First, the non-preference based measure may have different severity content than the preference-based measure. There are several causes for the overprediction of low utility values. The proposed solution involves the use of a different algorithm for patients in poor health, where health status is determined using available information from a condition-specific non-preference-based measure. The current study explores whether the causes of overprediction of utility values for patients in poor health found in the literature can inform a method to minimize that overprediction.
While it is unlikely for such overprediction to be a problem in all samples, given that many studies have reasonably high mean EQ-5 D values, it is likely to occur in patient (sub) samples containing a significant proportion of individuals in poor health. Another study, mapping the modified Rankin scale measurement, which assesses disability after stroke, on EQ-5 D reports decreased accuracy for patients in poor health and significant overprediction of low values. For instance, a study mapping SF-12 on EQ-5 D report predicted values under 0.5 to be notably higher than observed values, for both 2 nd and 4 th order models. Criteria for the quality of a mapping algorithm do not currently exist although it is well known that utilities estimated by mapping algorithms typically have larger errors for lower utility values and mapped EQ-5 D utilities show a systematic overprediction of utility values for patients in poor health. The resulting mapping equation is used to estimate the utility values of the preferenced-based measure in the study dataset where such a measure is absent. A mapping algorithm can be estimated by regressing a non-preference-based measure onto a preference-based measure on a dataset external to your study dataset. Mapped EQ-5 D utility values are accepted as evidence in cost-utility analyses by reimbursement agencies such as the National Institute of Health and Clinical Excellence (NICE) (see § 5.4.6) but suffer from non-trivial problems like the overprediction of utility values for patients in poor health. In recent years there has been an increasing amount of publications concerned with 'mapping' condition specific measures on EQ-5 D to estimate EQ-5 D utility values. Specifying a separate mapping algorithm to predict utility values for individuals in poor health greatly reduces overprediction, but does not fully solve the problem. The cut-off points found in this study represent severely impaired health. Cut-off points on a disease specific questionnaire can be identified through association with the causes of overprediction. Guidelines can be developed on when the use of a mapping algorithms is inappropriate, for instance through the identification of cut-off points. Mapping algorithms overpredict utility values for patients in poor health but are used in cost-effectiveness analyses nonetheless. A HAQ separate algorithm could not be estimated due to data restrictions. The QLQ-C30 separate algorithm performed better than existing mapping algorithms for predicting utility values for patients in poor health, but still did not accurately predict mean utility values. Separate algorithms are here proposed to predict utility values for patients in poor health, where these are selected using cut-off points for HAQ-DI (> 2.0) and QLQ C-30 (< 45 average of QLQ C-30 functioning scales). The large decrement of reporting 'extreme problems' in the EQ-5 D tariff, few observations with the most severe level in any EQ-5 D dimension and many observations at the least severe level in any EQ-5 D dimension led to a bimodal distribution of EQ-5 D index values, which is related to the overprediction of utility values for patients in poor health.
ResultsĪll mapping algorithms suffer from overprediction of utility values for patients in poor health. Poor health states are defined using a cut-off point for QLQ-C30 and HAQ, which is determined using association with EQ-5 D values.
Separate mapping algorithms are estimated for poor health states.
Three existing datasets are used to estimate mapping algorithms and assess existing mapping algorithms from the literature mapping the cancer-specific EORTC-QLQ C-30 and the arthritis-specific Health Assessment Questionnaire (HAQ) onto the EQ-5 D. This paper is concerned with the question of why overestimation of EQ-5 D utility values occurs for patients in poor health, and explores possible solutions.
#Disease dmg eq how to#
Currently there are no guidelines on how to deal with this problem. Yet many mapping algorithms have been found to systematically overpredict EQ-5 D utility values for patients in poor health. An increasing amount of studies report mapping algorithms which predict EQ-5 D utility values using disease specific non-preference-based measures.
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