11/04/2009 – HIV Vaccine Trails: The first, the Step and The Thai

Erin Gabriel

There have been more than 100 HIV vaccine trials since 1988. There have been 3 main types of trials and not one has shown a substantial reduction in acquisition until the Thai trial that combined two previously non-efficacious vaccine types. The two types of vaccines were tested previously in the STEP trial and the VaxGen trials. The STEP trial was stopped early due to lack of efficacy along with some evidence of harm. The long term follow-up has shown that actual harm is unlikely and I will discuss the different approaches to analyzing this challenging data. The VaxGen trial found no effect of the purely antibody vaccine. The Thai trial has caused a stir with it’s greater than 30% estimated effect. I will discuss the results and why it should give us hope for the future.

10/28/2009 – Estimation and Inference for Measures of Accuracy of a Medical Test in the Presence of Verification Bias.

Michael Sachs

Often, the accuracy of a medical test for diagnosing or predicting a binary event is measured against some definitive assessment of that binary event. Sometimes, for ethical or practical reasons, a definitive assessment of the event is not possible on all the subjects in a study sample. In such studies where not all subjects are definitively diagnosed, naive estimates of measures of accuracy may be subject to bias. This type of bias is called verification bias. The most common measure of accuracy of a medical test is the Receiver Operating Characteristic (ROC) Curve. Other measures that can also be scientifically relevant include Positive and Negative Predictive Values, covariate-specific ROC curves, covariate-adjusted ROC curves, the Predictiveness Curve, the Proportion of Explained Variation, and Total Gain. In his investigation, we propose to review these measures and consider their estimation in the presence of verification bias. Existing estimation methods are based on missing data procedures such as reweighting, imputation, and “doubly-robust” procedures that are hybrids of reweighting and imputation. We also consider a newly proposed method based on the theory of semi-parametric biased sampling models. Preliminary study suggests that the doubly-robust procedures offer the best balance between efficiency and robustness to model misspecification. Finally, we propose to investigate study design considerations and make recommendations for sample-size and whether to do a single phase or a two-phase study in situations where one would like to estimate a particular measure with a specified degree of precision.

10/21/2009 — The Stepped Wedge Design: Outstanding Issues

Tanya Granston

The stepped wedge design of cluster-randomized trials has been growing in popularity and becomes increasingly relevant due to the need to efficiently evaluate the rollout of interventions that are individually efficacious in a community setting.  The design becomes especially practical for HIV/AIDS prevention and intervention trials, as the areas in most need are in resource limited settings where it is very likely not feasible to introduce interventions all at once and where there are substantial clusters/communities/groups to provide answers regarding intervention effects.  However, there remain limitations and outstanding issues to this design, which could potentially limit its usefulness where it’s most needed, and there are interesting extensions to the design that need to be explored.

10/14/2009 — A Quasi-Likelihood Approach to Genome Wide Association Studies

Matthew Bryan

A common issue in Genome Wide Association Studies (GWAS) is relatedness between subjects due to restricting sampling to a confined homogenous population.  As a result, the genetic data can be correlated across subject which may not be accounted for by common genetic testing methods.  This presentation will discuss a method suggested by Timothy Thornton and Mary McPeek that is designed to account for relatedness between subjects for genetic data.  The method uses a quasi-likelihood model approach to generate a quasi-likelihood score statistic.  This statistic can be shown to be locally most powerful for testing genetic associations with a phenotype.  Thus the method is expected to provide higher power for detecting small genetic effects.

10/07/2009 — Glucose, Insulin, and Incident Hypertension in the Multi-Ethnic Study of Atherosclerosis

Greg Levin

Diabetes mellitus and hypertension commonly coexist, but the nature of this link is not well understood. We examined whether diabetes and higher concentrations of fasting serum glucose and insulin were associated with increased risk of developing incident hypertension in the community-based Multi-Ethnic Study of Atherosclerosis. In addition, we explored whether these associations may be mediated by kidney disease and diminished arterial elasticity. Of 3,513 participants free of hypertension at baseline, 965 (27%) developed incident hypertension over 4.7 years median follow-up. Compared to participants with baseline fasting glucose < 100 mg/dL, relative risk of hypertension was 1.16 [95% CI: 0.96, 1.40] among participants with glucose 100-125 mg/dL and 1.41 [1.17, 1.71] among participants with diabetes, adjusting for age, gender, race/ethnicity, education, physical activity, smoking, alcohol use, body mass index, and waist circumference (p=0.0015). Higher concentrations of fasting glucose and insulin within their normal ranges were also associated with increased risk of incident hypertension.  Further adjustment for serum cystatin C, urine albumin-creatinine ratio, and arterial elasticity measured by tonometry attenuated the magnitudes of these associations by approximately 50%. In conclusion, diabetes and higher concentrations of glucose and insulin were associated with increased risks of developing hypertension. The results also suggest that these associations may be mediated by damage to the kidney and arterial wall, but this hypothesis should be studied further.

In the process of carrying out this study, we encountered several challenging statistical and epidemiological questions. These included discussions about the merits of baseline adjustment in analyses of change (in the presence of measurement error), the choice of regression model in the setting of longitudinal measurements and a dichotomous outcome, and the difficulty in attempting to identify mechanisms in a causal pathway (diagnosing mediation).