• Causal inference
  • Data Science Pedagogy
  • Large-scale medical data


  • PhD in Biostatistics, 2018

    Vanderbilt University

  • MS in Biostatistics, 2013

    Washington University in St. Louis School of Medicine

  • BA in Religious Studies and Romance Language Studies, 2012

    UNC Chapel Hill

Selected Publications

Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value—a second-generation p-value (pδ)–that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses (pδ = 1), or with alternative hypotheses (pδ = 0), or when the data are inconclusive (0 < pδ < 1). Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priori specifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses.
In PLoS One, 2018

Medications that impact insulin sensitivity or cause weight gain may increase heart failure risk. Our aim was to compare heart failure and cardiovascular death outcomes among patients initiating sulfonylureas for diabetes mellitus treatment versus metformin.
Methods and Results
National Veterans Health Administration databases were linked to Medicare, Medicaid, and National Death Index data. Veterans aged ≥18 years who initiated metformin or sulfonylureas between 2001 and 2011 and whose creatinine was <1.4 (females) or 1.5 mg/dL (males) were included. Each metformin patient was propensity score‐matched to a sulfonylurea initiator. The outcome was hospitalization for acute decompensated heart failure as the primary reason for admission or a cardiovascular death. There were 126 867 and 79 192 new users of metformin and sulfonylurea, respectively. Propensity score matching yielded 65 986 per group. Median age was 66 years, and 97% of patients were male; hemoglobin A1c 6.9% (6.3, 7.7); body mass index 30.7 kg/m2 (27.4, 34.6); and 6% had heart failure history. There were 1236 events (1184 heart failure hospitalizations and 52 cardiovascular deaths) among sulfonylurea initiators and 1078 events (1043 heart failure hospitalizations and 35 cardiovascular deaths) among metformin initiators. There were 12.4 versus 8.9 events per 1000 person‐years of use (adjusted hazard ratio 1.32, 95%CI 1.21, 1.43). The rate difference was 4 heart failure hospitalizations or cardiovascular deaths per 1000 users of sulfonylureas versus metformin annually.
Predominantly male patients initiating treatment for diabetes mellitus with sulfonylurea had a higher risk of heart failure and cardiovascular death compared to similar patients initiating metformin.
In Journal of the American Heart Association, 2017

Location bias occurs when a reader detects a false lesion in a subject with disease and the falsely detected lesion is considered a true positive. In this study, we examine the effect of location bias in two large MRMC ROC studies, comparing three ROC scoring methods. We compare one method that only uses the maximum confidence score and does not take location bias into account (maxROC), and two methods that take location bias into account: the region of interest ROC (ROI–ROC) and the free-response ROC (FROC). In both studies, when comparing two modalities’ ROC areas without adjusting for location bias, the effect size depends on the difference in the frequency of location bias between the two modalities. When the difference in frequency is small, the effect size is similar whether the location bias is corrected for or not. However, when the frequency of location bias is dissimilar, failure to correct for the location bias favors the modality with higher false positive rate. Location bias should be corrected when the next step in the clinical management of the patient depends on the specific location of the detected lesion and/or when the frequency of the bias is dissimilar between the two modalities.
In Statistics in Biopharmaceutical Research, 2016

Recent Publications

More Publications

. Comparative Safety of Sulfonylurea and Metformin Monotherapy on the Risk of Heart Failure: A Cohort Study. In Journal of the American Heart Association, 2017.


. Location Bias in ROC Studies. In Statistics in Biopharmaceutical Research, 2016.

. Secondary consent to biospecimen use in a prostate cancer biorepository. In BMC Research Notes, 2016.


. Quantitative evaluation of the community research fellows training program. In Frontiers in public health, 2015.



Vanderbilt University

  • Frank Harrell’s Regression Modeling Strategies (2017)
  • Statistical Collaboration in Health Sciences Teaching Assistant (2015)
  • Modern Regression Analysis Teaching Assistant (2015)
  • Principles of Modern Biostatistics Teaching Assistant (2014)

Washington University in St. Louis

  • Introduction to SAS Teaching Assistant (2013)
  • Introduction to Clinical Epidemiology Teaching Assistant (2013)
  • Randomized Controlled Trials Teaching Assistant (2013)

Online Courses


  • R
  • Math GRE
  • SAS
  • Introduction to Biostatistics