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The Campbell Collaboration Systematic Review

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Title: The Campbell Collaboration Systematic Review


1
The Campbell CollaborationSystematic Review
  • Presented by
  • Harris Cooper
  • University of Missouri-Columbia
  • Larry V. Hedges
  • University of Chicago

2
A Campbell Systematic Review
  • Is meant to synthesize evidence on social and
    behavioral interventions and public policy

3
A Campbell Systematic Review
  • Is primarily concerned with
  • Evidence on overall intervention or policy
    effectiveness and how effectiveness is influenced
    by variations in
  • Process
  • Implementation
  • Intervention components
  • Recipients
  • Other factors

4
A Campbell Systematic Review
  • Uses systematic, transparent rules to define,
    gather, summarize, integrate and present research
    evidence

5
How have the methods of systematic reviewing
developed?
6
Statistical procedures for the integration of
research have existed for 100 years
  • 1904 K. Pearson. Report on certain enteric fever
    inoculation statistics. British Medical Journal,
    3, 1243-1246
  • 1932 R. A. Fisher. Statistical Methods for
    Research Workers. London Oliver Boyd.
  • it sometimes happens that although few or no
    statistical tests can be claimed individually as
    significant, yet the aggregate gives an
    impression that the probabilities are lower than
    would have been obtained by chance. (p.99)
  • Birge RT (1932). The calculation of errors by
    the method of least squares. Physical Review,
    40, 207-227.

7
Statistical procedures for the integration of
research have existed for 100 years
  • Additional references to early work can be found
    in
  • Chalmers, I., Hedges, L.V. Cooper, H (in
    press). A brief history of research synthesis.
    Evaluation and Health Professions.
  • Olkin, I. (1990). History and goals. In K.W.
    Wachter M.L. Straf (Eds.). The future of
    meta-analysis. New York Russell Sage Foundation

8
Modern interest in statistical synthesis of
research exploded in the mid-1970s
  • 1976 G. V. Glass Primary, secondary, and
    meta-analysis of research. Educational
    Researcher, 5, 3-8.
  • Meta-analysis is the statistical analysis of a
    large collection of analysis results from
    individual studies for purposes of integrating
    the findings (p.3).
  • 1977 F. Schmidt J. Hunter. Development of a
    general solution to the problem of validity
    generalization. Journal of Applied Psychology,
    62, 529-540.
  • 1997 M. Hunt. How science takes stock The story
    of meta-analysis. New York Russell Sage
    Foundation

9
Use of statistical procedures was precipitated by
the increase of social science research
  • 1978 R. Rosenthal D. Rubin. Interpersonal
    expectancy effects The first 345 studies.
    Behavioral and Brain Sciences, 3, 377-415.
  • 1979 G. V. Glass M. L. Smith. Meta-analysis of
    research on class size and achievement.
    Educational Evaluation and Policy Analysis, 1,
    2-16.
  • 1979 J. Hunter, F. Schmidt R. Hunter.
    Differential validity of employment tests by
    race A comprehensive review and analysis.
    Psychological Bulletin, 86, 721-735

10
and by demonstrations of flaws in traditional
reviewing procedures
  • 1980 H. Cooper R. Rosenthal. Statistical
    versus traditional procedures for summarizing
    research findings. Psychological Bulletin, 87,
    442-449.

11
The first textbooks on statistical procedures
appeared in the 1980s
  • 1981 G. V. Glass, B. McGraw M. L. Smith.
    Meta-Analysis in Social Research. Beverly Hills,
    CA Sage.
  • 1982 J. Hunter, F. Schmidt G. Jackson.
    Meta-Analysis Cumulating Research Findings
    Across Studies. Beverly Hills, CA Sage.
  • 1984 R. Rosenthal. Meta-Analytic Procedures for
    Social Research. Beverly Hills, CA Sage

12
The first textbooks on statistical procedures
appeared in the 1980s
  • 1985 L. V. Hedges I. Olkin. Statistical
    methods for meta-analysis. Orlando, FL Academic
    Press.
  • 1986 F. Wolf. Meta-analysis Quantitative
    Methods for Research Synthesis. Beverly Hills,
    CA Sage.

13
...while, simultaneously, a scientific paradigm
for research synthesis emerged
  • 1971 K. Feldman. Using the work of others Some
    observations on reviewing and integrating.
    Sociology of Education, 4, 86-102.
  • 1971 R. Light P. Smith. Accumulating evidence
    Procedures for resolving contradictions among
    research studies. Harvard Educational Review, 41,
    429-471.
  • 1980 G. Jackson. Methods for integrative
    reviews. Review of Educational Research, 50,
    438-460.

14
...while, simultaneously, a scientific paradigm
for research synthesis emerged
  • 1982 H. Cooper. Scientific guidelines for
    conducting integrative research reviews. Review
    of Educational Research, 52, 291-302.
  • the integration of separate research projects
    involves scientific inferences as central to the
    validity of knowledge as the inferences made in
    primary research. (p.291)
  • Most important, the methodological choices at
    each review stage may engender threats to the
    validity of the reviews conclusions. (p.292)
  • Because of the increasing role that reviews play
    in our definition of knowledge, it seems that
    these adjustments in procedures are inevitable if
    behavioral scientists hope to retain their claim
    to objectivity. (P.301)

15
The Integrative Review Conceptualized as a
Research Project
  • Stage of a Research Synthesis
  • Problem Formulation
  • Data Collection
  • Data Evaluation
  • Data Analysis
  • Public Presentation

16
The Integrative Review Conceptualized as a
Research Project
  • Characteristics of Each Stage
  • Research Question Asked
  • Primary Function in Review
  • Procedural Differences That Create Variation in
    Review Conclusions
  • Sources of Potential Invalidity in Review
    Conclusion

17
The first texts on scientific research synthesis
appeared shortly thereafter
  • 1984 Harris Cooper. The integrative research
    review A systematic approach. Beverly Hills, CA
    Sage.
  • 1984 Richard Light David Pillemer. Summing Up
    The Science of Research Reviewing. Cambridge, MA
    Harvard University Press.

18
These were followed by other excellent texts
  • Some treat it from the perspective of particular
    research design conceptualizations
  • Eddy, Hasselblad Shachter, 1992 Mullen, 1989
  • Some are tied to particular software packages
  • Johnson, 1989 Bushman Wang, 1999
  • Some of which treated research synthesis
    generally
  • Cooper Hedges, 1994 Lipsey Wilson, 2001
  • And some look at potential future developments in
    research synthesis
  • Wachter Straf, 1990 Cook, et.al., 1992

19
What is a C2 Review Protocol?
  • A C2 Review Protocol is a document that
  • Sets out the reviewers intentions with regard to
    the topic and the methods to be used in carrying
    out a proposed review
  • Is meant for inclusion in the Campbell Database
    of Systematic Reviews

20
What should a C2 Protocol contain?
  • Cover Sheet
  • Background for the Review
  • Objectives for the Review

21
What should a C2 Protocol contain?
  • Methods
  • Criteria for inclusion and exclusion of studies
    in the review
  • Search strategy for identification of relevant
    studies
  • Description of methods used in the component
    studies
  • Criteria for determination of independent
    findings
  • Details of study coding categories
  • Statistical procedures and conventions
  • Treatment of qualitative research

22
What should a C2 Protocol contain?
  • Timeframe
  • Plans for Updating the Review
  • Acknowledgements
  • Statement Concerning Conflict of Interest
  • References
  • Tables

23
What are the key aspects of methods in C2 Reviews?
  • Criteria for inclusion and exclusion of studies
    in the review
  • Search strategy for identification of relevant
    studies
  • Description of methods used in primary research
  • Criteria for determining independent findings

24
What are the key aspects of methods in C2 Reviews?
  • Details of coding of study characteristics
  • Statistical procedures and conventions
  • Treatment of qualitative research

25
What is the major source of bias in systematic
reviews?
26
Selection biases due to systematic non-inclusion
of studies that give different results than other
studies
  • Non-inclusion may happen because
  • The studies were never identified
  • The studies were identified, but never retrieved
  • Bias exists in evaluation of studies for
    inclusion

27
Selection biases due to systematic non-inclusion
of studies that give different results than other
studies
  • A particularly important source of selection bias
    is publication bias associated with non-inclusion
    of studies with statistically insignificant
    results
  • Publication selection effects can produce very
    large biases

28
Such biases are problematic because they do not
cancel across studies
  • A systematic review that does not control for
    selection bias may be just as biased as a single
    study
  • However the review may be more misleading because
    it seems to be more precise

29
How are study results used in systematic reviews?
30
Study results are usually represented
quantitatively as effect sizes
  • Effect sizes are chosen to be comparable (to mean
    the same thing) across all of the studies in the
    review.
  • Sometimes the effect size will be as simple as
    the raw mean difference between the treatment and
    control groups

31
Study results are usually represented
quantitatively as effect sizes
  • However, when the outcome is measure by different
    instruments in different studies, the raw mean
    difference may not be comparable across studies.
    In such cases, standardized effect size may be
    used.
  • The standardized mean difference (the raw mean
    difference divided by the standard deviation) is
    often used as an effect size measure
  • The correlation coefficient is also used as an
    effect size measure

32
Study results are usually represented
quantitatively as effect sizes
  • When studies have dichotomous outcomes, other
    effect sizes are typically used
  • The odds ratio between treatment and control
    groups
  • The rate ratio (ratio of proportions) between
    treatment and control groups
  • The rate difference (difference in proportions)
    between treatment and control groups

33
Study results are usually represented
quantitatively as effect sizes
  • When studies use a quantitative independent
    variable, raw regression coefficients or
    partially or fully standardized regression
    coefficients can be used as effect sizes.

34
Study results are usually represented
quantitatively as effect sizes
  • In rare cases, p-values may be used to represent
    study results.

35
Why do we need special methods for systematic
reviews?
  • Cant we just see how many studies found results?
  • We usually interpret studies by determining
    whether they found an effect that was big enough
    to be statistically significant.
  • We could just see if a large proportion of
    studies found the effect.

36
Why do we need special methods for systematic
reviews?
  • Such a strategy is usually called vote-counting.
    Each study metaphorically casts a vote for or
    against effectiveness of the treatment

37
Why do we need special methods for systematic
reviews?
  • Intuitive though it may be, vote counting can be
    shown to have terrible properties
  • Vote counting has low power it often fails to
    find effects even when they exist
  • The power doesnt necessarily increase, even as
    the amount of evidence (the number of studies
    increases)
  • In fact, the chance that vote counting detects
    effects that exist in all studies may tend to
    zero as the amount of evidence increases!

38
Why do we need special methods for systematic
reviews?
  • Vote counting also gives little insight about the
    size of effects or the consistency of effects
    across studies.

39
Are the effect sizes from every study equal?
  • Even if every study in the review is equally free
    of bias, the effect sizes do not contain the same
    amount of information.

40
Are the effect sizes from every study equal?
  • Sample size usually varies substantially from
    study to study
  • Large studies provide more precise information
    about effect size than small studies
  • Large variation in sample sizes and therefore
    large variation in precision across studies, is
    common in systematic reviews

41
Are the effect sizes from every study equal?
  • The precision of a studys effect size is
    characterized by its sampling error variance
  • Sampling error variances can usually be computed
    from formulas involving sample size and effect
    size

42
Are the effect sizes from every study equal?
  • Systematic reviews often contain a graphical
    presentation of each studys effect size and
    precision, called a Forrest plot.

43
Are the effect sizes from every study equal?
  • A Forrest plot gives each studys effect size and
    a 95 confidence interval for the effect
  • The width of the confidence interval indicates
    the uncertainty of a studys effect size estimate

44
Are the effect sizes from every study equal?
  • Often there is a dot denoting the effect size
    whose area corresponds to the relative sample
    size
  • Overlap between the confidence intervals
    represents consistency of the effect sizes across
    studies

45
How are the results combined across studies?
  • Effect sizes are usually combined across studies
    by averaging.
  • Since effect sizes usually differ in precision,
    we usually want to give more weight to studies
    with greater precision (that is smaller sampling
    error variance)

46
How are the results combined across studies?
  • Effect sizes are usually combined across This
    leads to a weighted average of the form
  • ? wi Ti
  • ? wi
  • Where the Ti are the effect sizes and the weights
    wi are the inverses of the sampling error
    variances

47
How are the results combined across studies?
  • The weighted average has precision (sampling
    error variance) proportional to the inverse of
    the sum of the weights
  • _1 _
  • ? wi

48
Is the average effect size the only important
summary?
  • Average is important, but variation in effect
    sizes across studies is important too.
  • The variation we care about is the degree to
    which the true effect sizes differ across
    studies
  • Total variation Sampling error variation True
    variation

49
Is the average effect size the only important
summary?
  • The standard deviation of the effect sizes
    measures total variation, which includes sampling
    error variation and true variation in effect
    sizes.

50
Is the average effect size the only important
summary?
  • There are two strategies for assessing true
    variation in effect sizes across studies
  • Tests of homogeneity, which are statistical tests
    of the hypothesis that the true effects are equal
    across studies
  • Estimates of the between-study variance component

51
Is the average effect size the only important
summary?
  • The between-studies variance component is a
    quantitative estimate of true effect size
    variation across studies.
  • The homogeneity test is a test that the variance
    component is zero.

52
How do we account for variation in effects across
studies?
  • We account for variation across studies by using
    statistical models.
  • Just as in primary research there are basically
    two modeling approaches
  • Analogues to Analysis of Variance
  • Analogues to Multiple Regression

53
How do we account for variation in effects across
studies?
  • Analogues to Analysis of Variance allow us to
    compare groups of studies, for example whether
    one group of studies (such as studies conducted
    in the US) has a different average effect than
    another (such as studies conducted in Europe).

54
How do we account for variation in effects across
studies?
  • Analogues to Multiple Regression allow us to
    determine the relation of a quantitative study
    characteristic (such as treatment intensity or
    duration) and effect size.

55
How do we account for variation in effects across
studies?
  • It is important to recognize that comparisons
    among groups of experiments are observational
    (correlational) studies.

56
What about studies that have many results?
  • Sometimes a study produces many results that
    could lead to the computation of many effect
    sizes. How should each effect size be treated?

57
What about studies that have many results?
  • The answer depends on whether each result (each
    effect size is computed from data on different
    individuals.
  • Effect sizes computed from the same individuals
    (e.g., from different outcome measures from the
    same people or by comparing different groups to
    the same control groups) are statistically
    dependent

58
What about studies that have many results?
  • Statistically dependent effect sizes do not
    contain independent information, therefore two
    dependent effect sizes contain less (often much
    less) information than two independent effect
    sizes, even if they have the same sampling error
    variance

59
What about studies that have many results?
  • Effect sizes computed from different individuals
    are statistically independent and therefore can
    be treated for most purposes as if they came from
    different studies

60
What about studies that have many results?
  • We usually try to obtain statistically
    independent effect sizes because the analysis and
    interpretation is much simpler.

61
How do we check on the robustness of findings in
systematic reviews?
  • It is critical to check the robustness of
    findings by sensitivity analyses of various
    kinds.
  • Sensitivity analyses check the effect of various
    choices of methods made in review.

62
How do we check on the robustness of findings in
systematic reviews?
  • Important kinds of sensitivity analyses include
    examination of the impact kind
  • Particular studies on results of the review
  • Synthesis methods on results of the review
  • Primary study methodology on results of the
    review
  • Study heterogeneity on results of the review
  • Publication bias on results of the review

63
How do we check on the robustness of findings in
systematic reviews?
  • Sensitivity analyses should be tailored to the
    systematic review at hand.
  • Different sensitivity analyses will be
    appropriate for reviews in different areas.

64
What are the currently established C2 methods
groups?
65
The Statistics Group
  • Will focus primarily on statistical methods used
    to develop summary indicators of study results
    and how to combine these across studies
  • The Statistics group will provide
  • advise to review groups and the C2 Steering
    Committee on statistical methods
  • training and support
  • research on statistical methods
  • monitor the quality of statistical aspects of C2
    reviews
  • a forum for discussion of statistical issues

66
The Quasi-Experimental Design Group
  • Will focus primarily on critically assessing the
    power and limitations of trials which do not use
    random assignments
  • These designs will be assessed with regard to
    validity and generalizability
  • The group will also refine methods of
    meta-analysis for non-randomized trials
  • The group will make recommendations to C2
    regarding software and inclusion of
    non-randomized studies in the Campbell Library

67
The Process and Implementation Group
  • Will focus primarily on both quantitative and
    qualitative methods for uncovering those parts of
    the implementation process that might influence
    the success or failure of an intervention
  • These might include the population under study,
    characteristics of the intervention itself and
    the non-intervention condition, the setting, and
    the outcome variables

68
The Editorial Review Group
  • Will serve as the editorial team for reviews
    undertaken by other methods groups

69
What future C2 methods groups might be
established?
70
Literature Searching Date Retrieval
  • Publication bias
  • Prospective registers
  • Coding techniques and reliability

71
Research Design
  • Research quality judgments
  • Cluster randomized trials

72
Statistical Issues
  • Effect size metrics
  • Stochastically dependent effect sizes
  • Explanatory models
  • Fixed, random, and multilevel effects
  • Missing data
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