Thank you Michael, I also think there is a lot to be said too about the results of adjusting for potential confounders. Obviously I could elaborate but two issues are important. 1. Adjustment for potential confounders should be as thorough as possible. e.g ever/never smoked does not adjust for smoking history for most diseases - some but by no means most. Therefore smoking remains a potent potential confounder after adjustment. So look for inadequate adjustments before believing causation. 2. When adjustment is as through as is feasible for all known confounders are properly measured and there remains a residual significant effect then interpretation may lean on by how much the estimate changes before and after adjustment. If by only a small amount then the association looks causal. Clearly this is not fool proof because orthogonal unknown confounders could still explain the effect. But such things are probably quite rare on current knowledge. Certainly most things correlate a little - certainly known and unknown risk factors might. But really if an unknown uncorrelated risk factor exists, or is likely to exist, the burden then shifts somewhat to suggesting a plausible hypothesis for what it might be. That can't be a bad thing either. All best and thanks again Klim >>> Michael Stuart <[log in to unmask]> 18/03/2009 21:35 >>> Dear Colleagues: We would like to receive feedback from list serve readers on criteria we have devised for assessing causality in non-experimental studies. Like many others, we assess efficacy of interventions by starting with RCTs, first evaluating studies for validity; then, if a study is rated as valid, we examine the reported results for clinically usefulness. We have formulated a set of primary criteria that may be useful in identifying situations in which, without experimental design, there is likely to be a true cause and effect relationship between the exposure or intervention and an observed outcome. We hypothesize that several major criteria may be the most important considerations in determining efficacy when valid RCTs are not available. We believe that these criteria represent what appears to be a conservative approach, and we do not believe they necessarily exclude other possibilities. We are considering submitting a paper to this effect, but would like to get a reaction from readers of this list serve (we are not including references here) to see if the criteria seem to hold. CriteriaTheorized as Possibly Reliable Indicators of Efficacy (Uncertain Strength of Evidence) Criteria 1. 1a) There is extremely low likelihood of improvement without some intervention; and, 1b) Any effects are highly likely to be attributable to the intervention (e.g., a single intervention or technology was utilized and the likelihood of patients utilizing co-interventions is low or co-interventions were equivalent in compared groups [note: equivalence should take into account considerations such as administration, duration, dosing, etc.]); and, 1c) Convincing sustained improvement is documented following use of the intervention or technology. OR Criteria 2. 2a) Illness or condition has a predictable course or outcome (e.g., symptoms, diagnostic signs, progression or resolution of the condition are predictable); and, 2b) Any effects in this population are likely to be attributable to the intervention (e.g., plausible confounders have been considered, a single intervention or technology was utilized and the likelihood of patients utilizing co-interventions is low or co-interventions were equivalent in groups being compared [note: equivalence should take into account considerations such as administration, duration, dosing, etc.]); and, 2c) Convincing sustained improvement in this particular population is documented following use of the intervention or technology with results demonstrably and significantly earlier than what i s predicted based on reliable knowledge of natural history. Secondary Considerations: Our Thinking And then in postulating what might be reasonable secondary considerations for determining efficacy in observational studies we came up with the following: Considerations Lending Support to the Evidence Factoring in the following conditions may decrease uncertainty: a. Immediacy of effectwhich may increase confidence in the effectiveness of the intervention or technology not only because of temporal proximity, but also because a delay in effect provides more opportunity for application of a co-intervention which could be a confounder. When considering immediacy, caution is urged because the effect could be due to potential of confounding. b. Dramatic effects(e.g., around 80% or greater relative risk reduction). When considering dramatic effects, caution is urged because the effects could be due to the strength of a confounder. For example, The Cochrane Handbook states that observational evidence with large effect sizes where a dose-response gradient exists and all plausible confounding would most likely demonstrate a reduced effect size, may be of moderate or even high quality (Higgins JPT, Green S (2008) Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.0 [updated February 2008]. Available from www.cochrane-handbook.org ( http://www.cochrane-handbook.org/ ).accessed 08/07/2008. Section 12.2.3). However, we remain cautious about this because confounding may result in large differences between groups. c. Dose/response relationship. When considering dose/response relationship, caution is urged because patient expectation bias may result in increased perception of benefit or increased placebo effect if patient is aware of the greater dosing through increase in side effects, for example. d. Consistent changes in multiple outcomes. When considering consistent change, caution is urged because the change in multiple outcomes may be due to confounding (e.g., a causal association between an unknown confounder and each of the outcomes). Again, we are most appreciative of peopleâs thoughts. Michael E Stuart MD President & Medical Director, Delfini Group Clinical Asst Professor, UW School of Medicine 6831 31st Ave N.E. Seattle, Washington 98115 206-854-3680 Mobile Phone 206-527-6146 Home Office [log in to unmask] www.delfini.org Klim McPherson Visiting Professor of Public Health Epidemiology Fellow of New College Nuffield Dept of Obstetrics & Gynaecology Oxford University Womens Centre, Level3 John Radcliffe Hospital Headington Oxford OX3 9DU Tel: 01865 222937 Mobile: 07711335993 Home 01865 558743 [log in to unmask] [log in to unmask]