# Chapter 1 Literature Review

Misuse of p-values and lack of reproducibility in scientific discoveries have been a cause for concern in the scientic world, leading to proposals of new ways to define significance. Benjamin et. al have shown that the Bayes factor equivalents for commonly used p-values only correspond to “weak” evidence in the Bayes factor characterization (Benjamin et al., 2017). They suggest reducing the p-value threshold in studies with less power, but acknowledge that hypothesis testing with thresholding is still an issue. Another approach proposes two calibrations of the p-value: as the lower bound of the Bayes factor under any alternative hypothesis, and as a posterior probability of the type 1 error in a Bayesian framework (Sellke, Bayarri, & Berger, 2001).

This problem has become a major issue in replicated studies, an effect known as the “winner’s curse” (Zöllner & Pritchard, 2007) or the Beavis effect (S. Xu, 2003). Zollner and Pritchard first define this in the context of genome-wide association scans (GWAS), which use stringent thresholds for significance, resulting in inflated effect sizes after selection, especially since these are calculated with the same data. Thus, replication studies underestimate the sample size necessary and do not have enough power to detect an effect. They suggest a conditional-likelihood based method to address this issue, proposing a computational algorithm to maximize over the the likelihood of the parameters conditional on the significance association at level $$\alpha$$, which results in less biased coefficient estimates (albeit with larger variance) and sample size estimates centered at the true value (Zöllner & Pritchard, 2007).

Zhong and Prentice also propose a similar method, but use a different parametrization and an asymptotic approximation instead of a computational one to find the estimators, which is more computationally efficient (2009). Ghosh et al. also define an approximate conditional likelihood, and propose two more estimators (other than the MLE): the mean of the (normalized) conditional likelihood, which can be interpreted as a posterior mean of the parameters under a flat prior, and a “compromise” estimator which is the average of the mean and MLE (Ghosh, Zou, & Wright, 2008). The combination estimator proves to have the most stable MSE accross the range of true values for the parameters. Their approach only requires summary statistics, so they further apply it to published datasets. The results are similar for the three conditional likelihood approaches.

Another method proposed to create bias-reduced estimates uses bootstrap re-sampling to correct for both the thresholding effect and the ranking effect, which is not addressed in the conditional likelihood methods because of the difficulty of specifying joint likelihoods for correlated variables (Sun et al., 2011). By using a sample-split approach, the detection and estimation datasets can be virtually independent. This is repeated multiple times in order to reduce variance in the results. The main drawback of this approach is its computational intensity.

Several authors have also proposed shrinkage-based methods in the effect detection step. Bacanu and Kendler use a soft threshold method to scale statistics such that their sum of squares do not overestimate the true mean and then find “suggestive” signals in a GWAS context by setting a threshold. This method does not address the winner’s curse directly, but provides a subset of the genome which can be futher analyzed or used in future studies (Bacanu & Kendler, 2013). Bigdelli et al. propose shrinking coefficient estimates by drawing a comparison between “winner’s curse adjustments” for effect sizes and multiple testing approaches for p-values, since both are used on the tail of their respective distributions. Their method transforms False Discovery Rate (FDR) adjusted p-values into the corresponding Z-score and uses that as the estimator (Bigdeli et al., 2016). Both Bigdelli and Bacanu assume the data is normally distributed. Storey and Tibshirani (2003), on the other hand, propose to adjust the value used for significance testing rather than the coefficients, choosing the FDR value as an alternative to the p-value.

Multiple Bayesian methods have also been proposed: Xu et al use a Bayesian approach to a logistic regression, selecting a spike and slab prior for the mean and an inverse gamma prior for the variance (2011). A beta prior for the proportion of each component in the prior, and the hyperparameters were estimated empirically. They also propose a Bayesian Model Average approach, which they recommend for instances with little prior information. Their results show that the Bayesian models had smaller variance than conditional likelihood methods, but still do not address the “ranking effect” (Sun et al., 2011), or implement a fully Bayesian approach because of the dependence on the threshold $$\alpha$$.

Ferguson et al propose an Empirical Bayes approach, which estimate the prior density distribution with the data (2013). This is a nonparametric estimate, but still depends on other specifications such as the number of bins, type of splines, etc. Using the empirical prior, the posterior is then calculated, from which the estimate and pseudo-Bayesian credible intervals are derived by considering the 5% and 95% points. This method resulted in better estimates in the higher density regions, but performed worse than conditional likelihood methods on the tails. Thus, the authors propose a combined method, which calculates both the empirical Bayes and the conditional likelihood confidence intervals, and picks the shortest one. One possible problem with this approach is the use of non-HPD intervals, which could change the tail behavior.

The Bayesian framework is also applied to power calculations specifically, defining “Bayesian power” as the marginal probability of finding significance in a replicated study given the original and the data. In this paper, a spike and slab prior is also used, but the hyperparameters are estimated empirically. The resulting power estimators are improved, but lead to downwards bias in the effect size (Jiang & Yu, 2016).