A non-linear beta-binomial regression model for mapping EORTC QLQ- C30 to the EQ-5D-3L in lung cancer patients: a comparison with existing approaches.
BACKGROUND
The performance of the Beta Binomial (BB) model is compared with several existing models for mapping the EORTC QLQ-C30 (QLQ-C30) on to the EQ-5D-3L using data from lung cancer trials.


METHODS
Data from 2 separate non small cell lung cancer clinical trials (TOPICAL and SOCCAR) are used to develop and validate the BB model. Comparisons with Linear, TOBIT, Quantile, Quadratic and CLAD models are carried out. The mean prediction error, R(2), proportion predicted outside the valid range, clinical interpretation of coefficients, model fit and estimation of Quality Adjusted Life Years (QALY) are reported and compared. Monte-Carlo simulation is also used.


RESULTS
The Beta-Binomial regression model performed 'best' among all models. For TOPICAL and SOCCAR trials, respectively, residual mean square error (RMSE) was 0.09 and 0.11; R(2) was 0.75 and 0.71; observed vs. predicted means were 0.612 vs. 0.608 and 0.750 vs. 0.749. Mean difference in QALY's (observed vs. predicted) were 0.051 vs. 0.053 and 0.164 vs. 0.162 for TOPICAL and SOCCAR respectively. Models tested on independent data show simulated 95% confidence from the BB model containing the observed mean more often (77% and 59% for TOPICAL and SOCCAR respectively) compared to the other models. All algorithms over-predict at poorer health states but the BB model was relatively better, particularly for the SOCCAR data.


CONCLUSIONS
The BB model may offer superior predictive properties amongst mapping algorithms considered and may be more useful when predicting EQ-5D-3L at poorer health states. We recommend the algorithm derived from the TOPICAL data due to better predictive properties and less uncertainty.
Cancer Research UK & UCL Cancer Trials Centre, Cancer Institute, University College London, 90 Tottenham Court Road (5th floor), London, W1T 4TJ, UK. Iftekhar.khan@ucl.ac.uk.
Iftekhar
I
Khan
Department of Applied Health Research, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK. steve.morris@ucl.ac.uk.
Stephen
S
Morris
D000328:Adult; D000465:Algorithms; D005260:Female; D006304:Health Status; D006801:Humans; D008175:Lung Neoplasms / Q000523:psychology*; D008297:Male; D015233:Models, Statistical; D011788:Quality of Life / Q000523:psychology*; D019057:Quality-Adjusted Life Years; D012044:Regression Analysis; D015203:Reproducibility of Results; D011795:Surveys and Questionnaires / Q000592:standards*