Alternative evaluation designs offer similar protections against bias
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There are natural, human tendencies to look for expected or even hoped for intervention effects, and hence reach erroneous conclusions from an evaluation. Comparisons of more and less rigorous evaluation methods suggest that the best way to show that an intervention ‘works’ is to use a weak non-randomised (quasi-experimental) study design without a concurrent control group [Whilst relatively sophisticated quasi-experimental designs hold considerable promise, further experience is needed to understand their properties, strengths and limitations in much the same way that it has taken decades to develop evidence-informed criteria to judge the validity and generalisability of trials [The understanding of known and unknown confounders within the context of healthcare systems is relatively poor compared to, say, biological systems. Multiple contextual influences on the outcomes of implementation strategies [Confident attribution is important given that the observed effect sizes of implementation interventions can be small, even if worthwhile from population perspectives. For example, a trial of a multifaceted implementation strategy found just over a 1% absolute difference in high-risk prescribing between intervention and control general practices, which translated into a cost-effective population benefit from reduced patient harm [