Original methods for EBCTCG meta-analyses

Introduction and methods sections reproduced from: Early Breast Cancer Trialists' Collaborative Group, "Treatment of Early Breast Cancer. Volume 1. Worldwide Evidence 1985-1990"

Oxford University Press 1990

Disclaimer: The following was reproduced using optical character recognition software. Every effort has been made to ensure that the text has been reproduced correctly. We apologise for any errors which may remain.


3.1 Systematic public availability of randomized trial results
3.2 Informative overviews even of somewhat different trials, despite heterogeneity of design and of size of treatment effect
3.3 Importance of reliably assessing MODERATE treatment effects
3.4 Two main reasons for using overviews of properly randomized trials to assess MODERATE treatment effects: avoiding selection biases and reducing random errors
3.5 Review of many trials, with no data-dependent omissions, to limit selection bias in assessment of treatment effects
3.6 Other selection biases either in design or in analysis of trials
3.7 Proper randomization (with no subsequent exclusions) to limit selection bias in design of trials of moderate treatment effects
3.8 Selection biases from subgroup analyses: statistical difficulty in the assessment of qualitative "interactions" and of quantitative "interactions"
3.9 Specific categories of patient or of treatment: data-dependent emphasis on subgroup analyses versus indirect extrapolation of overall analyses
3.10 Use of recurrence data, as well as mortality data, to study interactions unbiasedly
3.11 Use of overviews to assess effects of treatment on other specific causes of early death, or other rare endpoints
3.12 Use of overviews to improve the reliability of data from particular trials


4.1 Summary of the inclusion criteria for trials in the present overviews
4.2 Principles of identification of trials: selection bias may be limited even without absolute completeness
4.3 Practice of identification of trials: multiple sources of information
4.4 Methods of seeking data from individual trials
4.5 Methods of checking data from individual trials
4.6 Methods of publishing data from individual trials


5.1 Medical assumptions: (1) modest sizes of treatment effects, and (2) differences of size but not of direction between effects in different circumstances
5.2 Comparisons only of like with like, based on "Observed minus Expected" (O-E) differences in each separate trial
5.3 Routine stratification of all main analyses by age (<50, 50+) and by year (1, 2, 3, 4, 5+) of follow-up
5.4 Additional stratification of selected analyses for the available information on nodal status or Estrogen Receptor status
5.5 Arithmetic procedures for calculation of Observed minus Expected (O-E) numbers of events among treatment-allocated patients, and for obtaining "two-sided" significance levels ( 2 P )
5.6 Interpretation of P-values: statistical significance and medical judgement
5.7 RESULTS OF RADIOTHERAPY TRIALS as an example of the summation of (O-E) values from different trials to provide an overall test of the "null hypothesis" of no treatment effect
5.8 Use of (O-E) values to provide a description of the typical reduction in the odds of treatment failure (i.e. to describe the "alternative hypothesis")
5.9 Graphical display methods for separate trial results, and for an overview
5.10 Similarities between risk ratios, death rate ratios and odds ratios when event rates are low
5.11 Logrank "year of death" analyses for statistical significance tests, and for estimation of reductions in annual odds of death
5.12 Life-table estimation for descriptive purposes
5.13 Test of heterogeneity between several different trial results
5.14 Arithmetic details of tests for trend, for heterogeneity and for interaction
5.15 Practical meaning of a clear effect of treatment in a single large trial
5.16 Practical meaning of a clear effect of treatment in an overview of many trials
5.17 Fixed-effect "assumption-free" methods, and random-effect "assumed representativeness" methods



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