Dr. Manuel Wiesenfarth

Area of Work

- Bayesian methods in clinical trials
- Validation of clinical prediction models / in machine learning
- Adaptive clinical trials for precision oncology
- Regularization and smoothing

Selected publications

Wiesenfarth M, Reinke A, Landman BA, Eisenmann M, Saiz LA, Cardoso MJ, Maier-Hein L, Kopp-Schneider A. (2021) Methods and open-source toolkit for analyzing and visualizing challenge results. Science Reports 11(1):2369. doi: 10.1038/s41598-021-82017-6.
Accompanying R package challengeR available on GitHub: https://github.com/wiesenfa/challengeR.

Wiesenfarth M, Calderazzo S. (2020) Quantification of prior impact in terms of effective current sample size. Biometrics, 76(1), 326-336. doi: 10.1111/biom.13124

Calderazzo S, Wiesenfarth M, Kopp-Schneider A. (2020) A decision-theoretic approach to Bayesian clinical trial design and evaluation of robustness to prior-data conflict. Biostatistics, doi: 10.1093/biostatistics/kxaa027

Kopp-Schneider A, Calderazzo S, Wiesenfarth M. (2020) Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control. Biometrical Journal 62(2):361-374. doi: 10.1002/bimj.201800395

Kopp-Schneider A, Wiesenfarth M, Witt R, Edelmann D, Witt O, Abel U. (2019) Monitoring futility and efficacy in phase II trials with Bayesian posterior distributions-A calibration approach. Biometrical Journal 61: 488-502. doi: 10.1002/bimj.201700209

Bonekamp D, Kohl S, Wiesenfarth M, ..., Schlemmer H, Maier-Hein K. (2018). Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology, 289(1), 128-137. doi: 10.1148/radiol.2018173064

Kesch C, Radtke JP, Wintsche A, Wiesenfarth M, ..., Duensing S. (2018) Correlation between genomic index lesions and mpMRI and Ga-PSMA-PET/CT imaging features in primary prostate cancer. Scientific reports 12;8(1):16708. doi: 10.1038/s41598-018-35058-3.

Lipka D.B, Witte T, Toth R, Yang J, Wiesenfarth M, ... & Plass C (2017). RAS-pathway mutation patterns define epigenetic subclasses in juvenile myelomonocytic leukemia. Nature communications, 8(1), 2126. doi: 10.1038/s41467-017-02177-w

Frölich M, Huber M, Wiesenfarth M (2017). The finite sample performance of semi-and non-parametric estimators for treatment effects and policy evaluation. Computational Statistics & Data Analysis, 115, 91-102. doi: 10.1016/j.csda.2017.05.007

Klasen S, Krivobokova T, Greb F, Lahoti, R, Pasaribu SH, Wiesenfarth M. (2016). International income poverty measurement: which way now?. The Journal of Economic Inequality, 14(2), 199-225.

Wiesenfarth M, Hisgen M, Kneib T, Cadarso-Suarez C (2014). Bayesian Nonparametric Instrumental Variables Regression based on Penalized Splines and Dirichlet Process Mixtures. Journal of Business & Economic Statistics, 32(3), 468-482.
Accompanying R package bayesIV Package source / Windows binaries

Wiesenfarth M, Krivobokova T, Klasen S, Sperlich S (2012). Direct Simultaneous Inference in Additive Models and its Application to Model Undernutrition. Journal of the American Statistical Association, 107(500):1286-1296.
Accompanying R package AdaptFitOS Available on CRAN: https://cran.r-project.org/web/packages/AdaptFitOS/

Wiesenfarth M, Kneib T (2010). Bayesian Geoadditive Sample Selection Models. Journal of the Royal Statistical Society: Series C, 59(3): 381-404.
Accompanying R package bayesIV  Package source / Windows binaries