133 related articles for article (PubMed ID: 34590286)
1. Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring.
de Viron S; Trotta L; Schumacher H; Lomp HJ; Höppner S; Young S; Buyse M
Ther Innov Regul Sci; 2022 Jan; 56(1):130-136. PubMed ID: 34590286
[TBL] [Abstract][Full Text] [Related]
2. Central statistical monitoring: detecting fraud in clinical trials.
Pogue JM; Devereaux PJ; Thorlund K; Yusuf S
Clin Trials; 2013 Apr; 10(2):225-35. PubMed ID: 23283577
[TBL] [Abstract][Full Text] [Related]
3. Detection of atypical data in multicenter clinical trials using unsupervised statistical monitoring.
Trotta L; Kabeya Y; Buyse M; Doffagne E; Venet D; Desmet L; Burzykowski T; Tsuburaya A; Yoshida K; Miyashita Y; Morita S; Sakamoto J; Praveen P; Oba K
Clin Trials; 2019 Oct; 16(5):512-522. PubMed ID: 31331195
[TBL] [Abstract][Full Text] [Related]
4. A computationally simple central monitoring procedure, effectively applied to empirical trial data with known fraud.
van den Bor RM; Vaessen PWJ; Oosterman BJ; Zuithoff NPA; Grobbee DE; Roes KCB
J Clin Epidemiol; 2017 Jul; 87():59-69. PubMed ID: 28412468
[TBL] [Abstract][Full Text] [Related]
5. Strategies for dealing with fraud in clinical trials.
Herson J
Int J Clin Oncol; 2016 Feb; 21(1):22-7. PubMed ID: 26194810
[TBL] [Abstract][Full Text] [Related]
6. The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials.
Buyse M; George SL; Evans S; Geller NL; Ranstam J; Scherrer B; Lesaffre E; Murray G; Edler L; Hutton J; Colton T; Lachenbruch P; Verma BL
Stat Med; 1999 Dec; 18(24):3435-51. PubMed ID: 10611617
[TBL] [Abstract][Full Text] [Related]
7. Data fraud in clinical trials.
George SL; Buyse M
Clin Investig (Lond); 2015; 5(2):161-173. PubMed ID: 25729561
[TBL] [Abstract][Full Text] [Related]
8. Statistical challenges for central monitoring in clinical trials: a review.
Oba K
Int J Clin Oncol; 2016 Feb; 21(1):28-37. PubMed ID: 26499195
[TBL] [Abstract][Full Text] [Related]
9. Data-driven risk identification in phase III clinical trials using central statistical monitoring.
Timmermans C; Venet D; Burzykowski T
Int J Clin Oncol; 2016 Feb; 21(1):38-45. PubMed ID: 26233672
[TBL] [Abstract][Full Text] [Related]
10. Detecting Data Quality Issues in Clinical Trials: Current Practices and Recommendations.
Knepper D; Fenske C; Nadolny P; Bedding A; Gribkova E; Polzer J; Neumann J; Wilson B; Benedict J; Lawton A
Ther Innov Regul Sci; 2016 Jan; 50(1):15-21. PubMed ID: 30236017
[TBL] [Abstract][Full Text] [Related]
11. Statistical Monitoring in Clinical Trials: Best Practices for Detecting Data Anomalies Suggestive of Fabrication or Misconduct.
Knepper D; Lindblad AS; Sharma G; Gensler GR; Manukyan Z; Matthews AG; Seifu Y
Ther Innov Regul Sci; 2016 Mar; 50(2):144-154. PubMed ID: 30227005
[TBL] [Abstract][Full Text] [Related]
12. Dynamic methods for ongoing assessment of site-level risk in risk-based monitoring of clinical trials: A scoping review.
Cragg WJ; Hurley C; Yorke-Edwards V; Stenning SP
Clin Trials; 2021 Apr; 18(2):245-259. PubMed ID: 33611927
[TBL] [Abstract][Full Text] [Related]
13. An Appraisal of the Carlisle-Stouffer-Fisher Method for Assessing Study Data Integrity and Fraud.
Mascha EJ; Vetter TR; Pittet JF
Anesth Analg; 2017 Oct; 125(4):1381-1385. PubMed ID: 28786843
[TBL] [Abstract][Full Text] [Related]
14. A survey on statistical methods for health care fraud detection.
Li J; Huang KY; Jin J; Shi J
Health Care Manag Sci; 2008 Sep; 11(3):275-87. PubMed ID: 18826005
[TBL] [Abstract][Full Text] [Related]
15. Detecting medical prescriptions suspected of fraud using an unsupervised data mining algorithm.
Haddad Soleymani M; Yaseri M; Farzadfar F; Mohammadpour A; Sharifi F; Kabir MJ
Daru; 2018 Dec; 26(2):209-214. PubMed ID: 30460618
[TBL] [Abstract][Full Text] [Related]
16. The role of data audits in detecting scientific misconduct. Results of the FDA program.
Shapiro MF; Charrow RP
JAMA; 1989 May; 261(17):2505-11. PubMed ID: 2704109
[TBL] [Abstract][Full Text] [Related]
17. Research misconduct and data fraud in clinical trials: prevalence and causal factors.
George SL
Int J Clin Oncol; 2016 Feb; 21(1):15-21. PubMed ID: 26289019
[TBL] [Abstract][Full Text] [Related]
18. Clinician-trialist rounds: 19. Faux pas or fraud? Identifying centers that have fabricated their data in your multi-center trial.
Pogue J; Sackett DL
Clin Trials; 2014 Feb; 11(1):128-30. PubMed ID: 24096634
[No Abstract] [Full Text] [Related]
19. Scientific Misconduct (Fraud) in Medical Writing.
Mavrogenis AF; Panagopoulos GN; Megaloikonomos PD; Panagopoulos VN; Mauffrey C; Quaile A; Scarlat MM
Orthopedics; 2018 Mar; 41(2):e176-e183. PubMed ID: 29377051
[TBL] [Abstract][Full Text] [Related]
20. Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases.
Massi MC; Ieva F; Lettieri E
BMC Med Inform Decis Mak; 2020 Jul; 20(1):160. PubMed ID: 32664923
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]