Alternatives to randomisation in the evaluation of public-health interventions: statistical analysis and causal inference. Issue 7 (6th August 2009)

• Record Type: Journal Article
• Authors: Cousens, S; Hargreaves, J; Bonell, C; Armstrong, B; Thomas, J; Kirkwood, B R; Hayes, R
• Is Part Of: Journal of epidemiology and community health. Volume 65:Issue 7(2011)
• Page Start: 576
• Page End: 581
• Publication Date: 2009-08-06
• Subjects: Evaluation studies -- statistics -- confounding -- propensity scores -- instrumental variables -- public health -- randomised trails; Public health -- Periodicals; Epidemiology -- Periodicals; 614.4
• DOI: 10.1136/jech.2008.082610
• Languages: English
• ISSNs: 0143-005X

Abstract

Abstract : Background: In non-randomised evaluations of public-health interventions, statistical methods to control confounding will usually be required. We review approaches to the control of confounding and discuss issues in drawing causal inference from these studies. Methods: Non-systematic review of literature and mathematical data-simulation. Results: Standard stratification and regression techniques will often be appropriate, but propensity scores may be useful where many confounders need to be controlled, and data are limited. All these techniques require that key putative confounders are measured accurately. Instrumental variables offer, in theory, a solution to the problem of unknown or unmeasured confounders, but identifying an instrument which meets the required conditions will often be challenging. Obtaining measurements of the outcome variable in both intervention and control groups before the intervention is introduced allows balance to be assessed, and these data may be used to help control confounding. However, imbalance in outcome measures at baseline poses challenges for the analysis and interpretation of the evaluation, highlighting the value of adopting a design strategy that maximises the likelihood of achieving balance. Finally, when it is not possible to have any concurrent control group, making multiple measures of outcome pre- and postintervention can enable the estimation of intervention effects with appropriate statistical models. Conclusion: For non-randomised designs, careful statistical analysis can help reduce bias by confounding in estimating intervention effects. However, investigators must report their methods thoroughly and be conscious and critical of the assumptions they must make whenever they adopt these designs.