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Meta-Analysis of Medical Device Data: Applications for Designing Studies and Reinforcing Clinical Evidence Chris Miller, M.S. Senior Medical Research Biostatistician NAMSA

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Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence discusses what meta analysis is as well as the potential benefits.

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Page 1: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

Meta-Analysis of Medical Device Data: Applications for Designing Studies and Reinforcing Clinical Evidence

Chris Miller, M.S.

Senior Medical Research Biostatistician

NAMSA

Chris Mullin
Overall comment - spicing things up with graphics, screen shots, etc would make the slides more visually engaging.
Page 2: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Overview What is Meta-Analysis? How to Use Meta-Analysis Potential Benefits Types of Meta-Analyses

Page 3: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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What is Meta-Analysis?

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Meta-analysis: a statistical technique that integrates findings to reach an “overarching” conclusion Combine the results of several studies to

increase power and precisions in the estimation of an effect

“An analysis of analyses”

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How to Use Meta-Analysis

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Research is time-consuming and difficult If an effect is modest, a very large sample size

is required

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Research is time-consuming and difficult If an effect is modest, a very large sample size

is required

Synthesizing evidence is difficult Treatments and diseases may change over time What if all studies on a treatment don’t agree?

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Why Conduct One?

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Create a historical, literature-based control Establish performance goal to run single-arm

study Reduce sample size for a randomized controlled

trial (RCT) (i.e., Bayesian prior) At minimum, get better estimates to plan RCT

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Create a historical, literature-based control Establish performance goal to run single-arm

study Reduce sample size for a randomized controlled

trial (RCT) (i.e., Bayesian prior) At minimum, get better estimates to plan RCT

Establish a non-inferiority margin

Page 11: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Create a historical, literature-based control Establish performance goal to run single-arm

study Reduce sample size for a randomized controlled

trial (RCT) (i.e., Bayesian prior) At minimum, get better estimates to plan RCT

Establish a non-inferiority margin Combine efficacy and safety data across

studies for more authoritative estimates of your device performance

Page 12: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Create a historical, literature-based control Establish performance goal to run single-arm

study Reduce sample size for a randomized

controlled trial (RCT) (i.e., Bayesian prior) At minimum, get better estimates to plan RCT

Establish a non-inferiority margin Combine efficacy and safety data across

studies for more authoritative estimates of your device performance

Make indirect comparisons between treatments

Page 13: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Potential Benefits

Page 14: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Improves estimates of effect size or precision

Page 15: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Improves estimates of effect size or precision

Resolve uncertainty or contradictory evidence

Page 16: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Improves estimates of effect size or precision

Resolve uncertainty or contradictory evidence

Answer new questions “Has the treatment become safer or more

effective in the past decade?” “If I have data on A vs. B and B vs. C, is there a

difference between A vs. C?”

Page 17: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Improves estimates of effect size or precision

Resolve uncertainty or contradictory evidence

Answer new questions “Has the treatment become safer or more

effective in the past decade?” “If I have data on A vs. B and B vs. C, is there a

difference between A vs. C?”

Allow for smaller or simpler study designs by drawing from historical evidence

Page 18: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Types of Meta-Analyses

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Types of data Individual participant data Aggregate data (most common)

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Types of data Individual participant data Aggregate data (most common)

Models Fixed-effects model

Weighted average of studies by inverse of variance (sample size)

Large studies will dominate estimate Assumes homogenous patient populations, same

intervention, outcome definitions (not realistic in most cases)

Page 21: Meta Analysis of Medical Device Data Applications for Designing Studies and Reinforcing Clinical Evidence

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Types of data Individual participant data Aggregate data (most common)

Models Fixed-effects model

Weighted average of studies by inverse of variance (sample size)

Large studies will dominate estimate Assumes homogenous patient populations, same

intervention, outcome definitions (not realistic in most cases)

Random-effects model Weighting of studies dependent on heterogeneity of estimates Relaxed assumptions on heterogeneity between studies Most common type of meta-analysis

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To view the complete Remote Training Series on Meta-Analysis of Medical Device Data: Applications for Study Design and Reinforcing Clinical Evidence Check out NAMSA’s Seminars

For information about the Clinical Research services NAMSA can offer you Visit our Clinical Research page

For additional information Download our brochure on Clinical Research Contact us at [email protected].