Valerie Welty dissertation defense – March 8
PhD candidate Valerie F. Welty will defend her dissertation on Wednesday, March 8, at 3 p.m. Central Time, on-site and online. Her advisor is Jeffrey D. Blume. All are invited.
The in-person event will be held in Room 11105 (11th floor) at 2525 West End Avenue.
For access to the event stream, please contact the department at biostatistics[at]vumc[dot]org.
On False Discovery Rates for Second-Generation p-Values
The False Discovery Rate (FDR) was introduced as an alternative multiple comparisons adjustment to controlling the family wise error rate. While many variations of the FDR have been proposed, the weakness of the p-value – namely that it does not consider the scientific relevance of the finding – remains a challenge for these methods. The second-generation p-value (SGPV, Blume et al. 2018) is an alternative that uses the scientific relevance of the findings to screen out false discoveries. It does this by employing an interval null hypothesis to denote null and practically null findings. In this dissertation, we explore and define false discovery concepts and quantities for SGPVs. First, we provide a review of fundamental FDR concepts, including empirical estimation. Second, we extend the definition and estimation of the positive false discovery rate (pFDR) to SGPVs. The pFDR is the appropriate measurement of the reliability of a set of findings in a large-scale inference procedure. Finally, we examine FDR control using SGPV methods, and compare SGPV false discovery quantities with other multiple testing methods. We find that the handling and identification of null effects is critical, as FDR methods routinely misclassify tiny departures from the null as findings, especially in large samples. The SGPV framework directly addresses this, but at the cost of some global performance properties. Overall, SGPV methods can be beneficial for impactful scientific inference, and further work on empirical estimation schemes is needed for wide adoption in practice.