2005
DOI: 10.1007/s11136-004-0834-7
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Missing forms and dropout in the TME quality of life substudy

Abstract: Objective: Missing forms may pose problems in health related quality of life (QOL) studies, because the absence of a QOL measure may be related to the patient's health and hence to the patient's QOL itself. Studying patterns of missingness, dropout, and the possible impact of missing data on QOL measures is an important step in reporting outcomes of QOL studies. We study patterns of dropout and evaluate the impact of missing forms in the TME QOL substudy. Methods: Patients with rectal cancer, randomized to rec… Show more

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Cited by 4 publications
(3 citation statements)
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“…This is not surprising in a naturalistic, longitudinal study of patients and caregivers coping with a terminal and demanding illness that makes it unlikely that 5 years of continuous and available data could be obtained, not least because many patients die within that time frame. However, the strength of our data analysis (MLM) is that complete data across the span of the study are not necessary to fit a model, and MLM is even considered ideal for longitudinal quality-of-life studies in which number of data points and intervals between data points both vary [58,59]. Additionally, for missing centering variables that were imputed (e.g., time since diagnosis), we used an acceptable multiple random imputation method [39,60].…”
Section: Discussionmentioning
confidence: 99%
“…This is not surprising in a naturalistic, longitudinal study of patients and caregivers coping with a terminal and demanding illness that makes it unlikely that 5 years of continuous and available data could be obtained, not least because many patients die within that time frame. However, the strength of our data analysis (MLM) is that complete data across the span of the study are not necessary to fit a model, and MLM is even considered ideal for longitudinal quality-of-life studies in which number of data points and intervals between data points both vary [58,59]. Additionally, for missing centering variables that were imputed (e.g., time since diagnosis), we used an acceptable multiple random imputation method [39,60].…”
Section: Discussionmentioning
confidence: 99%
“…However, the evidence provided here from the ALSAQ-5 Index would not tend to support this view. Furthermore, it is more likely that severely ill patients do not return questionnaires at all rather than omit certain questions (19). Nevertheless, researchers would be advised to undertake a sensitivity analysis by comparing results gained from their original dataset, which includes the missing data, to those gained after implementing the EM algorithm.…”
Section: Discussionmentioning
confidence: 99%
“…Differences in paid and unpaid labor and quality of life between randomization groups were evaluated with linear mixed model analyses with time as a within-subjects factor, because this analysis method takes into account repeated measures and possibly nonignorable drop-out. 30 Linear and ordinal regression analysis were performed at each time point to study the sensitivity of our results to the assumptions of the linear mixed model, because the distribution of paid labor was not entirely normal. The results of the linear and ordinal regression analyses did not substantially differ, and linear mixed model analyses were therefore considered appropriate.…”
Section: Analysesmentioning
confidence: 99%