Zobrazeno 1 - 7
of 7
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:
Marta Spreafico, Audinga-Dea Hazewinkel, Michiel A. J. van de Sande, Hans Gelderblom, Marta Fiocco
Publikováno v:
Cancers, Vol 16, Iss 16, p 2880 (2024)
Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strateg
Externí odkaz:
https://doaj.org/article/3067038a65c042b9a62cc89ad9d62902
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
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
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
Autor:
Audinga-Dea Hazewinkel, Carlo Lancia, Jakob Anninga, Michiel van de Sande, Jeremy Whelan, Hans Gelderblom, Marta Fiocco
Publikováno v:
BMJ Open, 12(3). BMJ PUBLISHING GROUP
BMJ Open, 12(3):e053083. BMJ PUBLISHING GROUP
BMJ Open, 12(3):e053083. BMJ PUBLISHING GROUP
ObjectivesInvestigating 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 lo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0c8827a0a5a4fc3a65435a9afe95fe0
https://bmjopen.bmj.com/content/12/3/e053083
https://bmjopen.bmj.com/content/12/3/e053083