ObjectivesThis review addresses the common problem of missing patient-reported outcome (PRO) data in clinical trials by assessing the current practice of their statistical handling as reported in... Show moreObjectivesThis review addresses the common problem of missing patient-reported outcome (PRO) data in clinical trials by assessing the current practice of their statistical handling as reported in publications of randomized controlled trials (RCTs) in patients with breast cancer.Study Design and SettingWe searched PubMed to identify RCTs evaluating biomedical treatments in breast cancer patients with at least one PRO endpoint published between January 2019 and February 2022. Two reviewers independently assessed the eligibility of the publications for this scoping review and extracted prespecified information on missing PRO data and related statistical practices.ResultsOf 1,598 publications identified, 118 trials met the inclusion criteria. Eighty-eight (74.6%) trials reported the extent of missing data, with 11 (9.3%) not containing any missing PRO data. Twenty-one (19.6%) trials explicitly stated the statistical approach for handling missing data, with a preference for single imputation over multiple imputation approaches (57.2%/19.0%). Only six (5.6%) trials reported a sensitivity analysis to examine the extent to the results being affected by changes in assumptions made about missing PRO data.ConclusionInternational efforts to raise awareness of the importance of accurately reporting state-of-the-art handling of missing PRO data are not yet fully reflected in the current literature of breast cancer RCTs. Show less
This thesis addresses potential threats to the validity of observational epidemiological studies. Examples of these potential sources of bias are confounding, missing data, selection bias, and... Show moreThis thesis addresses potential threats to the validity of observational epidemiological studies. Examples of these potential sources of bias are confounding, missing data, selection bias, and measurement error. Although various methods have been developed to mitigate these biases, it is often unclear which methods can be used in which empirical settings. It is also common that issues discussed in methodological studies are overlooked in clinical research. Thus, we investigated problems ofmissing data, selection bias, and measurement error occurring in several specific observational settings and discuss how to optimally handle them. Show less
This thesis describes studies on methods for answering questions about causality, specifically so-called what-if questions, in the presence of methodological obstacles such as confounding, missing... Show moreThis thesis describes studies on methods for answering questions about causality, specifically so-called what-if questions, in the presence of methodological obstacles such as confounding, missing data, and measurement error. Show less
Objectives: Epidemiologic studies often suffer from incomplete data, measurement error (or misclassification), and confounding. Each of these can cause bias and imprecision in estimates of exposure... Show moreObjectives: Epidemiologic studies often suffer from incomplete data, measurement error (or misclassification), and confounding. Each of these can cause bias and imprecision in estimates of exposure-outcome relations. We describe and compare statistical approaches that aim to control all three sources of bias simultaneously.Study Design and Setting: We illustrate four statistical approaches that address all three sources of bias, namely, multiple imputation for missing data and measurement error, multiple imputation combined with regression calibration, full information maximum likelihood within a structural equation modeling framework, and a Bayesian model. In a simulation study, we assess the performance of the four approaches compared with more commonly used approaches that do not account for measurement error, missing values, or confounding.Results: The results demonstrate that the four approaches consistently outperform the alternative approaches on all performance metrics (bias, mean squared error, and confidence interval coverage). Even in simulated data of 100 subjects, these approaches perform well.Conclusion: There can be a large benefit of addressing measurement error, missing values, and confounding to improve the estimation of exposure-outcome relations, even when the available sample size is relatively small. (C) 2020 The Authors. Published by Elsevier Inc. Show less
Missing data is a problem that occurs frequently in many scientific areas. The most sophisticatedmethod for dealing with this problem is multiple imputation. Contrary to other methods, like... Show moreMissing data is a problem that occurs frequently in many scientific areas. The most sophisticatedmethod for dealing with this problem is multiple imputation. Contrary to other methods, like listwise deletion, this method does not throw away information, and partly repairs the problem ofsystematic dropout. Although from a theoretical point of view multiple imputation is consideredto be the optimal method, many applied researchers are reluctant to use it because of persistentmisconceptions about this method. Instead of providing an(other) overview of missing data methods, or extensively explaining how multiple imputation works, this article aims specifically atrebutting these misconceptions, and provides applied researchers with practical arguments supporting them in the use of multiple imputation. Show less
The aim of this thesis was to study the link between hearing loss, language skills, and social functioning in deaf and hard of hearing (DHH) children. Sufficient language skills are an... Show moreThe aim of this thesis was to study the link between hearing loss, language skills, and social functioning in deaf and hard of hearing (DHH) children. Sufficient language skills are an essential prerequisite to develop appropriate communication skills, in order to join in conversations with others. Both their hearing loss and their diminished communication skills prevent DHH children from learning by observing their surroundings (incidental learning). As a result, DHH children showed more difficulty in understanding others’ thoughts and wishes (Theory of Mind or ToM). DHH teenagers reported to have difficulties with understanding others’ emotions and showed lower levels of prosocial behavior. Higher communication skills, but not language skills, were related to better ToM development and higher empathic abilities. Second, the role of early identification and intervention of hearing loss on the social-emotional development of DHH children was studied. This was illustrated in a longitudinal study showing that early cochlear implantation resulted in higher language and communication skills. In turn, these improved skills prevented the development of early signs of psychopathology. To conclude, this thesis shows that in order to stimulate the social-emotional development of DHH children, their opportunities for incidental learning have to be increased. Show less
