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Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice.

ABSTRACT: We introduce a general response model that allows for several simple restrictions, resulting in other models such as the extended Rasch model. For the extended Rasch model, a dynamic Bayesian estimation procedure is provided, which is able to deal with data sets that change over time, and possibly include many missing values. To ensure comparability over time, a data augmentation method is used, which provides an augmented person-by-item data matrix and reproduces the sufficient statistics of the complete data matrix. Hence, longitudinal comparisons can be easily made based on simple summaries, such as proportion correct, sum score, etc. As an illustration of the method, an example is provided using data from a computer-adaptive practice mathematical environment.

SUBMITTER: Brinkhuis MJS

PROVIDER: S-EPMC7003866 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice.

Brinkhuis Matthieu J S MJS Maris Gunter G

The British journal of mathematical and statistical psychology 20190318 1

We introduce a general response model that allows for several simple restrictions, resulting in other models such as the extended Rasch model. For the extended Rasch model, a dynamic Bayesian estimation procedure is provided, which is able to deal with data sets that change over time, and possibly include many missing values. To ensure comparability over time, a data augmentation method is used, which provides an augmented person-by-item data matrix and reproduces the sufficient statistics of th ...[more]

PMID: 30883704

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Project description:This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpart.

Project description:BACKGROUND:People with cerebral palsy experience limitations in performing activities of daily living. Rehabilitation practitioners seek valid instruments to measure changes in the performance of those activities. The Pediatric Evaluation of Disability Inventory Computer Adaptive Test (PEDI-CAT) is a new tool to assess functioning in children and youth with various health conditions. Its validity needs to be evaluated in a way that is consistent with the theoretical model on which it was based. We aimed to evaluate the fit of daily activity and mobility items and children with CP to the Rasch model and to compare the performance in daily activities and mobility of older children, adolescents, and young adults with CP based on manual function and gross motor function limitations. METHODS:Eighty-three parents of children and youth of 8-20 years old (mean age: 11.6) with different severity levels of cerebral palsy participated in this study. Ninety-one items of the PEDI-CAT Daily Activities and Mobility domains were analyzed through Rasch analysis to evaluate relative item difficulty and participant ability. Participants were described according to the Manual Ability (MACS) (level I: 21.7%; II: 32.5%; III: 24.1%; IV: 7.2% and V: 3.6%) and the Gross Motor Function (GMFCS) (level I: 37.3%; II: 26.5%; III: 6%; IV: 18.1%; and V: 7.2%) classification systems levels. RESULTS:Our data fit the Rasch Model. Parents had difficulty distinguishing some PEDI-CAT response categories. Participants from MACS and GMFCS levels IV and V showed lower ability to perform relatively more difficult items. There was a floor effect in both domains. Only 7.7% of the items presented differential item functioning when individuals with mild MACS and GMFCS levels (I, II) and moderate level (III) and individuals with moderate (III) and severe levels (IV, V) were compared. CONCLUSIONS:PEDI-CAT daily activities and mobility domains are valid to evaluate children, adolescents and youth with CP of different severities, but the addition of items to these domains is recommended in order to address their floor effect.

Project description:ObjectiveIn oncology, extrapolation of clinical outcomes beyond trial duration is traditionally achieved by parametric survival analysis using population-level outcomes. This approach may not fully capture the benefit/risk profile of immunotherapies due to their unique mechanisms of action. We evaluated an alternative approach-dynamic modeling-to predict outcomes in patients with advanced renal cell carcinoma. We compared standard parametric fitting and dynamic modeling for survival estimation of nivolumab and everolimus using data from the phase III CheckMate 025 study.MethodsWe developed two statistical approaches to predict longer-term outcomes (progression, treatment discontinuation, and survival) for nivolumab and everolimus, then compared these predictions against follow-up clinical trial data to assess their proximity to observed outcomes. For the parametric survival analyses, we selected a probability distribution based on its fit to observed population-level outcomes at 14-month minimum follow-up and used it to predict longer-term outcomes. For dynamic modeling, we used a multivariate Cox regression based on patient-level data, which included risk scores, and probability and duration of response as predictors of longer-term outcomes. Both sets of predictions were compared against trial data with 26- and 38-month minimum follow-up.ResultsBoth statistical approaches led to comparable fits to observed trial data for median progression, discontinuation, and survival. However, beyond the trial duration, mean survival predictions differed substantially between methods for nivolumab (30.8 and 51.5 months), but not everolimus (27.2 and 29.8 months). Longer-term follow-up data from CheckMate 025 and phase I/II studies resembled dynamic model predictions for nivolumab.ConclusionsDynamic modeling can be a good alternative to parametric survival fitting for immunotherapies because it may help better capture the longer-term benefit/risk profile and support health-economic evaluations of immunotherapies.

Dataset Information

Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice.

Publications

Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice.

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