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

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Serbian KINDL questionnaire for quality of life assessments in healthy children and adolescents: reproducibility and construct validity

<p>Abstract</p> <p>Background</p> <p>The KINDL questionnaire is frequently used to evaluate quality of life (QOL) and the impacts of health conditions on children's everyday living. The objectives of this study were to assess the reproducibility and construct validity of the Serbian KINDL for QOL assessments in healthy children and adolescents.</p> <p>Methods</p> <p>Five hundred and sixty-four healthy children and adolescents completed the KINDL. Reproducibility was analyzed using the intraclass correlation coefficient (ICC). Confirmatory factor analysis (CFA) was performed to assess the structure of the KINDL construct validity.</p> <p>Results</p> <p>The intraclass correlation coefficients ranged from 0.03 to 0.84 for the subscales and total score. A second order CFA model as originally hypothesized was tested: items (24), primary factors (six subscales), and one secondary factor (QOL). The fit indexes derived from a CFA failed to yield appropriate fit between the data and the hypothesized model.</p> <p>Conclusion</p> <p>Majority of the subscales and total KINDL possess appropriate reproducibility for group comparisons. However, a CFA failed to confirm the structure of the original measurement model, indicating that the Serbian version should be revised before wider use for QOL assessments in healthy children and adolescent.</p

Mapping the disease-specific LupusQoL to the SF-6D

Purpose To derive a mapping algorithm to predict SF-6D utility scores from the non-preference-based LupusQoL and test the performance of the developed algorithm on a separate independent validation data set. Method LupusQoL and SF-6D data were collected from 320 patients with systemic lupus erythematosus (SLE) attending routine rheumatology outpatient appointments at seven centres in the UK. Ordinary least squares (OLS) regression was used to estimate models of increasing complexity in order to predict individuals’ SF-6D utility scores from their responses to the LupusQoL questionnaire. Model performance was judged on predictive ability through the size and pattern of prediction errors generated. The performance of the selected model was externally validated on an independent data set containing 113 female SLE patients who had again completed both the LupusQoL and SF-36 questionnaires. Results Four of the eight LupusQoL domains (physical health, pain, emotional health, and fatigue) were selected as dependent variables in the final model. Overall model fit was good, with R2 0.7219, MAE 0.0557, and RMSE 0.0706 when applied to the estimation data set, and R2 0.7431, MAE 0.0528, and RMSE 0.0663 when applied to the validation sample. Conclusion This study provides a method by which health state utility values can be estimated from patient responses to the non-preference-based LupusQoL, generalisable beyond the data set upon which it was estimated. Despite concerns over the use of OLS to develop mapping algorithms, we find this method to be suitable in this case due to the normality of the SF-6D data

General population normative data for the EORTC QLQ-C30 health-related quality of life questionnaire based on 15,386 persons across 13 European countries, Canada and the Unites States

OBJECTIVE: The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 health-related quality of life questionnaire is one of the most widely used cancer-specific health-related quality of life questionnaires worldwide. General population norm data can facilitate the interpretation of QLQ-C30 data obtained from cancer patients. This study aimed at systematically collecting norm data from the general population to develop European QLQ-C30 norm scores and to generate comparable norm data for individual countries in Europe and North America. METHODS: We collected QLQ-C30 data from the general population across 11 European Union (EU) countries, Russia, Turkey, Canada and United States (n \u3e /= 1000/country). Representative samples were stratified by sex and age groups (18-39, 40-49, 50-59, 60-69 and \u3e /= 70 years). After applying weights based on the United Nations population distribution statistics, we calculated QLQ-C30 domain scores to generate a \u27European QLQ-C30 Norm\u27 based on the EU countries. Further, we calculated QLQ-C30 norm scores for all 15 individual countries. RESULTS: A total of 15,386 respondents completed the online survey. For the EU sample, most QLQ-C30 domains showed differences by sex/age, with men scoring somewhat better health than women, while age effects varied across domains. Substantially larger differences were seen in inter-country comparisons, with Austrian and Dutch respondents reporting consistently better health compared with British and Polish respondents. CONCLUSIONS: This study is the first to systematically collect EORTC QLQ-C30 general population norm data across Europe and North America applying a consistent data collection method across 15 countries. These new norm data facilitate valid intra-country as well as inter-country comparisons and QLQ-C30 score interpretation

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

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Is health-related quality of life associated with the risk of low-energy wrist fracture: a case-control study

<p>Abstract</p> <p>Background</p> <p>Some risk factors for low-energy wrist fracture have been identified. However, self-reported measures such as health-related quality of life (HRQOL) have not been examined as potential risk factors for wrist fracture. The aims of this study were to compare HRQOL prior to a low-energy wrist fracture in elderly patients (≥ 50 years) with HRQOL in age- and sex-matched controls, and to explore the association between HRQOL and wrist fracture after adjusting for known risk factors for fracture such as age, weight, osteoporosis and falls.</p> <p>Methods</p> <p>Patients with a low-energy wrist fracture (n = 181) and age- and sex-matched controls (n = 181) were studied. Shortly after fracture (median 10 days), patients assessed their HRQOL before fracture using the Short Form 36 (SF-36). Statistical tests included <it>t </it>tests and multivariate logistic regression analysis.</p> <p>Results</p> <p>Several dimensions of HRQOL were significantly associated with wrist fracture. The direction of the associations with wrist fracture varied between the different sub-dimensions of the SF-36. After controlling for demographic and clinical variables, higher scores on <it>general health </it>(odds ratio (OR) = 1.31, 95% confidence interval (CI) = 1.10–1.56), <it>bodily pain </it>(OR = 1.18, 95% CI = 1.03–1.34) and <it>mental health </it>(OR = 1.39, 95% CI = 1.09–1.79) were related to an increased chance of being a wrist fracture patient rather than a control. In contrast, higher scores on <it>physical role limitation </it>(OR = 0.87, 95% CI = 0.79–0.95) and <it>social function </it>(OR = 0.65, 95% CI 0.53–0.80) decreased this chance. Significant associations with wrist fracture were also found for living alone (OR = 1.91, 95% CI 1.07–3.4), low body mass index (BMI) (OR = 0.92, 95% CI 0.86–0.98), osteoporosis (OR = 3.30, 95% CI 1.67–6.50) and previous falls (OR = 2.01, 95% CI 1.16–3.49).</p> <p>Conclusion</p> <p>Wrist fracture patients perceive themselves to be as healthy as the controls before fracture. Our data indicate that patients with favourable and unfavourable HRQOL measures may be at increased risk of wrist fracture.</p

Trajectories of self-rated health in people with diabetes: Associations with functioning in a prospective community sample

© 2013 Schmitz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Self-rated health (SRH) is a single-item measure that is one of the most widely used measures of general health in population health research. Relatively little is known about changes and the trajectories of SRH in people with chronic medical conditions. The aims of the present study were to identify and describe longitudinal trajectories of self-rated health (SRH) status in people with diabetes. Methods: A prospective community study was carried out between 2008 and 2011. SRH was assessed at baseline and yearly at follow-ups (n=1288). Analysis was carried out through trajectory modeling. The trajectory groups were subsequently compared at 4 years follow-up with respect to functioning. Results: Four distinct trajectories of SRH were identified: 1) 72.2% of the participants were assigned to a persistently good SRH trajectory; 2) 10.1% were assigned to a persistently poor SRH trajectory; 3) mean SRH scores changed from good to poor for one group (7.3%); while 4) mean SRH scores changed from poor to medium/good for another group (10.4%). Those with a persistently poor perception of health status were at higher risk for poor functioning at 4 years follow-up than those whose SRH scores decreased from good to poor. Conclusions: SRH is an important predictor for poor functioning in diabetes, but the trajectory of SRH seems to be even more important. Health professionals should pay attention to not only SRH per se, but also changes in SRH over time.This work was supported by Operating Grant MOP-84574 from the Canadian Institutes of Health Research (CIHR). GG was supported by a doctoral fellowship from the CIHR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript