Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions
Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values ( HSUVs ) from data obtained from another health-related quality of life ( HRQoL ) measure .
There have been several method ological developments in this area since a previous review up to 2007 .
Mapping the Paediatric Quality of Life Inventory (PedsQL™) Generic Core Scales onto the Child Health Utility Index–9 Dimension (CHU-9D) Score for Economic Evaluation in Children
Background The Paediatric Quality of Life Inventory ( PedsQL ™ ) question naire is a widely used , generic instrument design ed for measuring health-related quality of life ( HRQoL ) ; however , it is not preference-based and therefore not suitable for cost – utility analysis . The Child Health Utility Index–9 Dimension ( CHU-9D ) , however , is a preference-based instrument that has been primarily developed to support cost – utility analysis . Objective This paper presents a method for estimating CHU-9D index scores from responses to the PedsQL ™ using data from a r and omised controlled trial of prednisolone therapy for treatment of childhood corticosteroid-sensitive nephrotic syndrome . Methods HRQoL data were collected from children at r and omisation , week 16 , and months 12 , 18 , 24 , 36 and 48 . Observations on children aged 5 years and older were pooled across all data collection timepoints and were then r and omised into an estimation ( n = 279 ) and validation ( n = 284 ) sample . A number of models were developed using the estimation data before internal validation . The best model was chosen using multi-stage selection criteria . Results Most of the models developed accurately predicted the CHU-9D mean index score . The best performing model was a generalised linear model ( mean absolute error = 0.0408 ; mean square error = 0.0035 ) . The proportion of index scores deviating from the observed scores by < 0.03 was 53 % . Conclusions The mapping algorithm provides an empirical tool for estimating CHU-9D index scores and for conducting cost – utility analyses within clinical studies that have only collected PedsQL ™ data . It is valid for children aged 5 years or older . Caution should be exercised when using this with children younger than 5 years , older adolescents ( > 13 years ) or patient groups with particularly poor quality of life . IS RCT N Registry
Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions
Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values ( HSUVs ) from data obtained from another health-related quality of life ( HRQoL ) measure .
There have been several method ological developments in this area since a previous review up to 2007 .