DDMODEL00000223: Novakovic_2016_multiplesclerosis_cladribine_irt

  public model
Short description:
Item Response Theory model applied to modeling of disease progression in multiple sclerosis, as measured by EDSS. Model has been developed on data from Phase 3 clinical trial of cladribine tablets.
Original code
  • Application of Item Response Theory to Modeling of Expanded Disability Status Scale in Multiple Sclerosis.
  • Novakovic AM, Krekels EH, Munafo A, Ueckert S, Karlsson MO
  • The AAPS journal, 9/2016
  • Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden. ana.kalezic@farmbio.uu.se.
  • In this study, we report the development of the first item response theory (IRT) model within a pharmacometrics framework to characterize the disease progression in multiple sclerosis (MS), as measured by Expanded Disability Status Score (EDSS). Data were collected quarterly from a 96-week phase III clinical study by a blinder rater, involving 104,206 item-level observations from 1319 patients with relapsing-remitting MS (RRMS), treated with placebo or cladribine. Observed scores for each EDSS item were modeled describing the probability of a given score as a function of patients' (unobserved) disability using a logistic model. Longitudinal data from placebo arms were used to describe the disease progression over time, and the model was then extended to cladribine arms to characterize the drug effect. Sensitivity with respect to patient disability was calculated as Fisher information for each EDSS item, which were ranked according to the amount of information they contained. The IRT model was able to describe baseline and longitudinal EDSS data on item and total level. The final model suggested that cladribine treatment significantly slows disease-progression rate, with a 20% decrease in disease-progression rate compared to placebo, irrespective of exposure, and effects an additional exposure-dependent reduction in disability progression. Four out of eight items contained 80% of information for the given range of disabilities. This study has illustrated that IRT modeling is specifically suitable for accurate quantification of disease status and description and prediction of disease progression in phase 3 studies on RRMS, by integrating EDSS item-level data in a meaningful manner.
Ana Novakovic
Context of model development: Disease Progression model; Clinical end-point;
Discrepancy between implemented model and original publication: none;
Long technical model description: Observed scores for each EDSS item were modeled describing the probability of a given score as a function of patients’ disability variable using a logistic model. Model development was conducted in five sequential steps: development of the baseline model; development of disease progression model based on placebo data; development of the exposure-response model based on data from patients on cladribine treatment; development of the covariate model; and model evaluation. For the disease progression model, linear and non-linear (e.g., power and asymptotic) relationships were explored to describe the change in IRT disability over time. For more detailed techincal description, please refer to the original publication.;
Model compliance with original publication: Yes;
Model implementation requiring submitter’s additional knowledge: No;
Modelling context description: The EDSS is a summarized measure which ranges from 0 (normal neurological exam) to 10 (death due to MS) in incremental steps of 0.5. Despite its wide use and acceptance, there are several perceived problems with the use of the scale, such as limited inter-rater reproducibility, bimodal distribution of the scale, and potentially unequal steps, mostly due to its ordinal nature. In the past, EDSS has been modeled either as a continuous variable or as an ordered categorical variable with considerable simplification of the scale (20 categories combined into 5 – 6 categories). Instead of modeling changes in the composite score over time, application of IRT allows derivation of underlying/ unobserved latent variable from observed subscores and model the changes in that latent variable over time. ;
Modelling task in scope: estimation;
Nature of research: Approval phase/Registration trial (Phase III);
Therapeutic/disease area: CNS;
Annotations are correct.
This model is not certified.
  • Model owner: Ana Novakovic
  • Submitted: Oct 12, 2016 10:56:36 AM
  • Last Modified: Oct 12, 2016 10:58:43 AM
  • Version: 7 public model Download this version
    • Submitted on: Oct 12, 2016 10:58:43 AM
    • Submitted by: Ana Novakovic
    • With comment: Edited model metadata online.
  • Version: 5 public model Download this version
    • Submitted on: Oct 12, 2016 10:56:36 AM
    • Submitted by: Ana Novakovic
    • With comment: Model revised without commit message