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UID:86@ern-lung.eu
DTSTART;TZID=Europe/Berlin:20230929T180000
DTEND;TZID=Europe/Berlin:20230929T190000
DTSTAMP:20230904T105338Z
URL:https://ern-lung.eu/events/ejp-rd-online-training-item-response-models
 -for-analysing-assessments-in-rare-diseases-29-september/
SUMMARY:EJP RD Online Training: Item response models for analysing assessme
 nts in rare diseases (29 September)
DESCRIPTION:Clinical outcome assessments (COA)\, incorporating multiple ind
 ividual items (i.e.\, tasks\, tests\, questions and ratings)\, are used 
 to follow the progression of many rare diseases. The most common way of u
 tilizing the results of such assessments is to calculate the total score 
 and then perform inference regarding disease progression rate and treatm
 ent effects using this. An alternative strategy is to analyse all the item
  level data under a joint model. Commonly this is done under the Item Resp
 onse Theory (IRT) concept originally developed in the psychometrics field.
  In IRT models\, it is assumed that the different items all reflect an und
 erlying disease severity (the US FDA use the term “reflective indicator
 ” model in its guidance for COAs). It is therefore possible to model all
  item level data as dependent on a common latent variable that is allowed 
 to change with time\, treatment and other covariates. Item level data i
 s typically categorical and IRT models have some distinct numerical advant
 ages: (i) it treats data using the appropriate numerical assumption (total
  score analyses typically assume data to be continuous)\, (ii) IRT models 
 naturally adhere to the bounded nature of the scale\, which total score mo
 dels struggle to do\, (iii) it is robust to missing item level data\, and\
 , most importantly (iv) it distinguishes the differing information content
  between items. These properties result in higher power\, or higher precis
 ion of effect size\, for IRT than total score models. There are even more 
 options for the analysis of COAs and these include (i) disease staging\, (
 ii) time-to-deterioration\, (iii) responder definitions\, and (iv) individ
 ual item level response. Longitudinal IRT models representing informatio
 n at the most granular level with both respect to item responses and time 
 can be valuable tools in evaluating trial design and analysis options. Eve
 n the use of modified COAs can be assessed using such models and under dif
 ferent analysis models. The use of IRT models comes with assumptions and a
 ssessments of adequacy of these is a central aspect of IRT model developme
 nt. 
ATTACH;FMTTYPE=image/jpeg:https://ern-lung.eu/wp-content/uploads/2023/09/E
 JP-RD-SEPTEMBER-29.png
CATEGORIES:PCD Core
LOCATION:https://www.ejprarediseases.org/event/ejp-rd-online-training-item-
 response-models-for-analysing-assessments-in-rare-diseases/
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