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Treating osteoporosis in Canada: what clinical efficacy data should be considered by policy decision makers?

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Abstract

Summary

Using a Markov state-transition model, we estimated fractures averted with risedronate using two different types of clinical efficacy data. Summary data, as opposed to individual patient data (IPD), underestimated the number of fractures averted when applied in a specified high risk population. The choice of clinical efficacy data is an important consideration in health economic models evaluating osteoporosis therapies.

Introduction

This paper contrasts fracture reduction estimates for risedronate utilizing efficacy data from two approaches to meta-analysis: summary data versus individual patient data. We also examined differences in fracture reduction explained by varied cohort selection, especially the inclusion of low- versus high-risk populations.

Methods

Using a Markov state-transition model, we compared fractures averted over 3 years in a hypothetical cohort by inputting fracture risk reduction estimates (risedronate versus placebo) from two data sources (summary data versus IPD). The cohort consisted of 100,000 Canadian women, age ≥65 years with osteoporosis (WHO criteria T-score ≤ -2.5) and prevalent morphometric vertebral fracture.

Results

Non-vertebral fractures averted with risedronate were: 3,571 and 6,584 per 100,000 women for summary data and IPD, respectively. For vertebral fractures, the numbers were 8,552 and 10,127. When IPD versus summary data was used, an additional 3,013 more non-vertebral fractures and 1,575 vertebral fractures were averted.

Discussion

Relative risk estimates from IPD analyses were the best choice for modelling fracture outcomes when applied in a specified high-risk population. In addition to superior statistical methodology, they utilized RCT cohorts that are more representative of higher risk patients requiring treatment (osteoporotic women ≥65 years with a prevalent vertebral fracture).

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Disclaimers

This manuscript was funded by the Alliance for Better Bone Health, Procter & Gamble Pharmaceuticals Canada, and sanofi-aventis Canada Inc.

Conflicts of interest

Jonathan D. Adachi is a consultant for Amgen; Astra Zeneca, Eli Lilly; GlaxoSmithKline; Merck Frosst; Novartis; Proctor & Gamble; Roche; sanofi-aventis; Servier; and Wyeth (clinical trials: Eli Lilly; GlaxoSmithKline; Merck; Novartis; Pfizer; Proctor & Gamble; sanofi-aventis; Servier; and Wyeth). Alexandra Papaioannou is a consultant and adviser for Amgen, Eli Lilly, Merck Frosst, Novartis, Proctor & Gamble, sanofi-aventis, and Servier (clinical trials: Amgen, Eli Lilly, Merck, Novartis, Proctor & Gamble, and sanofi-aventis). William D. Leslie received speaker fees, research honoraria, and unrestricted research grants from Merck Frosst Canada Ltd; research honoraria and unrestricted educational grants from The Alliance for Better Bone Health: sanofi-aventis and Procter & Gamble Pharmaceuticals Canada, Inc.; unrestricted research grants from Novartis Pharmaceuticals Canada, Inc.; unrestricted educational grants from Genzyme Canada. Valery Walker is a paid consultant for Alliance for Better Bone Health, Procter & Gamble Pharmaceuticals Canada, and sanofi-aventis Canada Inc. Courtney C. Kennedy and George Ioannidis have no disclosures.

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Authors and Affiliations

  1. Department of Medicine, St Joseph’s, Healthcare–McMaster University, 25 Charlton Avenue East, Suite 501, Hamilton, Ontario, L8N 1Y2, Canada

    J. D. Adachi, C. C. Kennedy, A. Papaioannou & G. Ioannidis

  2. Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada

    W. D. Leslie

  3. i3 Innovus, Burlington, Ontario, Canada

    V. Walker

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  1. J. D. Adachi
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Correspondence to J. D. Adachi.

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Adachi, J.D., Kennedy, C.C., Papaioannou, A. et al. Treating osteoporosis in Canada: what clinical efficacy data should be considered by policy decision makers?. Osteoporos Int 20, 1785–1793 (2009). https://doi.org/10.1007/s00198-009-0870-9

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  • DOI: https://doi.org/10.1007/s00198-009-0870-9

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