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1.
Zamanian, A.* ; Ahmidi, N. & Drton, M.*: Assessable and interpretable sensitivity analysis in the pattern graph framework for nonignorable missingness mechanisms. Stat. Med. 42, 5419-5450 (2023)
2.
Mishra, A.K.* & Müller, C.L.: Negative binomial factor regression with application to microbiome data analysis. Stat. Med. 41, 2786-2803 (2022)
3.
Simonetto, C. et al.: Simulating the dynamics of atherosclerosis to the incidence of myocardial infarction, applied to the KORA population. Stat. Med. 40, 3299-3312 (2021)
4.
Sommer, A. ; Leray, E.* ; Lee, Y.* & Bind, M.A.C.*: Assessing environmental epidemiology questions in practice with a causal inference pipeline: An investigation of the air pollution-multiple sclerosis relapses relationship. Stat. Med. 40, 1321-1335 (2021)
5.
Kurz, C.F. & Hatfield, L.A.*: Identifying and interpreting subgroups in health care utilization data with count mixture regression models. Stat. Med. 38, 4423-4435 (2019)
6.
Köhler, M. ; Umlauf, N.* & Greven, S.*: Nonlinear association structures in flexible Bayesian additive joint models. Stat. Med. 37, 4771-4788 (2018)
7.
Gong, G.* ; Quante, A.S. ; Terry, M.B.* & Whittemore, A.S.*: Assessing the goodness of fit of personal risk models. Stat. Med. 33, 3179-3190 (2014)
8.
Hess, W.* ; Schwarzkopf, L. ; Hunger, M. & Holle, R.: Competing-risks duration models with correlated random effects: An application to dementia patients' transition histories. Stat. Med. 33, 3919-3931 (2014)
9.
Rapsomaniki, E.* ; White, I.R.* ; Wood, A.M.* ; Thompson, S.G.* & Emerging Risk Factors Collaboration (Döring, A. ; Meisinger, C.): A framework for quantifying net benefits of alternative prognostic models. Stat. Med. 31, 114-130 (2012)
10.
Finner, H.* et al.: How to link call rate and p-values for Hardy-Weinberg equilibrium as measures of genome-wide SNP data quality. Stat. Med. 29, 2347-2358 (2010)
11.
Barnett, A.G.* ; Dobson, A.J.* & WHO MONICA Project (Löwel, H. ; Hörmann, A. ; Gostomzyk, J.G. ; Bolte, H.-D.): Estimating trends and seasonality in coronary heart disease. Stat. Med. 23, 3505-3523 (2004)
12.
Kaiser, J.C. & Heidenreich, W.F.: Comparing regression methods for the two-stage clonal expansion model of carcinogenesis. Stat. Med. 23, 3333-3350 (2004)
13.
Heid, I.M. et al.: On the potential of measurement error to induce differential bias on odds ratio estimates : An example from radon epidemiology. Stat. Med. 21, 3261-3278 (2002)
14.
Heidenreich, W.F. ; Wellmann, J. ; Jacob, P. & Wichmann, H.-E.: Mechanistic modelling in large case-control studies of lung cancer risk from smoking. Stat. Med. 21, 3055-3070 (2002)
15.
Hauptmann, M. ; Lubin, J.H.* ; Rosenberg, P.* ; Wellmann, J.* & Kreienbrock, L.*: The use sliding time windows for the exploratory analysis of temporal effects of smoking histories on lung cancer risk. Stat. Med. 19, 2185-2194 (2000)
16.
Reitmeir, P. & Wassmer, G.*: Resampling-based methods for the analysis of multiple endpoints in clinical trials. Stat. Med. 18, 3453-3462 (1999)
17.
Lehmacher, W.: Analysis of the Crossover Design in the Presence of Residual Effects. Stat. Med. 10:891-899 (1991)
18.
Lehmacher, W.: Analysis of the crossover design in the presence of residual effects. Stat. Med. 10, 891-899 (1991)
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