TY - JOUR AB - BACKGROUND: This article describes how withdrawals and exclusions of study participants can be managed in COVID-19-cohort studies by NUKLEUS (German: NUM Klinische Epidemiologie- und Studienplattform), using NAPKON (German: Nationales Pandemie Kohorten Netz). The aim of this manuscript was to describe, how partial withdrawals can be performed so that most of the data and bio-samples can be kept for research purposes. METHODS: The study has taken all signed informed consents (ICs) of study participants into account in order to develop a method how partial withdrawals can be developed and installed. The informed consents, which comprise of mandatory and optional modules were investigated to find out which optional modules can be withdrawn from without withdrawing consent from the whole study. RESULTS: Withdrawals refer to signed ICs including mandatory and optional modules. Withdrawals can be submitted verbally or in writing, and regarding the IC, as a whole, or only partially. Consequently, implemented withdrawals for NAPKON cohorts comprise partial withdrawals with partial or no data deletion or complete withdrawals with data deletion. Thus, more data is still available for research purpose, which would have been lost without the possibility of partial withdrawals. In NAPKON, a total of 3,97% of the participants have submitted a withdrawal or have been excluded from the study if the inclusion criteria were no longer met. CONCLUSIONS: This manuscript is to the author's knowledge one of the first article related to withdrawals within COVID-19-studies (NAPKON). The processes serve as 'best practice' examples for planning and establishing withdrawal processes in medical research. AU - Valentin, H.* AU - Rau, H.* AU - Fiedler-Lacombe, L.* AU - Blumentritt, A.* AU - Heim, E.* AU - Rudolph, A.* AU - Leyh, K.* AU - Kraus, M. AU - Lorenz-Depiereux, B. AU - Chaplinskaya, I.* AU - Schäfer, C.* AU - Schaller, J.* AU - Vehreschild, J.J.* AU - Stecher, M.* AU - Scherer, M.* AU - Witzenrath, M.* AU - Balzuweit, B.* AU - Schreiber, S.* AU - Bahmer, T.* AU - Lieb, W.* AU - Cordes, S.* AU - Hoffmann, W.* AU - Hans, S.* AU - Stahl, D.* C1 - 74841 C2 - 57627 CY - Campus, 4 Crinan St, London N1 9xw, England TI - Managing withdrawals and exclusions of study participants in COVID-19-research by NUKLEUS. JO - BMC Med. Res. Methodol. VL - 25 IS - 1 PB - Bmc PY - 2025 ER - TY - JOUR AB - BACKGROUND: There is a growing awareness of the need to adequately integrate sex and gender into health-related research. Although it is widely known that the entangled dimensions sex/gender are not comprehensively considered in most studies to date, current publications of conceptual considerations and guidelines often only give recommendations for certain stages of the research process and - to the best of our knowledge - there is a lack of a detailed guidance that accompanies each step of the entire research process. The interdisciplinary project "Integrating gender into environmental health research" (INGER) aimed to fill this gap by developing a comprehensive checklist that encourages sex/gender transformative research at all stages of the research process of quantitative health research. In the long term this contributes to a more sex/gender-equitable research. METHODS: The checklist builds on current guidelines on sex/gender in health-related research. Starting from important key documents, publications from disciplines involved in INGER were collected. Furthermore, we used a snowball method to include further relevant titles. The identification of relevant publications was continued until saturation was reached. 55 relevant publications published between 2000 and 2021 were identified, assessed, summarised and included in the developed checklist. After noticing that most publications did not cover every step of the research process and often considered sex/gender in a binary way, the recommendations were modified and enriched based on the authors' expertise to cover every research step and to add further categories to the binary sex/gender categories. RESULTS: The checklist comprises 67 items in 15 sections for integrating sex/gender in quantitative health-related research and addresses aspects of the whole research process of planning, implementing and analysing quantitative health studies as well as aspects of appropriate language, communication of results to the scientific community and the public, and research team composition. CONCLUSION: The developed comprehensive checklist goes beyond a binary consideration of sex/gender and thus enables sex/gender-transformative research. Although the project INGER focused on environmental health research, no aspects that were specific to this research area were identified in the checklist. The resulting comprehensive checklist can therefore be used in different quantitative health-related research fields. AU - Hartig, C.* AU - Horstmann, S.* AU - Jacke, K.* AU - Kraus, U. AU - Dandolo, L.* AU - Schneider, A.E. AU - Palm, K.* AU - Bolte, G.* C1 - 71455 C2 - 56101 CY - Campus, 4 Crinan St, London N1 9xw, England TI - A deeper consideration of sex/gender in quantitative health research: A checklist for incorporating multidimensionality, variety, embodiment, and intersectionality throughout the whole research process. JO - BMC Med. Res. Methodol. VL - 24 IS - 1 PB - Bmc PY - 2024 ER - TY - JOUR AB - BACKGROUND: The statistical analysis of health care cost data is often problematic because these data are usually non-negative, right-skewed and have excess zeros for non-users. This prevents the use of linear models based on the Gaussian or Gamma distribution. A common way to counter this is the use of Two-part or Tobit models, which makes interpretation of the results more difficult. In this study, I explore a statistical distribution from the Tweedie family of distributions that can simultaneously model the probability of zero outcome, i.e. of being a non-user of health care utilization and continuous costs for users. METHODS: I assess the usefulness of the Tweedie model in a Monte Carlo simulation study that addresses two common situations of low and high correlation of the users and the non-users of health care utilization. Furthermore, I compare the Tweedie model with several other models using a real data set from the RAND health insurance experiment. RESULTS: I show that the Tweedie distribution fits cost data very well and provides better fit, especially when the number of non-users is low and the correlation between users and non-users is high. CONCLUSION: The Tweedie distribution provides an interesting solution to many statistical problems in health economic analyses. AU - Kurz, C.F. C1 - 52583 C2 - 44031 CY - London TI - Tweedie distributions for fitting semicontinuous health care utilization cost data. JO - BMC Med. Res. Methodol. VL - 17 IS - 1 PB - Biomed Central Ltd PY - 2017 ER - TY - JOUR AB - BACKGROUND: Creating study identifiers and assigning them to study participants is an important feature in epidemiologic studies, ensuring the consistency and privacy of the study data. The numbering system for identifiers needs to be random within certain number constraints, to carry extensions coding for organizational information, or to contain multiple layers of numbers per participant to diversify data access. Available software can generate globally-unique identifiers, but identifier-creating tools meeting the special needs of epidemiological studies are lacking. We have thus set out to develop a software program to generate IDs for epidemiological or clinical studies. RESULTS: Our software IDGenerator creates unique identifiers that not only carry a random identifier for a study participant, but also support the creation of structured IDs, where organizational information is coded into the ID directly. This may include study center (for multicenter-studies), study track (for studies with diversified study programs), or study visit (baseline, follow-up, regularly repeated visits). Our software can be used to add a check digit to the ID to minimize data entry errors. It facilitates the generation of IDs in batches and the creation of layered IDs (personal data ID, study data ID, temporary ID, external data ID) to ensure a high standard of data privacy. The software is supported by a user-friendly graphic interface that enables the generation of IDs in both standard text and barcode 128B format. CONCLUSION: Our software IDGenerator can create identifiers meeting the specific needs for epidemiologic or clinical studies to facilitate study organization and data privacy. IDGenerator is freeware under the GNU General Public License version 3; a Windows port and the source code can be downloaded at the Open Science Framework website: https://osf.io/urs2g/ . AU - Olden, M.* AU - Holle, R. AU - Heid, I.* AU - Stark, K.* C1 - 49495 C2 - 31628 CY - London TI - IDGenerator: Unique identifier generator for epidemiologic or clinical studies. JO - BMC Med. Res. Methodol. VL - 16 PB - Biomed Central Ltd PY - 2016 ER - TY - JOUR AB - BACKGROUND: Missing values are a frequent issue in human studies. In many situations, multiple imputation (MI) is an appropriate missing data handling strategy, whereby missing values are imputed multiple times, the analysis is performed in every imputed data set, and the obtained estimates are pooled. If the aim is to estimate (added) predictive performance measures, such as (change in) the area under the receiver-operating characteristic curve (AUC), internal validation strategies become desirable in order to correct for optimism. It is not fully understood how internal validation should be combined with multiple imputation. METHODS: In a comprehensive simulation study and in a real data set based on blood markers as predictors for mortality, we compare three combination strategies: Val-MI, internal validation followed by MI on the training and test parts separately, MI-Val, MI on the full data set followed by internal validation, and MI(-y)-Val, MI on the full data set omitting the outcome followed by internal validation. Different validation strategies, including bootstrap und cross-validation, different (added) performance measures, and various data characteristics are considered, and the strategies are evaluated with regard to bias and mean squared error of the obtained performance estimates. In addition, we elaborate on the number of resamples and imputations to be used, and adopt a strategy for confidence interval construction to incomplete data. RESULTS: Internal validation is essential in order to avoid optimism, with the bootstrap 0.632+ estimate representing a reliable method to correct for optimism. While estimates obtained by MI-Val are optimistically biased, those obtained by MI(-y)-Val tend to be pessimistic in the presence of a true underlying effect. Val-MI provides largely unbiased estimates, with a slight pessimistic bias with increasing true effect size, number of covariates and decreasing sample size. In Val-MI, accuracy of the estimate is more strongly improved by increasing the number of bootstrap draws rather than the number of imputations. With a simple integrated approach, valid confidence intervals for performance estimates can be obtained. CONCLUSIONS: When prognostic models are developed on incomplete data, Val-MI represents a valid strategy to obtain estimates of predictive performance measures. AU - Wahl, S. AU - Boulesteix, A.L.* AU - Zierer, A. AU - Thorand, B. AU - Avan de Wiel, M.* C1 - 49819 C2 - 40971 CY - London TI - Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation. JO - BMC Med. Res. Methodol. VL - 16 PB - Biomed Central Ltd PY - 2016 ER - TY - JOUR AB - Background: In the last years, the importance of independent validation of the prediction ability of a new gene signature has been largely recognized. Recently, with the development of gene signatures which integrate rather than replace the clinical predictors in the prediction rule, the focus has been moved to the validation of the added predictive value of a gene signature, i.e. to the verification that the inclusion of the new gene signature in a prediction model is able to improve its prediction ability. Methods: The high-dimensional nature of the data from which a new signature is derived raises challenging issues and necessitates the modification of classical methods to adapt them to this framework. Here we show how to validate the added predictive value of a signature derived from high-dimensional data and critically discuss the impact of the choice of methods on the results. Results: The analysis of the added predictive value of two gene signatures developed in two recent studies on the survival of leukemia patients allows us to illustrate and empirically compare different validation techniques in the high-dimensional framework. Conclusions: The issues related to the high-dimensional nature of the omics predictors space affect the validation process. An analysis procedure based on repeated cross-validation is suggested. AU - de Bin, R.* AU - Herold, T. AU - Boulesteix, A.-L.* C1 - 43081 C2 - 36035 TI - Added predictive value of omics data: Specific issues related to validation illustrated by two case studies. JO - BMC Med. Res. Methodol. VL - 14 PY - 2014 ER - TY - JOUR AB - ABSTRACT: BACKGROUND: Health-related quality of life (HRQL) has become an increasingly important outcome parameter in clinical trials and epidemiological research. HRQL scores are typically bounded at both ends of the scale and often highly skewed. Several regression techniques have been proposed to model such data in cross-sectional studies, however, methods applicable in longitudinal research are less well researched. This study examined the use of beta regression models for analyzing longitudinal HRQL data using two empirical examples with distributional features typically encountered in practice. METHODS: We used SF-6D utility data from a German older age cohort study and stroke-specific HRQL data from a randomized controlled trial. We described the conceptual differences between mixed and marginal beta regression models and compared both models to the commonly used linear mixed model in terms of overall fit and predictive accuracy. RESULTS: At any measurement time, the beta distribution fitted the SF-6D utility data and strokespecific HRQL data better than the normal distribution. The mixed beta model showed better likelihood-based fit statistics than the linear mixed model and respected the boundedness of the outcome variable. However, it tended to underestimate the true mean at the upper part of the distribution. Adjusted group means from marginal beta model and linear mixed model were nearly identical but differences could be observed with respect to standard errors. CONCLUSIONS: Understanding the conceptual differences between mixed and marginal beta regression models is important for their proper use in the analysis of longitudinal HRQL data. Beta regression fits the typical distribution of HRQL data better than linear mixed models, however, if focus is on estimating group mean scores rather than making individual predictions, the two methods might not differ substantially. AU - Hunger, M. AU - Döring, A. AU - Holle, R. C1 - 11186 C2 - 30541 TI - Longitudinal beta regression models for analyzing health-related quality of life scores over time. JO - BMC Med. Res. Methodol. VL - 12 PB - Biomed Central Ltd. PY - 2012 ER - TY - JOUR AB - BACKGROUND: Although aging is accompanied by diminished functioning, many elderly individuals preserve a sense of well-being. While the concept of "successful aging" has been popular for many decades, little is known about its psycho-physiologic and endocrine underpinnings. KORA-Age is a population-based, longitudinal study designed to determine the prevalence of successfully aged men and women between 65 and 94 years old in the MONICA/KORA Augsburg cohort of randomly selected inhabitants. Specifically, we aim to identify predictors of successful aging and to elucidate bio-psychosocial mechanisms that maintain mental health and successful adaptation despite adverse experiences of life and aging. METHODS/DESIGN: Components of successful aging were assessed in a telephone survey of 4,127 participants (2008-2009) enrolled in the MONICA/KORA cohort, on average, 13 years earlier. Psychosocial, somatic and behavioural predictors are used to determine factors that contribute to successful aging. An age-stratified random sub-sample (n = 1,079) participated in a personal interview where further psychological mechanisms that may underlie successful adaptation (resilience, social support, attachment) were examined. The interactions among neuroendocrine systems in the aging process are investigated by studying the cortisol/dehydroepiandrosterone-sulfate ratio, the level of insulin-like growth factor I, and oxytocin. DISCUSSION: Longitudinal determinants of successful aging can be assessed based on a follow-up of an average of 13 years. A comprehensive analysis of biological as well as physio-psychological information provides a unique opportunity to investigate relevant outcomes such as resilience and frailty in the elderly population. AU - Lacruz, M.-E. AU - Emeny, R.T. AU - Bickel, H.* AU - Cramer, B.* AU - Kurz, A.* AU - Bidlingmaier, M.* AU - Huber, D.* AU - Klug, G.* AU - Peters, A. AU - Ladwig, K.-H. C1 - 1246 C2 - 27352 TI - Mental health in the aged: Prevalence, covariates and related neuroendocrine, cardiovascular and inflammatory factors of successful aging. JO - BMC Med. Res. Methodol. VL - 10 PB - BioMed Central Ltd. PY - 2010 ER -