Klein, Nadja

[["dc.bibliographiccitation.firstpage","1232"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Biometrical journal. Biometrische Zeitschrift"],["dc.bibliographiccitation.lastpage","1246"],["dc.bibliographiccitation.volume","59"],["dc.contributor.author","Duarte, Elisa"],["dc.contributor.author","de Sousa, Bruno"],["dc.contributor.author","Cadarso-Suárez, Carmen"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Rodrigues, Vítor"],["dc.date.accessioned","2018-03-13T15:08:55Z"],["dc.date.available","2018-03-13T15:08:55Z"],["dc.date.issued","2017"],["dc.description.abstract","Studies addressing breast cancer risk factors have been looking at trends relative to age at menarche and menopause. These studies point to a downward trend of age at menarche and an upward trend for age at menopause, meaning an increase of a woman's reproductive lifespan cycle. In addition to studying the effect of the year of birth on the expectation of age at menarche and a woman's reproductive lifespan, it is important to understand how a woman's cohort affects the correlation between these two variables. Since the behavior of age at menarche and menopause may vary with the geographic location of a woman's residence, the spatial effect of the municipality where a woman resides needs to be considered. Thus, a Bayesian multivariate structured additive distributional regression model is proposed in order to analyze how a woman's municipality and year of birth affects a woman's age of menarche, her lifespan cycle, and the correlation of the two. The data consists of 212,517 postmenopausal women, born between 1920 and 1965, who attended the breast cancer screening program in the central region of Portugal."],["dc.identifier.doi","10.1002/bimj.201600245"],["dc.identifier.pmid","28660685"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/13026"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.relation.eissn","1521-4036"],["dc.title","Studying the relationship between a woman's reproductive lifespan and age at menarche using a Bayesian multivariate structured additive distributional regression model"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","187"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Medical Research Methodology"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Martins, Rui"],["dc.contributor.author","Sousa, Bruno d."],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Hohberg, Maike"],["dc.contributor.author","Klein, Nadja"],["dc.contributor.author","Duarte, Elisa"],["dc.contributor.author","Rodrigues, Vítor"],["dc.date.accessioned","2022-08-04T12:02:10Z"],["dc.date.available","2022-08-04T12:02:10Z"],["dc.date.issued","2022-07-11"],["dc.date.updated","2022-07-25T11:18:52Z"],["dc.description.abstract","Background Due to contradictory results in current research, whether age at menopause is increasing or decreasing in Western countries remains an open question, yet worth studying as later ages at menopause are likely to be related to an increased risk of breast cancer. Using data from breast cancer screening programs to study the temporal trend of age at menopause is difficult since especially younger women in the same generational cohort have often not yet reached menopause. Deleting these younger women in a breast cancer risk analyses may bias the results. The aim of this study is therefore to recover missing menopause ages as a covariate by comparing methods for handling missing data. Additionally, the study makes a contribution to understanding the evolution of age at menopause for several generations born in Portugal between 1920 and 1970. Methods Data from a breast cancer screening program in Portugal including 278,282 women aged 45–69 and collected between 1990 and 2010 are used to compare two approaches of imputing age at menopause: (i) a multiple imputation methodology based on a truncated distribution but ignoring the mechanism of missingness; (ii) a copula-based multiple imputation method that simultaneously handles the age at menopause and the missing mechanism. The linear predictors considered in both cases have a semiparametric additive structure accommodating linear and non-linear effects defined via splines or Markov random fields smoothers in the case of spatial variables. Results Both imputation methods unveiled an increasing trend of age at menopause when viewed as a function of the birth year for the youngest generation. This trend is hidden if we model only women with an observed age at menopause. Conclusion When studying age at menopause, missing ages must be recovered with an adequate procedure for incomplete data. Imputing these missing ages avoids excluding the younger generation cohort of the screening program in breast cancer risk analyses and hence reduces the bias stemming from this exclusion. In addition, imputing the not yet observed ages of menopause for mostly younger women is also crucial when studying the time trend of age at menopause otherwise the analysis will be biased."],["dc.identifier.citation","BMC Medical Research Methodology. 2022 Jul 11;22(1):187"],["dc.identifier.doi","10.1186/s12874-022-01658-x"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112645"],["dc.language.iso","en"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Copula function"],["dc.subject","Distributional regression"],["dc.subject","GJRM"],["dc.subject","Incomplete data"],["dc.subject","Menopause"],["dc.subject","Smoothing"],["dc.title","Is age at menopause decreasing? – The consequences of not completing the generational cohort"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]