Shannon Grant

The Cox proportional hazards (PH) regression model is commonly used to analyze censored failure time data. The portion of the hazard that depends on covariates is assumed to be constant across time; this concept is known as the PH assumption. The PH regression model has been extended to accommodate updated values of covariates, known as time-varying covariates (TVCs). Many methods for formally testing the PH assumption have been proposed and assessed for power in models containing only constant covariates. Some tests are appropriate for models containing TVCs. I will present results from a simulation study assessing the power of tests for goodness of fit in the presence of TVCs and apply the tests to data from the Stanford heart transplant study.

Danping Liu

The receiver operating characteristic (ROC) curve is a powerful tool to evaluate the classification accuracy of a biomarker. In many large-scale screening tests, the gold standard is subject to missingness due to high cost or harmfulness to the patient. A complete-case analysis would yield biased results, known as “verification bias”. Under missing at random (MAR) assumption, several methods are proposed to correct for the verification bias based on imputation, inverse probability weighting, or both. The model framework is built on a location-scale model for the marker value, with unspecified residual distribution. Bias-corrected estimation of three different types of ROC curves are discussed: (i) the ordinary ROC curve as a plot of sensitivity and specificity, (ii) the covariate-specific ROC curve when the marker value is associated with individual covariates, and (iii) the adjusted ROC (AROC) curve as an “average” of covariate-specific ROC curves. The proposed method is applied to the data from Alzheimer’s disease research.

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.

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.

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.