Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches

Background: Missing data is classified as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). Knowing the mechanism is useful in identifying the most appropriate analysis. The first aim was to compare different methods for identifying this missing data mechanism to determine if they gave consistent conclusions. Secondly, to investigate whether the reminder-response data can be utilised to help identify the missing data mechanism. Methods: Five clinical trial datasets that employed a reminder system at follow-up were used. Some quality of life questionnaires were initially missing, but later recovered through reminders. Four methods of determining the missing data mechanism were applied. Two response data scenarios were considered. Firstly, immediate data only; secondly, all observed responses (including reminder-response). Results: In three of five trials the hypothesis tests found evidence against the MCAR assumption. Logistic regression suggested MAR, but was able to use the reminder-collected data to highlight potential MNAR data in two trials. Conclusion: The four methods were consistent in determining the missingness mechanism. One hypothesis test was preferred as it is applicable with intermittent missingness. Some inconsistencies between the two data scenarios were found. Ignoring the reminder data could potentially give a distorted view of the missingness mechanism. Utilising reminder data allowed the possibility of MNAR to be considered.The Chief Scientist Office of the Scottish Government Health Directorate. Research Training Fellowship (CZF/1/31

Predicting missing quality of life data that were later recovered : an empirical comparison of approaches

Peer reviewedPostprin

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Background: Randomised controlled trials (RCTs) are perceived as the gold-standard method for evaluating healthcare interventions, and increasingly include quality of life (QoL) measures. The observed results are susceptible to bias if a substantial proportion of outcome data are missing. The review aimed to determine whether imputation was used to deal with missing QoL outcomes. Methods: A random selection of 285 RCTs published during 2005/6 in the British Medical Journal, Lancet, New England Journal of Medicine and Journal of American Medical Association were identified. Results: QoL outcomes were reported in 61 (21%) trials. Six (10%) reported having no missing data, 20 (33%) reported ≤ 10% missing, eleven (18%) 11%–20% missing, and eleven (18%) reported >20% missing. Missingness was unclear in 13 (21%). Missing data were imputed in 19 (31%) of the 61 trials. Imputation was part of the primary analysis in 13 trials, but a sensitivity analysis in six. Last value carried forward was used in 12 trials and multiple imputation in two. Following imputation, the most common analysis method was analysis of covariance (10 trials). Conclusion: The majority of studies did not impute missing data and carried out a complete-case analysis. For those studies that did impute missing data, researchers tended to prefer simpler methods of imputation, despite more sophisticated methods being available.The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health Directorate. Shona Fielding is also currently funded by the Chief Scientist Office on a Research Training Fellowship (CZF/1/31)

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Peer reviewedPublisher PD

Psychometric properties of the Iranian interview-administered version of the World Health Organization's Quality of Life Questionnaire (WHOQOL-BREF): A population-based study

<p>Abstract</p> <p>Background</p> <p>The objective of the current study was to translate and validate the Iranian version of the WHOQOL-BREF.</p> <p>Methods</p> <p>A forward-backward translation procedure was followed to develop the Iranian version of the questionnaire. A stratified random sample of individuals aged 18 and over completed the questionnaire in Tehran, Iran. Psychometric properties of the instrument including reliability (internal consistency, and test-retest analysis), validity (known groups' comparison and convergent validity), and items' correlation with their hypothesized domains were assessed.</p> <p>Results</p> <p>In all 1164 individuals entered into the study. The mean age of the participants was 36.6 (SD = 13.2) years, and the mean years of their formal education was 10.7 (SD = 4.4). In general the questionnaire received well and all domains met the minimum reliability standards (Cronbach's alpha and intra-class correlation > 0.7), except for social relationships (alpha = 0.55). Performing known groups' comparison analysis, the results indicated that the questionnaire discriminated well between subgroups of the study samples differing in their health status. Since the WHOQOL-BREF demonstrated statistically significant correlation with the Iranian version of the SF-36 as expected, the convergent validity of the questionnaire was found to be desirable. Correlation matrix also showed satisfactory results in all domains except for social relationships.</p> <p>Conclusion</p> <p>This study has provided some preliminary evidence of the reliability and validity of the WHOQOL-BREF to be used in Iran, though further research is required to challenge the problems of reliability in one of the dimensions and the instrument's factor structure.</p

Outcome based subgroup analysis: a neglected concern

A subgroup of clinical trial subjects identified by baseline characteristics is a proper subgroup while a subgroup determined by post randomization events or measures is an improper subgroup. Both types of subgroups are often analyzed in clinical trial papers. Yet, the extensive scrutiny of subgroup analyses has almost exclusively attended to the former. The analysis of improper subgroups thereby not only flourishes in numerous disguised ways but also does so without a corresponding awareness of its pitfalls. Comparisons of the grade of angina in a heart disease trial, for example, usually include only the survivors. This paper highlights some of the distinct ways in which outcome based subgroup analysis occurs, describes the hazards associated with it, and proposes a simple alternative approach to counter its analytic bias. Data from six published trials show that outcome based subgroup analysis, like proper subgroup analysis, may be performed in a post-hoc fashion, overdone, selectively reported, and over interpreted. Six hypothetical trial scenarios illustrate the forms of hidden bias related to it. That bias can, however, be addressed by assigning clinically appropriate scores to the usually excluded subjects and performing an analysis that includes all the randomized subjects. A greater level of awareness about the practice and pitfalls of outcome based subgroup analysis is needed. When required, such an analysis should maintain the integrity of randomization. This issue needs greater practical and methodologic attention than has been accorded to it thus far

Impaired health-related quality of life in children and adolescents with chronic conditions: a comparative analysis of 10 disease clusters and 33 disease categories/severities utilizing the PedsQL™ 4.0 Generic Core Scales

<p>Abstract</p> <p>Background</p> <p>Advances in biomedical science and technology have resulted in dramatic improvements in the healthcare of pediatric chronic conditions. With enhanced survival, health-related quality of life (HRQOL) issues have become more salient. The objectives of this study were to compare generic HRQOL across ten chronic disease clusters and 33 disease categories/severities from the perspectives of patients and parents. Comparisons were also benchmarked with healthy children data.</p> <p>Methods</p> <p>The analyses were based on over 2,500 pediatric patients from 10 physician-diagnosed disease clusters and 33 disease categories/severities and over 9,500 healthy children utilizing the PedsQL™ 4.0 Generic Core Scales. Patients were recruited from general pediatric clinics, subspecialty clinics, and hospitals.</p> <p>Results</p> <p>Pediatric patients with diabetes, gastrointestinal conditions, cardiac conditions, asthma, obesity, end stage renal disease, psychiatric disorders, cancer, rheumatologic conditions, and cerebral palsy self-reported progressively more impaired overall HRQOL than healthy children, respectively, with medium to large effect sizes. Patients with cerebral palsy self-reported the most impaired HRQOL, while patients with diabetes self-reported the best HRQOL. Parent proxy-reports generally paralleled patient self-report, with several notable differences.</p> <p>Conclusion</p> <p>The results demonstrate differential effects of pediatric chronic conditions on patient HRQOL across diseases clusters, categories, and severities utilizing the PedsQL™ 4.0 Generic Core Scales from the perspectives of pediatric patients and parents. The data contained within this study represents a larger and more diverse population of pediatric patients with chronic conditions than previously reported in the extant literature. The findings contribute important information on the differential effects of pediatric chronic conditions on generic HRQOL from the perspectives of children and parents utilizing the PedsQL™ 4.0 Generic Core Scales. These findings with the PedsQL™ have clinical implications for the healthcare services provided for children with chronic health conditions. Given the degree of reported impairment based on PedsQL™ scores across different pediatric chronic conditions, the need for more efficacious targeted treatments for those pediatric patients with more severely impaired HRQOL is clearly and urgently indicated.</p