Skip to main content

Table 1 Key features and limitations of different approaches to assessing adverse effects of healthcare interventions

From: Drug safety assessment in clinical trials: methodological challenges and opportunities

Study design Key features Limitations
Spontaneous or voluntary reporting systems, including journal-published case reports Captures very wide range of events No denominator or control group, difficult to quantify risk
  Particularly useful for detecting signals of rare (low background incidence in treated population) and/or unexpected events (e.g., new unrecognized pathology) Format and type of information differs substantially among regulators and journals
  Sophisticated statistical techniques have been developed for signal detection Clinical details may be incomplete, causality uncertain
   Selective reporting or under-reporting of cases
Randomized clinical trials Randomization reduces possibility of confounding at baseline Rigid recruitment criteria may lead to exclusion of patients who are at risk of adverse effects
  Certain adverse effects can be prospectively specified for detailed monitoring Powered for detection of significant difference between groups for beneficial effect, estimates for adverse effects may lack precisions
  Intervention is typically well defined  
Non-randomized studies ‘Real-world’ use with more generalizable data and longer follow-up Monitoring for rare or unexpected events may be less rigorous, and the trials may not be of sufficient duration to detect long-term problems
  Potentially able to specify and assess rare events as primary outcomes in case control designs Non-randomized nature is susceptible to confounding
  May be able to explore relationship to dose, duration and patient susceptibility factors Drug exposures are often based on computerized records rather than dispensing or actual use
Meta-analysis of controlled observational studies and/or trials Pooled analysis has greater power to detect significant differences, even with rare events Reliant on quality of primary data
   Missing or unreported data on adverse events is a major problem, as are the statistical techniques of pooling sparse data
  Aims to summarize complete data set and can evaluate consistency of findings among studies Susceptible to selective outcome reporting of primary studies
Heterogeneity within the pooled analysis