151 related articles for article (PubMed ID: 38822392)
1. Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): summary of findings and assessment of existing guidelines.
Rufibach K; Beyersmann J; Friede T; Schmoor C; Stegherr R
Trials; 2024 May; 25(1):353. PubMed ID: 38822392
[TBL] [Abstract][Full Text] [Related]
2. Survival analysis for AdVerse events with VarYing follow-up times (SAVVY)-estimation of adverse event risks.
Stegherr R; Schmoor C; Beyersmann J; Rufibach K; Jehl V; Brückner A; Eisele L; Künzel T; Kupas K; Langer F; Leverkus F; Loos A; Norenberg C; Voss F; Friede T
Trials; 2021 Jun; 22(1):420. PubMed ID: 34187527
[TBL] [Abstract][Full Text] [Related]
3. Estimating and comparing adverse event probabilities in the presence of varying follow-up times and competing events.
Stegherr R; Schmoor C; Lübbert M; Friede T; Beyersmann J
Pharm Stat; 2021 Nov; 20(6):1125-1146. PubMed ID: 34002935
[TBL] [Abstract][Full Text] [Related]
4. Statistical issues in the analysis of adverse events in time-to-event data.
Allignol A; Beyersmann J; Schmoor C
Pharm Stat; 2016 Jul; 15(4):297-305. PubMed ID: 26929180
[TBL] [Abstract][Full Text] [Related]
5. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
[TBL] [Abstract][Full Text] [Related]
6. Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): Rationale and statistical concept of a meta-analytic study.
Stegherr R; Beyersmann J; Jehl V; Rufibach K; Leverkus F; Schmoor C; Friede T
Biom J; 2021 Mar; 63(3):650-670. PubMed ID: 33145854
[TBL] [Abstract][Full Text] [Related]
7. Quantitative assessment of adverse events in clinical trials: Comparison of methods at an interim and the final analysis.
Hollaender N; Gonzalez-Maffe J; Jehl V
Biom J; 2020 May; 62(3):658-669. PubMed ID: 31756032
[TBL] [Abstract][Full Text] [Related]
8. Improved confidence intervals for a difference of two cause-specific cumulative incidence functions estimated in the presence of competing risks and random censoring.
Scosyrev E
Biom J; 2020 Oct; 62(6):1394-1407. PubMed ID: 32227361
[TBL] [Abstract][Full Text] [Related]
9. On estimands and the analysis of adverse events in the presence of varying follow-up times within the benefit assessment of therapies.
Unkel S; Amiri M; Benda N; Beyersmann J; Knoerzer D; Kupas K; Langer F; Leverkus F; Loos A; Ose C; Proctor T; Schmoor C; Schwenke C; Skipka G; Unnebrink K; Voss F; Friede T
Pharm Stat; 2019 Mar; 18(2):166-183. PubMed ID: 30458579
[TBL] [Abstract][Full Text] [Related]
10. Doubly robust estimator of risk in the presence of censoring dependent on time-varying covariates: application to a primary prevention trial for coronary events with pravastatin.
Kawahara T; Shinozaki T; Matsuyama Y
BMC Med Res Methodol; 2020 Jul; 20(1):204. PubMed ID: 32736528
[TBL] [Abstract][Full Text] [Related]
11. Eliciting adverse effects data from participants in clinical trials.
Allen EN; Chandler CI; Mandimika N; Leisegang C; Barnes K
Cochrane Database Syst Rev; 2018 Jan; 1(1):MR000039. PubMed ID: 29372930
[TBL] [Abstract][Full Text] [Related]
12. The future of Cochrane Neonatal.
Soll RF; Ovelman C; McGuire W
Early Hum Dev; 2020 Nov; 150():105191. PubMed ID: 33036834
[TBL] [Abstract][Full Text] [Related]
13. Estimating a population cumulative incidence under calendar time trends.
Hansen SN; Overgaard M; Andersen PK; Parner ET
BMC Med Res Methodol; 2017 Jan; 17(1):7. PubMed ID: 28077076
[TBL] [Abstract][Full Text] [Related]
14. Adverse events in single-arm clinical trials with non-fatal time-to-event efficacy endpoint: from clinical questions to methods for statistical analysis.
Tassistro E; Bernasconi DP; Valsecchi MG; Antolini L
BMC Med Res Methodol; 2024 Jan; 24(1):3. PubMed ID: 38172810
[TBL] [Abstract][Full Text] [Related]
15. A note on variance estimation of the Aalen-Johansen estimator of the cumulative incidence function in competing risks, with a view towards left-truncated data.
Allignol A; Schumacher M; Beyersmann J
Biom J; 2010 Feb; 52(1):126-37. PubMed ID: 20140901
[TBL] [Abstract][Full Text] [Related]
16. Non-parametric estimation of transition probabilities in non-Markov multi-state models: The landmark Aalen-Johansen estimator.
Putter H; Spitoni C
Stat Methods Med Res; 2018 Jul; 27(7):2081-2092. PubMed ID: 29846146
[TBL] [Abstract][Full Text] [Related]
17. Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator.
Willems S; Schat A; van Noorden MS; Fiocco M
Stat Methods Med Res; 2018 Feb; 27(2):323-335. PubMed ID: 26988930
[TBL] [Abstract][Full Text] [Related]
18. Treatment with disease-modifying drugs for people with a first clinical attack suggestive of multiple sclerosis.
Filippini G; Del Giovane C; Clerico M; Beiki O; Mattoscio M; Piazza F; Fredrikson S; Tramacere I; Scalfari A; Salanti G
Cochrane Database Syst Rev; 2017 Apr; 4(4):CD012200. PubMed ID: 28440858
[TBL] [Abstract][Full Text] [Related]
19. Non-parametric inference of adverse events under informative censoring.
Nishikawa M; Tango T; Ogawa M
Stat Med; 2006 Dec; 25(23):3981-4003. PubMed ID: 16526008
[TBL] [Abstract][Full Text] [Related]
20. Competing time-to-event endpoints in cardiology trials: a simulation study to illustrate the importance of an adequate statistical analysis.
Rauch G; Kieser M; Ulrich S; Doherty P; Rauch B; Schneider S; Riemer T; Senges J
Eur J Prev Cardiol; 2014 Jan; 21(1):74-80. PubMed ID: 22964966
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
