meta analysis of medical device data applications for designing studies and reinforcing clinical...
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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.TRANSCRIPT
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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Overview What is Meta-Analysis? How to Use Meta-Analysis Potential Benefits Types of Meta-Analyses
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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
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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
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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
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Potential Benefits
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Improves estimates of effect size or precision
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Improves estimates of effect size or precision
Resolve uncertainty or contradictory 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?”
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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
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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)
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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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