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Paul Gustafson

As seen in: Wiley Online Library, MDPI, Occupational and Environmental Medicine (OEM), Edutopia, St. Cloud Times, Grand Forks Herald, Bemidji Pioneer, Park Rapids Enterprise, Journal of Clinical Epidemiology, Scene
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Articles

Expected Value of Sample Information Calculations for Risk Prediction Model Development

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By Abdollah Safari, Paul Gustafson, Mohsen Sadatsafavi
| Wiley Online Library Verified
A.Safari, P.Gustafson, and M.Sadatsafavi, “Expected Value of Sample Information Calculations for Risk Prediction Model Development,” Statistics in Medicine45, no. 8-9 (2026): e70484, https://doi.org/10.1002/sim.70484. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available in R Library at https://search.r-project.org/CRAN/refmans/predtools/html/gusto.html.
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Bayesian Sample Size Calculations for External Validation Studies of Risk Prediction Models

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By Mohsen Sadatsafavi, Paul Gustafson, Solmaz Setayeshgar
| Wiley Online Library Verified
1 Introduction Developing risk prediction models or validating existing models in a new population represents a significant investment in time, resources, and expertise. Like other empirical experiments, the design of such studies should be based on objective, transparent, and defensible principles. A particular aspect of study design is the sample size of such studies. The field has witnessed significant recent developments on this front [1-8].
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Partial identification with categorical data and nonignorable missing outcomes

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By Daniel Daly-Grafstein, Paul Gustafson
| Wiley Online Library Verified
1 Introduction Missing outcomes are a common feature of epidemiological data. Missingness is commonly categorized as either missing at random (MAR) or nonignorable missingness (also called missing not at random, or MNAR), where in the latter case the probability of missingness depends on the missing values themselves (Rubin, 1976). When data have nonignorable missingness, a missing data mechanism must be incorporated into the model when performing inference.
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