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Neurology and Therapy

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Open Access 01-06-2018 | Brief Report

Understanding the Disease Course and Therapeutic Benefit of Tafamidis Across Real-World Studies of Hereditary Transthyretin Amyloidosis with Polyneuropathy: A Proof of Concept for Integrative Data Analytic Approaches

Authors: Daniel Serrano, Christopher B. Atzinger, Marc F. Botteman

Published in: Neurology and Therapy | Issue 1/2018

Abstract

Introduction

Hereditary transthyretin (TTR) amyloidosis with polyneuropathy (hATTR-PN) is a rare, autosomal dominant amyloidosis characterized primarily by progressive ascending sensorimotor neuropathy often associated with autonomic involvement. hATTR-PN is caused by a mutation in the TTR gene leading to protein misfolding and amyloid accumulation in peripheral nerves and vital organs. The latest global prevalence estimates point to 10,000 cases worldwide, with an upper end of about 40,000. Tafamidis has been approved in over 40 countries for delaying neurologic disease progression in early-stage hATTR-PN. Multiple observational studies have examined clinical outcomes in hATTR-PN patients treated with tafamidis in the routine clinical setting. Integrative data analysis (IDA) is a technique for optimally constructing synthetic treatment and control cohorts from multiple independent studies, which allows meta-analysis of patient-level data. Herein, we provide a proof of concept for the application of IDA to real-world and natural history hATTR-PN data. IDA permits increased understanding of outcomes in tafamidis-treated and untreated persons with hATTR-PN by optimally pooling all available information.

Methods

Summary statistics corresponding to the Neuropathy Impairment Score-Lower Limb (NIS-LL) from five published studies were pooled, converted to change from baseline means and variances, and analyzed using IDA. IDA-based synthetic cohorts were generated by averaging across studies stratified on treatment versus control cohort. Trends in change from baseline in each study and the corresponding synthetic cohorts were plotted. Patient-level data were simulated from the synthetic cohort trends in a Monte Carlo simulation to highlight the ability to contrast synthetic cohort trends using the mixed model for repeated measures (MMRM).

Results

The average sample size among the five studies was 71 (37–128) patients. The average NIS-LL trends indicated that tafamidis-treated patients experienced slower progression in neuropathy compared to untreated patients. Synthetic cohort trends reflected the trends observed in the contributing studies, while simultaneously shrinking the width of corresponding confidence bands. Monte Carlo simulation results demonstrated precise recovery of the synthetic cohort and time-dependent simulated NIS-LL means by the MMRM.

Discussion

This proof of concept demonstrates the utility of IDA-based synthetic cohorts for increased precision in characterizing and testing hypotheses about treatment outcomes and prognosis in hATTR-PN.

Funding

Pfizer.

Plain Language Summary

Plain language summary available for this article.

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Title: Understanding the Disease Course and Therapeutic Benefit of Tafamidis Across Real-World Studies of Hereditary Transthyretin Amyloidosis with Polyneuropathy: A Proof of Concept for Integrative Data Analytic Approaches
Authors: Daniel Serrano
Christopher B. Atzinger
Marc F. Botteman
Publication date: 01-06-2018
Publisher: Springer Healthcare
Published in: Neurology and Therapy / Issue 1/2018
Print ISSN: 2193-8253
Electronic ISSN: 2193-6536
DOI: https://doi.org/10.1007/s40120-018-0096-x

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Understanding the Disease Course and Therapeutic Benefit of Tafamidis Across Real-World Studies of Hereditary Transthyretin Amyloidosis with Polyneuropathy: A Proof of Concept for Integrative Data Analytic Approaches

Abstract

Introduction

Methods

Results

Discussion

Funding

Plain Language Summary

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Introduction

Methods

Results

Discussion

Funding

Plain Language Summary

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