Extending the long-term survivor mixture model with random effects for clustered survival data

2010 ◽  
Vol 54 (9) ◽  
pp. 2103-2112 ◽  
Author(s):  
Xin Lai ◽  
Kelvin K.W. Yau
Keyword(s):  
Mixture Model ◽  
Random Effects ◽  
Survival Data ◽  
Clustered Survival Data ◽  
Term Survivor

scholarly journals A NEW MULTILEVEL MODELING APPROACH FOR CLUSTERED SURVIVAL DATA

2020 ◽  
Vol 36 (4) ◽  
pp. 707-750 ◽  
Author(s):  
Jinfeng Xu ◽  
Mu Yue ◽  
Wenyang Zhang
Keyword(s):  
Multilevel Modeling ◽  
Random Effects ◽  
Survival Data ◽  
Birth Interval ◽  
Fixed Effects ◽  
Estimation Procedure ◽  
Unknown Parameters ◽  
Modeling Approach ◽  
Clustered Survival Data ◽  
The Impact

In multilevel modeling of clustered survival data, to account for the differences among different clusters, a commonly used approach is to introduce cluster effects, either random or fixed, into the model. Modeling with random effects may lead to difficulties in the implementation of the estimation procedure for the unknown parameters of interest because the numerical computation of multiple integrals may become unavoidable when the cluster effects are not scalars. On the other hand, if fixed effects are used, there is a danger of having estimators with large variances because there are too many nuisance parameters involved in the model. In this article, using the idea of the homogeneity pursuit, we propose a new multilevel modeling approach for clustered survival data. The proposed modeling approach does not have the potential computational problem as modeling with random effects, and it also involves far fewer unknown parameters than modeling with fixed effects. We also establish asymptotic properties to show the advantages of the proposed model and conduct intensive simulation studies to demonstrate the performance of the proposed method. Finally, the proposed method is applied to analyze a dataset on the second-birth interval in Bangladesh. The most interesting finding is the impact of some important factors on the length of the second-birth interval variation over clusters and its homogeneous structure.

scholarly journals Multilevel model with random effects for clustered survival data with multiple failure outcomes

2018 ◽  
Vol 38 (6) ◽  
pp. 1036-1055 ◽  
Author(s):  
Richard Tawiah ◽  
Kelvin K.W. Yau ◽  
Geoffrey J. McLachlan ◽  
Suzanne K. Chambers ◽  
Shu-Kay Ng
Keyword(s):  
Random Effects ◽  
Survival Data ◽  
Multilevel Model ◽  
Clustered Survival Data

scholarly journals A score test for assessing the cured proportion in the long-term survivor mixture model

2009 ◽  
Vol 28 (27) ◽  
pp. 3454-3466 ◽  
Author(s):  
Yun Zhao ◽  
Andy H. Lee ◽  
Kelvin K. W. Yau ◽  
Valerie Burke ◽  
Geoffrey J. McLachlan
Keyword(s):  
Mixture Model ◽  
Score Test ◽  
Term Survivor

scholarly journals Gut microbiome diversity is an independent predictor of survival in cervical cancer patients receiving chemoradiation

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Travis T. Sims ◽  
Molly B. El Alam ◽  
Tatiana V. Karpinets ◽  
Stephanie Dorta-Estremera ◽  
Venkatesh L. Hegde ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Radiation Therapy ◽  
Gut Microbiota ◽  
Cancer Patients ◽  
Gut Microbiome ◽  
Compositional Variation ◽  
Short Term ◽  
Microbiome Diversity ◽  
Term Survivor

AbstractDiversity of the gut microbiome is associated with higher response rates for cancer patients receiving immunotherapy but has not been investigated in patients receiving radiation therapy. Additionally, current studies investigating the gut microbiome and outcomes in cancer patients may not have adjusted for established risk factors. Here, we sought to determine if diversity and composition of the gut microbiome was independently associated with survival in cervical cancer patients receiving chemoradiation. Our study demonstrates that the diversity of gut microbiota is associated with a favorable response to chemoradiation. Additionally, compositional variation among patients correlated with short term and long-term survival. Short term survivor fecal samples were significantly enriched in Porphyromonas, Porphyromonadaceae, and Dialister, whereas long term survivor samples were significantly enriched in Escherichia Shigella, Enterobacteriaceae, and Enterobacteriales. Moreover, analysis of immune cells from cervical tumor brush samples by flow cytometry revealed that patients with a high microbiome diversity had increased tumor infiltration of CD4+ lymphocytes as well as activated subsets of CD4 cells expressing ki67+ and CD69+ over the course of radiation therapy. Modulation of the gut microbiota before chemoradiation might provide an alternative way to enhance treatment efficacy and improve treatment outcomes in cervical cancer patients.

scholarly journals Is It Definitely Clear That Long-Term Survival after Breast Cancer Surgery Is Not Affected by Anaesthetics?

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3390
Author(s):  
Mats Enlund
Keyword(s):  
Breast Cancer ◽  
Survival Data ◽  
Retrospective Studies ◽  
Breast Cancer Surgery ◽  
Breast Cancer Patients ◽  
Term Survival ◽  
Long Term Survival ◽  
Significant Difference ◽  
One Year

Retrospective studies indicate that cancer survival may be affected by the anaesthetic technique. Propofol seems to be a better choice than volatile anaesthetics, such as sevoflurane. The first two retrospective studies suggested better long-term survival with propofol, but not for breast cancer. Subsequent retrospective studies from Asia indicated the same. When data from seven Swedish hospitals were analysed, including 6305 breast cancer patients, different analyses gave different results, from a non-significant difference in survival to a remarkably large difference in favour of propofol, an illustration of the innate weakness in the retrospective design. The largest randomised clinical trial, registered on clinicaltrial.gov, with survival as an outcome is the Cancer and Anesthesia study. Patients are here randomised to propofol or sevoflurane. The inclusion of patients with breast cancer was completed in autumn 2017. Delayed by the pandemic, one-year survival data for the cohort were presented in November 2020. Due to the extremely good short-term survival for breast cancer, one-year survival is of less interest for this disease. As the inclusions took almost five years, there was also a trend to observe. Unsurprisingly, no difference was found in one-year survival between the two groups, and the trend indicated no difference either.

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