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521 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. State-of-the-art in selection of variables and functional forms in multivariable analysis -- outstanding issues

Author

Sauerbrei, Willi, Perperoglou, Aris, Schmid, Matthias, Abrahamowicz, Michal, Becher, Heiko, Binder, Harald, Dunkler, Daniela, Harrell Jr, Frank E., Royston, Patrick, and Heinze, Georg

Subjects
Statistics - Methodology
Abstract

How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc 'traditional' approaches to variable selection have been in use for at least 50 years. Similarly, methods for determining functional forms for continuous variables were first suggested many years ago. More recently, many alternative approaches to address these two challenges have been proposed, but knowledge of their properties and meaningful comparisons between them are scarce. To define a state-of-the-art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge many outstanding issues in multivariable modelling remain. Our main aims are to identify and illustrate such gaps in the literature and present them at a moderate technical level to the wide community of practitioners, researchers and students of statistics. We briefly discuss general issues in building descriptive regression models, strategies for variable selection, different ways of choosing functional forms for continuous variables, and methods for combining the selection of variables and functions. We discuss two examples, taken from the medical literature, to illustrate problems in the practice of modelling. Our overview revealed that there is not yet enough evidence on which to base recommendations for the selection of variables and functional forms in multivariable analysis. Such evidence may come from comparisons between alternative methods. In particular, we highlight seven important topics that require further investigation and make suggestions for the direction of further research.

Published
2019

7. 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
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8. 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
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12. 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
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20. Development of the Instrument to assess the Credibility of Effect Modification Analyses (ICEMAN) in randomized controlled trials and meta-analyses

Author

Schandelmaier, Stefan, Briel, Matthias, Varadhan, Ravi, Schmid, Christopher H., Devasenapathy, Niveditha, Hayward, Rodney A., Gagnier, Joel, Borenstein, Michael, van der Heijden, Geert J.M.G., Dahabreh, Issa J., Sun, Xin, Sauerbrei, Willi, Walsh, Michael, Ioannidis, John P.A., Thabane, Lehana, and Guyatt, Gordon H.

Subjects
Periodical publishing -- Analysis -- Surveys, Medical research -- Analysis -- Surveys, Health
Abstract

BACKGROUND: Most randomized controlled trials (RCTs) and meta-analyses of RCTs examine effect modification (also called a subgroup effect or interaction), in which the effect of an intervention varies by another variable (e.g., age or disease severity). Assessing the credibility of an apparent effect modification presents challenges; therefore, we developed the Instrument for assessing the Credibility of Effect Modification Analyses (ICEMAN). METHODS: To develop ICEMAN, we established a detailed concept; identified candidate credibility considerations in a systematic survey of the literature; together with experts, performed a consensus study to identify key considerations and develop them into instrument items; and refined the instrument based on feedback from trial investigators, systematic review authors and journal editors, who applied drafts of ICEMAN to published claims of effect modification. RESULTS: The final instrument consists of a set of preliminary considerations, core questions (5 for RCTs, 8 for meta-analyses) with 4 response options, 1 optional item for additional considerations and a rating of credibility on a visual analogue scale ranging from very low to high. An accompanying manual provides rationales, detailed instructions and examples from the literature. Seventeen potential users tested ICEMAN; their suggestions improved the user-friendliness of the instrument. INTERPRETATION: The Instrument for assessing the Credibility of Effect Modification Analyses offers explicit guidance for investigators, systematic reviewers, journal editors and others considering making a claim of effect modification or interpreting a claim made by others., Investigators who conduct randomized controlled trials (RCTs) and meta-analyses of RCTs often perform analyses of effect modification to assess whether intervention effects might vary by another variable such as age, [...]

Published
2020
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23. 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
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24. 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
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25. Prognostic factor research

Author

Riley, Richard D, additional, Moons, Karel GM, additional, Hayden, Jill A, additional, Sauerbrei, Willi, additional, and Altman, Douglas G, additional

Published
2019
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42. Prevention of Cervical Cancer

Author

Hillemanns, Peter, Friese, Klaus, Dannecker, Christian, Klug, Stefanie, Seifert, Ulrike, Iftner, Thomas, Hädicke, Juliane, Löning, Thomas, Horn, Lars, Schmidt, Dietmar, Ikenberg, Hans, Steiner, Manfred, Freitag, Ulrich, Siebert, Uwe, Sroczynski, Gaby, Sauerbrei, Willi, Beckmann, Matthias W., Gebhardt, Marion, Friedrich, Michael, Münstedt, Karsten, Schneider, Achim, Kaufmann, Andreas, Petry, K. Ulrich, Schäfer, Axel P. A., Pawlita, Michael, Weis, Joachim, Mehnert, Anja, Fehr, Mathias, Grimm, Christoph, Reich, Olaf, Arbyn, Marc, Kleijnen, Jos, Wesselmann, Simone, Nothacker, Monika, Follmann, Markus, Langer, Thomas, and Jentschke, Matthias

Subjects
HPV, zervikale intraepitheliale Neoplasie (CIN), cervical cancer, zervikale Präkanzerosen, cervical precancerous condition, Zervixkarzinom, GebFra Science, Guideline/Leitlinie, cervical intraepithelial neoplasia (CIN), female genital diseases and pregnancy complications
Abstract

Aims Annual opportunistic screening for cervical carcinoma has been done in Germany since 1971. The creation of this S3 guideline meets an important need, outlined in the National Cancer Plan, with regard to screening for cervical cancer, as this guideline aims to provide important information and support for planned organized screening for cervical cancer in Germany. Methods With the financial support of German Cancer Aid, 21 professional societies developed evidence-based statements and recommendations (classified using the GRADE system) for the screening, management and treatment of precancerous conditions of the cervix. Two independent scientific institutes compiled systematic reviews for this guideline. Recommendations The second part of this short summary deals with the triage, treatment and follow-up care of cervical dysplasia. With regard to those women who do not participate in screening, the guideline authors recommend sending out repeat invitation letters or an HPV self-collection kit. Colposcopy should be carried out for further investigation if cytology findings are Pap II-p and HPV test results are positive or if the results of an HPV 16 or HPV 18 screening test are positive. A single abnormal Pap smear should be triaged and investigated using HPV testing or p16/Ki67 dual staining.

Published
2019

43. Meta‐analysis of non‐linear exposure‐outcome relationships using individual participant data: A comparison of two methods

Author

White, Ian R., Kaptoge, Stephen, Royston, Patrick, and Sauerbrei, Willi

Subjects
Male, prognostic research, Models, Statistical, multivariate meta‐analysis, Coronary Disease, fractional polynomials, Middle Aged, random effects models, Body Mass Index, Meta-Analysis as Topic, Nonlinear Dynamics, meta‐analysis, Risk Factors, Humans, Female, Mortality, Research Articles, Research Article
Abstract

Non‐linear exposure‐outcome relationships such as between body mass index (BMI) and mortality are common. They are best explored as continuous functions using individual participant data from multiple studies. We explore two two‐stage methods for meta‐analysis of such relationships, where the confounder‐adjusted relationship is first estimated in a non‐linear regression model in each study, then combined across studies. The “metacurve” approach combines the estimated curves using multiple meta‐analyses of the relative effect between a given exposure level and a reference level. The “mvmeta” approach combines the estimated model parameters in a single multivariate meta‐analysis. Both methods allow the exposure‐outcome relationship to differ across studies. Using theoretical arguments, we show that the methods differ most when covariate distributions differ across studies; using simulated data, we show that mvmeta gains precision but metacurve is more robust to model mis‐specification. We then compare the two methods using data from the Emerging Risk Factors Collaboration on BMI, coronary heart disease events, and all‐cause mortality (>80 cohorts, >18 000 events). For each outcome, we model BMI using fractional polynomials of degree 2 in each study, with adjustment for confounders. For metacurve, the powers defining the fractional polynomials may be study‐specific or common across studies. For coronary heart disease, metacurve with common powers and mvmeta correctly identify a small increase in risk in the lowest levels of BMI, but metacurve with study‐specific powers does not. For all‐cause mortality, all methods identify a steep U‐shape. The metacurve and mvmeta methods perform well in combining complex exposure‐disease relationships across studies.

Published
2018

44. Towards guidance for issues in statistical analyses of observational studies

Author

Sauerbrei, Willi

Subjects
ddc: 610, 610 Medical sciences, Medicine, STRATOS
Abstract

The validity and practical utility of observational medical research depends critically on good study design, appropriate statistical analysis methods, accurate interpretation of results, and consideration of sources of potential bias in the data. Statistical methodology has seen substantial development[for full text, please go to the a.m. URL], 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

Published
2021
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45. Outstanding issues in selection of variables and functional forms in multivariable analysis

Author

Sauerbrei, Willi

Subjects
shrinkage, ddc: 610, fractional polynomials, 610 Medical sciences, Medicine, function selection, variable selection
Abstract

Willi Sauerbrei on behalf of TG2. How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc ‘traditional’ approaches to variable selection have been in use for at least 50 years. Similarly, methods[for full text, please go to the a.m. URL], 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

Published
2021

46. Variable selection and shrinkage in low-dimensional data

Author

Kipruto, Edwin and Sauerbrei, Willi

Subjects
shrinkage, ddc: 610, 610 Medical sciences, Medicine, descriptive models, variable selection
Abstract

Background: Over the last two decades combination of variable selection and shrinkage has grown in popularity due to the need to correct for overestimation bias and to improve the prediction accuracy of a model. Several statistical approaches have been developed such as the non-negative garrote (NNG)[for full text, please go to the a.m. URL], 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

Published
2021

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