Vol 14: The performance of sequence symmetry analysis as a tool for post-market surveillance of newly marketed medicines: a simulation study.Reportar como inadecuado



 Vol 14: The performance of sequence symmetry analysis as a tool for post-market surveillance of newly marketed medicines: a simulation study.


Vol 14: The performance of sequence symmetry analysis as a tool for post-market surveillance of newly marketed medicines: a simulation study. - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Descargar gratis o leer online en formato PDF el libro: Vol 14: The performance of sequence symmetry analysis as a tool for post-market surveillance of newly marketed medicines: a simulation study.
This article is from BMC Medical Research Methodology, volume 14.AbstractBackground: Sequence symmetry analysis SSA is a potential tool for rapid detection of adverse drug events ADRs associated with newly marketed medicines utilizing computerized claims data. SSA is robust to patient specific confounders but it is sensitive to the underlying utilization trends in the medicines of interest. Methods to adjust for utilisation trends have been developed, however, there has been no systematic investigation to assess the performance of SSA when variable prescribing trends occur. The objective of this study was to evaluate the validity of SSA as a signal detection tool for newly marketed medicines. Methods: Randomly simulated prescription supplies for a population of 1 million were generated for two medicines, DrugA medicine of interest and DrugB medicine indicative of an adverse event. Scenarios were created by varying medicine utilization trends for a newly marketed medicine DrugA. In addition, the magnitude of association between DrugA and DrugB was varied. For each scenario 1000 simulations were generated. Average Adjusted Sequence Ratios ASR, bootstrapped 95% confidence intervals CIs, percentage of CIs which covered the expected ASR and percent relative bias were calculated. Results: When no association was simulated between DrugA and DrugB, over 95% of SSA CIs covered the expected ASR ASR = 1 and relative bias was 1% or less irrespective of medicine utilization trends. In scenarios where DrugA and DrugB were associated ASR = 2, unadjusted SRs were underestimated by between 11.7 and 15.3%. After adjustment for trend, ASR estimates were close to expected with relative bias less than 1%. Power was over 80% in all scenarios except for one scenario in which medicine uptake was gradual and the effect of interest was weak ASR = 1.2. Conclusions: Adjustment for underlying medicine utilization patterns effectively overcomes potential under-ascertainment bias in SSA analyses. SSA may be effectively applied as a safety signal detection tool for newly marketed medicines where sufficiently large health claim data are available.



Autor: Pratt, Nicole L; Ilomaki, Jenni; Raymond, Chris; Roughead, Elizabeth E

Fuente: https://archive.org/







Documentos relacionados