Modeling and inference for survival analysis problems typically revolves around different functions related to the survival distribution. Here, we focus on the mean residual life (MRL) function, which provides the expected remaining lifetime given that a subject has survived (i.e. is event-free) up to a particular time. This function is of direct interest in reliability, medical, and actuarial fields. In addition to its practical interpretation, the MRL function characterizes the survival distribution. We develop general Bayesian nonparametric inference for MRL functions built from a Dirichlet process mixture model for the associated survival distribution. The resulting model for the MRL function admits a representation as a mixture of the kernel MRL functions with time-dependent mixture weights. This model structure allows for a wide range of shapes for the MRL function. Particular emphasis is placed on the selection of the mixture kernel, taken to be a gamma distribution, to obtain desirable properties for the MRL function arising from the mixture model. The inference method is illustrated with a data set of two experimental groups and a data set involving right censoring. The supplementary material available at Biostatistics online provides further results on empirical performance of the model, using simulated data examples. The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Purpose: To correlate the damage produced by particles of different types and qualities to cell survival on the basis of nanodosimetric analysis and advanced DNA structures in the cell nucleus. Methods: A Monte Carlo code was developed to simulate subnuclear DNA chromatin fibers (CFs) of 30nm utilizing a mean-free-path approach common to radiation transport. The cell nucleus was modeled as a spherical region containing 6000 chromatin-dense domains (CDs) of 400nm diameter, with additional CFs modeled in a sparser interchromatin region. The Geant4-DNA code was utilized to produce a particle track database representing various particles at different energies and dose quantities. These tracks were used to stochastically position the DNA structures based on their mean free path to interaction with CFs. Excitation and ionization events intersecting CFs were analyzed using the DBSCAN clustering algorithm for assessment of the likelihood of producing DSBs. Simulated DSBs were then assessed based on their proximity to one another for a probability of inducing cell death. Results: Variations in energy deposition to chromatin fibers match expectations based on differences in particle track structure. The quality of damage to CFs based on different particle types indicate more severe damage by high-LET radiation than low-LET radiation of identical particles. In addition, the model indicates more severe damage by protons than of alpha particles of same LET, which is consistent with differences in their track structure. Cell survival curves have been produced showing the L-Q behavior of sparsely ionizing radiation. Conclusion: Initial results indicate the feasibility of producing cell survival curves based on the Monte Carlo cell nucleus method. Accurate correlation between simulated DNA damage to cell survival on the basis of nanodosimetric analysis can provide insight into the biological responses to various radiation types. Current efforts are directed at producing cell
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Full Text Available Abstract Background Relative survival is commonly used for studying survival of cancer patients as it captures both the direct and indirect contribution of a cancer diagnosis on mortality by comparing the observed survival of the patients to the expected survival in a comparable cancer-free population. However, existing methods do not allow estimation of the impact of isolated conditions (e.g., excess cardiovascular mortality on the total excess mortality. For this purpose we extend flexible parametric survival models for relative survival, which use restricted cubic splines for the baseline cumulative excess hazard and for any time-dependent effects. Methods In the extended model we partition the excess mortality associated with a diagnosis of cancer through estimating a separate baseline excess hazard function for the outcomes under investigation. This is done by incorporating mutually exclusive background mortality rates, stratified by the underlying causes of death reported in the Swedish population, and by introducing cause of death as a time-dependent effect in the extended model. This approach thereby enables modeling of temporal trends in e.g., excess cardiovascular mortality and remaining cancer excess mortality simultaneously. Furthermore, we illustrate how the results from the proposed model can be used to derive crude probabilities of death due to the component parts, i.e., probabilities estimated in the presence of competing causes of death. Results The method is illustrated with examples where the total excess mortality experienced by patients diagnosed with breast cancer is partitioned into excess cardiovascular mortality and remaining cancer excess mortality. Conclusions The proposed method can be used to simultaneously study disease patterns and temporal trends for various causes of cancer-consequent deaths. Such information should be of interest for patients and clinicians as one way of improving prognosis after cancer is
Clinical and pathological indicators are inadequate for prognosis of stage II and III colorectal carcinoma (CRC). In this study, we utilized the activity of regulatory factors, univariate Cox regression and random forest for variable selection and developed a multivariate Cox model to predict the overall survival of Stage II/III colorectal carcinoma in GSE39582 datasets (469 samples). Patients in low-risk group showed a significant longer overall survival and recurrence-free survival time than those in high-risk group. This finding was further validated in five other independent datasets (GSE14333, GSE17536, GSE17537, GSE33113, and GSE37892). Besides, associations between clinicopathological information and risk score were analyzed. A nomogram including risk score was plotted to facilitate the utilization of risk score. The risk score model is also demonstrated to be effective on predicting both overall and recurrence-free survival of chemotherapy received patients. After performing Gene Set Enrichment Analysis (GSEA) between high and low risk groups, we found that several cell-cell interaction KEGG pathways were identified. Funnel plot results showed that there was no publication bias in these datasets. In summary, by utilizing the regulatory activity in stage II and III colorectal carcinoma, the risk score successfully predicts the survival of 1021 stage II/III CRC patients in six independent datasets.
Survival modeling techniques are increasingly being used as part of decision modeling for health economic evaluations. As many models are available, it is imperative for interested readers to know about the steps in selecting and using the most suitable ones. The objective of this paper is to propose a tutorial for the application of appropriate survival modeling techniques to estimate transition probabilities, for use in model-based economic evaluations, in the absence of individual patient data (IPD). An illustration of the use of the tutorial is provided based on the final progression-free survival (PFS) analysis of the BOLERO-2 trial in metastatic breast cancer (mBC). An algorithm was adopted from Guyot and colleagues, and was then run in the statistical package R to reconstruct IPD, based on the final PFS analysis of the BOLERO-2 trial. It should be emphasized that the reconstructed IPD represent an approximation of the original data. Afterwards, we fitted parametric models to the reconstructed IPD in the statistical package Stata. Both statistical and graphical tests were conducted to verify the relative and absolute validity of the findings. Finally, the equations for transition probabilities were derived using the general equation for transition probabilities used in model-based economic evaluations, and the parameters were estimated from fitted distributions. The results of the application of the tutorial suggest that the log-logistic model best fits the reconstructed data from the latest published Kaplan-Meier (KM) curves of the BOLERO-2 trial. Results from the regression analyses were confirmed graphically. An equation for transition probabilities was obtained for each arm of the BOLERO-2 trial. In this paper, a tutorial was proposed and used to estimate the transition probabilities for model-based economic evaluation, based on the results of the final PFS analysis of the BOLERO-2 trial in mBC. The results of our study can serve as a basis for any model
Recent studies suggest that subtotal resection (STR) followed by radiation therapy (RT) is an appealing alternative to gross total resection (GTR) for craniopharyngioma, but it remains controversial. We conducted a meta-analysis to determine whether GTR is superior to STR with RT for craniopharyngioma. A systematic search was performed for articles published until October 2017 in the PubMed, Embase, and Cochrane Central databases. The endpoints of interest are overall survival and progression-free survival. Pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated using a fixed or random-effects model. The data were analyzed using Review Manager 5.3 software. A total of 744 patients (seven cohort studies) were enrolled for analyses. There were no significant differences between the GTR and STR with RT groups when the authors compared the pooled HRs at the end of the follow-up period. Overall survival (pooled HR = 0.76, 95% CI: 0.46-1.25, P = 0.28) and progression-free survival (pooled HR = 1.52, 95% CI: 0.42-5.44, P = 0.52) were similar between the two groups. The current meta-analysis suggests that GTR and STR with RT have the similar survival outcomes for craniopharyngioma. Copyright 2018 Elsevier Inc. All rights reserved. 2ff7e9595c
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