Method of study
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Strengths
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Limitations
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Randomised Controlled Trials and meta-analyses of such trials
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• Gold standard evidence for causal relationship by virtue of randomisation to treatment
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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
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Linked data on individuals
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• 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
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• 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
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Ecological studies
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• Simple and cheap to do because use existing data on medicines and health outcomes
• Directly examine relationships between population medicine use and health outcomes
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• 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
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Epidemiological modelling
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• Mathematical synthesis of epidemiological data on the disease and clinical trial data on safety and efficacy of medicines
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• 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
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