Comparison of three longitudinal analysis models for the health-related quality of life in oncology: a simulation study.

BACKGROUND: Health-Related Quality of Life (HRQoL) is an important endpoint in oncology clinical trials aiming to investigate the clinical benefit of new therapeutic strategies for the patient. However, the longitudinal analysis of HRQoL remains complex and unstandardized. There is clearly a need to propose accessible statistical methods and meaningful results for clinicians. The objective of this study was to compare three strategies for longitudinal analyses of HRQoL data in oncology clinical trials through a simulation study. METHODS: The methods proposed were: the score and mixed model (SM); a survival analysis approach based on the time to HRQoL score deterioration (TTD); and the longitudinal partial credit model (LPCM). Simulations compared the methods in terms of type I error and statistical power of the test of an interaction effect between treatment arm and time. Several simulation scenarios were explored based on the EORTC HRQoL questionnaires and varying the number of patients (100, 200 or 300), items (1, 2 or 4) and response categories per item (4 or 7). Five or 10 measurement times were considered, with correlations ranging from low to high between each measure. The impact of informative missing data on these methods was also studied to reflect the reality of most clinical trials. RESULTS: With complete data, the type I error rate was close to the expected value (5%) for all methods, while the SM method was the most powerful method, followed by LPCM. The power of TTD is low for single-item dimensions, because only four possible values exist for the score. When the number of items increases, the power of the SM approach remained stable, those of the TTD method increases while the power of LPCM remained stable. With 10 measurement times, the LPCM was less efficient. With informative missing data, the statistical power of SM and TTD tended to decrease, while that of LPCM tended to increase. CONCLUSIONS: To conclude, the SM model was the most powerful model, irrespective of the scenario considered, and the presence or not of missing data. The TTD method should be avoided for single-item dimensions of the EORTC questionnaire. While the LPCM model was more adapted to this kind of data, it was less efficient than the SM model. These results warrant validation through comparisons on real data.

Health-related quality-of-life as co-primary endpoint in randomized clinical trials in oncology.

Overall survival (OS) has been considered as the most relevant primary endpoint but trials using OS often require large numbers of patients and long-term follow-up. Therefore composite endpoints, which are assessed earlier, are frequently used as primary endpoint but suffer from important limitations specially a lack of validation as surrogate of OS. Therefore, Health-related quality of life (HRQoL) could be considered as an outcome to judge efficacy of a treatment. An alternative approach would be to combine HRQoL with composite endpoints as co-primary endpoint to ensure a clinical benefit for patients of a new therapy. The decision rules of such design, the procedure to control the Type I error and the determination of sample size remain questions to debate. Here, we discusses HRQoL as co-primary endpoints in randomized clinical trials in oncology and provide some solutions to promote such design.