Biomedical subjects

Peter M Rothwell

Publications and source records attributed to Peter M Rothwell.

56 records · Page 4Linked to original sources

PubMedDate not supplied

Treating individuals 2. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation.

Large pragmatic trials provide the most reliable data about the effects of treatments, but should be designed, analysed, and reported to enable the most effective use of treatments in routine practice. Subgroup analyses are important if there are potentially large differences between groups in the risk of a poor outcome with or without treatment, if there is potential heterogeneity of treatment effect in relation to pathophysiology, if there are practical questions about when to treat, or if there are doubts about benefit in specific groups, such as elderly people, which are leading to potentially inappropriate undertreatment. Analyses must be predefined, carefully justified, and limited to a few clinically important questions, and post-hoc observations should be treated with scepticism irrespective of their statistical significance. If important subgroup effects are anticipated, trials should either be powered to detect them reliably or pooled analyses of several trials should be undertaken. Formal rules for the planning, analysis, and reporting of subgroup analyses are proposed.

Data Interpretation, Statistical↗

PubMedDate not supplied

Treating individuals 3: from subgroups to individuals: general principles and the example of carotid endarterectomy.

Clinicians often have to make treatment decisions based on the likelihood that an individual patient will benefit. In this article we consider the relevance of relative and absolute risk reductions, and draw attention to the importance of expressing the results of trials and subgroup analyses in terms of absolute risk. We describe the limitations of univariate subgroup analysis in situations in which there are several determinants of treatment effect, and review the potential for targeting treatments with risk models, especially when benefit is probably going to be dependent on the absolute risk of adverse outcomes with or without treatment. The ability to systematically take into account the characteristics of an individual patient and their interactions, to consider the risks and benefits of interventions separately if needed, and to provide patients with personalised estimates of their likelihood of benefit is shown using the example of endarterectomy for symptomatic carotid stenosis.

Carotid Stenosis↗