Assessment of score- and Rasch-based methods for group comparison of longitudinal patient-reported outcomes with intermittent missing data (informative and non-informative)
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The purpose of this study was to identify the most adequate strategy for group comparison of longitudinal patient-reported outcomes in the presence of possibly informative intermittent missing data. Models coming from classical test theory (CTT) and item response theory (IRT) were compared.
Two groups of patients’ responses to dichotomous items with three times of assessment were simulated. Different cases were considered: presence or absence of a group effect and/or a time effect, a total of 100 or 200 patients, 4 or 7 items and two different values for the correlation coefficient of the latent trait between two consecutive times (0.4 or 0.9). Cases including informative and non-informative intermittent missing data were compared at different rates (15, 30 %). These simulated data were analyzed with CTT using score and mixed model (SM) and with IRT using longitudinal Rasch mixed model (LRM). The type I error, the power and the bias of the group effect estimations were compared between the two methods.
This study showed that LRM performs better than SM. When the rate of missing data rose to 30 %, estimations were biased with SM mainly for informative missing data. Otherwise, LRM and SM methods were comparable concerning biases. However, regardless of the rate of intermittent missing data, power of LRM was higher compared to power of SM.
In conclusion, LRM should be favored when the rate of missing data is higher than 15 %. For other cases, SM and LRM provide similar results.
KeywordsIRT CTT Rasch models Longitudinal data PROs Missing data
This study was supported by the Ligue Nationale Contre le Cancer and the Comité de Loire-Atlantique de la Ligue Contre le Cancer.
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