Modelling Decline in Cognition to Decline in Function in Alzheimer's Disease.

Objectives: The study aimed to evaluate and quantify the temporal link between cognitive and functional decline, and assess the impact of the apolipoprotein E4 (APOE-e4) genotype on Alzheimer's disease (AD) progression.

Methods: A nonlinear mixed-effects Emax model was developed using longitudinal data from 659 patients with dementia due to AD sourced from the Alzheimer's disease neuroimaging initiative (ADNI) database. A cognitive decline model was first built using a cognitive subscale of the AD assessment scale (delayed word recall) as the endpoint, followed by a functional decline model, using the functional assessment questionnaire (FAQ) as the endpoint.

Development and validation of a Bayesian survival model for inclusion body myositis.

Background: Associations between disease characteristics and payer-relevant outcomes can be difficult to establish for rare and progressive chronic diseases with sparse available data. We developed an exploratory bridging model to predict premature mortality from disease characteristics, and using inclusion body myositis (IBM) as a representative case study.

Methods: Candidate variables that may be potentially associated with premature mortality were identified by disease experts and from the IBM literature.

Predicting Chronic Spontaneous Urticaria Symptom Return After Omalizumab Treatment Discontinuation: Exploratory Analysis.

Background: Omalizumab is highly effective in controlling chronic spontaneous urticaria (CSU) symptoms; however, patients can experience symptom return on treatment discontinuation. Pivotal clinical trials have identified 2 categories of patients who experience symptom return: rapid and slow.

Objective: The objective of this study was to identify potential predictors of the speed of symptom return after stopping omalizumab treatment.

Linked Patient-Reported Outcomes Data From Patients With Multiple Sclerosis Recruited on an Open Internet Platform to Health Care Claims Databases Identifies a Representative Population for Real-Life Data Analysis in Multiple Sclerosis.

Background: An enormous amount of information relevant to public health is being generated directly by online communities.

Objective: To explore the feasibility of creating a dataset that links patient-reported outcomes data, from a Web-based survey of US patients with multiple sclerosis (MS) recruited on open Internet platforms, to health care utilization information from health care claims databases. The dataset was generated by linkage analysis to a broader MS population in the United States using both pharmacy and medical claims data sources.

Patterns of Treatment Switching in Multiple Sclerosis Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research.

Background: Social media analysis has rarely been applied to the study of specific questions in outcomes research.

Objective: The aim was to test the applicability of social media analysis to outcomes research using automated listening combined with filtering and analysis of data by specialists. After validation, the process was applied to the study of patterns of treatment switching in multiple sclerosis (MS).