Table 1 Approaches to studying the effects of medicines on health outcomes
From: Assessing the impact of prescribed medicines on health outcomes
| Method of study | Strengths | Limitations |
|---|---|---|
| Randomised Controlled Trials and meta-analyses of such trials | • Gold standard evidence for causal relationship by virtue of randomisation to treatment |
May not predict effects of medicines on health outcomes because: • May be too small to detect rare adverse events • May be too short to detect long term adverse effects • May exclude high risk patients e.g. those with comorbidity • May involve optimal treatment and compliance |
| Linked data on individuals |
• Links data on medicine use and health outcomes in individuals • Closer to routine clinical practice than evidence from RCTs • Cheap and quick to do retrospectively |
• Confounding by indication: patients who use medicines are at a higher risk of a disease • Limited assessment of confounders e.g. comorbidity, OTC drugs, alcohol & tobacco • Often uses treated morbidity as a proxy for comorbidity |
| Ecological studies |
• Simple and cheap to do because use existing data on medicines and health outcomes • Directly examine relationships between population medicine use and health outcomes |
• Use aggregate rather than individual level data • Crude measures of medicine use e.g. drug sales or scripts • Limited capacity to exclude alternative explanations such as changes in risk factors, and increased use of other treatments |
| Epidemiological modelling | • Mathematical synthesis of epidemiological data on the disease and clinical trial data on safety and efficacy of medicines |
• Simplifications of complex natural history of disease • Uncertainties about long term effects of medicines (addressed by sensitivity analyses) • Underdeveloped in studies of effects of medicines on health outcomes |