Search

Your search keyword '"Sauerbrei, Willi"' showing total 22 results
Start Over Author "Sauerbrei, Willi" Publication Year Range Last 3 years
22 results on '"Sauerbrei, Willi"'

1. Exhuming nonnegative garrote from oblivion using suitable initial estimates- illustration in low and high-dimensional real data

Author

Kipruto, Edwin and Sauerbrei, Willi

Subjects
Statistics - Methodology
Abstract

The nonnegative garrote (NNG) is among the first approaches that combine variable selection and shrinkage of regression estimates. When more than the derivation of a predictor is of interest, NNG has some conceptual advantages over the popular lasso. Nevertheless, NNG has received little attention. The original NNG relies on least-squares (OLS) estimates, which are highly variable in data with a high degree of multicollinearity (HDM) and do not exist in high-dimensional data (HDD). This might be the reason that NNG is not used in such data. Alternative initial estimates have been proposed but hardly used in practice. Analyzing three structurally different data sets, we demonstrated that NNG can also be applied in HDM and HDD and compared its performance with the lasso, adaptive lasso, relaxed lasso, and best subset selection in terms of variables selected, regression estimates, and prediction. Replacing OLS by ridge initial estimates in HDM and lasso initial estimates in HDD helped NNG select simpler models than competing approaches without much increase in prediction errors. Simpler models are easier to interpret, an important issue for descriptive modelling. Based on the limited experience from three datasets, we assume that the NNG can be a suitable alternative to the lasso and its extensions. Neutral comparison simulation studies are needed to better understand the properties of variable selection methods, compare them and derive guidance for practice.

Published
2022

2. Effects of Influential Points and Sample Size on the Selection and Replicability of Multivariable Fractional Polynomial Models

Author

Sauerbrei, Willi, Kipruto, Edwin, and Balmford, James

Subjects
Statistics - Methodology
Abstract

The multivariable fractional polynomial (MFP) procedure combines variable selection with a function selection procedure (FSP). For continuous variables, a closed test procedure is used to decide between no effect, linear, FP1 or FP2 functions. Influential observations (IPs) and small sample size can both have an impact on a selected fractional polynomial model. In this paper, we used simulated data with six continuous and four categorical predictors to illustrate approaches which can help to identify IPs with an influence on function selection and the MFP model. Approaches use leave-one or two-out and two related techniques for a multivariable assessment. In seven subsamples we also investigated the effects of sample size and model replicability. For better illustration, a structured profile was used to provide an overview of all analyses conducted. The results showed that one or more IPs can drive the functions and models selected. In addition, with a small sample size, MFP might not be able to detect non-linear functions and the selected model might differ substantially from the true underlying model. However, if the sample size is sufficient and regression diagnostics are carefully conducted, MFP can be a suitable approach to select variables and functional forms for continuous variables., Comment: Main paper and a supplementary combined

Published
2022

6. Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI): stakeholder views, objectives, and procedures

Author

Bui, Cat, Emechebe, Nnadozie, Kamalakar, Rajesh, Papadopoulos, Elektra, Sail, Kavita, Sen, Rohini, Turner, Sean C, Cocks, Kim, Reijneveld, Jaap, Gerlinger, Christoph, Keating, Karen, Su, Yun, Wolf, Birgit, Ge, Miaomiao, Ingelgaard, Anders, Peil, Barbara, Voorhaar, Maarten, Wong, Brendon, Dekanic Arbanas, Gracia, Kuljanic, Karin, Petranovic, Duska, Rede, Ivana, Arraras, Juan, Coons, Stephen Joel, Eremenco, Sonya, Murray, Lindsey, Reeve, Bryce, De Vries, Corinne, Herold, Ralf, Pignatti, Francesco, Machingura, Abigirl, Martinelli, Francesca, Musoro, Jammbe, Piccart, Martine, Barriuso, Jorge, Cherny, Nathan, Dafni, Ourania, De Vries, Elisabeth, Gyawali, Bishal, Kiesewetter, Barbara, Oosting, Sjoukje, Roitberg, Felipe, Rumpold, Gerhard, Schoepf, Felix, Tschuggnall, Michael, Black, Jennifer, Sasseville, Maxime, Soltys, Katherine, Ferrer, Montserrat, Garin, Olatz, Vilagut, Gemma, Schürmann, Christoph, Thomas, Stefanie, Wieseler, Beate, Snyder, Claire, Alonso Abad, Ariel, Bogaerts, Kris, Brackx, Febe, Molenberghs, Geert, Verbeke, Geert, Frigolett Catalan, Cristián, Choi, Jan, Thomassen, Doranne, Geissler, Jan, Sauerbrei, Willi, Gross, Franziska, Pilz, Micha Johannes, Barbachano, Yolanda, Campbell, Lisa, Rantell, Khadija, Desplanques, Gregoire, Regnault, Antoine, Morgan, Kate, Plate, Ananda, ten Seldam, Silene, Terada, Mitsumi, Mizusawa, Junki, Mitchell, Sandra, Wilder Smith, Ashley, Reksten, Tove Ragna, Schiel, Anja, Hansen Joseph, Kenth Louis, Campbell, Alicyn, Cappelleri, Joseph, de Besi, Patrizia, Russell-Smith, Alexander, Sandin, Rickard, Mamolo, Carla, Brundage, Michael, Tu, Dongsheng, Groenvold, Mogens, Petersen, Morten, Cleeland, Charlie, Williams, Lori, Shelley Wang, Xin, Ringash, Jolie, Calvert, Melanie, Cruz Rivera, Samantha, Aiyegbusi, Olalekan Lee, Goetghebeur, Els, Liu, Limin, Van Lancker, Kelly, Boele, Florien, Gilbert, Alexandra, Peacock, Rosemary, Basch, Ethan, King, Madeleine, Rutherford, Claudia, Bhatnagar, Vishal, Fiero, Mallorie, Horodniceanu, Erica, Johnson, Laura Lee, Kluetz, Paul, Rodriguez, Lisa, Pe, Madeline, Alanya, Ahu, Falk, Ragnhild Sorum, Amdal, Cecilie Delphin, Bjordal, Kristin, Chang, Jane, Cislo, Paul, Coens, Corneel, Dirven, Linda, Speck, Rebecca M, Fitzgerald, Kristina, Galinsky, Jayne, Giesinger, Johannes M, Holzner, Bernhard, Le Cessie, Saskia, O'Connor, Daniel, Oliver, Kathy, Pawar, Vivek, Quinten, Chantal, Schlichting, Michael, Ren, Jinma, Roychoudhury, Satrajit, Taphoorn, Martin J B, Velikova, Galina, Wintner, Lisa M, Griebsch, Ingolf, and Bottomley, Andrew

Published
2023
Full Text
View/download PDF

7. Single-arm studies involving patient-reported outcome data in oncology: a literature review on current practice

Author

Aiyegbusi, Olalekan Lee, Alanya, Ahu, Barbachano, Yolanda, Basch, Ethan, Calvert, Melanie, Campbell, Alicyn, Cappelleri, Joseph, Choi, Jungyeon, Cruz Rivera, Samantha, Delphin Amdal, Cecilie, Fiero, Mallorie, Goetghebeur, Els, Kamalakar, Rajesh, Keating, Karen, Kluetz, Paul, le Cessie, Saskia, Liu, Limin, Molenberghs, Geert, Musoro, Jammbe, Ness, David, Rantell, Khadija, Regnault, Antoine, Reijneveld, Jaap, Roychoudhury, Satrajit, Rumpold, Gerhard, Russell-Smith, Alexander, Rutherford, Claudia, Sail, Kavita, Sandin, Rickard, Sasseville, Maxime, Sauerbrei, Willi, Schiel, Anja, Schlichting, Michael, Soltys, Kathy, Sorum Falk, Ragnhild, Su, Yun, ten Seldam, Silene, Thomassen, Doranne, Van Lancker, Kelly, Musoro, Jammbe Z, Amdal, Cecilie Delphin, Cappelleri, Joseph C, Falk, Ragnhild Sørum, Fiero, Mallorie H, and Reijneveld, Jaap C

Published
2023
Full Text
View/download PDF

11. Regression without regrets –initial data analysis is a prerequisite for multivariable regression.

Author

Heinze, Georg, Baillie, Mark, Lusa, Lara, Sauerbrei, Willi, Schmidt, Carsten Oliver, Harrell, Frank E., and Huebner, Marianne

Subjects
REGRESSION analysis, INDEPENDENT variables, INFERENTIAL statistics, STATISTICAL models, DATA integrity
Abstract

Statistical regression models are used for predicting outcomes based on the values of some predictor variables or for describing the association of an outcome with predictors. With a data set at hand, a regression model can be easily fit with standard software packages. This bears the risk that data analysts may rush to perform sophisticated analyses without sufficient knowledge of basic properties, associations in and errors of their data, leading to wrong interpretation and presentation of the modeling results that lacks clarity. Ignorance about special features of the data such as redundancies or particular distributions may even invalidate the chosen analysis strategy. Initial data analysis (IDA) is prerequisite to regression analyses as it provides knowledge about the data needed to confirm the appropriateness of or to refine a chosen model building strategy, to interpret the modeling results correctly, and to guide the presentation of modeling results. In order to facilitate reproducibility, IDA needs to be preplanned, an IDA plan should be included in the general statistical analysis plan of a research project, and results should be well documented. Biased statistical inference of the final regression model can be minimized if IDA abstains from evaluating associations of outcome and predictors, a key principle of IDA. We give advice on which aspects to consider in an IDA plan for data screening in the context of regression modeling to supplement the statistical analysis plan. We illustrate this IDA plan for data screening in an example of a typical diagnostic modeling project and give recommendations for data visualizations. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

16. Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI): stakeholder views, objectives, and procedures

Author

Pe, Madeline, primary, Alanya, Ahu, additional, Falk, Ragnhild Sorum, additional, Amdal, Cecilie Delphin, additional, Bjordal, Kristin, additional, Chang, Jane, additional, Cislo, Paul, additional, Coens, Corneel, additional, Dirven, Linda, additional, Speck, Rebecca M, additional, Fitzgerald, Kristina, additional, Galinsky, Jayne, additional, Giesinger, Johannes M, additional, Holzner, Bernhard, additional, Le Cessie, Saskia, additional, O'Connor, Daniel, additional, Oliver, Kathy, additional, Pawar, Vivek, additional, Quinten, Chantal, additional, Schlichting, Michael, additional, Ren, Jinma, additional, Roychoudhury, Satrajit, additional, Taphoorn, Martin J B, additional, Velikova, Galina, additional, Wintner, Lisa M, additional, Griebsch, Ingolf, additional, Bottomley, Andrew, additional, Bui, Cat, additional, Emechebe, Nnadozie, additional, Kamalakar, Rajesh, additional, Papadopoulos, Elektra, additional, Sail, Kavita, additional, Sen, Rohini, additional, Turner, Sean C, additional, Cocks, Kim, additional, Reijneveld, Jaap, additional, Gerlinger, Christoph, additional, Keating, Karen, additional, Su, Yun, additional, Wolf, Birgit, additional, Ge, Miaomiao, additional, Ingelgaard, Anders, additional, Peil, Barbara, additional, Voorhaar, Maarten, additional, Wong, Brendon, additional, Dekanic Arbanas, Gracia, additional, Kuljanic, Karin, additional, Petranovic, Duska, additional, Rede, Ivana, additional, Arraras, Juan, additional, Coons, Stephen Joel, additional, Eremenco, Sonya, additional, Murray, Lindsey, additional, Reeve, Bryce, additional, De Vries, Corinne, additional, Herold, Ralf, additional, Pignatti, Francesco, additional, Machingura, Abigirl, additional, Martinelli, Francesca, additional, Musoro, Jammbe, additional, Piccart, Martine, additional, Barriuso, Jorge, additional, Cherny, Nathan, additional, Dafni, Ourania, additional, De Vries, Elisabeth, additional, Gyawali, Bishal, additional, Kiesewetter, Barbara, additional, Oosting, Sjoukje, additional, Roitberg, Felipe, additional, Rumpold, Gerhard, additional, Schoepf, Felix, additional, Tschuggnall, Michael, additional, Black, Jennifer, additional, Sasseville, Maxime, additional, Soltys, Katherine, additional, Ferrer, Montserrat, additional, Garin, Olatz, additional, Vilagut, Gemma, additional, Schürmann, Christoph, additional, Thomas, Stefanie, additional, Wieseler, Beate, additional, Snyder, Claire, additional, Alonso Abad, Ariel, additional, Bogaerts, Kris, additional, Brackx, Febe, additional, Molenberghs, Geert, additional, Verbeke, Geert, additional, Frigolett Catalan, Cristián, additional, Choi, Jan, additional, Thomassen, Doranne, additional, Geissler, Jan, additional, Sauerbrei, Willi, additional, Gross, Franziska, additional, Pilz, Micha Johannes, additional, Barbachano, Yolanda, additional, Campbell, Lisa, additional, Rantell, Khadija, additional, Desplanques, Gregoire, additional, Regnault, Antoine, additional, Morgan, Kate, additional, Plate, Ananda, additional, ten Seldam, Silene, additional, Terada, Mitsumi, additional, Mizusawa, Junki, additional, Mitchell, Sandra, additional, Wilder Smith, Ashley, additional, Reksten, Tove Ragna, additional, Schiel, Anja, additional, Hansen Joseph, Kenth Louis, additional, Campbell, Alicyn, additional, Cappelleri, Joseph, additional, de Besi, Patrizia, additional, Russell-Smith, Alexander, additional, Sandin, Rickard, additional, Mamolo, Carla, additional, Brundage, Michael, additional, Tu, Dongsheng, additional, Groenvold, Mogens, additional, Petersen, Morten, additional, Cleeland, Charlie, additional, Williams, Lori, additional, Shelley Wang, Xin, additional, Ringash, Jolie, additional, Calvert, Melanie, additional, Cruz Rivera, Samantha, additional, Aiyegbusi, Olalekan Lee, additional, Goetghebeur, Els, additional, Liu, Limin, additional, Van Lancker, Kelly, additional, Boele, Florien, additional, Gilbert, Alexandra, additional, Peacock, Rosemary, additional, Basch, Ethan, additional, King, Madeleine, additional, Rutherford, Claudia, additional, Bhatnagar, Vishal, additional, Fiero, Mallorie, additional, Horodniceanu, Erica, additional, Johnson, Laura Lee, additional, Kluetz, Paul, additional, and Rodriguez, Lisa, additional

Published
2023
Full Text
View/download PDF

17. Single-arm studies involving patient-reported outcome data in oncology: a literature review on current practice

Author

Liu, Limin, primary, Choi, Jungyeon, additional, Musoro, Jammbe Z, additional, Sauerbrei, Willi, additional, Amdal, Cecilie Delphin, additional, Alanya, Ahu, additional, Barbachano, Yolanda, additional, Cappelleri, Joseph C, additional, Falk, Ragnhild Sørum, additional, Fiero, Mallorie H, additional, Regnault, Antoine, additional, Reijneveld, Jaap C, additional, Sandin, Rickard, additional, Thomassen, Doranne, additional, Roychoudhury, Satrajit, additional, Goetghebeur, Els, additional, le Cessie, Saskia, additional, Aiyegbusi, Olalekan Lee, additional, Basch, Ethan, additional, Calvert, Melanie, additional, Campbell, Alicyn, additional, Cappelleri, Joseph, additional, Cruz Rivera, Samantha, additional, Delphin Amdal, Cecilie, additional, Fiero, Mallorie, additional, Kamalakar, Rajesh, additional, Keating, Karen, additional, Kluetz, Paul, additional, Liu, Limin, additional, Molenberghs, Geert, additional, Musoro, Jammbe, additional, Ness, David, additional, Rantell, Khadija, additional, Reijneveld, Jaap, additional, Rumpold, Gerhard, additional, Russell-Smith, Alexander, additional, Rutherford, Claudia, additional, Sail, Kavita, additional, Sasseville, Maxime, additional, Schiel, Anja, additional, Schlichting, Michael, additional, Soltys, Kathy, additional, Sorum Falk, Ragnhild, additional, Su, Yun, additional, ten Seldam, Silene, additional, and Van Lancker, Kelly, additional

Published
2023
Full Text
View/download PDF

21. Review of guidance papers on regression modeling in statistical series of medical journals.

Author

Wallisch, Christine, Bach, Paul, Hafermann, Lorena, Klein, Nadja, Sauerbrei, Willi, Steyerberg, Ewout W., Heinze, Georg, and Rauch, Geraldine

Subjects
REGRESSION analysis, STATISTICAL models, POISSON regression, MEDICAL research personnel, MEDICAL misconceptions, LOGISTIC regression analysis, NONLINEAR oscillators, ELECTRONIC journals
Abstract

Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

22. Data-Driven Simulations to Assess the Impact of Study Imperfections in Time-to-Event Analyses.

Author

Abrahamowicz M, Beauchamp ME, Boulesteix AL, Morris TP, Sauerbrei W, Kaufman JS, and Stratos Simulation Panel OBOT

Abstract

Quantitative bias analysis (QBA) permits assessment of the expected impact of various imperfections of the available data on the results and conclusions of a particular real-world study. This article extends QBA methodology to multivariable time-to-event analyses with right-censored endpoints, possibly including time-varying exposures or covariates. The proposed approach employs data-driven simulations, which preserve important features of the data at hand while offering flexibility in controlling the parameters and assumptions that may affect the results. First, the steps required to perform data-driven simulations are described, and then two examples of real-world time-to-event analyses illustrate their implementation and the insights they may offer. The first example focuses on the omission of an important time-invariant predictor of the outcome in a prognostic study of cancer mortality, and permits separating the expected impact of confounding bias from non-collapsibility. The second example assesses how imprecise timing of an interval-censored event - ascertained only at sparse times of clinic visits - affects its estimated association with a time-varying drug exposure. The simulation results also provide a basis for comparing the performance of two alternative strategies for imputing the unknown event times in this setting. The R scripts that permit the reproduction of our examples are provided., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results