Incomplete quality of life data in lung transplant research: comparing cross sectional, repeated measures ANOVA, and multi-level analysis

• Main Authors: van der Bij Wim, Span Mark M, Post Wendy J, Vermeulen Karin M, Koëter Gerard H, TenVergert Elisabeth M
• Format: Article
• Language: English
• Published: BMC 2005-09-01
• Series: Respiratory Research
• Online Access: http://respiratory-research.com/content/6/1/101

<p>Abstract</p> <p>Background</p> <p>In longitudinal studies on Health Related Quality of Life (HRQL) it frequently occurs that patients have one or more missing forms, which may cause bias, and reduce the sample size. Aims of the present study were to address the problem of missing data in the field of lung transplantation (LgTX) and HRQL, to compare results obtained with different methods of analysis, and to show the value of each type of statistical method used to summarize data.</p> <p>Methods</p> <p>Results from cross-sectional analysis, repeated measures on complete cases (ANOVA), and a multi-level analysis were compared. The scores on the dimension 'energy' of the Nottingham Health Profile (NHP) after transplantation were used to illustrate the differences between methods.</p> <p>Results</p> <p>Compared to repeated measures ANOVA, the cross-sectional and multi-level analysis included more patients, and allowed for a longer period of follow-up. In contrast to the cross sectional analyses, in the complete case analysis, and the multi-level analysis, the correlation between different time points was taken into account. Patterns over time of the three methods were comparable. In general, results from repeated measures ANOVA showed the most favorable energy scores, and results from the multi-level analysis the least favorable. Due to the separate subgroups per time point in the cross-sectional analysis, and the relatively small number of patients in the repeated measures ANOVA, inclusion of predictors was only possible in the multi-level analysis.</p> <p>Conclusion</p> <p>Results obtained with the various methods of analysis differed, indicating some reduction of bias took place. Multi-level analysis is a useful approach to study changes over time in a data set where missing data, to reduce bias, make efficient use of available data, and to include predictors, in studies concerning the effects of LgTX on HRQL.</p>