This document discusses meta-analysis and its uses in medical device research. Meta-analysis statistically combines findings from multiple studies to reach a stronger conclusion. It can improve estimates of a treatment's effects, resolve uncertain or contradictory evidence from individual studies, and allow for smaller clinical studies by drawing from existing evidence. There are two main types of meta-analysis: individual participant data uses raw data from each subject, while aggregate data meta-analysis (more common) analyzes summary results across studies.

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

2. 2
Overview
 What is Meta-Analysis?
 How to Use Meta-Analysis
 Potential Benefits
 Types of Meta-Analyses

3. What is Meta-Analysis?
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4.  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
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 “An analysis of analyses”

5. How to Use Meta-Analysis
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6.  Research is time-consuming and difficult
 If an effect is modest, a very large sample size
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is required

7.  Research is time-consuming and difficult
 If an effect is modest, a very large sample size
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is required
 Synthesizing evidence is difficult
 Treatments and diseases may change over time
 What if all studies on a treatment don’t agree?

8. Why Conduct One?
8

9.  Create a historical, literature-based control
 Establish performance goal to run single-arm
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study
 Reduce sample size for a randomized controlled
trial (RCT) (i.e., Bayesian prior)
 At minimum, get better estimates to plan RCT

10.  Create a historical, literature-based control
 Establish performance goal to run single-arm
10
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

11.  Create a historical, literature-based control
 Establish performance goal to run single-arm
11
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

12.  Create a historical, literature-based control
 Establish performance goal to run single-arm
12
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

13. Potential Benefits
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14.  Improves estimates of effect size or
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precision

15.  Improves estimates of effect size or
15
precision
 Resolve uncertainty or contradictory
evidence

16.  Improves estimates of effect size or
16
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?”

17.  Improves estimates of effect size or
17
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

18. 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

22.  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
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NAMSA can offer you
 Visit our Clinical Research page
 For additional information
 Download our brochure on Clinical Research
 Contact us at clientcare@namsa.com.