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1. Gütemaße und Kriterien bei der Anwendung von Propensity Scores

Author

Kuß, Oliver and Strobel, Alexandra

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Infectious and parasitic diseases, RC109-216
Abstract

Propensity scores (PS) have been established as a valid alternative to conventional regression models when evaluating non-randomized treatment studies. The PS describes the probability for an individual to receive a treatment, conditional on a set of observed covariates. PS analyses are performed in two steps. In the first step, the PS is generally estimated via logistic regression. In the second step, the actual treatment effect is estimated. The quality of a PS analysis depends on whether it is possible to achieve a sufficient balance of the patient characteristics in the treatment groups in the first step. This is the only way to ensure that these patient characteristics do not bias the estimate of the treatment effect. Various measures have been proposed to measure this balance, e.g. the standardized difference or the z-difference. Closely related to the balance of patient characteristics (and thus also a measure of the quality for a PS model) is the overlap, i.e. the similarity in the distribution of the estimated propensity scores in the two treatment groups. A valid comparison of the treatments is only possible in regions of sufficient overlap. In this article, the two concepts of balance and overlap are presented and discussed using an example from cardiac surgery.

Published
2024
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6. A non-parametric proportional risk model to assess a treatment effect in time-to-event data

Author

Ameis, Lucia, Kuß, Oliver, Hoyer, Annika, and Möllenhoff, Kathrin

Subjects
Statistics - Methodology
Abstract

Time-to-event analysis often relies on prior parametric assumptions, or, if a non-parametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk. In this paper, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new non-parametric model to directly estimate the relative risk of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial investigating the effect of dapagliflozin on the risk of first hospitalization for heart failure., Comment: 18 pages, 5 figures

Published
2023

7. Measuring covariate balance in weighted propensity score analyses by the weighted z-difference

Author

Filla, Tim, Schwender, Holger, and Kuß, Oliver

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Propensity score (PS) methods have been increasingly used in recent years when assessing treatment effects in nonrandomized studies. In terms of statistical methods, a number of new PS weighting methods were developed, and it was shown that they can outperform PS matching in efficiency of treatment effect estimation in different simulation settings. For assessing balance of covariates in treatment groups, PS weighting methods commonly use the weighted standardized difference, despite some deficiencies of this measure like, for example, the distribution of the weighted standardized difference depending on the sample size and on the distribution of weights. We introduce the weighted z-difference as a balance measure in PS weighting analyses and demonstrate its usage in a simulation study and by applying it to an example from cardiac surgery. The weighted z-difference is computationally simple and can be calculated for continuous, binary, ordinal and nominal covariates. By using Q-Q-plots we can compare the balance of PS weighted samples immediately to the balance in perfectly matched PS samples and to the expected balance in a randomized trial., Comment: 13 pages, 5 figures, 1 table

Published
2022

9. Depression symptoms, wellbeing, health-related quality of life, and diabetes-related distress in novel subtypes of recent-onset diabetes in Germany: a 5-year observational follow-up study

Author

Al-Hasani, Hadi, Belgardt, Bengt-Frederik, Bönhof, Gidon, Geerling, Gerd, Herder, Christian, Icks, Andrea, Jandeleit-Dahm, Karin, Kotzka, Jörg, Kuss, Oliver, Lammert, Eckhard, Rathmann, Wolfgang, Roden, Michael, Schlesinger, Sabrina, Schrauwen-Hinderling, Vera, Szendroedi, Julia, Trenkamp, Sandra, Wagner, Robert, Sommer, Jana, Borgmann, Sandra Olivia, Gontscharuk, Veronika, Zaharia, Oana Patricia, Maalmi, Haifa, Strassburger, Klaus, Schön, Martin, Burkart, Volker, Pfeiffer, Andreas F H, Bornstein, Stefan, Blüher, Matthias, Seissler, Jochen, Birkenfeld, Andreas L, and Meyhöfer, Svenja

Published
2024
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14. Analysis of type 2 diabetes heterogeneity with a tree-like representation: insights from the prospective German Diabetes Study and the LURIC cohort

Author

Al-Hasani, Hadi, Belgardt, Bengt-Frederik, Bönhof, Gidon J., Geerling, Gerd, Herder, Christian, Icks, Andrea, Jandeleit-Dahm, Karin, Kotzka, Jörg, Kuss, Oliver, Lammert, Eckhard, Rathmann, Wolfgang, Roden, Michael, Schlesinger, Sabrina, Schrauwen-Hinderling, Vera, Szendroedi, Julia, Trenkamp, Sandra, Wagner, Robert, Schön, Martin, Prystupa, Katsiaryna, Mori, Tim, Zaharia, Oana P, Bódis, Kálmán, Bombrich, Maria, Möser, Clara, Yurchenko, Iryna, Kupriyanova, Yuliya, Strassburger, Klaus, Bobrov, Pavel, Nair, Anand T N, Bönhof, Gidon J, Strom, Alexander, Delgado, Graciela E, Kaya, Sema, Guthoff, Rainer, Stefan, Norbert, Birkenfeld, Andreas L, Hauner, Hans, Seissler, Jochen, Pfeiffer, Andreas, Blüher, Matthias, Bornstein, Stefan, Meyhöfer, Svenja, Burkart, Volker, Schrauwen-Hinderling, Vera B, Kleber, Marcus E, Niessner, Alexander, März, Winfried, and Pearson, Ewan R

Published
2024
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18. Sphingolipid subtypes differentially control proinsulin processing and systemic glucose homeostasis

Author

Griess, Kerstin, Rieck, Michael, Müller, Nadine, Karsai, Gergely, Hartwig, Sonja, Pelligra, Angela, Hardt, Robert, Schlegel, Caroline, Kuboth, Jennifer, Uhlemeyer, Celina, Trenkamp, Sandra, Jeruschke, Kay, Weiss, Jürgen, Peifer-Weiss, Leon, Xu, Weiwei, Cames, Sandra, Yi, Xiaoyan, Cnop, Miriam, Beller, Mathias, Stark, Holger, Kondadi, Arun Kumar, Reichert, Andreas S., Markgraf, Daniel, Wammers, Marianne, Häussinger, Dieter, Kuss, Oliver, Lehr, Stefan, Eizirik, Decio, Lickert, Heiko, Lammert, Eckhard, Roden, Michael, Winter, Dominic, Al-Hasani, Hadi, Höglinger, Doris, Hornemann, Thorsten, Brüning, Jens C., and Belgardt, Bengt-Frederik

Published
2023
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20. Framework and baseline examination of the German National Cohort (NAKO)

Author

Peters, Annette, Peters, Annette, Greiser, Karin Halina, Göttlicher, Susanne, Ahrens, Wolfgang, Albrecht, Maren, Bamberg, Fabian, Bärnighausen, Till, Becher, Heiko, Berger, Klaus, Beule, Achim, Boeing, Heiner, Bohn, Barbara, Bohnert, Kerstin, Braun, Bettina, Brenner, Hermann, Bülow, Robin, Castell, Stefanie, Damms-Machado, Antje, Dörr, Marcus, Ebert, Nina, Ecker, Margit, Emmel, Carina, Fischer, Beate, Franzke, Claus-Werner, Gastell, Sylvia, Giani, Guido, Günther, Matthias, Günther, Kathrin, Günther, Klaus-Peter, Haerting, Johannes, Haug, Ulrike, Heid, Iris M., Heier, Margit, Heinemeyer, Diana, Hendel, Thomas, Herbolsheimer, Florian, Hirsch, Jochen, Hoffmann, Wolfgang, Holleczek, Bernd, Hölling, Heike, Hörlein, Andreas, Jöckel, Karl-Heinz, Kaaks, Rudolf, Karch, André, Karrasch, Stefan, Kartschmit, Nadja, Kauczor, Hans-Ulrich, Keil, Thomas, Kemmling, Yvonne, Klee, Bianca, Klüppelholz, Birgit, Kluttig, Alexander, Kofink, Lisa, Köttgen, Anna, Kraft, Daniel, Krause, Gérard, Kretz, Lisa, Krist, Lilian, Kühnisch, Jan, Kuß, Oliver, Legath, Nicole, Lehnich, Anna-Therese, Leitzmann, Michael, Lieb, Wolfgang, Linseisen, Jakob, Loeffler, Markus, Macdonald, Anke, Maier-Hein, Klaus H., Mangold, Nina, Meinke-Franze, Claudia, Meisinger, Christa, Melzer, Juliane, Mergarten, Björn, Michels, Karin B., Mikolajczyk, Rafael, Moebus, Susanne, Mueller, Ulrich, Nauck, Matthias, Niendorf, Thoralf, Nikolaou, Konstantin, Obi, Nadia, Ostrzinski, Stefan, Panreck, Leo, Pigeot, Iris, Pischon, Tobias, Pschibul-Thamm, Irene, Rathmann, Wolfgang, Reineke, Achim, Roloff, Stefanie, Rujescu, Dan, Rupf, Stefan, Sander, Oliver, Schikowski, Tamara, Schipf, Sabine, Schirmacher, Peter, Schlett, Christopher L., Schmidt, Börge, Schmidt, Georg, Schmidt, Martin, Schöne, Gina, Schulz, Holger, Schulze, Matthias B., Schweig, Alexandra, Sedlmeier, Anja M., Selder, Sonja, Six-Merker, Julia, Sowade, Ramona, Stang, Andreas, Stegle, Oliver, Steindorf, Karen, Stübs, Gunthard, Swart, Enno, Teismann, Henning, Thiele, Inke, Thierry, Sigrid, Ueffing, Marius, Völzke, Henry, Waniek, Sabina, Weber, Andrea, Werner, Nicole, Wichmann, H.-Erich, Willich, Stefan N., Wirkner, Kerstin, Wolf, Kathrin, Wolff, Robert, Zeeb, Hajo, Zinkhan, Melanie, and Zschocke, Johannes

Published
2022
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26. Statistical Analysis in the German National Cohort (NAKO) – Specific Aspects and General Recommendations

Author

Kuss, Oliver, Becher, Heiko, Wienke, Andreas, Ittermann, Till, Ostrzinski, Stefan, Schipf, Sabine, Schmidt, Carsten O, Leitzmann, Michael, Pischon, Tobias, Krist, Lilian, Roll, Stephanie, Sand, Matthias, Pohlabeln, Hermann, Rach, Stefan, Jöckel, Karl-Heinz, Stang, Andreas, Mueller, Ulrich A, Werdecker, Andrea, Westerman, Ronny, Greiser, Karin H, and Michels, Karin B

Published
2022
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27. Modeling Chronic Disease Mortality by Methods From Accelerated Life Testing.

Author

Zamsheva, Marina, Kluttig, Alexander, Wienke, Andreas, and Kuss, Oliver

Abstract

We propose a parametric model for describing chronic disease mortality from cohort data and illustrate its use for Type 2 diabetes. The model uses ideas from accelerated life testing in reliability theory and conceptualizes the occurrence of a chronic disease as putting the observational unit to an enhanced stress level, which is supposed to shorten its lifetime. It further addresses the issue of semi‐competing risk, that is, the asymmetry of death and diagnosis of disease, where the disease can be diagnosed before death, but not after. With respect to the cohort structure of the data, late entry into the cohort is taken into account and prevalent as well as incident cases inform the analysis. We finally give an extension of the model that allows age at disease diagnosis to be observed not exactly, but only partially within an interval. Model parameters can be straightforwardly estimated by Maximum Likelihood, using the assumption of a Gompertz distribution we show in a small simulation study that this works well. Data of the Cardiovascular Disease, Living and Ageing in Halle (CARLA) study, a population‐based cohort in the city of Halle (Saale) in the eastern part of Germany, are used for illustration. [ABSTRACT FROM AUTHOR]

Published
2024
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28. A discrete time‐to‐event model for the meta‐analysis of full ROC curves.

Author

Stoye, Ferdinand Valentin, Tschammler, Claudia, Kuss, Oliver, and Hoyer, Annika

Subjects
TYPE 2 diabetes, MEDICAL screening, STATISTICAL models, SENSITIVITY & specificity (Statistics), DIAGNOSIS methods, RECEIVER operating characteristic curves
Abstract

The development of new statistical models for the meta‐analysis of diagnostic test accuracy studies is still an ongoing field of research, especially with respect to summary receiver operating characteristic (ROC) curves. In the recently published updated version of the "Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy", the authors point to the challenges of this kind of meta‐analysis and propose two approaches. However, both of them come with some disadvantages, such as the nonstraightforward choice of priors in Bayesian models or the requirement of a two‐step approach where parameters are estimated for the individual studies, followed by summarizing the results. As an alternative, we propose a novel model by applying methods from time‐to‐event analysis. To this task we use the discrete proportional hazard approach to treat the different diagnostic thresholds, that provide means to estimate sensitivity and specificity and are reported by the single studies, as categorical variables in a generalized linear mixed model, using both the logit‐ and the asymmetric cloglog‐link. This leads to a model specification with threshold‐specific discrete hazards, avoiding a linear dependency between thresholds, discrete hazard, and sensitivity/specificity and thus increasing model flexibility. We compare the resulting models to approaches from the literature in a simulation study. While the estimated area under the summary ROC curve is estimated comparably well in most approaches, the results depict substantial differences in the estimated sensitivities and specificities. We also show the practical applicability of the models to data from a meta‐analysis for the screening of type 2 diabetes. [ABSTRACT FROM AUTHOR]

Published
2024
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29. A re‐analysis of about 60,000 sparse data meta‐analyses suggests that using an adequate method for pooling matters.

Author

Schulz, Maxi, Kramer, Malte, Kuss, Oliver, and Mathes, Tim

Subjects
STATISTICAL accuracy, DATABASES, ODDS ratio, CONFIDENCE intervals, SENSITIVITY analysis
Abstract

In sparse data meta‐analyses (with few trials or zero events), conventional methods may distort results. Although better‐performing one‐stage methods have become available in recent years, their implementation remains limited in practice. This study examines the impact of using conventional methods compared to one‐stage models by re‐analysing meta‐analyses from the Cochrane Database of Systematic Reviews in scenarios with zero event trials and few trials. For each scenario, we computed one‐stage methods (Generalised linear mixed model [GLMM], Beta‐binomial model [BBM], Bayesian binomial‐normal hierarchical model using a weakly informative prior [BNHM‐WIP]) and compared them with conventional methods (Peto‐Odds‐ratio [PETO], DerSimonian‐Laird method [DL] for zero event trials; DL, Paule‐Mandel [PM], Restricted maximum likelihood [REML] method for few trials). While all methods showed similar treatment effect estimates, substantial variability in statistical precision emerged. Conventional methods generally resulted in smaller confidence intervals (CIs) compared to one‐stage models in the zero event situation. In the few trials scenario, the CI lengths were widest for the BBM on average and significance often changed compared to the PM and REML, despite the relatively wide CIs of the latter. In agreement with simulations and guidelines for meta‐analyses with zero event trials, our results suggest that one‐stage models are preferable. The best model can be either selected based on the data situation or, using a method that can be used in various situations. In the few trial situation, using BBM and additionally PM or REML for sensitivity analyses appears reasonable when conservative results are desired. Overall, our results encourage careful method selection. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet is associated with lower visceral and hepatic lipid content in recent‐onset type 1 diabetes and type 2 diabetes.

Author

Schaefer, Edyta, Lang, Alexander, Kupriyanova, Yuliya, Bódis, Kálmán B., Weber, Katharina S., Buyken, Anette E., Barbaresko, Janett, Kössler, Theresa, Kahl, Sabine, Zaharia, Oana‐Patricia, Szendroedi, Julia, Herder, Christian, Schrauwen‐Hinderling, Vera B., Wagner, Robert, Kuss, Oliver, Roden, Michael, and Schlesinger, Sabrina

Subjects
DASH diet, TYPE 2 diabetes, TYPE 1 diabetes, PEOPLE with diabetes, BODY mass index, ADIPOSE tissues
Abstract

Aim: To investigate the associations of the Dietary Approaches to Stop Hypertension (DASH) score with subcutaneous (SAT) and visceral (VAT) adipose tissue volume and hepatic lipid content (HLC) in people with diabetes and to examine whether changes in the DASH diet were associated with changes in these outcomes. Methods: In total, 335 participants with recent‐onset type 1 diabetes (T1D) and type 2 diabetes (T2D) from the German Diabetes Study were included in the cross‐sectional analysis, and 111 participants in the analysis of changes during the 5‐year follow‐up. Associations between the DASH score and VAT, SAT and HLC and their changes were investigated using multivariable linear regression models by diabetes type. The proportion mediated by changes in potential mediators was determined using mediation analysis. Results: A higher baseline DASH score was associated with lower HLC, especially in people with T2D (per 5 points: −1.5% [−2.7%; −0.3%]). Over 5 years, a 5‐point increase in the DASH score was associated with decreased VAT in people with T2D (−514 [−800; −228] cm3). Similar, but imprecise, associations were observed for VAT changes in people with T1D (−403 [−861; 55] cm3) and for HLC in people with T2D (−1.3% [−2.8%; 0.3%]). Body mass index and waist circumference changes explained 8%‐48% of the associations between DASH and VAT changes in both groups. In people with T2D, adipose tissue insulin resistance index (Adipo‐IR) changes explained 47% of the association between DASH and HLC changes. Conclusions: A shift to a DASH‐like diet was associated with favourable VAT and HLC changes, which were partly explained by changes in anthropometric measures and Adipo‐IR. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Meta-Analysis of Full ROC Curves with Flexible Parametric Distributions of Diagnostic Test Values

Author

Hoyer, Annika and Kuss, Oliver

Abstract

Diagnostic accuracy studies often evaluate diagnostic tests at several threshold values, aiming to make recommendations on optimal thresholds for use in practice. Methods for meta-analysis of full receiver operating characteristic (ROC) curves have been proposed but still have deficiencies. We recently proposed a parametric approach that is based on bivariate time-to-event models for interval-censored data to this task. To increase the flexibility of that approach, to cover a wide range of distributions of diagnostic test values and to address the open point of model selection, we here suggest to use the generalized "F" family of distributions that includes previously used distributions for the bivariate time-to-event model as special cases. The results of a simulation study are given as well as an illustration by an example of population-based screening for type 2 diabetes mellitus.

Published
2020
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37. Meta-Analysis of Full ROC Curves: Additional Flexibility by Using Semiparametric Distributions of Diagnostic Test Values

Author

Hoyer, Annika and Kuss, Oliver

Abstract

Diagnostic test accuracy studies frequently report on sensitivities and specificities for more than one threshold of the diagnostic test under study. Although it is obvious that the information from all thresholds should be used for a meta-analysis, in practice, frequently, only a single pair of sensitivity and specificity is selected. To overcome this disadvantage, we recently proposed a statistical model for the meta-analysis of such full receiver operating characteristic (ROC) curves that uses the relationship between a ROC curve and a bivariate model for interval-censored data. In this model, diagnostic tests values reported by the single studies were assumed to follow a parametric distribution. We propose a generalization of this model that allows for a flexible semiparametric modelling of the underlying distribution of the diagnostic test values by using the idea of piecewise constant hazard modelling. We show the results of a simulation study that indicates that the approach works reasonably well in practice. Finally, we illustrate the model by the example of population-based screening for type 2 diabetes mellitus.

Published
2019
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38. Methods to Calculate Uncertainty in the Estimated Overall Effect Size from a Random-Effects Meta-Analysis

Author

Veroniki, Areti Angeliki, Jackson, Dan, Bender, Ralf, Kuss, Oliver, Langan, Dean, Higgins, Julian P. T., Knapp, Guido, and Salanti, Georgia

Abstract

Meta-analyses are an important tool within systematic reviews to estimate the overall effect size and its confidence interval for an outcome of interest. If heterogeneity between the results of the relevant studies is anticipated, then a random-effects model is often preferred for analysis. In this model, a prediction interval for the true effect in a new study also provides additional useful information. However, the DerSimonian and Laird method--frequently used as the default method for meta-analyses with random effects--has been long challenged due to its unfavorable statistical properties. Several alternative methods have been proposed that may have better statistical properties in specific scenarios. In this paper, we aim to provide a comprehensive overview of available methods for calculating point estimates, confidence intervals, and prediction intervals for the overall effect size under the random-effects model. We indicate whether some methods are preferable than others by considering the results of comparative simulation and real-life data studies.

Published
2019
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43. Assessing the potential for precision medicine in body weight reduction with regard to type 2 diabetes mellitus therapies: A meta‐regression analysis of 120 randomized controlled trials

Author

Vargas, Kris G., primary, Rütten, Tobias, additional, Siemes, Benedikt, additional, Brockmeyer, Maximilian, additional, Parco, Claudio, additional, Hoss, Alexander, additional, Schlesinger, Sabrina, additional, Jung, Christian, additional, Roden, Michael, additional, Kelm, Malte, additional, Wolff, Georg, additional, and Kuss, Oliver, additional

Published
2024
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46. Erfassung selbst berichteter kardiovaskulärer und metabolischer Erkrankungen in der NAKO Gesundheitsstudie: Methoden und erste Ergebnisse

Author

Jaeschke, Lina, Steinbrecher, Astrid, Greiser, Karin Halina, Dörr, Marcus, Buck, Thomas, Linseisen, Jakob, Meisinger, Christa, Ahrens, Wolfgang, Becher, Heiko, Berger, Klaus, Braun, Bettina, Brenner, Hermann, Castell, Stefanie, Fischer, Beate, Franzke, Claus-Werner, Gastell, Sylvia, Günther, Kathrin, Hoffmann, Wolfgang, Holleczek, Bernd, Jagodzinski, Annika, Kaaks, Rudolf, Kluttig, Alexander, Krause, Gérard, Krist, Lilian, Kuß, Oliver, Lehnich, Anna-Therese, Leitzmann, Michael, Lieb, Wolfgang, Löffler, Markus, Michels, Karin B., Mikolajczyk, Rafael, Peters, Annette, Schikowski, Tamara, Schipf, Sabine, Schmidt, Börge, Schulze, Matthias, Völzke, Henry, Willich, Stefan N., and Pischon, Tobias

Published
2020
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47. Selbst berichtete Infektionen in der NAKO Gesundheitsstudie – Einordnung in die gegenwärtige Forschungslandschaft

Author

Hassenstein, Max J, Aarabi, Ghazal, Ahnert, Peter, Becher, Heiko, Franzke, Claus-Werner, Fricke, Julia, Krause, Gérard, Glöckner, Stephan, Gottschick, Cornelia, Karch, André, Kemmling, Yvonne, Kerrinnes, Tobias, Lange, Berit, Mikolajczyk, Rafael, Nieters, Alexandra, Ott, Jördis J, Ahrens, Wolfgang, Berger, Klaus, Meinke-Franze, Claudia, Gastell, Sylvia, Günther, Kathrin, Greiser, Karin Halina, Holleczek, Bernd, Horn, Johannes, Jaeschke, Lina, Jagodzinski, Annika, Jansen, Lina, Jochem, Carmen, Jöckel, Karl-Heinz, Kaaks, Rudolf, Krist, Lilian, Kuß, Oliver, Langer, Susan, Legath, Nicole, Leitzmann, Michael, Lieb, Wolfgang, Loeffler, Markus, Mangold, Nina, Michels, Karin B., Meisinger, Christa, Obi, Nadia, Pischon, Tobias, Schikowski, Tamara, Schipf, Sabine, Schulze, Matthias B., Stang, Andreas, Waniek, Sabina, Wirkner, Kerstin, Willich, Stefan N., and Castell, Stefanie

Published
2020
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48. Selbstberichtete Krebserkrankungen in der NAKO Gesundheitsstudie: Erfassungsmethoden und erste Ergebnisse

Author

Nimptsch, Katharina, Jaeschke, Lina, Chang-Claude, Jenny, Kaaks, Rudolf, Katzke, Verena, Michels, Karin B., Franzke, Claus-Werner, Obi, Nadia, Becher, Heiko, Kuß, Oliver, Schikowski, Tamara, Schulze, Matthias B., Gastell, Sylvia, Hoffmann, Wolfgang, Schipf, Sabine, Ahrens, Wolfgang, Günther, Kathrin, Krist, Lilian, Keil, Thomas, Jöckel, Karl-Heinz, Schmidt, Börge, Brenner, Hermann, Holleczek, Bernd, Fischer, Beate, Leitzmann, Michael, Lieb, Wolfgang, Berger, Klaus, Krause, Gérard, Löffler, Markus, Schmidt-Pokrzywniak, Andrea, Mikolajczyk, Rafael, Linseisen, Jakob, Greiser, Karin Halina, and Pischon, Tobias

Published
2020
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49. Häufigkeiten muskuloskelettaler Symptome und Erkrankungen in der bevölkerungsbezogenen NAKO Gesundheitsstudie

Author

Schmidt, Carsten Oliver, Günther, Klaus-Peter, Goronzy, Jens, Albrecht, Katinka, Chenot, Jean-François, Callhoff, Johanna, Richter, Adrian, Kasch, Richard, Ahrens, Wolfgang, Becher, Heiko, Berger, Klaus, Brenner, Hermann, Fischer, Beate, Franzke, Claus-Werner, Hoffmann, Wolfgang, Holleczek, Bernd, Jaeschke, Lina, Jenning, Carsten, Jöckel, Karl-Heinz, Kaaks, Rudolf, Keil, Thomas, Kluttig, Alexander, Krause, Gérard, Kuß, Oliver, Leitzmann, Michael, Lieb, Wolfgang, Linseisen, Jakob, Löffler, Markus, Meinke-Franze, Claudia, Meisinger, Christa, Michels, Karin B., Mikolajczyk, Rafael, Obi, Nadia, Peters, Annette, Pischon, Tobias, Schikowski, Tamara, Schipf, Sabine, Specker, Christof, Völzke, Henry, Wirkner, Kerstin, Zink, Angela, and Sander, Oliver

Published
2020
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50. Blutdruckmessung in der NAKO – methodische Unterschiede, Blutdruckverteilung und Bekanntheit der Hypertonie im Vergleich zu anderen bevölkerungsbezogenen Studien in Deutschland

Author

Schikowski, Tamara, Wigmann, Claudia, Fuks, Kateryna B., Schipf, Sabine, Heier, Margit, Neuhauser, Hannelore, Sarganas, Giselle, Ahrens, Wolfgang, Becher, Heiko, Berger, Klaus, Brenner, Hermann, Castell, Stefanie, Damms-Machado, Antje, Dörr, Marcus, Ebert, Nina, Efremov, Ljupcho, Emmel, Carina, Felix, Stephan B., Fischer, Beate, Franzke, Claus-Werner, Gastell, Sylvia, Günther, Kathrin, Haerting, Johannes, Ittermann, Till, Jaeschke, Lina, Jagodzinski, Annika, Jöckel, Karl-Heinz, Kaaks, Rudolphe, Kalinowski, Sonja, Keil, Thomas, Kemmling, Yvonne, Kluttig, Alexander, Krist, Lilian, Kuss, Oliver, Legath, Nicole, Leitzmann, Michael, Lieb, Wolfgang, Löffler, Markus, Meinke-Franze, Claudia, Michels, Karin B., Mikolajczyk, Rafael, Moebus, Susanne, Nuding, Sebastian, Peters, Annette, Pischon, Tobias, Rückert-Eheberg, Ina-Maria, Schöttker, Ben, Schmidt, Börge, Schmidt, Carsten Oliver, Schulze, Matthias B., Stang, Andreas, Thiele, Inke, Thierry, Sigrid, Thorand, Barbara, Völzke, Henry, Waniek, Sabina, Werdan, Karl, Wirkner, Kerstin, and Greiser, Karin Halina

Published
2020
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