariate, and time-to-event processes) linked through the random effects that characterize the underlying individual-specific longitudinal processes. We discuss in detail special cases of the model class, which are offered to jointly model HIV dynamic response in the presence of CD4 covariate process with measurement errors and time to decrease in CD4/CD8 ratio, to provide a tool to assess antiretroviral treatment and to monitor disease progression. We illustrate the proposed methods using the data from a clinical trial study of HIV treatment. The findings from this research suggest that the joint models with a skew-normal distribution may provide more reliable and robust results if the data exhibit skewness, and particularly the results may be important for HIV/AIDS studies in providing quantitative guidance to better understand the virologic responses to antiretroviral treatment.

Within-Group Differences Between Native-Born and Foreign-Born Black Men on Prostate Cancer Risk Reduction and Early Detection Practices

To better address prostate cancer disparities, we investigated the differences among US-born, African-born, and Caribbean-born Black men on prostate cancer risk reduction and early detection behaviors. Data were collected from over 3,400 Black men in five cities in Florida. One-way analysis of variance was used to explore the ethnic variations among the three study groups. We found that there were significant differences among the three groups. The US-born Black men had the highest knowledge, were most likely to have health insurance, and consume the most meat compared to African-born, and Caribbean-born Black men. African-born Black men were most likely to use chemoprevention products and discuss prostate cancer risk-reduction and early detection with a physician. Given the significant number of foreign-born Blacks in the US, it is important to disaggregate the data of US-born and foreign-born Blacks to develop effective programs and policies to address the needs of each group.

Multilevel methods for modeling observed sequences of family interaction

Observation of interaction plays a central role in family research. This article discusses how to analyze sequential data generated by discrete microcoding methods to test hypotheses about family interaction. Current methods for studying sequential data are presented, and their limits are discussed. Building on recent applications of contingency table analysis to such data, a multilevel log-linear model is presented that can specify and estimate indicators of individual behavioral tendencies and antecedent-consequent relationships among behaviors, both within and across samples of families. An example of this method is presented using data from a study of couples facing job loss. Potential extensions of this framework for future research are discussed.