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
Measurement error is common in epidemiologic research and may affect the validity of research results. It is therefore important to scrutinise the effects of measurement error in epidemiologic... Show moreMeasurement error is common in epidemiologic research and may affect the validity of research results. It is therefore important to scrutinise the effects of measurement error in epidemiologic research. Even simple forms of measurement error, for instance random measurement error in an exposure, can introduce bias in exposure-outcome associations. And even though there are situations in which measurement error does not introduce bias in the exposure-outcome association, for instance in case of random measurement error in a continuous outcome, it nearly always affects the precision and power of a study. In addition, other forms of measurement error, for example systematic measurement error or differential measurement error in an exposure, covariate or outcome, can affect exposure-outcome associations in complex ways that may not easily be anticipated. Adjusting for measurement error using measurement error correction methods may thus be necessary to obtain reliable estimates of exposure-outcome associations.To facilitate measurement error correction, information about the underlying measurement error mechanism (i.e., model) and its parameters is needed. The measurement error model can sometimes be estimated from internal or external validation data, replicates data or calibration data. Collection and the use of such measurement error mechanism data will likely improve the quality of epidemiologic analyses in the presence of measurement error. This can be done through the application of measurement error correction methods, which adjust the analyses taking into account the information from the measurement error mechanism. Alternatively, in the absence of concrete data about the mechanisms or the parameters of measurement error, sensitivity analysis for measurement error can be used, in which the impact on the epidemiologic analyses of one or a range of hypothesised measurement error mechanisms or their parameters can be investigated. The studies described in the thesis were set out to improve the understanding of the impact of measurement error, to facilitate the application of measurement error correction methods, to improve the design of epidemiologic studies when measurement error in a variable is suspected and, to develop tools to quantitatively assess the impact of measurement error in epidemiologic research. Show less
Groenwold, R.H.H.; Shofty, I.; Miocevic, M.; Smeden, M. van; Klugkist, I. 2018
