Accuracy and concordance of measurement methods to assess non-adherence after renal transplantation - A prospective study - Lieb, Marietta; Hepp, Tobias; Schiffer, Mario; Opgenoorth, Mirian; Erim, Yesim (BMC Nephrology, 2020)
Background: Non-adherence (NA) to immunosuppressants (IS) among renal transplant recipients (RTRs) is associated with higher risk of allograft rejection, graft loss, and mortality. A precise measurement of NA is indispensable, although its prevalence differs greatly depending on the respective measurement methods. The objective of this study was to assess the accuracy and concordance of different measurement methods of NA in patients after renal transplantation. Design and methods: This was a single-center prospective observational study. At baseline (T0), NA was measured via physicians' estimates (PE), self-reports (SR), and tacrolimus trough level variability (CV%) in 78 RTRs. A Visual Analogue Scale (VAS, 0-100%) was applied both for SR and PE. In addition, we used BAASIS© for SR and a 5-point Likert scale for PE. NA was measured prospectively via electronic monitoring (EM, VAICA©) during a three month period. Meanwhile, all participants received phone calls in a two week interval (T1-T6) during which SRs were given. Results: Seventy-eight RTRs participated in our study. At t0, NA rates of 6.4%, 28.6%, and 15.4% were found for PE, SR, and CV%, respectively. No correlation was found between these methods. During the study, the percentages of self-reported and electronically monitored adherence remained high, with a minimum mean of 91.2% for the strictest adherence measure (Timing Adherence ±30 min). Our results revealed a moderate to high association between SR and EM. In contrast to PE and CV%, SR significantly predicted electronically monitored adherence. Overall, a decreasing effect of electronically monitored adherence was found for both taking and timing adherence (±2 h, ±30 min) over the course of the study. Discussion: The moderate to high concordance of SR and EM suggests that both methods measure NA equally accurately. SR seems to be a method that can adequately depict electronically monitored NA and may represent a good and economical instrument to assess NA in clinical practice. The increased adherence at the beginning of the study and its subsequent decrease suggests an intervention effect. Surveillance of IS intake via EM with intermittent phone calls could improve adherence on a short-term basis. To establish long-term effects, further research is necessary.
CRIS-Team Scopus Importer:2020-04-14
Keywords: Accuracy; Adherence; Electronic monitoring; IS trough level variability; Measurement methods; Physicians' estimates; Prospective study; Renal transplant recipients; Self-report

Adaptive step-length selection in gradient boosting for Gaussian location and scale models - Zhang, Boyao; Hepp, Tobias; Greven, Sonja; Bergherr, Elisabeth (Computational Statistics, 2022)
Tuning of model-based boosting algorithms relies mainly on the number of iterations, while the step-length is fixed at a predefined value. For complex models with several predictors such as Generalized additive models for location, scale and shape (GAMLSS), imbalanced updates of predictors, where some distribution parameters are updated more frequently than others, can be a problem that prevents some submodels to be appropriately fitted within a limited number of boosting iterations. We propose an approach using adaptive step-length (ASL) determination within a non-cyclical boosting algorithm for Gaussian location and scale models, as an important special case of the wider class of GAMLSS, to prevent such imbalance. Moreover, we discuss properties of the ASL and derive a semi-analytical form of the ASL that avoids manual selection of the search interval and numerical optimization to find the optimal step-length, and consequently improves computational efficiency. We show competitive behavior of the proposed approaches compared to penalized maximum likelihood and boosting with a fixed step-length for Gaussian location and scale models in two simulations and two applications, in particular for cases of large variance and/or more variables than observations. In addition, the underlying concept of the ASL is also applicable to the whole GAMLSS framework and to other models with more than one predictor like zero-inflated count models, and brings up insights into the choice of the reasonable defaults for the step-length in the simpler special case of (Gaussian) additive models.
CRIS-Team Scopus Importer:2022-02-11
Keywords: GAMLSS; Gradient boosting; Shrinkage; Step-Length; Variable selection

Addendum to: Approaches to Regularized Regression - A Comparison between Gradient Boosting and the Lasso - Hepp, Tobias; Schmid, Matthias; Gefeller, Olaf; Waldmann, Elisabeth; Mayr, Andreas (Methods of Information in Medicine, 2019)
CRIS-Team Scopus Importer:2019-07-30

An Update on Statistical Boosting in Biomedicine - Mayr, Andreas; Hofner, Benjamin; Waldmann, Elisabeth; Hepp, Tobias; Meyer, Sebastian; Gefeller, Olaf (Computational and Mathematical Methods in Medicine, 2017)
Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.
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Apparent Diffusion Coefficient (ADC) Histogram Analysis in Parotid Gland Tumors: Evaluating a Novel Approach for Differentiation between Benign and Malignant Parotid Lesions Based on Full Histogram Distributions. - Hepp, Tobias; Wüst, Wolfgang; Heiß, Rafael; May, Matthias; Kopp, Markus; Wetzl, Matthias; Treutlein, Christoph; Uder, Michael; Wiesmüller, Marco (Diagnostics, 2022)
The aim of this study was to assess the diagnostic value of ADC distribution curves for differentiation between benign and malignant parotid gland tumors and to compare with mean ADC values. 73 patients with parotid gland tumors underwent head-and-neck MRI on a 1.5 Tesla scanner prior to surgery and histograms of ADC values were extracted. Histopathological results served as a reference standard for further analysis. ADC histograms were evaluated by comparing their similarity to a reference distribution using Chi2-test-statistics. The assumed reference distribution for benign and malignant parotid gland lesions was calculated after pooling the entire ADC data. In addition, mean ADC values were determined. For both methods, we calculated and compared the sensitivity and specificity between benign and malignant parotid gland tumors and three subgroups (pleomorphic adenoma, Warthin tumor, and malignant lesions), respectively. Moreover, we performed cross-validation (CV) techniques to estimate the predictive performance between ADC distributions and mean values. Histopathological results revealed 30 pleomorphic adenomas, 22 Warthin tumors, and 21 malignant tumors. ADC histogram distribution yielded a better specificity for detection of benign parotid gland lesions (ADChistogram: 75.0% vs. ADCmean: 71.2%), but mean ADC values provided a higher sensitivity (ADCmean: 71.4% vs. ADChistogram: 61.9%). The discrepancies are most pronounced in the differentiation between malignant and Warthin tumors (sensitivity ADCmean: 76.2% vs. ADChistogram: 61.9%; specificity ADChistogram: 81.8% vs. ADCmean: 68.2%). Using CV techniques, ADC distribution revealed consistently better accuracy to differentiate benign from malignant lesions ("leave-one-out CV" accuracy ADChistogram: 71.2% vs. ADCmean: 67.1%). ADC histogram analysis using full distribution curves is a promising new approach for differentiation between primary benign and malignant parotid gland tumors, especially with respect to the advantage in predictive performance based on CV techniques.
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Approaches to Regularized Regression - A Comparison between Gradient Boosting and the Lasso - Hepp, Tobias; Schmid, Matthias; Gefeller, Olaf; Waldmann, Elisabeth; Mayr, Andreas (Methods of Information in Medicine, 2016)
Penalization and regularization techniques for statistical modeling have attracted increasing attention in biomedical research due to their advantages in the presence of high-dimensional data. A special focus lies on algorithms that incorporate automatic variable selection like the least absolute shrinkage operator (lasso) or statistical boosting techniques.Focusing on the linear regression framework, this article compares the two most-common techniques for this task, the lasso and gradient boosting, both from a methodological and a practical perspective.We describe these methods highlighting under which circumstances their results will coincide in low-dimensional settings. In addition, we carry out extensive simulation studies comparing the performance in settings with more predictors than observations and investigate multiple combinations of noise-to-signal ratio and number of true non-zero coeffcients. Finally, we examine the impact of different tuning methods on the results.Both methods carry out penalization and variable selection for possibly highdimensional data, often resulting in very similar models. An advantage of the lasso is its faster run-time, a strength of the boosting concept is its modular nature, making it easy to extend to other regression settings.Although following different strategies with respect to optimization and regularization, both methods imply similar constraints to the estimation problem leading to a comparable performance regarding prediction accuracy and variable selection in practice.
EVALuna2:5566

A Taxonomy for Psycho-Oncological Intervention Techniques in an Acute Care Hospital in Germany - Madl, Martina; Lieb, Marietta; Schieber, Katharina; Hepp, Tobias; Erim, Yesim (Oncology Research and Treatment, 2021)
Background: Due to the establishment of a nationwide certification system for cancer centers in Germany, the availability of psycho-oncological services for cancer patients has increased substantially. However, little is known about the specific intervention techniques that are applied during sessions in an acute care hospital, since a standardized taxonomy is lacking. With this study, we aimed at the investigation of psycho-oncological intervention techniques and the development of a comprehensive and structured taxonomy thereof. Methods: In a stepwise procedure, a team of psycho-oncologists generated a data pool of interventions and definitions that were tested in clinical practice during a pilot phase. After an adaptation of intervention techniques, interrater reliability (IRR) was attained by rating 10 previously recorded psycho-oncological sessions. A classification of interventions into superordinate categories was performed, supported by cluster analysis. Results: Between April and June 2017, 980 psycho-oncological sessions took place. The experts agreed on a total number of 22 intervention techniques. An IRR of 89% for 2 independent psycho-oncological raters was reached. The 22 techniques were classified into 5 superordinate categories. Discussion/Conclusion: We developed a comprehensive and structured taxonomy of psycho-oncological intervention techniques in an acute care hospital that provides a standardized basis for systematic research and applied care. We expect our work to be continuously subjected to further development: future research should evaluate and expand our taxonomy to other contexts and care settings.
CRIS-Team WoS Importer:2021-07-30

Auswirkungen der Ausgangsbeschränkungen im Zusammenhang mit Covid-19 auf Supermarktbesuche in Deutschland - Hepp, Tobias; Marquart, Pia; Jauck, Christoph; Gefeller, Olaf (Gesundheitswesen, 2021)
Background The lockdown measures imposed in Germany starting in mid-March had a significantimpact on public life. While the avoidance of social contacts was possible in many areas, the responsibility to provide themselves with food was placed in the hands of private households. Objectives To investigate whether and to what extent the restrictions led to changes regarding visits to supermarkets using cars. Materials and Methods Data were available on the parking movements on 136 parking spaces of supermarkets in Germany between the beginning of February and the end of May. The analyses focused on changes in the total number of parking events per day and slot as well as theirduration. Results Regarding the parking events, there was a clear reduction until the end of April, interrupted by an increase around Easter. After the introduction of mandatory masking, the values recovered to just below the level before lockdown. The duration changed relatively slightly. Initially it decreased, but later rose slightly above the level observed before the restrictions. Overthe medium term, there was a slight reduction of parking events and an extension of their duration by a little more than 5%. Discussion Despite the explicit exclusion of food purchases from the lockdown measures, a dynamic adaptation behaviour of the population to the changing circumstances is also evident in this context. In terms of parking movements, a lasting effect on shopping behaviourseems unlikely.
CRIS-Team Scopus Importer:2021-02-12
Keywords: Behavior change; Coronavirus; Covid-19; Risk perception

Clinical Pharmacist Counselling Improves Long-term Medication Safety and Patient-reported Outcomes in Anti-TNF-treated Patients With Inflammatory Bowel Diseases: The Prospective, Randomized AdPhaNCED Trial. - Plechschmidt, Johannes; Fietkau, Konstantin; Hepp, Tobias; Dietrich, Peter; Fischer, Sarah; Krebs, Sabine; Neurath, Markus; Dörje, Frank; Atreya, Raja (Inflammatory Bowel Diseases, 2025)
BACKGROUND: Antitumor necrosis factor (anti-TNF) antibody treatment has led to marked improvements in the management of patients with inflammatory bowel diseases (IBDs). Nevertheless, anti-TNF therapy is associated with potential adverse drug reactions (ADRs). Our prospective, randomized trial investigated the effect of intensified clinical pharmacist counselling in a multidisciplinary team on medication safety in anti-TNF-treated IBD patients. METHODS: Patients with IBD with ongoing anti-TNF treatment were enrolled in our tertiary center AdPhaNCED trial and randomized to either receive conventional standard of care (control group) or additional clinical pharmacist counselling (intervention group) over 12 months. The primary end point consisted of the number and severity of ADRs associated with anti-TNF therapy. Secondary end points included patient satisfaction with medication information and medication safety. RESULTS: One hundred twenty-seven IBD patients were included in this study. Anti-TNF-related ADRs were significantly lower in the intervention compared with the control group (0.20 vs 0.32 [mean] ADR/patient/month, P = .006) after 12 months. The risk of more severe ADRs (Common Terminology Criteria for Adverse Events [CTCAE] grade ≥2) was significantly higher in the control compared with the intervention group (hazard ratio, 0.34; P = .001). The probability of ADR resolution (hazard ratio, 2.02; P < .001) and patient satisfaction with medication information (14.82 vs 11.60; P < .001) were significantly higher in the intervention group compared with the control group. CONCLUSIONS: Our study results demonstrate that intensified pharmacist counselling significantly reduces the occurrence and severity of therapy-related ADRs and improves patient satisfaction. Clinical pharmacists should therefore be part of a holistic approach to IBD care delivered by a multidisciplinary team.
EVALuna2:566019
Keywords: inflammatory bowel diseases; patient reported outcomes; adverse drug reaction management; medication safety; patient support program

Corrigendum to "Probing for Sparse and Fast Variable Selection with Model-Based Boosting" - Thomas, Janek; Hepp, Tobias; Mayr, Andreas; Bischl, Bernd (Computational and Mathematical Methods in Medicine, 2018)
[This corrects the article DOI: 10.1155/2017/1421409.].
EVALuna2:35692

Diagnostic Utility of Interleukin-6 in Early-Onset Sepsis among Term Newborns: Impact of Maternal Risk Factors and CRP Evaluation. - Schleier, Maria; Lubig, Julia; Kehl, Sven; Hebert, Steven; Wölfle, Joachim; van der Donk, Adriana; Bär, Alisa; Reutter, Heiko Martin; Hepp, Tobias; Morhart, Patrick (Children, 2023)
(1) Background: Interleukin-6 (IL-6) levels act as an early infection marker preceding C-reactive protein (CRP) elevation. This study seeks to analyze IL-6 behavior in suspected early-onset sepsis (EOS) cases among term newborns, comparing it to that of CRP and evaluating IL-6's diagnostic utility. We also aim to assess the impact of maternal risk factors on EOS in term newborns, quantifying their influence for informed decision making. (2) Methods: The retrospective data analysis included 533 term newborns who were admitted to our hospital because of suspected EOS. IL-6, CRP, and the impact of maternal risk factors were analyzed in the context of EOS using binomial test, Chi-squared test, logistic and linear regression. (3) Results: In the cases of EOS, both IL-6 and CRP were elevated. The increase in CRP can be predicted by the initial increase in IL-6 levels. Among the assessed risk factors, intrapartum maternal fever (adjusted odds ratio 18.1; 95% CI (1.7-4.1)) was identified as the only risk factor significantly associated with EOS. (4) Conclusions: Employing IL-6 as an early infection marker enhanced EOS diagnostic precision due to its detectable early rise. However, caution is required, as elevations in IL-6 and CRP levels do not exclusively indicate EOS. Increased CRP levels in healthy newborns with maternal risk factors may be attributed to dynamics of vaginal labor.
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Die gesundheitsökonomische Evaluation der GLICEMIA 2.0-Studie als Beispiel für eine komplexe Intervention - Forster, Cordula; Prax, Katja; Jaensch, Peter; Müller, Sebastian; Hepp, Tobias; Schlager, Helmut; Friedland, Kristina; Zerth, Jürgen (Gesundheitswesen, 2022)
BACKGROUND: A piggyback approach was used to evaluate the cost-effectiveness of the prevention program delivered at the point of care pharmacy in the GLICEMIA 2.0 study that sought to guide participants in the intervention group to improved glycemic control in type 2 diabetes with sustained incentivization of lifestyle changes, therapeutic compliance, and adherence. The control group received passive medication management and monitoring. METHODS: Primary endpoint of the GLICEMIA 2.0 study was the stabilization of HbA1c values. For health economic evaluation, incremental differences in output changes were examined, defined as the difference in the distribution of the HbA1c values between both groups over time. Direct program costs and anticipated indirect costs of service utilization were used as cost parameters. A net monetary benefit approach was used to validate cost-effectiveness thresholds via the formation of ICER values. RESULTS: The intervention group had significantly higher reductions in HbA1c-values. Risk stratification of initial HbA1c showed (short-term) cost effectiveness for initially higher HbA1c values. Due to the limited study period, no long-term differences in medical resource utilization could be assessed. CONCLUSION: The GLICEMIA program indicates considerable effectiveness potentials, especially for high-risk patients. The study design seems to have assisted the intervention group's adherence in contrast to the control group. Detailed impacts within the complex intervention could not be validated due to restrictions of the study design as a complex intervention. Overall, statements about effect sustainability and further utilization rates progressions are limited due to a lack of follow-up.
CRIS-Team Scopus Importer:2022-12-23

Evaluating the Use of Neonatal Colonization Screening for Empiric Antibiotic Therapy of Sepsis and Pneumonia - Bär, Alisa; Schmitt-Grohé, Sabina; Held, Jürgen; Lubig, Julia; Hanslik, Gregor; Fahlbusch, Fabian; Reutter, Heiko Martin; Wölfle, Joachim; van der Donk, Adriana; Schleier, Maria; Hepp, Tobias; Morhart, Patrick (Antibiotics, 2023)
(1) Background: Since 2013, weekly screening for multidrug-resistant Gram-negative (MDRGN) bacteria has been performed in German neonatal intensive care units (NICU). National guidelines recommend considering these colonization analyses for antibiotic treatment regimens. Our retrospective single center study provides insight into the clinical dichotomy of bacterial colonization and infection rates in neonates. (2) Methods: We analyzed microbiological data of neonates admitted to our tertiary level NICU over nine years. Colonization with MDRGN/Serratia marcescens (SERMA) was compared to microbiological findings in sepsis and pneumonia. (3) Results: We analyzed 917 blood and 1799 tracheal aspirate samples. After applying criteria from the Nosocomial Infection Surveillance for Neonates (NEO-KISS), we included 52 and 55 cases of sepsis and pneumonia, respectively; 19.2% of sepsis patients and 34.5% of pneumonia patients had a prior colonization with MDRGN bacteria or SERMA. In these patients, sepsis was not attributable to MDRGN bacteria yet one SERMA, while in pneumonias, ten MDRGN bacteria and one SERMA were identified. We identified late-onset pneumonia and cesarean section as risk factors for MDRGN/SERMA acquisition. (4) Conclusions: Colonization screening is a useful tool for hygiene surveillance. However, our data suggest that consideration of colonization with MDRGN/SERMA might promote extensive use of last resort antibiotics in neonates.
CRIS-Team Scopus Importer:2023-03-10
Keywords: colonization; MDR; MDRGN; multidrug-resistant bacteria; multidrug-resistant Gram-negative bacteria; neonatal infection; neonates; pneumonia; screening; sepsis

Improved confidence intervals for finite mixture regression based on resampling techniques - Griesbach, Colin; Hepp, Tobias (Computational Statistics, 2025)
Finite Mixture Regression is a popular approach for regression settings with present but unobserved sub-populations. Over the past decades an extensive toolbox has been developed covering various kinds of distributions and effect types. As for any other thorough statistical analysis, reporting of e.g. confidence intervals for the parameters of the latent models is of high practical relevance. However, standard theory neglects the additional variability arising from also estimating class assignments, which consequently leads to the corresponding uncertainty estimates usually being too optimistic. In this work we propose an alternative resampling technique to construct confidence intervals for the regression coefficients of finite mixture regression models. The routine relies on bootstrapping which already proved useful for different problems arising for mixture models in the past and is evaluated via various simulation studies and two real world applications. Overall, the routine performs consistently better in terms of holding the type-I error threshold and is less computationally expensive than alternative approaches.
CRIS-Team Scopus Importer:2025-08-15
Keywords: Mixture models; Regression; Resampling; Uncertainty

Interleukin-6 secretion upon acute psychosocial stress as a potential predictor of psychotherapy outcome in posttraumatic stress disorder - Rhein, Cosima; Hepp, Tobias; Kraus, Olga; von Majewski, Kristin; Lieb, Marietta; Rohleder, Nicolas; Erim, Yesim (Journal of Neural Transmission, 2021)
Posttraumatic stress disorder (PTSD) is a severe mental disorder that can develop after a traumatic event. PTSD has been reported to be associated with activation of the innate immune system, as measured by increased levels of pro-inflammatory cytokines. While it is well known that PTSD patients display increased levels of interleukin 6 (IL-6) when compared with healthy controls, the relationship between cytokine secretion and treatment outcome has been hardly investigated yet. The aim of this study was to assess the potential association of inflammatory activation and therapy outcome in PTSD. Before therapeutic intervention, we applied the Trier Social Stress Test (TSST) as a method to elicit psychosocial stress and an acute inflammatory response. IL-6 levels were measured in blood plasma of PTSD patients at different time points before and after the TSST. Severity of depressive, trauma-related, and somatic symptoms was assessed before and 8 weeks after trauma-focused treatment in a multimodal day clinic setting. We showed that high reactivity of IL-6 to psychosocial stress at the beginning of the therapy was associated with a negative therapy outcome in PTSD, especially regarding depressive symptoms. This study suggests plasma IL-6 reactivity as a potential molecular marker to predict treatment outcome in PTSD.
CRIS-Team Scopus Importer:2021-05-28
Keywords: BDI-II; Interleukin 6; Posttraumatic stress disorder; Psychotherapy; Therapy outcome; Trier Social Stress Test

Latent class distributional regression for the estimation of non-linear reference limits from contaminated data sources - Hepp, Tobias; Zierk, Jakob; Rauh, Manfred; Metzler, Markus; Mayr, Andreas (BMC Bioinformatics, 2020)
Background: Medical decision making based on quantitative test results depends on reliable reference intervals, which represent the range of physiological test results in a healthy population. Current methods for the estimation of reference limits focus either on modelling the age-dependent dynamics of different analytes directly in a prospective setting or the extraction of independent distributions from contaminated data sources, e.g. data with latent heterogeneity due to unlabeled pathologic cases. In this article, we propose a new method to estimate indirect reference limits with non-linear dependencies on covariates from contaminated datasets by combining the framework of mixture models and distributional regression. Results: Simulation results based on mixtures of Gaussian and gamma distributions suggest accurate approximation of the true quantiles that improves with increasing sample size and decreasing overlap between the mixture components. Due to the high flexibility of the framework, initialization of the algorithm requires careful considerations regarding appropriate starting weights. Estimated quantiles from the extracted distribution of healthy hemoglobin concentration in boys and girls provide clinically useful pediatric reference limits similar to solutions obtained using different approaches which require more samples and are computationally more expensive. Conclusions: Latent class distributional regression models represent the first method to estimate indirect non-linear reference limits from a single model fit, but the general scope of applications can be extended to other scenarios with latent heterogeneity.
CRIS-Team Scopus Importer:2020-11-20
Keywords: Distributional regression; Finite mixture models; Latent class regression; Reference limits

Longitudinal Assessment of Examiner Experience and the Accuracy of Sonographic Fetal Weight Estimation at Term - Faschingbauer, Florian; Heimrich, Jutta; Raabe, Eva; Kehl, Sven; Schneider, Michael; Schmid, Matthias; Beckmann, Matthias; Hepp, Tobias; Luebke, Anika; Mayr, Andreas; Schild, Ralf L. (Journal of Ultrasound in Medicine, 2017)
OBJECTIVES: To evaluate the influence of examiner experience on the accuracy of sonographic weight estimation and to further analyze examiners' individual learning curves. METHODS: In this multicenter study, 4613 sonographic weight estimations performed by 18 examiners at the beginning of their ultrasound training were included. To assess the effect of experience on the accuracy of weight estimation, a multivariable mixed regression model analysis was performed, with percentage error and absolute percentage error as outcome variables and the examiner, the examiner's experience (number of examinations), birth weight, gestational age, scan-to-delivery interval, and maternal body mass index as fixed effects and the perinatal center as random intercepts. To further analyze the individual learning curves of the examiners, the cumulative summation technique was used. RESULTS: Regression analyses showed a significant influence of the number of examinations on the accuracy of sonographic weight estimation after adjustment for the above-mentioned parameters (P < .001). A typical learning curve with improving accuracy was found until approximately 200 examinations. Between 200 and 300 examinations, the diagnostic performance started to deteriorate again, with a continuous decrease until the end of the study period. Cumulative summation charts representing individual learning curves varied greatly between different examiners. CONCLUSIONS: These findings indicate the great importance of continuous quality control systems in sonographic weight estimation.
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Mixture density networks for the indirect estimation of reference intervals - Hepp, Tobias; Zierk, Jakob; Rauh, Manfred; Metzler, Markus; Seitz, Sarem (BMC Bioinformatics, 2022)
Background: Reference intervals represent the expected range of physiological test results in a healthy population and are essential to support medical decision making. Particularly in the context of pediatric reference intervals, where recruitment regulations make prospective studies challenging to conduct, indirect estimation strategies are becoming increasingly important. Established indirect methods enable robust identification of the distribution of “healthy” samples from laboratory databases, which include unlabeled pathologic cases, but are currently severely limited when adjusting for essential patient characteristics such as age. Here, we propose the use of mixture density networks (MDN) to overcome this problem and model all parameters of the mixture distribution in a single step. Results: Estimated reference intervals from varying settings with simulated data demonstrate the ability to accurately estimate latent distributions from unlabeled data using different implementations of MDNs. Comparing the performance with alternative estimation approaches further highlights the importance of modeling the mixture component weights as a function of the input in order to avoid biased estimates for all other parameters and the resulting reference intervals. We also provide a strategy to generate partially customized starting weights to improve proper identification of the latent components. Finally, the application on real-world hemoglobin samples provides results in line with current gold standard approaches, but also suggests further investigations with respect to adequate regularization strategies in order to prevent overfitting the data. Conclusions: Mixture density networks provide a promising approach capable of extracting the distribution of healthy samples from unlabeled laboratory databases while simultaneously and explicitly estimating all parameters and component weights as non-linear functions of the covariate(s), thereby allowing the estimation of age-dependent reference intervals in a single step. Further studies on model regularization and asymmetric component distributions are warranted to consolidate our findings and expand the scope of applications.
CRIS-Team Scopus Importer:2022-08-12
Keywords: Distributional regression; Latent class regression; Mixture density networks; Reference intervals

mRNA expression of SMPD1 encoding acid sphingomyelinase decreases upon antidepressant treatment - Rhein, Cosima; Zoicas, Iulia; Marx, Lena M.; Zeitler, Stefanie; Hepp, Tobias; von Zimmermann, Claudia; Mühle, Christiane; Richter-Schmidinger, Tanja; Lenz, Bernd; Erim, Yesim; Reichel, Martin; Gulbins, Erich; et al. (International Journal of Molecular Sciences, 2021)
Major depressive disorder (MDD) is a severe psychiatric condition with key symptoms of low mood and lack of motivation, joy, and pleasure. Recently, the acid sphingomyelinase (ASM)/ceramide system has been implicated in the pathogenesis of MDD. ASM is a lysosomal glycoprotein that catalyzes the hydrolysis of sphingomyelin, an abundant component of membranes, into the bioactive sphingolipid ceramide, which impacts signaling pathways. ASM activity is inhibited by several common antidepressant drugs. Human and murine studies have confirmed that increased ASM activity and ceramide levels are correlated with MDD. To define a molecular marker for treatment monitoring, we investigated the mRNA expression of SMPD1, which encodes ASM, in primary cell culture models, a mouse study, and a human study with untreated MDD patients before and after antidepressive treatment. Our cell culture study showed that a common antidepressant inhibited ASM activity at the enzymatic level and also at the transcriptional level. In a genetically modified mouse line with depressive-like behavior, Smpd1 mRNA expression in dorsal hippocampal tissue was significantly decreased after treatment with a common antidepressant. The large human study showed that SMPD1 mRNA expression in untreated MDD patients decreased significantly after antidepressive treatment. This translational study shows that SMPD1 mRNA expression could serve as a molecular marker for treatment and adherence monitoring of MDD.
CRIS-Team Scopus Importer:2021-06-04
Keywords: Acid sphingomyelinase; Amitripty-line; Antidepressants; Biomarker; FIASMA; Fluoxetine; Major depressive disorder; SMPD1

Preventive Care in Type 2 Diabetes: Results of a Randomized, Controlled Trial in Community Pharmacies - Prax, Katja; Schmiedel, Karin; Hepp, Tobias; Schlager, Helmut; Friedland, Kristina (Diabetes Care, 2021)
CRIS-Team WoS Importer:2021-09-03

Probing for Sparse and Fast Variable Selection with Model-Based Boosting - Thomas, Janek; Hepp, Tobias; Mayr, Andreas; Bischl, Bernd (Computational and Mathematical Methods in Medicine, 2017)
We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time. A drawback of the fitting lies in the need of multiple model fits on slightly altered data (e.g., cross-validation or bootstrap) to find the optimal number of boosting iterations and prevent overfitting. In our proposed approach, we augment the data set with randomly permuted versions of the true variables, so-called shadow variables, and stop the stepwise fitting as soon as such a variable would be added to the model. This allows variable selection in a single fit of the model without requiring further parameter tuning. We show that our probing approach can compete with state-of-the-art selection methods like stability selection in a high-dimensional classification benchmark and apply it on three gene expression data sets.
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Psychosocial impact of prognostic genetic testing in uveal melanoma patients: a controlled prospective clinical observational study - Lieb, Marietta; Tagay, Sefik; Breidenstein, Anja; Hepp, Tobias; Le Guin, Claudia H.D.; Scheel, Jennifer Felicia; Lohmann, Dietmar R.; Bornfeld, Norbert; Teufel, Martin; Erim, Yesim (BMC Psychology, 2020)
BACKGROUND: The risk of metastases in uveal melanoma can accurately be estimated through genetic analysis of the tumor. A growing number of patients decide to receive information on their prognosis, although this can be extremely burdensome. Studies on the psychosocial impact of testing are sparse. The objective of this study was to examine traits of patients opting for prognostication, to investigate its psychosocial impact and the use of psycho-oncological services over time. We further examined characteristics of patients utilizing these services and risk factors of prolonged psychological distress. DESIGN AND METHODS: This study is a non-randomized controlled prospective clinical observational trial. Patients availing for prognostication formed the test group, while those who opted out constituted the observational group. The psychosocial impact of genetic testing was assessed with the following variables: resilience, social support, fear of tumor progression, depression, general distress, health-related quality of life, estimation of the perceived risk, and the utilization of psycho-oncological interventions. Data were assessed at five different time points over a period of 12 months. We applied binary logistic regression analysis, multiple linear regressions and a mixed model. RESULTS: Of 175 patients, 63 decided to obtain prognostic information. Treatment method (enucleation > brachytherapy), lower social support and higher general distress could significantly predict patient's choice for prognostic testing. After result announcement, perceived risk of metastases was significantly increased in patients with poor prognosis, while it decreased in those with good prognosis. Overall, a significant decrease over time appeared concerning fear of progression, general distress, depression and anxiety. Mental quality of life increased over time. The utilization of psycho-oncological interventions increased significantly after prognostication; however, this was equivalent in the test and observational groups. Female sex, higher general distress and higher anxiety predicted greater use of psycho-oncological interventions. DISCUSSION: Availing of prognostic testing is not associated with poorer subsequent psychological well-being. It rather may help to alleviate distress and promote a more realistic risk perception. However, psychological support should be available to all patients, independent of prognosis and treatment, especially considering that patients with low social support and high distress increasingly opt for prognostication.
CRIS-Team Scopus Importer:2020-02-11
Keywords: Controlled prospective clinical observation trial; Prognostic genetic testing; Psycho-oncological intervention; Psychosocial impact; Uveal melanoma

Significance Tests for Boosted Location and Scale Models with Linear Base-Learners - Hepp, Tobias; Schmid, Matthias; Mayr, Andreas (International Journal of Biostatistics, 2019)
Generalized additive models for location scale and shape (GAMLSS) offer very flexible solutions to a wide range of statistical analysis problems, but can be challenging in terms of proper model specification. This complex task can be simplified using regularization techniques such as gradient boosting algorithms, but the estimates derived from such models are shrunken towards zero and it is consequently not straightforward to calculate proper confidence intervals or test statistics. In this article, we propose two strategies to obtain p-values for linear effect estimates for Gaussian location and scale models based on permutation tests and a parametric bootstrap approach. These procedures can provide a solution for one of the remaining problems in the application of gradient boosting algorithms for distributional regression in biostatistical data analyses. Results from extensive simulations indicate that in low-dimensional data both suggested approaches are able to hold the type-I error threshold and provide reasonable test power comparable to the Wald-type test for maximum likelihood inference. In high-dimensional data, when gradient boosting is the only feasible inference for this model class, the power decreases but the type-I error is still under control. In addition, we demonstrate the application of both tests in an epidemiological study to analyse the impact of physical exercise on both average and the stability of the lung function of elderly people in Germany.
CRIS-Team Scopus Importer:2019-05-02
Keywords: boosting; GAMLSS; parametric bootstrap; permutation test; regularized regression

Women-only versus mixed-gender groups in multimodal, day clinic treatment of trauma-related disorders - Philipps, Anke; Hepp, Tobias; Silbermann, Andrea; Morawa, Eva; Stemmler, Mark; Erim, Yesim (Zeitschrift für Psychosomatische Medizin und Psychotherapie, 2022)
Background: In the German healthcare system benefits include inpatient and day clinic psychotherapy of trauma-related disorders, which are often provided in gender specific groups. Despite this widespread practice, the actual impact and potential outcome have been studied relatively scarcely and are still open to debate. The present study therefore examines whether the therapeutic relationships in the group vary depending on the group composition and whether this has an effect on therapy outcome. Methods: 66 patients (N = 55 women) were treated in our multimodal, day clinic groupbased treatment program and filled out symptom questionnaires (ETI, BDI-II, PHQ-15, Questionnaire on social support) at admission and discharge and the Group Questionnaire (GQ; including subscales positive bonding, positive working and negative relationships) at the end of each of the eight weeks of treatment. Patients took part in five women-only and six mixed-gender groups and underwent therapy in a closed group format, so that they began and ended therapy within the same group. All patients were diagnosed with trauma-related disorders, which included PTSD, cPTSD, depression, somatoform disorders and further mental diseases. Outcome data were examined using functional regression analysis and linear mixed models. Results: In women-only groups, positive bonding and positive working relationship were significantly higher than in mixed-gender groups. Negative relationship ratings were significantly lower in women-only groups, especially in the middle phase of treatment. However, no effect of an exclusively female group composition on treatment outcome was found, except for the higher increase of perceived social support for patients treated in women-only groups. Conclusions: Group relationships were considered more pleasant in women-only groups than in mixed-gender groups, but this had no effect on the symptom-based therapy outcome. To enhance feelings of safety and comfort, traumatized women should have the possibility to choose whether to be treated in a women-only or a mixed-gender group.
CRIS-Team Scopus Importer:2022-12-23
Keywords: Day Clinic; Group Relationships; PTSD; Trauma; Women-Only Groups