1 lecture 10: meta-analysis of intervention studies introduction to meta-analysis selection of...
TRANSCRIPT
1
Lecture 10:Meta-analysis of intervention
studies
• Introduction to meta-analysis
• Selection of studies
• Abstraction of information
• Quality scores
• Methods of analysis and presentation
• Sources of bias
2
Definitions
• Traditional (narrative) review:– Selective, biassed
• Systematic review (overview):– Synthesis of studies of a research question– Explicit methods for study selection, data
abstraction, and analysis (repeatable)
• Meta-analysis:– Quantitative pooling of study results
3 Source: l’Abbé et al, Ann Intren Med 1987, 107: 224-233
4
Protocol preparation
• Research question
• Study “population”:– search strategy– inclusion/exclusion criteria
5
Protocol: Search strategy
– computerized databases• E.g. Medline, CINAHL, Embase, Cochrane clinical trial database,
PubMed, Psychinfo
• test sensitivity and predictive value of search strategy
– hand-searches (reference list, relevant journals, colleagues)
– “grey” (unpublished) literature:
• pro: publication bias
• con: results less reliable
– Search strategy should be reliable
6
Identifying relevant studies for systematic reviews of RCTs in vision research (Dickerson, in Systematic Reviews, BMJ,1995)
• Sensitivity and “precision” of Medline searches• Gold standard:
– registry of RCTs in vision research• extensive computer and hand searches
• contacts with investigators to clarify design
• Sensitivity:– proportion of known RCTs identified by the search
• “Precision” (PV+):– proportion of publications identified by search that were
RCTs
7Source: Chalmers + Altman, Systematic Reviews, BMJ Publishing Group, 1995
8Source: Chalmers + Altman, Systematic Reviews, BMJ Publishing Group, 1995
9Source: Chalmers + Altman, Systematic Reviews, BMJ Publishing Group, 1995
10
Protocol preparation
• Study “population”:– inclusion/exclusion criteria:
• language
• study design
• outcome of interest
• etc.
Source: Data abstraction form for meta-analysis project
11
Protocol preparation
• Data collection:– standardized abstraction form– number of abstractors– blinding of abstractors– rules for resolving discrepancies (consensus,
other)– use of quality scores
12Source: l’Abbé et al, Ann Intren Med 1987, 107:224-233
13
Analysis
• Measure of effect:– odds ratio, risk/rate ratio– risk/rate difference – relative risk reduction
• Graphical methods:– conventional (individual studies)– cumulative– exploring heterogeneity
14
Analyses
• Pooling results:– is it appropriate?
– equivalent to pooling results from multi-centre trials
– fixed effect (e.g., Mantel-Haenzel) methods• assume that all trials have same underlying treatment effect
– random effect methods (e.g., DerSimonian & Laird):• allow for heterogeneity of treatment effects
15Source: Chalmers + Altman Systematic Reviews, BMJ Publishing Group, 1995
16Source: Chalmers + Altman Systematic Reviews, BMJ Publishing Group, 1995
17
Heterogeneity
• Sources of heterogeneity:– Study design
– Study population
– Intervention
– Methodological features
• Approaches:– Descriptive and graphical analyses
– Meta-regression• Effect size is outcome
18Source: l’Abbé et al, Ann Intren Med 1987, 107:224-233
19
Quality scores
• Rating scales and checklists to assess methodological quality of RCTs
• How should they be used?– Qualitative assessment– Exclusion of weaker studies– Weighting of estimates
20
Does quality of trials affect estimate of intervention efficacy? (Moher et al, 1998)
• Random sample of 11 meta-analyses of 127 RCTs • Replicated analysis • Used quality scales/measures• Results:
– masked abstraction provided higher quality score than unmasked
– low quality trials found stronger effects than high quality trials
– quality-weighted analysis resulted in lower statistical heterogeneity
21Source: Moher et al, Lancet 1998, 352: 609-13
22Source: Moher et al, Lancet 1998, 352; 609-13
23
Unresolved questions about meta-analysis
• Apples and oranges?– Between-study differences in study population,
design, outcome measures, etc.
• Inclusion of weak studies?
• Publication bias– methods to evaluate impact – - particularly with small studies
• Is it better to do good original studies?
24
Large trials vs meta-analyses of smaller trials (Cappelleri et al, 1996)
• Selected meta-analyses from Medline and Cochrane pregnancy and childbirth database with at least 1 “large” study and 2 smaller studies:– sample size approach (n=1000+) - 79 meta-analyses– statistical power approach (adequate size to detect treatment
effect from pooled analysis - 61 meta-analyses
• Results:– agreement between larger trials and meta-analysis 82-90%
using random effects models– greater disagreement using fixed effects models
25
Large trials vs meta-analyses of smaller trials (Cappelleri et al, 1996)
• Results:– agreement between larger trials and meta-analysis 82-
90% using random effects models
– greater disagreement using fixed effects models
• Conclusion:– large and small trial results generally agree
– each type of trial has advantages and disadvantages:• large trials provide more stable estimates of effect
• small trials may better effect heterogeneity of clinical populations
26
Risk ratios from large studies vs pooled smaller studies (Cappeleri et al,1996)
(Left- sample size approach; right - statistical power approach)
Source: Cappeleri et al, JAMA 1996, 276: 1332-1338
27
Source: Cappeleri et al, JAMA 1996, 276: 1332-1338
28
Discrepancies between meta-analyses and subsequent large RCTs (LeLorier et al, 1997)
• Compared results of 12 large (n=1000+) RCTs with results of 19 prior meta-analyses on same topics
29Source: Lelorier et al, NEJM 1997, 337: 536-42
30Source: Lelorier et al, NEJM 1997, 337: 536-42