1,721,316 research outputs found
Markers of prognosis and response to treatment: ready for clinical use in oncology? : A biostatistician's viewpoint
No full textRadial Basis Function Neural Networks for the Analysis of Survival Data
No full textThe growing interest in artificial neural networks for outcome prediction of oncological patients is motivated by the increasing number of variables related to patient and/or disease characteristics to be investigated and by the possible presence of complex prognostic effects. Neural networks suitable for survival data should consider censoring in a correct way to avoid suboptimal models. Starting from the relationship between survival regression models and generalized linear models with Poisson error, we proposed their extensions as feed-forward neural networks. In particular, radial basis function networks are considered, which can be implemented with standard statistical software. The proposed models can be applied in an exploratory framework, for a single event and in the presence of competing risks. An application of the proposed models to literature data is presented
Tecniche "high throughput" in patologia umana. Biostatistica. "High throughput" technique in human pathology. Biostatistics
No full textOld and new markers for breast cancer prognosis : the need for integrated research on quantitative issues
No full textEstimating Relapse Free Survival as a Net Probability : Regression Models and Graphical Representation : An Application of a Large Breast Cancer Case Series
Full text linkIn most clinical studies, the evaluation of the effect of a therapy and
the impact of prognostic factors is based on relapse-free survival.
Relapse free is a net survival, since it is interpreted as the relapsefree
probability that would be observed if all patients experienced
relapse sooner or later. Death without evidence of relapse prevents
the subsequent observation of relapse, acting in a semi-competing
risks framework. Relapse free survival is often estimated by
standard regression models after censoring times to death. The
association between relapse and death is thus accounted for.
However, to better estimate relapse free survival, a bivariate
distribution of times to events needs to be considered, for example
by means of copula models. We concentrate here on the copula
graphic estimator, for which a pertinent regression model has
been developed. No direct parametric estimation of the regression
coefficient for the covariates is available and the evaluation of the
impact of covariates on relapse free survival is based on graphical
representation for each covariate singularly. The advantage of this
approach is based on the relationship between net survival, and
crude cumulative incidences. Regression models can be fitted for
the latter quantities and the estimates can be used to compute
net survival through a copula structure. Our proposal is based
on flexible regression transformation model on crude cumulative
incidences based on pseudo-values. An overall view of the joint
association among covariates and relapse free survival is obtained
through Multiple Correspondence Analysis. Moreover cluster
analysis on MCA coordinates was used to synthesize covariate
patterns and to estimates the corresponding relapse free survival
curve. This approach has been applied to a large “historical” case
series of patients with breast cancer
Estimation of the Piecewise Exponential Model by Bayesian P-Splines via Gibbs Sampling: Robustness and Reliability of Posterior Estimates
Full text linkIn the investigation of disease dynamics, the effect of covariates on the hazard function is a major topic. Some recent smoothed estimation methods have been proposed, both frequentist and Bayesian, based on the relationship between penalized splines and mixed models theory. These approaches are also motivated by the possibility of using automatic procedures for determining the optimal amount of smoothing. However, estimation algorithms involve an analytically intractable hazard function, and thus require ad-hoc software routines. We propose a more user-friendly alternative, consisting in regularized estimation of piecewise exponential models by Bayesian P-splines. A further facilitation is that widespread Bayesian software, such as WinBUGS, can be used. The aim is assessing the robustness of this approach with respect to different prior functions and penalties. A large dataset from breast cancer patients, where results from validated clinical studies are available, is used as a benchmark to evaluate the reliability of the estimates. A second dataset from a small case series of sarcoma patients is used for evaluating the performances of the PE model as a tool for exploratory analysis. Concerning breast cancer data, the estimates are robust with respect to priors and penalties, and consistent with clinical knowledge. Concerning soft tissue sarcoma data, the estimates of the hazard function are sensitive with respect to the prior for the smoothing parameter, whereas the estimates of regression coefficients are robust. In conclusion, Gibbs sampling results an efficient computational strategy. The issue of the sensitivity with respect to the priors concerns only the estimates of the hazard function, and seems more likely to occur when non-large case series are investigated, calling for tailored solutions
Simple way to estimate the median time and compare survival distributions in analgesic trials under informative censoring [letter]
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