Jan Beyersmann

发表

A wild bootstrap approach for the Aalen–Johansen estimator

Martin Schumacher, Markus Pauly, Tobias Bluhmki, 2018, Biometrics.

Classical Regression Models for Competing Risks

Jan Beyersmann, Thomas H. Scheike, T. Scheike, 2013 .

Empirical Transition Matrix of Multi-State Models: The etm Package

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2011 .

Bayesian Phase II optimization for time-to-event data based on historical information

Jan Beyersmann, Frank Fleischer, Anja Bertsche, 2019, Statistical methods in medical research.

Time-to-event methodology improved statistical evaluation in register-based health services research.

Wolfgang Rathmann, Tobias Bluhmki, Jan Beyersmann, 2017, Journal of clinical epidemiology.

Quantifying the predictive accuracy of time‐to‐event models in the presence of competing risks

Harald Binder, Martin Schumacher, Jan Beyersmann, 2011, Biometrical journal. Biometrische Zeitschrift.

Boosting for high-dimensional time-to-event data with competing risks

Harald Binder, Martin Schumacher, Jan Beyersmann, 2009, Bioinform..

Joint modelling of progression-free and overall survival and computation of correlation measures

Jan Beyersmann, Matthias Meller, Kaspar Rufibach, 2018, 1810.10722.

Subdistribution hazard models for competing risks in discrete time.

Matthias Schmid, Steffen Schmitz-Valckenberg, Thomas Welchowski, 2018, Biostatistics.

Statistical issues in the analysis of adverse events in time‐to‐event data

Claudia Schmoor, Jan Beyersmann, Arthur Allignol, 2016, Pharmaceutical statistics.

On estimands and the analysis of adverse events in the presence of varying follow‐up times within the benefit assessment of therapies

Tim Friede, Norbert Benda, Frank Langer, 2018, Pharmaceutical statistics.

Simulating competing risks data in survival analysis

Martin Schumacher, Anika Buchholz, Jan Beyersmann, 2009, Statistics in medicine.

Estimating summary functionals in multistate models with an application to hospital infection data

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2011, Comput. Stat..

RE: "COMPARISON OF STATISTICAL APPROACHES FOR DEALING WITH IMMORTAL TIME BIAS IN DRUG EFFECTIVENESS STUDIES".

Martin Schumacher, Jan Beyersmann, Martin Wolkewitz, 2016, American journal of epidemiology.

Proportional subdistribution hazards modeling offers a summary analysis, even if misspecified

Martin Schumacher, Jan Beyersmann, Nadine Grambauer, 2010, Statistics in medicine.

Gerhard Tutz and Matthias Schmid. Modeling Discrete Time-to-Event Data. Heidelberg, Springer.: Book Review

Jan Beyersmann, 2018 .

Assessing assumptions for statistical analyses in randomised clinical trials

Per Winkel, Hein Putter, Janus Christian Jakobsen, 2019, BMJ Evidence-Based Medicine.

Prediction accuracy and variable selection for penalized cause‐specific hazards models

Axel Benner, Annette Kopp-Schneider, Maral Saadati, 2018, Biometrical journal. Biometrische Zeitschrift.

Proportional hazards models

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2012 .

Time-dependent covariates in the proportional subdistribution hazards model for competing risks.

Martin Schumacher, Jan Beyersmann, M. Schumacher, 2008, Biostatistics.

Two Pitfalls in Survival Analyses of Time-Dependent Exposure: A Case Study in a Cohort of Oscar Nominees

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2010 .

Modelling two cause‐specific hazards of competing risks in one cumulative proportional odds model?

Martin Schumacher, Jan Beyersmann, Kristin Ohneberg, 2017, Statistics in medicine.

An informal introduction to hazard-based analyses

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2012 .

Statistical models for complex data in clinical and epidemiological research.

Willi Sauerbrei, Claudia Schmoor, Jan Beyersmann, 2020, Biometrical journal. Biometrische Zeitschrift.

Further topics in multistate modelling

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2012 .

Nested exposure case-control sampling: a sampling scheme to analyze rare time-dependent exposures

Martin Schumacher, Jan Beyersmann, Jan Feifel, 2018, Lifetime data analysis.

Bootstrapping complex time‐to‐event data without individual patient data, with a view toward time‐dependent exposures

Hein Putter, Tobias Bluhmki, Jan Beyersmann, 2019, Statistics in medicine.

A competing risks approach for nonparametric estimation of transition probabilities in a non-Markov illness-death model

Jan Beyersmann, Arthur Allignol, Aurélien Latouche, 2013, Lifetime Data Analysis.

Competing Risks and Multistate Models with R

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2011 .

Understanding competing risks: a simulation point of view

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2011, BMC medical research methodology.

Estimating state occupation and transition probabilities in non-Markov multi-state models subject to both random left-truncation and right-censoring

Jan Beyersmann, Arthur Allignol, Alexandra Niessl, 2020 .

Estimating and comparing adverse event probabilities in the presence of varying follow‐up times and competing events

Tim Friede, Claudia Schmoor, Jan Beyersmann, 2020, Pharmaceutical statistics.

Optimizing the Design and Analysis of Clinical Trials for Antibacterials Against Multidrug-resistant Organisms: A White Paper From COMBACTE’s STAT-Net

Leonhard Held, Stephan Harbarth, Stavros Nikolakopoulos, 2018, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

The time-dependent bias and its effect on extra length of stay due to nosocomial infection.

Nicholas Graves, Jan Beyersmann, Arthur Allignol, 2011, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

A note on nonparametric quantile inference for competing risks and more complex multistate models

Martin Schumacher, Jan Beyersmann, 2008 .

A Random Time Interval Approach for Analysing the Impact of a Possible Intermediate Event on a Terminal Event

Jan Beyersmann, J. Beyersmann, 2007, Biometrical journal. Biometrische Zeitschrift.

Meta‐analysis for aggregated survival data with competing risks: a parametric approach using cumulative incidence functions

Guido Schwarzer, Federico Bonofiglio, Martin Schumacher, 2016, Research synthesis methods.

Risk factors for the development of nosocomial pneumonia and mortality on intensive care units: application of competing risks models

Martin Schumacher, Jan Beyersmann, Hajo Grundmann, 2008, Critical care.

Application of multistate models in hospital epidemiology: Advances and challenges

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2011, Biometrical journal. Biometrische Zeitschrift.

Time-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of bias.

Stephan Harbarth, Martin Schumacher, Jan Beyersmann, 2012, Journal of clinical epidemiology.

The impact of time‐dependent bias in proportional hazards modelling

Martin Schumacher, Jan Beyersmann, Martin Wolkewitz, 2008, Statistics in medicine.

A competing risks analysis of bloodstream infection after stem‐cell transplantation using subdistribution hazards and cause‐specific hazards

Martin Schumacher, Jan Beyersmann, Markus Dettenkofer, 2007, Statistics in medicine.

Competing Risks and Multistate Models

Martin Schumacher, Claudia Schmoor, Jan Beyersmann, 2012, Clinical Cancer Research.

A Note on Variance Estimation of the Aalen–Johansen Estimator of the Cumulative Incidence Function in Competing Risks, with a View towards Left‐Truncated Data

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2010, Biometrical journal. Biometrische Zeitschrift.

The health and economic burden of bloodstream infections caused by antimicrobial-susceptible and non-susceptible Enterobacteriaceae and Staphylococcus aureus in European hospitals, 2010 and 2011: a multicentre retrospective cohort study

Stefan Hagel, Stephan Harbarth, Martin Schumacher, 2016, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

Incidence densities in a competing events analysis.

Martin Schumacher, Jan Beyersmann, Markus Dettenkofer, 2010, American journal of epidemiology.

The Importance of Good Data, Analysis, and Interpretation for Showing the Economics of Reducing Healthcare-Associated Infection

Stephan Harbarth, Adrian G. Barnett, Matthew Samore, 2011, Infection Control & Hospital Epidemiology.

Misspecified regression model for the subdistribution hazard of a competing risk by Latouche A, Boisson V, Chevret S and Porcher R. Statistics in Medicine 2006; DOI: 10.2002/sim.2600

Martin Schumacher, Jan Beyersmann, M. Schumacher, 2007, Statistics in medicine.

Non-strange Weird Resampling for Complex Survival Data

Markus Pauly, Dennis Dobler, Jan Beyersmann, 2015, 1507.02838.

Nosocomial Infection, Length of Stay, and Time-Dependent Bias

Martin Schumacher, Thomas Kneib, Jan Beyersmann, 2009, Infection Control & Hospital Epidemiology.

An easy mathematical proof showed that time-dependent bias inevitably leads to biased effect estimation.

Martin Schumacher, Jan Beyersmann, Martin Wolkewitz, 2008, Journal of clinical epidemiology.

Transmission-associated nosocomial infections: prolongation of intensive care unit stay and risk factor analysis using multistate models.

Martin Schumacher, Jan Beyersmann, Hajo Grundmann, 2008, American journal of infection control.

The Kaplan-Meier Integral in the Presence of Covariates: A Review

Thomas A. Gerds, Martin Schumacher, Jan Beyersmann, 2017 .

A note on computing average state occupation times

Hein Putter, Jan Beyersmann, H. Putter, 2014 .

Nonparametric hypothesis testing

Martin Schumacher, Jan Beyersmann, Arthur Allignol, 2012 .

Incidence in ICU populations: how to measure and report it?

Martin Schumacher, Jan Beyersmann, Petra Gastmeier, 2014, Intensive Care Medicine.

Estimating the cost of health care-associated infections: mind your p's and q's.

Stephan Harbarth, Nicholas Graves, Jan Beyersmann, 2010, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.