1 / 36

Handout H6 The scope of meta-analysis: Meta-analysis of observational studies

Handout H6 The scope of meta-analysis: Meta-analysis of observational studies. Objectives. Understand the importance of systematic reviews of observational studies Understand the limitations of meta-analysis in observational studies

lainey
Download Presentation

Handout H6 The scope of meta-analysis: Meta-analysis of observational studies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Handout H6The scope of meta-analysis: Meta-analysis of observational studies Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  2. Objectives • Understand the importance of systematic reviews of observational studies • Understand the limitations of meta-analysis in observational studies • Understand the difficulties of avoiding publication bias in observational studies Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  3. Why do we need observational studies? • Randomisation may be • impossible • unnecessary • inappropriate Black, BMJ 1996 Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  4. Potentials of systematic reviews • More objective appraisal of the evidence than traditional narrative reviews applies equally to OS & RCT • May resolve uncertainty when original research, reviews and editorials disagree applies equally to OS & RCT • May generate promising research questions to be addressed in future studies applies equally to OS & RCT • Meta-analysis will enhance the precision of effect estimates, leading to reduced probability of false negative results BUT in OS may be a precise biased result Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  5. Meta-analysis • A statistical analysis which combines the results of several independent studies considered by the analyst to be ‘combinable’ Huque 1988 Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  6. Assumptions in meta-analysis • “Fixed-effects model”: Underlying effect is the same value (fixed) in each study. The differences between study results are solely due to the play of chance. • “Random-effects model”: Treatment effect for the individual studies are assumed to vary around some overall central effect Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  7. Fundamental difference in assumptions & how they apply to MA of RCT or observational studies • In meta-analysis of observational studies confounding, residual confounding and bias: • May introduce heterogeneity • May lead to misleading (albeit very precise) estimates • In well-conducted RCT there should not be confounding Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  8. Trial (Year) Mortality results from 33 trials of beta-blockers in secondary prevention after myocardial infarction Adapted from Freemantle et al BMJ 1999 Barber (1967) Reynolds (1972) Wilhelmsson (1974) Ahlmark (1974) Multicentre International (1975) Yusuf (1979) Andersen (1979) Rehnqvist (1980) Baber (1980) Wilcox Atenolol (1980) Wilcox Propanolol (1980) Hjalmarson (1981) Norwegian Multicentre (1981) Hansteen (1982) Julian (1982) BHAT (1982) Taylor (1982) Manger Cats (1983) Rehnqvist (1983) Australian-Swedish (1983) Mazur (1984) EIS (1984) Salathia (1985) Roque (1987) LIT 91987) Kaul (1988) Boissel (1990) Schwartz low risk (1992) Schwartz high risk (1992) SSSD (1993) Darasz (1995) Basu (1997) Aronow (1997) 0.80 (0.74 - 0.86) Overall (95% CI) 0.1 0.2 0.5 1 2 5 10 Relative risk (95% confidence interval)

  9. Study Allen Barongo Bollinger Bwayo Bwayo Cameron Carael Chao Chiasson Diallo Greenblatt Grosskurth Hira Hunter Konde-Luc Kreiss Malamba Mehendal Moss Nasio Pepin Quigley Sassan Sedlin Seed Simonsen Tyndall Urassa 1 Urassa 2 Urassa 3 Urassa 4 Urassa 5 Van de Perre 0.2 0.5 1 2 5 10 Relative risk (95% confidence interval) Results from 29 studies examining the association between intact foreskin and the risk of HIV infection in men Adapted from Van Howe Int J STD AIDS 1999

  10. Formaldehyde exposure and lung cancer 150 100 SMR (95% CI) 50 0 Anatomists, Funeral Directors Industrial Pathologists Embalmers Workers (3 Cohorts) (7 Cohorts) (14 Cohorts) Blair et al Scan J Work Environ Health 1990

  11. Dietary fat and breast cancer 1.8 1.6 1.4 Relative Risk (95% CI) 1.2 1.0 0.8 0.6 6 Cohort Studies 12 Case-Control Studies Boyd et al Br J Cancer 1993

  12. 3 2 Odds Ratio (95% CI) 1 0 7 Case-Control 9 Case-Control Studies with Studies without Blinding Blinding Intermittent sunlight exposure and melanoma Nelemans et al J Clin Epidemiol 1995

  13. Test of homogeneity • Examines the possibility of excess variability between the results of the different studies • Has low power if the number of studies is small • Can get a set of homogeneous but spurious findings Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  14. Beta-carotene and cardiovascular mortality Cohort Country Male health workers USA Social insurance, men Finland Social insurance, women Finland Male chemical workers Switzerland Hyperlipidaemic men USA Nursing home residents USA Cohorts combined 0.1 0.5 0.75 1 1.25 1.5 Relative risk (95% CI) Jah et al Ann Intern Med 1995

  15. Beta carotene and cardiovascular disease Egger et al. BMJ 1998;316:140-4

  16. “Well, so much for antioxidants.”

  17. Smoking and suicide Davey Smith et al Lancet 1992

  18. Smoking and homicide • Non-smoker 1.00 • 1-2 packs/day 1.71 (1.29-2.28) • 2+ packs/day 2.04 (1.32-3.15) Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  19. Fundamental difference in assumptions • In meta-analysis of observational studies confounding, residual confounding and bias: • May introduce heterogeneity • May lead to misleading (albeit very precise) estimates Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  20. What is the appropriate weighting factor? Inverse of variance?

  21. Case-control studies of Helicobacter pylori infection and CHD Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  22. Two case-control studies of Helicobacter pyloriinfection and CHD Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  23. Prospective and nested case-control studies of Helicobacter pylori infection and CHD Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  24. In RCT meta-analyses the appropriate study weights should relate to precision of effect estimates (e.g. inverse of variance). In observational meta-analyses this may not generally be the case. Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  25. Inverse of variance weighting • Can lead to magnification of pooled effect estimates when confounding and bias involved (e.g. H pylori) • Can lead to under-estimation of effect estimates when measurement error is important Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  26. Huxley, Lancet 2002

  27. Publication bias in MA / SR of observational studies

  28. Reporting bias in observational research Data collected (e.g cohort study) Data analysed Report written Paper published

  29. “I regret to inform you that the Journal of xxx will not be able to use your manuscript … We think the study is well-designed, with a fair follow-up and appropriate statistical analysis, but the negative results found can only be published as a Letter to the editor …” Rejection of ‘negative’ prospective cohort study finding of association of d-dimer with CHD. May 2006 Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  30. Genetic meta-analysis may be an exception … To the homogeneity and “spurious precision” problems… But may be particularly prone to publication bias Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  31. Funnel plot of meta–analysis of ACE I/D and CHD 0.0 0.1 Standard error 0.2 0.3 0.4 0.33 0.33 0.5 0.5 0.66 0.66 1.0 1.0 1.5 1.5 2.0 2.0 3.0 3.0 Odds ratio

  32. Conclusions • The principles of systematic reviews are applicable to any research design • Reviews of observational studies should always be systematic • Much attention should be given to exploring possible sources of heterogeneity • HOWEVER: Meta-analysis of observational studies will often produce misleading and spuriously precise estimates • Trial registers should solve much of the problems of publication bias in RCT, but trying to solve publication bias in observational studies impossible? Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

  33. Future work • We need to define optimal search strategies to identify epidemiological studies in the literature • We need validated instruments to assess the study quality at the design, conduct and analysis level • We need to improve the quality of reporting of epidemiological studies • We need to facilitate individual patient data analyses • We need to better define the place of meta-analysis in systematic reviews of epidemiological studies Topics in Meta-Analysis (Matthias Egger) Erasmus Summer Programme

More Related