Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Audinga-Dea Hazewinkel"'
Publikováno v:
Current Oncology, Vol 31, Iss 7, Pp 3630-3642 (2024)
Current prediction models for patients with ostosarcoma are restricted to predictions from a single, static point in time, such as diagnosis or surgery. These approaches discard information which becomes available during follow-up and may have an imp
Externí odkaz:
https://doaj.org/article/a6ab7dff96934085b4bd0a7177ace834
Autor:
Jeremy Whelan, Carlo Lancia, Jakob Anninga, Hans Gelderblom, Marta Fiocco, Audinga-Dea Hazewinkel, Michiel van de Sande
Publikováno v:
BMJ Open, Vol 12, Iss 3 (2022)
Objectives Investigating the effect of prognostic factors in a multistate framework on survival in a large population of patients with osteosarcoma. Of interest is how prognostic factors affect different disease stages after surgery, with stages of l
Externí odkaz:
https://doaj.org/article/77ce590c15754fbc9a46cd992679f379
Publikováno v:
Computational and Mathematical Methods in Medicine. 2022:1-18
Survival analysis deals with the expected duration of time until one or more events of interest occur. Time to the event of interest may be unobserved, a phenomenon commonly known as right censoring, which renders the analysis of these data challengi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d5368c40345a265284646a9de8b5579
https://doi.org/10.1155/2022/1176060
https://doi.org/10.1155/2022/1176060
Autor:
Nicola J Wiles, Kate Tilling, Jack Bowden, Tom Palmer, Kaitlin H Wade, Audinga-Dea Hazewinkel
Publikováno v:
Hazewinkel, A-D, Bowden, J, Wade, K H, Palmer, T, Wiles, N J & Tilling, K 2022, ' Sensitivity to missing not at random dropout in clinical trials : Use and interpretation of the trimmed means estimator ', Statistics in Medicine, vol. 41, no. 8, pp. 1462-1481 . https://doi.org/10.1101/2021.03.05.21252334, https://doi.org/10.1002/sim.9299
Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (e.g., due to perceived ineffectiveness of treatment, or adverse effects). In such scenario
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::466bd3f77d29801e758a24bb59b70490
https://doi.org/10.1002/sim.9299
https://doi.org/10.1002/sim.9299
Randomized controlled trials (RCTs) are considered the gold standard for assessing the causal effect of an exposure on an outcome, but are vulnerable to bias from missing data. When outcomes are missing not at random (MNAR), estimates from complete c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::591eeb63ae3311506e95362050d99cef
https://doi.org/10.1101/2022.04.15.22273918
https://doi.org/10.1101/2022.04.15.22273918
Recent years have seen increased interest in using machine learning (ML) methods for survival prediction, chiefly using big datasets with mixed datatypes and/or many predictors Model comparisons have frequently been limited to performance measure eva
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::39d7d5a04315ff52a426c09aa3f2ca9f
https://doi.org/10.1101/2022.03.29.22273112
https://doi.org/10.1101/2022.03.29.22273112