TUESDAY DECEMBER 4TH, 2018
NOON – 1:00PM
RAHN 2280

Presented by
Stefan Graw, M.Sc.
PhD Candidate
Department of Biostatistics
University of Kansas Medical Center

Abstract
When designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed pwrEWAS. The current implementation of pwrEWAS accommodates power estimation for two-group comparisons of DNAm (e.g. case vs control, exposed vs non-exposed, etc.), where methylation assessment is carried out using the Illumina Human Methylation BeadChip technology. Power is calculated using a semi-parametric simulation-based approach in which DNAm data is randomly generated from beta-distributions using CpG-specific means and variances estimated from one of several different existing DNAm data sets, chosen to cover the most common tissue-types used in EWAS. In addition to specifying the tissue type to be used for DNAm profiling, users are required to specify the sample size, number of differentially methylated CpGs, effect size(s) (Δ_β), target false discovery rate (FDR) and the number of simulated data sets, and have the option of selecting from several different statistical methods to perform differential methylation analyses. pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC). Here, we demonstrate how pwrEWAS can be applied in practice using a hypothetical EWAS. In addition, we report its computational efficiency across a variety of user settings. Both under- and overpowered studies unnecessarily deplete resources and even risk failure of a study. With pwrEWAS, we provide a user-friendly tool to help researchers circumvent these risks and to assist in the design and planning of EWAS.

Presented by: Fei Wan, PhD.

Tuesday, November 6th, 2018
12:00 – 1:00pm.
RAHN G230

Abstract

Observational studies are commonly used by cancer researchers to evaluate the relative effectiveness of different treatment options. However, confounding due to unmeasured or unknown variables can pose a serious issue when estimating treatment effects using observational data. To address this issue of unmeasured confounding, instrumental variable (IV) methods have become increasingly popular.
Specifically, the IV approach using two-stage residual inclusion (2SRI) has become a common analytic tool in studies of cancer therapies where the outcome of interest is overall or cancer-specific survival. However, despite its popularity, a compelling theoretical rationale has not been postulated nor have the limitations underlying the use of 2SRI in the context of survival outcomes been carefully laid out.
In this study we first provide a brief description of the concept of instrumental variables and their underlying assumptions. We then describe the 2SRI approach and provide examples from the literature of cancer comparative effectiveness studies that have used this approach. We show that the previous conclusion on the consistency of 2SRI in proportional hazards model relies on the unrealistic assumption that the effects of unmeasured confounders on treatment and outcome are proportional. Given a perfect instrumental variable, extension of 2SRI to proportional hazards model can generally result in biased estimates of treatment effect. We present a simple approach of assessing the bias of 2SRI as an omitted- variable-bias problem.
We conclude with our recommendations for the analysis of comparative effectiveness studies from observational data.