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Assessing quality of life in a randomized clinical trial: correcting for missing data.

Gunnes N, Seierstad TG, Aamdal S, Brunsvig PF, Jacobsen AB, Sundstrøm S, Aalen OO - BMC Med Res Methodol (2009)

Bottom Line: Comparison of the treatment arms shows a significant difference in mean score at the end of treatment.Use of proper methodology developed for analysing data subject to missingness is necessary to reduce potential estimation bias.The conclusions are robust for the choice of statistical methods.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, University of Oslo, P,O, Box 1122 Blindern, N-0317 Oslo, Norway. nina.gunnes@medisin.uio.no

ABSTRACT

Background: Health-related quality of life is a topic of current interest. This paper considers a randomized phase III study of radiation therapy with concurrent chemotherapy (docetaxel) versus radiation therapy alone in non-small cell lung cancer, stage III A/B. Longitudinal data on quality of life have been obtained through repeated administration of a multi-item questionnaire (EORTC QLQ-C30) developed by the European Organisation for Research and Treatment of Cancer. Missingness in the data is owing to patients having failed to complete the questionnaire at some of the scheduled filling-in times.

Methods: We have analysed a monotone (in terms of missingness) subset of the data as regards estimation of the mean score of a summary measure of self-reported quality of life in a hypothetical drop-out-free population at different points in time. Missingness is a difficult issue of great importance. We have therefore chosen to compare three different methods that are relatively easy to implement: the linear-increments method, the inverse-probability-weighting method and the Markov-process method. Single imputation has been applied in a supplementary analysis to fill in for all the non-consecutive missing score values prior to the execution of the estimation procedure.

Results: For the response in focus, the observed mean score at a certain time is larger than the estimated mean scores, which implies that the true mean score is easily overestimated unless the missingness is appropriately adjusted for. Comparison of the treatment arms shows a significant difference in mean score at the end of treatment.

Conclusion: Use of proper methodology developed for analysing data subject to missingness is necessary to reduce potential estimation bias. The quality of life of patients receiving radiation therapy with concurrent chemotherapy (docetaxel) appears somewhat worse than that of patients receiving radiation therapy alone in the period during which treatment is given. The conclusions are robust for the choice of statistical methods.

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Empirical standard errors of the estimated mean scores for an immortal cohort (with single imputation). The figure displays the empirical standard errors of the estimated mean scores (based on 1000 bootstrap samples) for arm A (upper panel) and arm B (lower panel) when considering an immortal cohort. Single imputation has been applied. The blue dotted-line curve corresponds to the IPW method, and the green dash-dotted-line curve corresponds to the LI method.
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Figure 6: Empirical standard errors of the estimated mean scores for an immortal cohort (with single imputation). The figure displays the empirical standard errors of the estimated mean scores (based on 1000 bootstrap samples) for arm A (upper panel) and arm B (lower panel) when considering an immortal cohort. Single imputation has been applied. The blue dotted-line curve corresponds to the IPW method, and the green dash-dotted-line curve corresponds to the LI method.

Mentions: Figure 5 displays the mean score estimates, plotted against time, for both treatment arms when considering an immortal cohort. By comparing the curves in Figure 1 and Figure 5, we see that the application of single imputation prior to the data analysis has not changed the observed and estimated mean scores very much. Figure 6 displays the empirical standard errors of the mean score estimates (based on 1000 bootstrap samples), plotted against time, for both treatment arms when considering an immortal cohort. It is evident that single imputation reduces the variability of the estimates.


Assessing quality of life in a randomized clinical trial: correcting for missing data.

Gunnes N, Seierstad TG, Aamdal S, Brunsvig PF, Jacobsen AB, Sundstrøm S, Aalen OO - BMC Med Res Methodol (2009)

Empirical standard errors of the estimated mean scores for an immortal cohort (with single imputation). The figure displays the empirical standard errors of the estimated mean scores (based on 1000 bootstrap samples) for arm A (upper panel) and arm B (lower panel) when considering an immortal cohort. Single imputation has been applied. The blue dotted-line curve corresponds to the IPW method, and the green dash-dotted-line curve corresponds to the LI method.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2698910&req=5

Figure 6: Empirical standard errors of the estimated mean scores for an immortal cohort (with single imputation). The figure displays the empirical standard errors of the estimated mean scores (based on 1000 bootstrap samples) for arm A (upper panel) and arm B (lower panel) when considering an immortal cohort. Single imputation has been applied. The blue dotted-line curve corresponds to the IPW method, and the green dash-dotted-line curve corresponds to the LI method.
Mentions: Figure 5 displays the mean score estimates, plotted against time, for both treatment arms when considering an immortal cohort. By comparing the curves in Figure 1 and Figure 5, we see that the application of single imputation prior to the data analysis has not changed the observed and estimated mean scores very much. Figure 6 displays the empirical standard errors of the mean score estimates (based on 1000 bootstrap samples), plotted against time, for both treatment arms when considering an immortal cohort. It is evident that single imputation reduces the variability of the estimates.

Bottom Line: Comparison of the treatment arms shows a significant difference in mean score at the end of treatment.Use of proper methodology developed for analysing data subject to missingness is necessary to reduce potential estimation bias.The conclusions are robust for the choice of statistical methods.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, University of Oslo, P,O, Box 1122 Blindern, N-0317 Oslo, Norway. nina.gunnes@medisin.uio.no

ABSTRACT

Background: Health-related quality of life is a topic of current interest. This paper considers a randomized phase III study of radiation therapy with concurrent chemotherapy (docetaxel) versus radiation therapy alone in non-small cell lung cancer, stage III A/B. Longitudinal data on quality of life have been obtained through repeated administration of a multi-item questionnaire (EORTC QLQ-C30) developed by the European Organisation for Research and Treatment of Cancer. Missingness in the data is owing to patients having failed to complete the questionnaire at some of the scheduled filling-in times.

Methods: We have analysed a monotone (in terms of missingness) subset of the data as regards estimation of the mean score of a summary measure of self-reported quality of life in a hypothetical drop-out-free population at different points in time. Missingness is a difficult issue of great importance. We have therefore chosen to compare three different methods that are relatively easy to implement: the linear-increments method, the inverse-probability-weighting method and the Markov-process method. Single imputation has been applied in a supplementary analysis to fill in for all the non-consecutive missing score values prior to the execution of the estimation procedure.

Results: For the response in focus, the observed mean score at a certain time is larger than the estimated mean scores, which implies that the true mean score is easily overestimated unless the missingness is appropriately adjusted for. Comparison of the treatment arms shows a significant difference in mean score at the end of treatment.

Conclusion: Use of proper methodology developed for analysing data subject to missingness is necessary to reduce potential estimation bias. The quality of life of patients receiving radiation therapy with concurrent chemotherapy (docetaxel) appears somewhat worse than that of patients receiving radiation therapy alone in the period during which treatment is given. The conclusions are robust for the choice of statistical methods.

Show MeSH
Related in: MedlinePlus