170 related articles for article (PubMed ID: 30612514)
1. Design of randomized controlled confirmatory trials using historical control data to augment sample size for concurrent controls.
Yuan J; Liu J; Zhu R; Lu Y; Palm U
J Biopharm Stat; 2019; 29(3):558-573. PubMed ID: 30612514
[TBL] [Abstract][Full Text] [Related]
2. Leveraging historical data into oncology development programs: Two case studies of phase 2 Bayesian augmented control trial designs.
Smith CL; Thomas Z; Enas N; Thorn K; Lahn M; Benhadji K; Cleverly A
Pharm Stat; 2020 May; 19(3):276-290. PubMed ID: 31903699
[TBL] [Abstract][Full Text] [Related]
3. Survival trial design and monitoring using historical controls.
Wu J; Xiong X
Pharm Stat; 2016 Sep; 15(5):405-11. PubMed ID: 27307025
[TBL] [Abstract][Full Text] [Related]
4. Sample size calculation based on efficient unconditional tests for clinical trials with historical controls.
Shan G; Moonie S; Shen J
J Biopharm Stat; 2016; 26(2):240-9. PubMed ID: 25551261
[TBL] [Abstract][Full Text] [Related]
5. Dynamic use of historical controls in clinical trials for rare disease research: A re-evaluation of the MILES trial.
Harun N; Gupta N; McCormack FX; Macaluso M
Clin Trials; 2023 Jun; 20(3):223-234. PubMed ID: 36927115
[TBL] [Abstract][Full Text] [Related]
6. Bayesian adaptive randomization design incorporating propensity score-matched historical controls.
Sawamoto R; Oba K; Matsuyama Y
Pharm Stat; 2022 Sep; 21(5):1074-1089. PubMed ID: 35278032
[TBL] [Abstract][Full Text] [Related]
7. Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials: Review of Methods and Opportunities.
Lim J; Walley R; Yuan J; Liu J; Dabral A; Best N; Grieve A; Hampson L; Wolfram J; Woodward P; Yong F; Zhang X; Bowen E
Ther Innov Regul Sci; 2018 Sep; 52(5):546-559. PubMed ID: 29909645
[TBL] [Abstract][Full Text] [Related]
8. Sample size computation in phase II designs combining the A'Hern design and the Sargent and Goldberg design.
Neven A; Mauer M; Hasan B; Sylvester R; Collette L
J Biopharm Stat; 2020 Mar; 30(2):305-321. PubMed ID: 31331234
[TBL] [Abstract][Full Text] [Related]
9. Group sequential design for historical control trials using error spending functions.
Wu J; Li Y
J Biopharm Stat; 2020 Mar; 30(2):351-363. PubMed ID: 31718458
[TBL] [Abstract][Full Text] [Related]
10. Design and analysis of a clinical trial using previous trials as historical control.
Schoenfeld DA; Finkelstein DM; Macklin E; Zach N; Ennist DL; Taylor AA; Atassi N;
Clin Trials; 2019 Oct; 16(5):531-538. PubMed ID: 31256630
[TBL] [Abstract][Full Text] [Related]
11. Calculating sample size in trials using historical controls.
Zhang S; Cao J; Ahn C
Clin Trials; 2010 Aug; 7(4):343-53. PubMed ID: 20573638
[TBL] [Abstract][Full Text] [Related]
12. Modified Goldilocks Design with strict type I error control in confirmatory clinical trials.
Zhan T; Zhang H; Hartford A; Mukhopadhyay S
J Biopharm Stat; 2020 Sep; 30(5):821-833. PubMed ID: 32297825
[TBL] [Abstract][Full Text] [Related]
13. Sample size considerations for historical control studies with survival outcomes.
Zhu H; Zhang S; Ahn C
J Biopharm Stat; 2016; 26(4):657-71. PubMed ID: 26098200
[TBL] [Abstract][Full Text] [Related]
14. Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials.
Ohigashi T; Maruo K; Sozu T; Gosho M
Stat Methods Med Res; 2022 Jul; 31(7):1392-1404. PubMed ID: 35379046
[TBL] [Abstract][Full Text] [Related]
15. Incorporating individual historical controls and aggregate treatment effect estimates into a Bayesian survival trial: a simulation study.
Brard C; Hampson LV; Gaspar N; Le Deley MC; Le Teuff G
BMC Med Res Methodol; 2019 Apr; 19(1):85. PubMed ID: 31018832
[TBL] [Abstract][Full Text] [Related]
16. Use of a historical control group in a noninferiority trial assessing a new antibacterial treatment: A case study and discussion of practical implementation aspects.
Dejardin D; Delmar P; Warne C; Patel K; van Rosmalen J; Lesaffre E
Pharm Stat; 2018 Mar; 17(2):169-181. PubMed ID: 29282862
[TBL] [Abstract][Full Text] [Related]
17. Adaptive extensions of a two-stage group sequential procedure for testing primary and secondary endpoints (I): unknown correlation between the endpoints.
Tamhane AC; Wu Y; Mehta CR
Stat Med; 2012 Aug; 31(19):2027-40. PubMed ID: 22729929
[TBL] [Abstract][Full Text] [Related]
18. Why caution is recommended with post-hoc individual patient matching for estimation of treatment effect in parallel-group randomized controlled trials: the case of acute stroke trials.
Jafari N; Hearne J; Churilov L
Stat Med; 2013 Nov; 32(25):4467-81. PubMed ID: 23761106
[TBL] [Abstract][Full Text] [Related]
19. Adaptive propensity score procedure improves matching in prospective observational trials.
Weber D; Uhlmann L; Schönenberger S; Kieser M
BMC Med Res Methodol; 2019 Jul; 19(1):150. PubMed ID: 31311500
[TBL] [Abstract][Full Text] [Related]
20. Adaptive extensions of a two-stage group sequential procedure for testing primary and secondary endpoints (II): sample size re-estimation.
Tamhane AC; Wu Y; Mehta CR
Stat Med; 2012 Aug; 31(19):2041-54. PubMed ID: 22733687
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
