Better-than-chance classification for signal detection

The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is particularly popular in neuroimaging and genetics. We provide evidence that using a classifier's accuracy as a test statistic can be an underpowered strategy for finding differences between populations, compared to a bona fide statistical test. It is also computationally more demanding than a statistical test. Via simulation, we compare test statistics that are based on classification accuracy, to others based on multivariate test statistics. We find that the probability of detecting differences between two distributions is lower for accuracy-based statistics. We examine several candidate causes for the low power of accuracy-tests. These causes include: the discrete nature of the accuracy-test statistic, the type of signal accuracy-tests are designed to detect, their inefficient use of the data, and their suboptimal regularization. When the purpose of the analysis is the evaluation of a particular classifier, not signal detection, we suggest several improvements to increase power. In particular, to replace V-fold cross-validation with the Leave-One-Out Bootstrap.Development and application of statistical models for medical scientific researc

All-Resolutions Inference for brain imaging

Development and application of statistical models for medical scientific researc

Provider Attitudes and Practice Patterns for Direct-Acting Antiviral Therapy for Patients With Hepatocellular Carcinoma

Background & Aims: Direct-acting antivirals (DAAs) are effective against hepatitis C virus and sustained virologic response is associated with reduced incidence of hepatocellular carcinoma (HCC). However, there is controversy over the use of DAAs in patients with active or treated HCC and uncertainty about optimal management of these patients. We aimed to characterize attitudes and practice patterns of hepatology practitioners in the United States regarding the use of DAAs in patients with HCC. Methods: We conducted a survey of hepatology providers at 47 tertiary care centers in 25 states. Surveys were sent to 476 providers and we received 279 responses (58.6%). Results: Provider beliefs about risk of HCC recurrence after DAA therapy varied: 48% responded that DAAs reduce risk, 36% responded that DAAs do not change risk, and 16% responded that DAAs increase risk of HCC recurrence. However, most providers believed DAAs to be beneficial to and reduce mortality of patients with complete response to HCC treatment. Accordingly, nearly all providers (94.9%) reported recommending DAA therapy to patients with early-stage HCC who received curative treatment. However, fewer providers recommended DAA therapy for patients with intermediate (72.9%) or advanced (57.5%) HCC undergoing palliative therapies. Timing of DAA initiation varied among providers based on HCC treatment modality: 49.1% of providers reported they would initiate DAA therapy within 3 months of surgical resection whereas 45.9% and 5.0% would delay DAA initiation for 3–12 months and >1 year post-surgery, respectively. For patients undergoing transarterial chemoembolization (TACE), 42.0% of providers would provide DAAs within 3 months of the procedure, 46.7% would delay DAAs until 3–12 months afterward, and 11.3% would delay DAAs more than 1 year after TACE. Conclusions: Based on a survey sent to hepatology providers, there is variation in provider attitudes and practice patterns regarding use and timing of DAAs for patients with HCC. Further studies are needed to characterize the risks and benefits of DAA therapy in this patient population

Discussion of "Gene hunting with hidden Markov model knockoffs'

Development and application of statistical models for medical scientific researc

The harmonic mean p-value: Strong versus weak control, and the assumption of independence

Development and application of statistical models for medical scientific researc