14 results on '"Sauerbrei, Willi"'
Search Results
2. 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
3. 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
4. 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, Sauerbrei, Willi, Emerging Risk Factors Collaboration, White, Ian R [0000-0002-6718-7661], and Apollo - University of Cambridge Repository
- Subjects
Male ,prognostic research ,Models, Statistical ,Coronary Disease ,fractional polynomials ,multivariate meta-analysis ,Middle Aged ,random effects models ,Body Mass Index ,meta-analysis ,Meta-Analysis as Topic ,Nonlinear Dynamics ,Risk Factors ,Humans ,Female ,Mortality - 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
- 2019
5. 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, Sauerbrei, Willi, and Emerging Risk Factors Collaboration
- Subjects
CHAOS theory ,CORONARY disease ,META-analysis ,MORTALITY ,BODY mass index ,STATISTICAL models - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
6. Bootstrap assessment of the stability of multivariable models
- Author
-
Royston, Patrick and Sauerbrei, Willi
- Subjects
mfpboot ,continuous covariates ,mfpboot_bif ,fractional polynomials ,mfp ,stability ,pmbevalfn ,multivariable modeling ,bagging ,pmbstabil ,pmbeval ,bootstrap ,Research Methods/ Statistical Methods ,fracpoly - Abstract
Assessing the instability of a multivariable model is important but is rarely done in practice. Model instability occurs when selected predictors—and for multivariable fractional polynomial modeling, selected functions of continuous predictors—are sensitive to small changes in the data. Bootstrap analysis is a useful technique for investigating variations among selected models in samples drawn at random with replacement. Such samples mimic datasets that are structurally similar to that under study and that could plausibly have arisen instead. The bootstrap inclusion fraction of a candidate variable usefully indicates the importance of the variable. We describe Stata tools for stability analysis in the context of the mfp command for multivariable model building. We offer practical guidance and illustrate the application of the tools to a study in prostate cancer.
- Published
- 2009
- Full Text
- View/download PDF
7. Two techniques for investigating interactions between treatment and continuous covariates in clinical trials
- Author
-
Royston, Patrick and Sauerbrei, Willi
- Subjects
mfpi_plot ,clinical trials ,treatment–covariate interaction ,continuous covariates ,fractional polynomials ,subpopulation treatment-effect pattern plot ,Research Methods/ Statistical Methods ,mfpi ,stepp_window ,stepp_tail ,stepp_plot - Abstract
There is increasing interest in the medical world in the possibility of tailoring treatment to the individual patient. Statistically, the relevant task is to identify interactions between covariates and treatments, such that the patient’s value of a given covariate influences how strongly (or even whether) they are likely to respond to a treatment. The most valuable data are obtained in randomized controlled clinical trials of novel treatments in comparison with a control treatment. We describe two techniques to detect and model such interactions. The first technique, multivariable fractional polynomials interaction, is based on fractional polynomials methodology, and provides a method of testing for continuous-bybinary interactions and by modeling the treatment effect as a function of a continuous covariate. The second technique, subpopulation treatment-effect pattern plot, aims to do something similar but is focused on producing a nonparametric estimate of the treatment effect, expressed graphically. Stata programs for both of these techniques are described. Real data for brain and breast cancer are used as examples.
- Published
- 2009
- Full Text
- View/download PDF
8. Modeling Variables With a Spike at Zero: Examples and Practical Recommendations.
- Author
-
Lorenz, Eva, Jenkner, Carolin, Sauerbrei, Willi, and Becher, Heiko
- Subjects
BREAST tumors ,CANCER ,EPIDEMIOLOGY ,LARYNGEAL tumors ,LUNG tumors ,REGRESSION analysis ,CASE-control method ,STATISTICAL models - Abstract
In most epidemiologic studies and in clinical research generally, there are variables with a spike at zero, namely variables for which a proportion of individuals have zero exposure (e.g., never smokers) and among those exposed the variable has a continuous distribution. Different options exist for modeling such variables, such as categorization where the nonexposed form the reference group, or ignoring the spike by including the variable in the regression model with or without some transformation or modeling procedures. It has been shown that such situations can be analyzed by adding a binary indicator (exposed/nonexposed) to the regression model, and a method based on fractional polynomials with which to estimate a suitable functional form for the positive portion of the spike-at-zero variable distribution has been developed. In this paper, we compare different approaches using data from 3 case-control studies carried out in Germany: the Mammary Carcinoma Risk Factor Investigation (MARIE), a breast cancer study conducted in 2002-2005 (Flesch-Janys et al., IntJ Cancer. 2008;123(4):933-941); the Rhein-Neckar Larynx Study, a study of laryngeal cancer conducted in 1998-2000 (Dietz et al., Int J Cancer. 2004;108(6):907-911); and a lung cancer study conducted in 1988-1993 (Jöckel et al., Int J Epidemiol. 1998; 27(4):549-560). Strengths and limitations of different procedures are demonstrated, and some recommendations for practical use are given. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
9. Modeling continuous covariates with a "spike" at zero: Bivariate approaches.
- Author
-
Jenkner, Carolin, Lorenz, Eva, Becher, Heiko, and Sauerbrei, Willi
- Abstract
In epidemiology and clinical research, predictors often take value zero for a large amount of observations while the distribution of the remaining observations is continuous. These predictors are called variables with a spike at zero. Examples include smoking or alcohol consumption. Recently, an extension of the fractional polynomial (FP) procedure, a technique for modeling nonlinear relationships, was proposed to deal with such situations. To indicate whether or not a value is zero, a binary variable is added to the model. In a two stage procedure, called FP-spike, the necessity of the binary variable and/or the continuous FP function for the positive part are assessed for a suitable fit. In univariate analyses, the FP-spike procedure usually leads to functional relationships that are easy to interpret. This paper introduces four approaches for dealing with two variables with a spike at zero (SAZ). The methods depend on the bivariate distribution of zero and nonzero values. Bi-Sep is the simplest of the four bivariate approaches. It uses the univariate FP-spike procedure separately for the two SAZ variables. In Bi-D3, Bi-D1, and Bi-Sub, proportions of zeros in both variables are considered simultaneously in the binary indicators. Therefore, these strategies can account for correlated variables. The methods can be used for arbitrary distributions of the covariates. For illustration and comparison of results, data from a case-control study on laryngeal cancer, with smoking and alcohol intake as two SAZ variables, is considered. In addition, a possible extension to three or more SAZ variables is outlined. A combination of log-linear models for the analysis of the correlation in combination with the bivariate approaches is proposed. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
10. Interaction of treatment with a continuous variable: simulation study of power for several methods of analysis.
- Author
-
Royston, Patrick and Sauerbrei, Willi
- Abstract
In a large simulation study reported in a companion paper, we investigated the significance levels of 21 methods for investigating interactions between binary treatment and a continuous covariate in a randomised controlled trial. Several of the methods were shown to have inflated type 1 errors. In the present paper, we report the second part of the simulation study in which we investigated the power of the interaction procedures for two sample sizes and with two distributions of the covariate (well and badly behaved). We studied several methods involving categorisation and others in which the covariate was kept continuous, including fractional polynomials and splines. We believe that the results provide sufficient evidence to recommend the multivariable fractional polynomial interaction procedure as a suitable approach to investigate interactions of treatment with a continuous variable. If subject-matter knowledge gives good arguments for a non-monotone treatment effect function, we propose to use a second-degree fractional polynomial approach, but otherwise a first-degree fractional polynomial (FP1) function with added flexibility (FLEX3) is the method of choice. The FP1 class includes the linear function, and the selected functions are simple, understandable, and transferable. Furthermore, software is available. We caution that investigation of interactions in one dataset can only be interpreted in a hypothesis-generating sense and needs validation in new data. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
11. Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response.
- Author
-
Binder, Harald, Sauerbrei, Willi, and Royston, Patrick
- Abstract
In observational studies, many continuous or categorical covariates may be related to an outcome. Various spline-based procedures or the multivariable fractional polynomial (MFP) procedure can be used to identify important variables and functional forms for continuous covariates. This is the main aim of an explanatory model, as opposed to a model only for prediction. The type of analysis often guides the complexity of the final model. Spline-based procedures and MFP have tuning parameters for choosing the required complexity. To compare model selection approaches, we perform a simulation study in the linear regression context based on a data structure intended to reflect realistic biomedical data. We vary the sample size, variance explained and complexity parameters for model selection. We consider 15 variables. A sample size of 200 (1000) and R2 = 0.2 (0.8) is the scenario with the smallest (largest) amount of information. For assessing performance, we consider prediction error, correct and incorrect inclusion of covariates, qualitative measures for judging selected functional forms and further novel criteria. From limited information, a suitable explanatory model cannot be obtained. Prediction performance from all types of models is similar. With a medium amount of information, MFP performs better than splines on several criteria. MFP better recovers simpler functions, whereas splines better recover more complex functions. For a large amount of information and no local structure, MFP and the spline procedures often select similar explanatory models. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
12. Analysing covariates with spike at zero: A modified FP procedure and conceptual issues.
- Author
-
Becher, Heiko, Lorenz, Eva, Royston, Patrick, and Sauerbrei, Willi
- Abstract
In epidemiology and in clinical research, risk factors often have special distributions. A common situation is that a proportion of individuals have exposure zero, and among those exposed, we have some continuous distribution. We call this a 'spike at zero'. Examples for this are smoking, duration of breastfeeding, or alcohol consumption. Furthermore, the empirical distribution of laboratory values and other measurements may have a semi-continuous distribution as a result of the lower detection limit of the measurement. To model the dose-response function, an extension of the fractional polynomial approach was recently proposed. In this paper, we suggest a modification of the previously suggested FP procedure. We first give the theoretical justification of this modified procedure by investigating relevant distribution classes. Here, we systematically derive the theoretical shapes of dose-response curves under given distributional assumptions (normal, log normal, gamma) in the framework of a logistic regression model. Further, we check the performance of the procedure in a simulation study and compare it to the previously suggested method, and finally we illustrate the procedures with data from a case-control study on breast cancer. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
13. Detecting an interaction between treatment and a continuous covariate: A comparison of two approaches
- Author
-
Sauerbrei, Willi, Royston, Patrick, and Zapien, Karina
- Subjects
- *
CLINICAL trials , *CLINICAL medicine , *MEDICAL experimentation on humans , *CANCER patients - Abstract
Abstract: In clinical trials, there is considerable interest in investigating whether a treatment effect is similar in all patients, or that some prognostic variable indicates a differential response to treatment. To examine this, a continuous predictor is usually categorized into groups according to one or more cutpoints. The treatment/covariate interaction is then analyzed in factorial fashion using multiplicative terms. The use of cutpoints raises several difficult issues for the analyst. It is preferable to keep continuous variables continuous in such a model. To achieve this, the MFP algorithm for multivariable model-building with fractional polynomials was recently extended to a new algorithm called multivariable fractional polynomial interaction (MFPI). With the latter, covariates may be binary, categorical or continuous, and cutpoints are avoided. MFPI is compared with a graphical technique, the subpopulation treatment-effect pattern plot or subpopulation treatment effect pattern plot (STEPP). Differences between MFPI and STEPP are illustrated by re-analysis of a randomized trial in kidney cancer. The stability of the two procedures is investigated by using the bootstrap. The Type I error probability of MFPI to ‘detect’ spurious interactions is estimated by simulation. MFPI and STEPP are found to exhibit similar treatment/covariate interactions. The tail-oriented variant of STEPP is found to give more stable and interpretable results than the sliding window variant. The type 1 error probabilty of MFPI is found to be close to its nominal value. [Copyright &y& Elsevier]
- Published
- 2007
- Full Text
- View/download PDF
14. Modeling exposures with a spike at zero: simulation study and practical application to survival data.
- Author
-
Lorenz, Eva, Jenkner, Carolin, Sauerbrei, Willi, and Becher, Heiko
- Subjects
- *
REGRESSION analysis , *BREAST cancer prognosis , *HORMONE receptors - Abstract
Risk and prognostic factors in epidemiological and clinical research are often semicontinuous such that a proportion of individuals have exposure zero, and a continuous distribution among those exposed. We call this a spike at zero (SAZ). Typical examples are consumption of alcohol and tobacco, or hormone receptor levels. To additionally model non-linear functional relationships for SAZ variables, an extension of the fractional polynomial (FP) approach was proposed. To indicate whether or not a value is zero, a binary variable is added to the model. In a two-stage procedure, called FP-spike, it is assessed whether the binary variable and/or the continuous FP function for the positive part is required for a suitable fit. In this paper, we compared the performance of two approaches – standard FP and FP-spike – in the Cox model in a motivating example on breast cancer prognosis and a simulation study. The comparisons lead to the suggestion to generally using FP-spike rather than standard FP when the SAZ effect is considerably large because the method performed better in real data applications and simulation in terms of deviance and functional form. Abbreviations: CI: confidence interval; FP: fractional polynomial; FP1: first degree fractional polynomial; FP2: second degree fractional polynomial; FSP: function selection procedure; HT: hormone therapy; OR: odds ratio; SAZ: spike at zero [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.