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Items: 1 to 20 of 108

1.

Propensity Score-Based Approaches in High Dimension for Pharmacovigilance Signal Detection: an Empirical Comparison on the French Spontaneous Reporting Database.

Courtois É, Pariente A, Salvo F, Volatier É, Tubert-Bitter P, Ahmed I.

Front Pharmacol. 2018 Sep 18;9:1010. doi: 10.3389/fphar.2018.01010. eCollection 2018.

2.

Class-imbalanced subsampling lasso algorithm for discovering adverse drug reactions.

Ahmed I, Pariente A, Tubert-Bitter P.

Stat Methods Med Res. 2018 Mar;27(3):785-797. doi: 10.1177/0962280216643116. Epub 2016 Apr 25.

PMID:
27114328
3.

Early detection of pharmacovigilance signals with automated methods based on false discovery rates: a comparative study.

Ahmed I, Thiessard F, Miremont-Salamé G, Haramburu F, Kreft-Jais C, Bégaud B, Tubert-Bitter P.

Drug Saf. 2012 Jun 1;35(6):495-506. doi: 10.2165/11597180-000000000-00000.

PMID:
22612853
4.

Comparison of statistical signal detection methods within and across spontaneous reporting databases.

Candore G, Juhlin K, Manlik K, Thakrar B, Quarcoo N, Seabroke S, Wisniewski A, Slattery J.

Drug Saf. 2015 Jun;38(6):577-87. doi: 10.1007/s40264-015-0289-5.

PMID:
25899605
5.

Bayesian model selection in logistic regression for the detection of adverse drug reactions.

Marbac M, Tubert-Bitter P, Sedki M.

Biom J. 2016 Nov;58(6):1376-1389. doi: 10.1002/bimj.201500098. Epub 2016 May 25.

PMID:
27225325
6.

Comparison of three methods (consensual expert judgement, algorithmic and probabilistic approaches) of causality assessment of adverse drug reactions: an assessment using reports made to a French pharmacovigilance centre.

Théophile H, Arimone Y, Miremont-Salamé G, Moore N, Fourrier-Réglat A, Haramburu F, Bégaud B.

Drug Saf. 2010 Nov 1;33(11):1045-54. doi: 10.2165/11537780-000000000-00000.

PMID:
20925441
7.

Exploration of statistical shrinkage parameters of disproportionality methods in spontaneous reporting system of China.

Wang J, Ye XF, Guo XJ, Zhu TT, Qi N, Hou YF, Zhang TY, Shi WT, Wei X, Liu YZ, Wu GZ, He J.

Pharmacoepidemiol Drug Saf. 2015 Sep;24(9):962-70. doi: 10.1002/pds.3811. Epub 2015 Jun 11.

PMID:
26095121
8.

vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use.

Caster O, Sandberg L, Bergvall T, Watson S, Norén GN.

Pharmacoepidemiol Drug Saf. 2017 Aug;26(8):1006-1010. doi: 10.1002/pds.4247. Epub 2017 Jun 27.

9.

Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank.

Caster O, Juhlin K, Watson S, Norén GN.

Drug Saf. 2014 Aug;37(8):617-28. doi: 10.1007/s40264-014-0204-5.

10.

The value of time-to-onset in statistical signal detection of adverse drug reactions: a comparison with disproportionality analysis in spontaneous reports from the Netherlands.

Scholl JH, van Puijenbroek EP.

Pharmacoepidemiol Drug Saf. 2016 Dec;25(12):1361-1367. doi: 10.1002/pds.4115. Epub 2016 Sep 30.

PMID:
27686554
11.

On variance estimate for covariate adjustment by propensity score analysis.

Zou B, Zou F, Shuster JJ, Tighe PJ, Koch GG, Zhou H.

Stat Med. 2016 Sep 10;35(20):3537-48. doi: 10.1002/sim.6943. Epub 2016 Mar 21.

12.

False discovery rate estimation for frequentist pharmacovigilance signal detection methods.

Ahmed I, Dalmasso C, Haramburu F, Thiessard F, Broët P, Tubert-Bitter P.

Biometrics. 2010 Mar;66(1):301-9. doi: 10.1111/j.1541-0420.2009.01262.x. Epub 2009 May 4.

PMID:
19432790
13.

Using classification tree analysis to generate propensity score weights.

Linden A, Yarnold PR.

J Eval Clin Pract. 2017 Aug;23(4):703-712. doi: 10.1111/jep.12744. Epub 2017 Mar 28.

14.

Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.

Linden A, Yarnold PR.

J Eval Clin Pract. 2018 Apr;24(2):380-387. doi: 10.1111/jep.12859. Epub 2017 Dec 12.

PMID:
29230910
15.

Effect of consumer reporting on signal detection: using disproportionality analysis.

Hammond IW, Rich DS, Gibbs TG.

Expert Opin Drug Saf. 2007 Nov;6(6):705-12. Review.

PMID:
17967159
16.

Mining association patterns of drug-interactions using post marketing FDA's spontaneous reporting data.

Ibrahim H, Saad A, Abdo A, Sharaf Eldin A.

J Biomed Inform. 2016 Apr;60:294-308. doi: 10.1016/j.jbi.2016.02.009. Epub 2016 Feb 20.

17.

An experimental investigation of masking in the US FDA adverse event reporting system database.

Wang HW, Hochberg AM, Pearson RK, Hauben M.

Drug Saf. 2010 Dec 1;33(12):1117-33. doi: 10.2165/11584390-000000000-00000.

PMID:
21077702
18.

A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system.

Noguchi Y, Ueno A, Otsubo M, Katsuno H, Sugita I, Kanematsu Y, Yoshida A, Esaki H, Tachi T, Teramachi H.

BMC Bioinformatics. 2018 Apr 5;19(1):124. doi: 10.1186/s12859-018-2137-y.

19.

Time Series Disturbance Detection for Hypothesis-Free Signal Detection in Longitudinal Observational Databases.

Whalen E, Hauben M, Bate A.

Drug Saf. 2018 Jun;41(6):565-577. doi: 10.1007/s40264-018-0640-8.

PMID:
29468602
20.

Bayesian pharmacovigilance signal detection methods revisited in a multiple comparison setting.

Ahmed I, Haramburu F, Fourrier-Réglat A, Thiessard F, Kreft-Jais C, Miremont-Salamé G, Bégaud B, Tubert-Bitter P.

Stat Med. 2009 Jun 15;28(13):1774-92. doi: 10.1002/sim.3586.

PMID:
19360795

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