SeriesCan meta-analysis help target interventions at individuals most likely to benefit?
Introduction
Systematic reviews of health-care interventions are an attempt to collate information from all relevant studies and, if deemed appropriate, combine their results using meta-analysis.1 This process inevitably brings together studies that are diverse in their designs (in terms of outcomes assessed and length of follow-up, for example), in the specific interventions used (method, intensity, and duration), and in the types of patients studied (demographic and clinical characteristics). Thus the results, based on such a broad range of evidence, can seem remote from the issue of how to treat individual patients, and even somewhat irrelevant to clinical practice.2 Nevertheless it is incontrovertible that treatment decisions should be based on evidence when it exists, and that good quality systematic reviews provide an essential mechanism in reviewing available evidence.3 The issue is how best to bridge the gap between evidence based on many patients and making decisions about treating individuals.
The larger randomised trials are, the less their results will be subject to chance. Many patients are needed to distinguish true treatment benefits that are clinically important, but moderate in size, from chance effects.4 Increasing numbers of patients by combining results across trials provides a principal rationale for meta-analysis.3 At the other extreme, n-of-1 trials attempt to isolate effective treatments for a particular individual;5 however, such trials can only be undertaken in specific clinical situations, for example, for treatments to relieve symptoms in chronic disorders, and do not provide evidence about medical policy that can be generalised to new patients. In between these extremes lies the aim of targeting interventions by identifying subgroups of patients most likely to benefit. Subgroup analyses within a clinical trial investigate the effects of an intervention for specific groups of patients—eg, defined by their clinical characteristics—in an attempt to refine how the treatment might best be used in practice.6 Such analyses are, however, inevitably plagued by chance effects—both wider confidence intervals due to the fewer patients involved, leading to more uncertain inferences, and false positive results arising from the multiplicity of subgroups typically investigated.7
Comparing patient subgroups within a meta-analysis might help to ameliorate the tension between decision making in clinical medicine and overall statements of evidence in systematic reviews. Researchers have suggested that meta-analysis should go beyond estimating one overall effect,8, 9 although this expansion has drawbacks.10 One aim of meta-analysis should be to estimate how treatment effectiveness varies according to patients' characteristics.11 In this article, we discuss the extent to which this aim is achievable, and investigate whether we can progress beyond the general statement that meta-analytic conclusions should be borne in mind in clinical decision making. In doing so, we need to distinguish the relative risk reductions usually summarised in meta-analyses from their implications for absolute risks, which describe how much patients benefit.
Section snippets
Conventional meta-analysis
To focus the discussion, we introduce a specific example. The effectiveness of platelet glycoprotein IIb/IIIa inhibitors (PGIs) in acute coronary syndromes (non-Q-wave infarction and unstable angina) has received much attention, being the subject of a Health Technology Assessment review,12 National Institute for Clinical Excellence guidance,13 and a Cochrane systematic review.14 Although PGIs reduce the risk of death and myocardial infarction in patients undergoing percutaneous coronary
Meta-regression
Meta-regression aims to relate the treatment effects recorded in different trials to the overall characteristics of those trials. We will consider the example of whether the effectiveness of PGIs is different between men and women. The basic characteristics of patients recruited into trials are usually reported fully in publications. For example, we can relate the odds ratio noted in every trial to the proportion of women in that study (figure 2). Meta-regression assesses the strength of the
Discussion
Identification of patient groups who benefit most from an intervention is never going to be easy, since it is a task for which enormous quantities of randomised evidence are necessary. Even in large trials, apparent subgroup differences can result merely from chance. Meta-analyses of large trials based on individual patient data allow subgroups to be contrasted within trials, and for these results to be combined across trials, producing more reliable evidence. Individual patient data also allow
References (48)
Can overall results of clinical trials be applied to all patients?
Lancet
(1995)- et al.
Summing up evidence: one answer is not always enough
Lancet
(1998) - et al.
Platelet glycoprotein IIb/IIIa inhibitors in acute coronary syndromes: a meta-analysis of all major randomised clinical trials
Lancet
(2002) Generalizing the results of randomized clinical trials
Control Clin Trials
(1994)- et al.
A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis
J Clin Epidemiol
(2002) - et al.
Subgroup analysis and other (mis)uses of baseline data in clinical trials
Lancet
(2000) - et al.
The design of prospective epidemiological studies: more subjects or better measurements?
J Clin Epidemiol
(1993) - et al.
Prediction of benefit from carotid endarterectomy in individual patients: a risk-modelling study
Lancet
(1999) - et al.
Effect of coronary artery bypass graft surgery on survival: overview of 10-year results from randomised trials by the Coronary Artery Bypass Graft Surgery Trialists Collaboration
Lancet
(1994) - et al.
Completeness of reporting trial results: effect on physicians' willingness to prescribe
Lancet
(1994)
Systematic reviews in health care: meta-analysis in context
Problems in the medical interpretation of overviews
Stat Med
Rationale for systematic reviews
BMJ
Why do we need systematic overviews of randomised trials?
Stat Med
Determining optimal therapy: randomized trials in individual patients
N Engl J Med
A consumers' guide to subgroup analyses
Ann Intern Med
Why sources of heterogeneity in meta-analysis should be investigated
BMJ
Benefits of heterogeneity in meta-analysis of data from epidemiologic studies
Am J Epidemiol
Meta-analysis: beyond the grand mean?
BMJ
A rapid and systematic review of the clinical effectiveness and cost-effectiveness of glycoprotein IIb/IIIa antagonists in the medical management of unstable angina
Health Technol Assess
Guidance on the use of glycoprotein IIb/IIIa inhibitors in the treatment of acute coronary syndromes (NICE Technology Appraisal Guidance No 12)
Platelet glycoprotein IIb/IIIa blockers for percutaneous coronary revascularization, and unstable angina and non-ST-segment elevation myocardial infarction (Cochrane Review)
Cochrane Database Syst Rev
A comparison of aspirin plus tirofiban with aspirin plus heparin for unstable angina
N Engl J Med
Inhibition of the platelet glycoprotein IIb/IIIa receptor with tirofiban in unstable angina and non-Q-wave myocardial infarction
N Engl J Med
Cited by (154)
Time points of outcome are often neglected in acupuncture meta-analyses: a methodological survey
2024, Journal of Clinical EpidemiologyKnowledge gaps in understanding the metabolic and clinical effects of excess folates/folic acid: A summary, and perspectives, from an NIH workshop
2020, American Journal of Clinical NutritionIndividual Participant Data Meta-analysis: Impact of Conduct Problem Severity, Comorbid Attention-Deficit/Hyperactivity Disorder and Emotional Problems, and Maternal Depression on Parenting Program Effects
2020, Journal of the American Academy of Child and Adolescent PsychiatryControversy and Debate on Meta-epidemiology. Paper 3: Causal inference from meta-epidemiology: a reasonable goal, or wishful thinking?
2020, Journal of Clinical Epidemiology