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Sequence symmetry analysis graphic adjustment for prescribing trends
Adrian Kym Preiss1, Elizabeth Ellen Roughead2, Nicole Leanne Pratt2
1Quality Use of Medicines and Pharmacy Research Centre, School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA, Australia. kym.preiss@unisa.edu.au.
BMC Medical Research Methodology
|July 11, 2019
Summary
A new curve-fit method enhances Sequence Symmetry Analysis (SSA) for adverse drug event detection by adjusting visualizations for prescribing trends. This improves risk estimation and data visualization in pharmacovigilance.
Area of Science:
- Pharmacovigilance and Drug Safety
- Biostatistics and Data Analysis
- Computational Methods in Medicine
Background:
- Sequence Symmetry Analysis (SSA) is a signal detection method for adverse drug event detection, providing risk estimates and data visualizations.
- Existing SSA methods adjust risk estimates for medicine use trends but lack visualization adjustment.
- This study aimed to develop and evaluate a novel adjustment method for prescribing trends within SSA visualizations.
Purpose of the Study:
- To develop and validate a curve-fit adjustment method for Sequence Symmetry Analysis (SSA) visualizations.
- To incorporate prescribing trend adjustments directly into the SSA data visualization.
- To evaluate the impact of this method on the accuracy of adverse drug event detection.
Main Methods:
- Developed a curve-fit method to smooth frequency distributions of incident medicine use.
- Normalized fitted curves to derive proportions reflecting prescribing trend differences.
- Applied these proportions to adjust unit counts in SSA visualizations and calculate adjusted sequence ratios.
- Compared the sensitivity and specificity of the curve-fit adjusted SSA with existing methods.
Main Results:
- Curve-fit adjusted SSA visualizations produced adjusted sequence ratios highly comparable to established methods (p=0.999, Kolmogorov-Smirnov test).
- Sensitivity and specificity derived from adjusted sequence ratios were practically identical to previous approaches.
- The curve-fit method visually represents sequence proportionality, supplementing the SSA visualization.
- Excluding common patient prescriptions from adjustment calculations improved SSA accuracy in specific cases.
Conclusions:
- The developed curve-fit method is equivalent to current literature methods for adjusting prescribing trends in SSA.
- A key advantage is the integration of trend adjustment directly into the SSA visualization.
- This approach offers an enhanced tool for adverse drug event detection and analysis.
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