TY - JOUR AB - Expectile regression, in contrast to classical linear regression, allows for heteroscedasticity and omits a parametric specification of the underlying distribution. This model class can be seen as a quantile‐like generalization of least squares regression. Similarly as in quantile regression, the whole distribution can be modeled with expectiles, while still offering the same flexibility in the use of semiparametric predictors as modern mean regression. However, even with no parametric assumption for the distribution of the response in expectile regression, the model is still constructed with a linear relationship between the fitted value and the predictor. If the true underlying relationship is nonlinear then severe biases can be observed in the parameter estimates as well as in quantities derived from them such as model predictions. We observed this problem during the analysis of the distribution of a self‐reported hearing score with limited range. Classical expectile regression should in theory adhere to these constraints, however, we observed predictions that exceeded the maximum score. We propose to include a response function between the fitted value and the predictor similarly as in generalized linear models. However, including a fixed response function would imply an assumption on the shape of the underlying distribution function. Such assumptions would be counterintuitive in expectile regression. Therefore, we propose to estimate the response function jointly with the covariate effects. We design the response function as a monotonically increasing P‐spline, which may also contain constraints on the target set. This results in valid estimates for a self‐reported listening effort score through nonlinear estimates of the response function. We observed strong associations with the speech reception threshold. AU - Spiegel, E AU - Kneib, T.* AU - von Gablenz, P.* AU - Otto‐Sobotka, F.* C1 - 61058 C2 - 50022 CY - 111 River St, Hoboken 07030-5774, Nj Usa SP - 1028-1051 TI - Generalized expectile regression with flexible response function. JO - Biom. J. VL - 63 IS - 5 PB - Wiley PY - 2021 SN - 0323-3847 ER - TY - JOUR AB - Mixture modeling is a popular approach to accommodate overdispersion, skewness, and multimodality features that are very common for health care utilization data. However, mixture modeling tends to rely on subjective judgment regarding the appropriate number of mixture components or some hypothesis about how to cluster the data. In this work, we adopt a nonparametric, variational Bayesian approach to allow the model to select the number of components while estimating their parameters. Our model allows for a probabilistic classification of observations into clusters and simultaneous estimation of a Gaussian regression model within each cluster. When we apply this approach to data on patients with interstitial lung disease, we find distinct subgroups of patients with differences in means and variances of health care costs, health and treatment covariates, and relationships between covariates and costs. The subgroups identified are readily interpretable, suggesting that this nonparametric variational approach to inference can discover valid insights into the factors driving treatment costs. Moreover, the learning algorithm we employed is very fast and scalable, which should make the technique accessible for a broad range of applications. AU - Kurz, C.F. AU - Stafford, S.* C1 - 60138 C2 - 49268 CY - 111 River St, Hoboken 07030-5774, Nj Usa SP - 1896-1908 TI - Isolating cost drivers in interstitial lung disease treatment using nonparametric Bayesian methods. JO - Biom. J. VL - 62 IS - 8 PB - Wiley PY - 2020 SN - 0323-3847 ER - TY - JOUR AB - The ultrafine particle measurements in the Augsburger Umweltstudie, a panel study conducted in Augsburg, Germany, exhibit measurement error from various sources. Measurements of mobile devices show classical possibly individual-specific measurement error; Berkson-type error, which may also vary individually, occurs, if measurements of fixed monitoring stations are used. The combination of fixed site and individual exposure measurements results in a mixture of the two error types. We extended existing bias analysis approaches to linear mixed models with a complex error structure including individual-specific error components, autocorrelated errors, and a mixture of classical and Berkson error. Theoretical considerations and simulation results show, that autocorrelation may severely change the attenuation of the effect estimations. Furthermore, unbalanced designs and the inclusion of confounding variables influence the degree of attenuation. Bias correction with the method of moments using data with mixture measurement error partially yielded better results compared to the usage of incomplete data with classical error. Confidence intervals (CIs) based on the delta method achieved better coverage probabilities than those based on Bootstrap samples. Moreover, we present the application of these new methods to heart rate measurements within the Augsburger Umweltstudie: the corrected effect estimates were slightly higher than their naive equivalents. The substantial measurement error of ultrafine particle measurements has little impact on the results. The developed methodology is generally applicable to longitudinal data with measurement error. AU - Deffner, V.* AU - Küchenhoff, H.* AU - Breitner-Busch, S. AU - Schneider, A.E. AU - Cyrys, J. AU - Peters, A. C1 - 53248 C2 - 44644 SP - 480-497 TI - Mixtures of Berkson and classical covariate measurement error in the linear mixed model: Bias analysis and application to a study on ultrafine particles. JO - Biom. J. VL - 60 IS - 3 PY - 2018 SN - 0323-3847 ER - TY - JOUR AB - The joint modeling of longitudinal and time-to-event data is an important tool of growing popularity to gain insights into the association between a biomarker and an event process. We develop a general framework of flexible additive joint models that allows the specification of a variety of effects, such as smooth nonlinear, time-varying and random effects, in the longitudinal and survival parts of the models. Our extensions are motivated by the investigation of the relationship between fluctuating disease-specific markers, in this case autoantibodies, and the progression to the autoimmune disease type 1 diabetes. Using Bayesian P-splines, we are in particular able to capture highly nonlinear subject-specific marker trajectories as well as a time-varying association between the marker and event process allowing new insights into disease progression. The model is estimated within a Bayesian framework and implemented in the R-package bamlss. AU - Köhler, M. AU - Umlauf, N.* AU - Beyerlein, A. AU - Winkler, C. AU - Ziegler, A.-G. AU - Greven, S.* C1 - 51703 C2 - 43412 CY - Hoboken SP - 1144-1165 TI - Flexible Bayesian additive joint models with an application to type 1 diabetes research. JO - Biom. J. VL - 59 IS - 6 PB - Wiley PY - 2017 SN - 0323-3847 ER - TY - JOUR AB - MotivationDiscrimination statistics describe the ability of a survival model to assign higher risks to individuals who experience earlier events: examples are Harrell's C-index and Royston and Sauerbrei's D, which we call the D-index. Prognostic covariates whose distributions are controlled by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination statistics in censored survival data. ObjectiveTo develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s). MethodWe define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration. ResultsThe proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were substantially smaller than unadjusted values. The study-specific standard deviation of baseline age was strongly associated with the unadjusted C-index and D-index but not significantly associated with the age-adjusted indices. ConclusionsThe proposed estimators improve meta-analysis comparisons, are easy to implement and give a more meaningful clinical interpretation. AU - White, I.R.* AU - Rapsomaniki, E.* AU - Emerging Risk Factors Collaboration (Meisinger, C.) C1 - 46437 C2 - 37581 CY - Hoboken SP - 592-613 TI - Covariate-adjusted measures of discrimination for survival data. JO - Biom. J. VL - 57 IS - 4 PB - Wiley-blackwell PY - 2015 SN - 0323-3847 ER - TY - JOUR AU - Heim, S.* AU - Hahn, K.R. AU - Auer, D.P.* AU - Fahrmeir, L.* C1 - 4130 C2 - 21938 SP - S.18 TI - Diffusion tensor magnetic resonance imaging in human brain research. JO - Biom. J. VL - 46 PY - 2004 SN - 0323-3847 ER - TY - JOUR AB - An S-estimator is defined for the one-way random effects model, analogous to an S-estimator in the model of i.i.d. random vectors. The estimator resembles the multivariate S-estimator with respect to existence and weak continuity. The proof of existence of the estimator yields in addition an upper bound for the breakdown point of the S-estimator of one of the variance components which is rather low. An improvement of the estimator is proposed which overcomes this deficiency. Nevertheless this estimator is an example that new problems of robustness arise in more structured models. AU - Wellmann, J. C1 - 21644 C2 - 19792 SP - 215-221 TI - Robustness of an S-Estimator in the One-Way Random Effects Model. JO - Biom. J. VL - 42 IS - 2 PY - 2000 SN - 0323-3847 ER - TY - JOUR AB - The Influence of the Incubation Period to Qualitative Aspects of the Spread of AIDS. AU - Lasser, R. AU - Obermaier, J. C1 - 17364 C2 - 10085 SP - 833-839 TI - The Influence of the Incubation Period to Qualitative Aspects of the Spread of AIDS. JO - Biom. J. VL - 31 IS - 7 PY - 1989 SN - 0323-3847 ER - TY - JOUR AU - Tritschler, J. C1 - 17182 C2 - 10274 TI - A Semi-Markov Modell for the Progression of the HVI-Infection. JO - Biom. J. VL - 2 PY - 1989 SN - 0323-3847 ER - TY - JOUR AU - Chambless, L. C1 - 17537 C2 - 10464 SP - 313-328 TI - On the Use of Two-Stage Cluster Samples in Epidemiological Population Studies. JO - Biom. J. VL - 30 PY - 1988 SN - 0323-3847 ER -