Ruolun Qiu

Combining patient-level and summary-level data for Alzheimer’s disease modeling and simulation: a beta regression meta-analysis

Our objective was to develop a beta regression (BR) model to describe the longitudinal progression of the 11 item Alzheimer’s disease (AD) assessment scale cognitive subscale (ADAS-cog) in AD patients in both natural history and randomized clinical trial settings, utilizing both individual patient and summary level literature data. Patient data from the coalition against major diseases database (3,223 patients), the Alzheimer’s disease neruroimaging initiative study database (186 patients), and summary data from 73 literature references (representing 17,235 patients) were fit to a BR drug-disease-trial model. Treatment effects for currently available acetyl cholinesterase inhibitors, longitudinal changes in disease severity, dropout rate, placebo effect, and factors influencing these parameters were estimated in the model. Based on predictive checks and external validation, an adequate BR meta-analysis model for ADAS-cog using both summary-level and patient-level data was developed. Baseline ADAS-cog was estimated from baseline MMSE score. Disease progression was dependent on time, ApoE4 status, age, and gender. Study drop out was a function of time, baseline age, and baseline MMSE. The use of the BR constrained simulations to the 0–70 range of the ADAS-cog, even when residuals were incorporated. The model allows for simultaneous fitting of summary and patient level data, allowing for integration of all information available. A further advantage of the BR model is that it constrains values to the range of the original instrument for simulation purposes, in contrast to methodologies that provide appropriate constraints only for conditional expectations.

Exposure‐response Analysis for Spontaneously Reported Dizziness in Pregabalin‐treated Patient With Generalized Anxiety Disorder

To describe the pregabalin exposure–adverse event (AE) (dizziness) relationship in patients with generalized anxiety disorder, separate models were developed for the incidence of AE and for the conditional severity of AE, given that an AE has occurred using patient data from six clinical studies. The incidence component was modeled using a nonlinear logistic regression model. The conditional severity component was modeled as an ordered categorical variable with a proportional odds model to capture the severity on any given day. A Markov element was introduced to account for the correlation between neighboring observations. The proportional odds model including a time course of appearance and disappearance of AE could adequately describe the time course of probability of dizziness. Incorporating a transition model including Markov elements improved the model fit and greatly improved the predictability of the time course of probability of dizziness.

Model-based analysis to support strategic decision-making: A case study from the development of a 5HT6 antagonist for the treatment of Alzheimer's disease

P3-367 MODEL-BASED ANALYSIS TO SUPPORT STRATEGIC DECISION-MAKING: A CASE STUDY FROM THE DEVELOPMENT OFA 5HT6 ANTAGONIST FOR THE TREATMENT OF ALZHEIMER’S DISEASE James Rogers, Dan Polhamus, Peter Lockwood, Yves Brault, Anne Desmet, Kaori Ito, Klaus Romero, Ruolun Qiu, Brian Corrigan, William Gillespie, Marc Gastonguay, Metrum Research Group, Tariffville, Connecticut, United States; Pfizer, Groton, Connecticut, United States; Pfizer, Paris, Ile-de-France, France; Pfizer, Paris, Ile-de-France, United States; Pfizer Inc., New London, Connecticut, United States; Critical Path Institute, Tucson, Arizona, United States; Pfizer Inc., Groton, Connecticut, United States; Pfizer, New London, Connecticut, United States.