Predicting EQ-5D-5L crosswalk from the PROMIS-29 profile for the United Kingdom, France, and Germany.

BACKGROUND: EQ-5D health state utilities (HSU) are commonly used in health economics to compute quality-adjusted life years (QALYs). The EQ-5D, which is country-specific, can be derived directly or by mapping from self-reported health-related quality of life (HRQoL) scales such as the PROMIS-29 profile. The PROMIS-29 from the Patient Reported Outcome Measures Information System is a comprehensive assessment of self-reported health with excellent psychometric properties. We sought to find optimal models predicting the EQ-5D-5L crosswalk from the PROMIS-29 in the United Kingdom, France, and Germany and compared the prediction performances with that of a US model. METHODS: We collected EQ-5D-5L and PROMIS-29 profiles and three samples representative of the general populations in the UK (n = 1509), France (n = 1501), and Germany (n = 1502). We used stepwise regression with backward selection to find the best models to predict the EQ-5D-5L crosswalk from all seven PROMIS-29 domains. We investigated the agreement between the observed and predicted EQ-5D-5L crosswalk in all three countries using various indices for the prediction performance, including Bland-Altman plots to examine the performance along the HSU continuum. RESULTS: The EQ-5D-5L crosswalk was best predicted in France (nRMSEFRA = 0.075, nMAEFRA = 0.052), followed by the UK (nRMSEUK = 0.076, nMAEUK = 0.053) and Germany (nRMSEGER = 0.079, nMAEGER = 0.051). The Bland-Altman plots show that the inclusion of higher-order effects reduced the overprediction of low HSU scores. CONCLUSIONS: Our models provide a valid method to predict the EQ-5D-5L crosswalk from the PROMIS-29 for the UK, France, and Germany.

Monitoring daily milk yields with a recursive test day repeatability model (Kalman filter).

Mixed model methodology and recursive estimation techniques (Kalman filter) were combined to detect significant changes in the performance level of both individual cows and an entire herd. Yields were predicted for the next time of recording using all data available up to that time. Predicted yields were compared with actual measurements. If the error of prediction, or innovation, exceeded +/- 2 times its standard deviation, the observation was considered to be significantly different from the former yield level. The data comprised 30,199 records for 135 cows and 366 d. Effects fitted in the model were test day, breed, and lactation class as fixed effects and cow within lactation number as a random effect. A lactation curve was fitted within lactation class. Of the observed milk yields, 9.2% deviated significantly from the expected value in a negative direction. None of the innovation of the fixed-day effect exceeded the threshold of two standard deviations. Compared with the results of rolling average, which were calculated as the average of a 10-d period, over 20% of the observations of the daily milk yield were classified differently by the two methods. The mixed-model method for recursive estimation takes better account of environmental and lactational effects influencing daily milk yields as the rolling average. The mixed-model recursive estimation method was applicable for the detection of suspicious (i.e., outside a specified prediction interval) observations of individual cows at the time of the actual recording.