- On the effect of adding clinical samples to validation studies of patient-reported outcome item banks
- A simulation study
- Quality of Life Research
- Volume | Issue number
- 25 | 7
- Pages (from-to)
- Document type
- Faculty of Social and Behavioural Sciences (FMG)
- Research Institute of Child Development and Education (RICDE)
To increase the precision of estimated item parameters of item response theory models for patient-reported outcomes, general population samples are often enriched with samples of clinical respondents. Calibration studies provide little information on how this sampling scheme is incorporated into model estimation. In a small simulation study the impact of ignoring the oversampling of clinical respondents on item and person parameters is illustrated.
Simulations were performed using two scenarios. Under the first it was assumed that regular and clinical respondents form two distinct distributions; under the second it was assumed that they form a single distribution. A synthetic item bank with quasi-trait characteristics was created, and item scores were generated from this bank for samples with varying percentages of clinical respondents. Proper (using a multi-group model, and sample weights, respectively, for Scenarios 1 and 2) and improper (ignoring oversampling) approaches for dealing with the clinical sample were contrasted using correlations and differences between true and estimated parameters.
Under the first scenario, ignoring the sampling scheme resulted in overestimation of both item and person parameters with bias decreasing with higher percentages of clinical respondents. Under the second, location and person parameters were underestimated with bias increasing in size with increasing percentage of clinical respondents. Under both scenarios, the standard error of the latent trait estimate was generally underestimated.
Ignoring the addition of extra clinical respondents leads to bias in item and person parameters, which may lead to biased norms and unreliable CAT scores. An appeal is made for researchers to provide more information on how clinical samples are incorporated in model estimation.
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