These days I like to discuss

- BB-8
- Postdoc-ing at Johns Hopkins Bloomberg School of Public Health
- Co-founding R-Ladies Nashville
- Causal inference
- Monty Python
- Vanderbilt’s Biostatistics PhD program
- Large-scale medical data
- Italian
- Data science pedagogy

over coffee…

Challenges in Augmenting Randomized Trials with Observational Health Records

Jul 29, 2019
2:55 PM

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-valueâ€”a second-generation p-value ($p_Î´$)â€“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 ($p_Î´$ = 1), or with alternative hypotheses ($p_Î´$ = 0), or when the data are inconclusive (0 < $p_Î´$ < 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

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 (ROIâ€“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

JS Liberman, **L Dâ€™Agostino McGowan**, RA Greevy, JA Morrow, MR Griffin, CL Roumie, CG Grijalva.
Mental health conditions and the risk of chronic opioid therapy among patients with rheumatoid arthritis: a retrospective veterans affairs cohort study.
In *Clinical Rheumatology*,
2020.

L Griffin, MJ Carter, R D’Agostino Jr, **L D’Agostino McGowan**.
Comparative Effectiveness of Two Collagen-containing Dressings: Oxidized Regenerated Cellulose (ORC)/Collagen/Silver-ORC Dressing Versus Ovine Collagen Extracellular Matrix.
In *Wounds: a compendium of clinical research and practice*,
2019.

H Wickham, M Averick, J Bryan, W Chang, **L Dâ€™Agostino McGowan**, R FranĂ§ois, G Grolemund, A Hayes, L Henry, J Hester, M Kuhn, T Lin Pedersen, E Miller, S Milton Bache, K MĂĽller, J Ooms, D Robinson, D Paige Seidel, V Spinu, K Takahashi, D Vaughan, C Wilke, K Woo, and H Yutani.
Welcome to the Tidyverse.
In *Journal of Open Source Software*,
2019.

DP Singh, A Gabriel, RP Silverman, LP Griffin, **L D’Agostino McGowan**, RB Dâ€™Agostino Jr.
Meta-analysis Comparing Outcomes of Two Different Negative Pressure Therapy Systems in Closed Incision Management..
In *Plastic and reconstructive surgery. Global open*,
2019.

BT Pun, MC Balas, MA Barnes-Daly, JL Thompson, JM Aldrich, J Barr, D Byrum, SS Carson, JW Devlin, HJ Engel, CL Esbrook, KD Hargett, LRRT Harmon, C Hielsberg, JC Jackson, TL Kelly, V Kumar, L Millner, A Morse, CS Perme, PJ Posa, KA Puntillo, WD Schweickert, JL Stollings, A Tan, **L D’Agostino McGowan**, EW Ely.
Caring for Critically Ill Patients with the ABCDEF Bundle: Results of the ICU Liberation Collaborative in Over 15,000 Adults.
In *Critical Care Medicine*,
2019.

- STA 363: Statistcal Learning (2020)
- STA 212: Statistical Models (2019)

- 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)

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

- Creating dynamic dashboards with the shinydashboard R package (2019)
- Understanding data and statistics in the medical literature (2019)
- ggplot2 in 2: Learn the ggplot2 R package in 2 hours (2019)

- R
- Math GRE
- SAS
- Introduction to Biostatistics