Illustration of the Weibull Shape Parameter Signal Detection Tool Using Electronic Healthcare Record Data

Sauzet O, Carvajal A, Escudero A, Molokhia M, Cornelius VR (2013)
Drug Safety 36(10): 995-1006.

Journal Article | Published | English

No fulltext has been uploaded

Author
; ; ; ;
Abstract
The WSP tool has previously been proposed as a method to detect signals for adverse drug reactions utilising time-to-event data without the need for a reference population. The aim of this study was to assess the performance of the tool on two well-known and two suspected adverse drug reactions for bisphosphonates that varied in both frequency and accuracy of reporting time. The use of the WSP tool was investigated on data from a matched population cohort study involving data from UK primary care patients exposed to oral bisphosphonates. Four listed/suspected ADRs were selected for investigation: headache, musculoskeletal pain, alopecia and carpal tunnel syndrome. For each suspected ADR, a graphical exploratory analysis was performed and the WSP tool was applied for two censoring periods each. Both of the well-known and common ADRs (headache and musculoskeletal pain) were detected using the WSP tool, and the signals were present regardless of the censoring intervals used. A signal was also detected when the event was uncommon and the timing was likely to be an accurate reflection of onset time (alopecia). This signal was only present for some of the censoring intervals. As anticipated, no signals were raised in the control groups for these events regardless of the censoring interval used. The suspected ADR, which was uncommon and where reporting times may not reflect onset time accurately (carpal tunnel syndrome), was not detected. A signal was raised in the control group but its false-positive nature was visible in the exploratory graphical analysis, which led to it (frequent but for only a limited number of consecutive dates). This study illustrates the usability and examines the reliability of the WSP tool as a method for signal detection in electronic health records. When the events are uncommon the success of this method may depend on the reporting time accurately reflecting the true event onset time. The study has shown that further work is required to define the censoring periods. The addition of a control group is not required but may enhance causal inference by showing that other causes than the exposure may lead to a signal.
Publishing Year
ISSN
eISSN
PUB-ID

Cite this

Sauzet O, Carvajal A, Escudero A, Molokhia M, Cornelius VR. Illustration of the Weibull Shape Parameter Signal Detection Tool Using Electronic Healthcare Record Data. Drug Safety. 2013;36(10):995-1006.
Sauzet, O., Carvajal, A., Escudero, A., Molokhia, M., & Cornelius, V. R. (2013). Illustration of the Weibull Shape Parameter Signal Detection Tool Using Electronic Healthcare Record Data. Drug Safety, 36(10), 995-1006.
Sauzet, O., Carvajal, A., Escudero, A., Molokhia, M., and Cornelius, V. R. (2013). Illustration of the Weibull Shape Parameter Signal Detection Tool Using Electronic Healthcare Record Data. Drug Safety 36, 995-1006.
Sauzet, O., et al., 2013. Illustration of the Weibull Shape Parameter Signal Detection Tool Using Electronic Healthcare Record Data. Drug Safety, 36(10), p 995-1006.
O. Sauzet, et al., “Illustration of the Weibull Shape Parameter Signal Detection Tool Using Electronic Healthcare Record Data”, Drug Safety, vol. 36, 2013, pp. 995-1006.
Sauzet, O., Carvajal, A., Escudero, A., Molokhia, M., Cornelius, V.R.: Illustration of the Weibull Shape Parameter Signal Detection Tool Using Electronic Healthcare Record Data. Drug Safety. 36, 995-1006 (2013).
Sauzet, Odile, Carvajal, Alfonso, Escudero, Antonio, Molokhia, Mariam, and Cornelius, Victoria R. “Illustration of the Weibull Shape Parameter Signal Detection Tool Using Electronic Healthcare Record Data”. Drug Safety 36.10 (2013): 995-1006.
This data publication is cited in the following publications:
This publication cites the following data publications:

1 Citation in Europe PMC

Data provided by Europe PubMed Central.

Analysis of the time-to-onset of osteonecrosis of jaw with bisphosphonate treatment using the data from a spontaneous reporting system of adverse drug events.
Nakamura M, Umetsu R, Abe J, Matsui T, Ueda N, Kato Y, Sasaoka S, Tahara K, Takeuchi H, Kinosada Y., J Pharm Health Care Sci 1(), 2015
PMID: 26819745

21 References

Data provided by Europe PubMed Central.


AUTHOR UNKNOWN, 0
A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database.
Park MY, Yoon D, Lee K, Kang SY, Park I, Lee SH, Kim W, Kam HJ, Lee YH, Kim JH, Park RW., Pharmacoepidemiol Drug Saf 20(6), 2011
PMID: 21472818
Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods.
Schuemie MJ, Coloma PM, Straatman H, Herings RM, Trifiro G, Matthews JN, Prieto-Merino D, Molokhia M, Pedersen L, Gini R, Innocenti F, Mazzaglia G, Picelli G, Scotti L, van der Lei J, Sturkenboom MC., Med Care 50(10), 2012
PMID: 22929992

AUTHOR UNKNOWN, 0
A method for estimating the probability of adverse drug reactions.
Naranjo CA, Busto U, Sellers EM, Sandor P, Ruiz I, Roberts EA, Janecek E, Domecq C, Greenblatt DJ., Clin. Pharmacol. Ther. 30(2), 1981
PMID: 7249508

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

D, Br Med J 330(7482), 2005

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Confounding by indication: an example of variation in the use of epidemiologic terminology.
Salas M, Hofman A, Stricker BH., Am. J. Epidemiol. 149(11), 1999
PMID: 10355372

Y, J Neurol Sci 270(1–2), 2008

Export

0 Marked Publications

Open Data PUB

Web of Science

View record in Web of Science®

Sources

PMID: 23673816
PubMed | Europe PMC

Search this title in

Google Scholar