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Statistics 542 Introduction to Clinical Trials Meta Analysis. Meta-Analysis. Alternatives? Occasionally Complementary? Yes Meta-Analysis Combination of similar studies using similar subjects and similar treatments and similar outcomes.

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meta analysis
Meta-Analysis

Alternatives? Occasionally

Complementary? Yes

Meta-Analysis

  • Combination of similar studies using similar subjects and similar treatments and similar outcomes
new method of analyzing health data stirs debate by lawrence k altman
New Method of Analyzing Health Data Stirs Debate by Lawrence K. Altman

Increasing use of a controversial statistical method to evaluate medical therapies and surgical procedures is beginning to affect profoundly the care of pregnant women and patients with cancer, heart disease and many other common conditions.

The method, known as meta-analysis promises to plan an increasingly important role in determining health risks, environmental hazards and national policy on payment for medical care.

Backers say technique can draw big, reliable conclusions from small, inconsistent findings.

Meta-analysis is a term derived from the Greek meaning an analysis that is more comprehensive. The larger numbers obtained by combining studies provide a greater statistical power than any of the individual studies. Researchers are often able to draw more reliable inferences or new conclusions from the combined results than from the smaller studies that may be inconclusive individually.

In earlier applications of meta-analysis, researchers evaluated intelligence quotients, government social welfare programs and many other topics. Meta-analysis has come to medicine late, but “it is now undergoing a boom in popularity,” said Dr. Thomas C. Chalmers, a distinguished physician of the Department of Veterans Affairs in Boston and a pioneer in methodology.

The method involves an analysis of previous analyses. It combines the results of a wide range of existing smaller studies and then applies one of several statistical techniques to discover more precisely what is known from previous research. It may also produce a unified result from diverse, apparently contradictory studies.

The technique has already shed new light on the effectiveness of medical therapies. Although it has not, in itself, revolutionized any medical treatment it has helped clear away the confusion caused by studies with scattered and apparently conflicting findings and has strengthen and confirmed findings from traditional clinical trials. NY Times 8/21/90

reference nih proceedings
Reference: NIH Proceedings

Methodologic Issues in Overviews

of Randomized Clinical Trials

NIH Conference

May 1986

Statistics in Medicine

Vol 6, No. 3, 1987

what is the purpose
What is the Purpose?

a. Testing for a treatment effect (rejecting the null hypothesis)

b. Evaluating a safety issue (rare events)

c. Estimating size of treatment effect in subgroups

d. Design of new studies

e. Develop practice guidelines

slide7
Ideal Meta Analysis

is

Randomized Multi-center Control Trial

  • Same protocol
  • Same treatment
  • Same type of subjects
  • Same outcome measure
issues in meta analysis
Issues in Meta Analysis
  • Differences Across Studies in:

a. Treatment

b. Control Group/Population

c. Time Span (Disease, Background Therapy)

d. Outcome Measures

  • Publication Bias
  • Completeness/Quality of Data
  • Access to Data
what studies should be included
What Studies Should Be Included?
  • All existing studies
  • All published studies
  • "Non-flawed" trials
  • Other selection criteria
meta analysis when 1
Meta-Analysis: When? (1)

Retrospective Analyses

  • Test Treatment Effect When:
    • Definitive answer not yet available
    • No more studies likely
    • Need to salvage available results
  • Develop Practice Guidelines
  • Design New Studies
meta analysis when 2
Meta-Analysis: When? (2)

Prospective Analyses

  • Not recommended
  • Better to design in advance proper multi-center trial(s)
meta analysis12
Meta-Analysis

Methodology Not New

  • Combining p-values, Fisher (1948)
  • Analysis of Variance, Fisher (1938)
  • Combining 2x2 Tables
    • Mantel-Haenszel (1959)
    • Cochran (1954)
odds ratio
Odds Ratio

OR = ad/bc

  • more explicitly
methods of meta analysis
Methods of Meta-Analysis
  • 1.0 Collapse Data
  • Collapsing can be misleading if there is qualitative interaction.
slide15

Methods of Meta Analysis

2. Graphical

  • See Figure
  • 95% CI for each study

(ad / bc) exp { ± 1.96 (1/a + 1/b + 1/c + 1/d) }

slide16

Apparent effects of fibrinolytic treatment on morality in the randomised trials of IV treatment of acute myocardial infarction. Stat in Med 7:890: 1988.

slide17

Comparison of meta-analysis of 12 RCTs of i.v.mixed drugs (double-blind) with i.v. metoprolol (double-blind) and i.v. atenlol (open study). Stat in Med 6(3): 320, 1987.

slide18

Comparison of meta-analysis of mortality in 11 RCTs and reinfarction rates in 10 RCTs of i.v. streptokinase with large co-operative study (GISSI). Stat in Med 6(3): 320, 1987.

slide19

Comparison of meta-analysis of 7 small RCTs of phenobarbital in the treatment of neonatal intra-cranial haemmorrhage with one large co-operative study (3 institutions). Endpoints are total infants with haemmorrhage and totals with severe haemorrhage (Grades III-IV) only. Stat in Med 6(3): 321, 1987.

slide21

Methods of Meta Analysis

  • 3. Blocking (Peto-MH)
    • Overall Estimate
  • Let O = ai
  • E = Ei Ei = (ai + ci)(ai + bi)
  • ni
  • V =  Vi Vi = (ai + ci) )(bi + di)(ci + di)(ai + bi
  • ni2 (ni - 1)
  • Z = O - E
  • CPooled OR
  • OR = exp { (O - E) / V }
  • 95% CI = exp { (O - E) / V ± 1.96 / }
slide22

Methods of Meta Analysis

4. Averaging P-values Fisher (1948)

Pi = P-value for ith trial

Z = -2log (Pi) ~2with 2N df

5. Averaging Test Statistics

e.g. wi = ni

meta analysis examples
Meta-Analysis Examples

Cardiology

  • Post MI Treatments

(e.g., beta-blockers, aspirin)

  • Thrombolytic Therapy

(e.g., streptokinase)

  • Anticoagulants
registries databases
Registries/Databases
  • Byar (1980) Biometrics
  • D\'Ambrosia, Ellenberg (1980) Biometrics
  • Starmer et al. (1980) Biometrics
  • Mantel (1983) Statistics in Medicine
registries databases25
Registries/Databases

Use Clinical Observational Series to:

  • Describe Clinical Practice
  • Identify Risk Factors
  • "Evaluate" Treatment
    • Historical
    • Concurrent
databases
Databases

Treatment Evaluation

  • Comparison Requires Risk Factor Comparability
    • Measured
    • Not Measured or Unknown
  • Statistical Models Usually Not Adequate
    • Association vs. Estimation
    • Model Only an Approximation
    • Small Portion of Outcome Explained
potential biases
Potential Biases
  • Time Trends (Decline in CHD Death)
  • Ascertainment
    • Changes in Diagnostic Criteria
    • Availability of Technology
  • Selection Bias
compliance adjustment
Compliance “Adjustment”

Coronary Drug Project (NEJM, 1980)

5 Year Mortality

Compliance Clofibrate Placebo

< 80% 24.6% 28.2%

> 80% 15.0% 15.1%

All 18.2% 19.4%

registries
Registries

Bias in Treatment Effect

(Peto, Biomedicine, 1978)

Trials of Anticoagulant Therapy

Design Studies Patients Effect

Historical 18 900 50% Reduction

Concurrent 8 3000 50% Reduction

RCT 6 3000 20% Reduction

slide30
PTCA
  • PTCA Registry
    • Tracked and compared usage
    • Lead to further trials
    • No PTCA vs. placebo
  • TIMI-II
    • Compared immediate vs. delayed PTCA
  • BARI
    • Compares PTCA vs. CABG
slide31
CABG
  • CASS RCT (Circulation, 1983)
    • Comparison of immediate vs. delayed CABG
  • CASS Registry ( J Clin Inv, 1983)
    • Prognostic value of Angiography
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