CAT Simulations Sample Clauses

CAT Simulations. To assess the efficiency of the extended and the original item bank, we performed an individual post hoc CAT simulation with each item bank, using the R package mirtCAT (Version 0.5; Chalmers, 2015). A CAT simulation is not an actual CAT administration, but selects the item responses and evaluates them as if they had been collected adaptively. We split the clinical and general population samples randomly into half; the first half of both samples was used for estimating the item parameters, the second half for simulating CAT. This method will prevent overfitting (▇▇▇▇▇▇, ▇▇▇▇▇▇▇▇▇▇, & ▇▇▇▇▇▇▇▇, 2001), which would have resulted in outcomes that are too optimistic. Note that we estimated the item parameters again to perform this analysis. Thus, the item parameters of the full clinical and general population sample are to be used as input for a future CAT, the item parameters of half of the clinical and general population sample are used in this study as input for simulating CAT. We chose to perform the primary CAT simulations on the clinical sample because clinical subjects were deemed the most relevant group to measure depression. In addition, we also performed CAT simulations with each item bank using the general population sample and briefly mention some main results. It could be expected that the efficiency gains are higher for the clinical sample compared to the general population sample because the information value of items is generally lower for respondents with low values of the latent trait (low levels of depression; Reise & ▇▇▇▇▇▇, 2009). The CAT simulations started with the item that had the highest item information value at the average value of the latent trait (θ = 0; ▇▇▇▇▇▇▇▇▇ & ▇▇▇▇▇, 2000; ▇▇▇▇▇▇, Dorans, Flaugher, Green, & ▇▇▇▇▇▇▇, 2000). Consequently, the CAT simulations started with the original item Emotional Distress – Depression item bank, Item 36 (EDDEP36) I felt unhappy for both the extended and the original item bank (note that we use the original US item coding; ▇▇▇.▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇.▇▇▇). The depression latent trait scores (θ) were then estimated with the Bayesian method maximum a posteriori (MAP; ▇▇▇▇▇▇▇▇▇ & ▇▇▇▇▇, 2000), and a standard error (SE) was calculated. The CAT simulation stopped selecting new items when the patient’s θ reached a prespecified value of the SE. Otherwise, new items were selected using the highest item information at the provisional θ estimate until the prespecified value of the SE was obtained or when all i...
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