The article "Conducting and Interpreting Disproportionality Analyses Derived from Spontaneous Reporting Systems" by Cutroneo et al. (2024) provides a comprehensive overview of disproportionality analyses (DAs) in the context of spontaneous reporting systems for adverse drug reactions (ADRs). DAs are recognized as a crucial tool for early signal detection in post-marketing surveillance, but they cannot be used as standalone assessments of drug-related risks. The authors emphasize the importance of combining DAs with clinical judgment and multidisciplinary team efforts to ensure accurate and reliable results. The article discusses various methods for performing DAs, including proportional reporting ratios (PRRs), reporting odds ratios (RORs), and Bayesian statistics such as the Information Component (IC). It highlights the need for careful interpretation of DAs, noting common pitfalls such as misinterpretation of results, overestimation of causality, and inappropriate extrapolation of findings. Key points covered in the article include: 1. **Methodological Aspects**: The rationale, design, and reporting of DAs. 2. **Signal Detection and Validation**: The role of DAs in signal detection and the importance of subsequent validation steps. 3. **Integrating Measures of Disproportionality**: Checking the novelty of suspected ADRs and the strength of evidence from supporting reports. 4. **Rationale for Conducting DAs**: The value of DAs in early detection of rare and unexpected ADRs, especially for new drugs or long-term use of old medications. 5. **Interpreting DAs**: Avoiding common pitfalls such as misinterpreting negative findings and potential benefits. 6. **Inappropriate Use of DAs**: Issues like using DAs as direct measures of risk, comparing drugs without proper comparators, and using DAs for already known drug-event associations. The authors also discuss the importance of integrating DAs with other data sources, such as electronic health records (EHRs), to enhance signal detection and validation. They emphasize the need for a multidisciplinary approach and the importance of transparent and reproducible reporting to support regulatory and clinical decision-making. Overall, the article aims to provide a balanced perspective on the strengths and limitations of DAs, highlighting their role in early signal detection and the need for careful interpretation and validation to ensure reliable and actionable results.The article "Conducting and Interpreting Disproportionality Analyses Derived from Spontaneous Reporting Systems" by Cutroneo et al. (2024) provides a comprehensive overview of disproportionality analyses (DAs) in the context of spontaneous reporting systems for adverse drug reactions (ADRs). DAs are recognized as a crucial tool for early signal detection in post-marketing surveillance, but they cannot be used as standalone assessments of drug-related risks. The authors emphasize the importance of combining DAs with clinical judgment and multidisciplinary team efforts to ensure accurate and reliable results. The article discusses various methods for performing DAs, including proportional reporting ratios (PRRs), reporting odds ratios (RORs), and Bayesian statistics such as the Information Component (IC). It highlights the need for careful interpretation of DAs, noting common pitfalls such as misinterpretation of results, overestimation of causality, and inappropriate extrapolation of findings. Key points covered in the article include: 1. **Methodological Aspects**: The rationale, design, and reporting of DAs. 2. **Signal Detection and Validation**: The role of DAs in signal detection and the importance of subsequent validation steps. 3. **Integrating Measures of Disproportionality**: Checking the novelty of suspected ADRs and the strength of evidence from supporting reports. 4. **Rationale for Conducting DAs**: The value of DAs in early detection of rare and unexpected ADRs, especially for new drugs or long-term use of old medications. 5. **Interpreting DAs**: Avoiding common pitfalls such as misinterpreting negative findings and potential benefits. 6. **Inappropriate Use of DAs**: Issues like using DAs as direct measures of risk, comparing drugs without proper comparators, and using DAs for already known drug-event associations. The authors also discuss the importance of integrating DAs with other data sources, such as electronic health records (EHRs), to enhance signal detection and validation. They emphasize the need for a multidisciplinary approach and the importance of transparent and reproducible reporting to support regulatory and clinical decision-making. Overall, the article aims to provide a balanced perspective on the strengths and limitations of DAs, highlighting their role in early signal detection and the need for careful interpretation and validation to ensure reliable and actionable results.