1,721,176 research outputs found
A comparison of cross validation and information criteria for survival analysis neural networks regularization selection
No full textMultiple correspondence analysis in S-PLUS
No full textMultiple correspondence analysis (MCA) is a multivariate method for analyzing multidimensional contingency tables. General software procedures to perform MCA are available. Among them SAS Proc CORRESP, SPAD CORMU procedure and the mca function of the MASS library in S-PLUS are probably the most used. However, CORRESP and CORMU output is different from that of mca function. The aim of this short note is showing how to obtain from mca function results compatible with those achieved with SAS or SPAD. A modified code is proposed in order to obtain the same coordinate system computed by SAS and SPAD. Moreover, the computation of the contributions of the levels of the factors to the inertia explained by each axis, the squared cosine of each factor level and the re-evaluation of the inertia explained by each axis have been added in order to improve the interpretations of the results of the decomposition. (copyright) 2005 Elsevier Ireland Ltd. All rights reserved
Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards
No full textIn the presence of competing risks, the estimation of crude cumulative incidence, i.e. the probability of a specific failure as time progresses, has received much attention in the methodological literature. It is possible to estimate crude cumulative incidence starting from models defined on cause-specific hazards or to adopt regression strategies modeling directly the quantity of interest. A generalized linear model based on discrete cause-specific hazard is used to obtain the crude cumulative incidence and its asymptotic variance. The model allows inference both on cause-specific hazard and on crude cumulative incidence in the presence of time dependent effects. Standard software can be used to compute all quantities of interest. A trial of chemoprevention of leukoplakia is considered for illustrative purposes, where different patterns of risk are suspected for the different causes of treatment failure
Comments on the use of a single or multiple probe-set approach for microarray-based analyses of routine molecular markers in breast cancer
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Penalized estimation for competing risks regression with applications to high-dimensional covariates
No full textHigh-dimensional regression has become an increasingly important topic for many research fields. For example, biomedical research generates an increasing amount of data to characterize patients' bio-profiles (e.g. from a genomic high-throughput assay). The increasing complexity in the characterization of patients' bio-profiles is added to the complexity related to the prolonged follow-up of patients with the registration of the occurrence of possible adverse events. This information may offer useful insight into disease dynamics and in identifying subset of patients with worse prognosis and better response to the therapy. Although in the last years the number of contributions for coping with high and ultra-high-dimensional data in standard survival analysis have increased (Witten and Tibshirani, 2010. Survival analysis with high-dimensional covariates. Statistical Methods in Medical Research 19(1), 29-51), the research regarding competing risks is less developed (Binder and others, 2009. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25(7), 890-896). The aim of this work is to consider how to do penalized regression in the presence of competing events. The direct binomial regression model of Scheike and others (2008. Predicting cumulative incidence probability by direct binomial regression. Biometrika 95(1), 205-220) is reformulated in a penalized framework to possibly fit a sparse regression model. The developed approach is easily implementable using existing high-performance software to do penalized regression. Results from simulation studies are presented together with an application to genomic data when the endpoint is progression-free survival. An R function is provided to perform regularized competing risks regression according to the binomial model in the package timereg (Scheike and Martinussen, 2006. Dynamic Regression models for survival data. New York: Springer), available through CRAN
Comparative benefit from small tumour size and adjuvant chemotherapy: clues for explaining breast cancer mortality decline
Full text linkBackground: Breast cancer mortality steadily declined from the 1990s and this has been attributed to early detection and/or to improvements in therapy. Which of those two has had the greater impact is a subject of contention.Methods: A database of 386 patients, enrolled in a randomized clinical trial on the effect of adjuvant chemotherapy (CMF), was analysed. The probabilities of recurrence and death were estimated by the Fine and Gray's model and by the Cox model. Time dependent covariate and interaction effects were investigated by additive models. Absolute risk reductions (ARR) related to adjuvant treatment or to tumour size [diameter ≤ 2 cm (T1) or >2 cm (T2/T3)] were estimated. Results: CMF-related reduction in recurrence emerges early, reaches a maximum level at 3 years and persists at a constant level thereafter. Tumour-size-related recurrence reduction, after a maximum at 3 years, displays a progressive regular reduction approaching zero. Patients with any tumour size, when given CMF, exhibit mortality reduction that displays an early regular increase and continues to a persistent plateau. In contrast, tumour-size-related mortality reduction reaches a maximum at 5-7 years and then regularly drops to very low values for patients of both trial arms. Conclusions: Findings reveal that there is a different time-dependent benefit from chemotherapy and from smaller tumour size at diagnosis. The benefit from adjuvant chemotherapy is long-lasting for patients with any tumour size while the early benefit of diagnosing smaller tumours substantially decreases afterwards. Treatment improvements have probably had greater impact on the mortality reduction than mammography screening
Clinical useful measures for the study of competing risks in survival analysis
No full textThe possible occurrence of multiple events during follow-up is a common situation in several clinical studies. Treatment failure, as the event firstly occurring, may be due to causes having di erent clinical implications in planning therapeutic strategies. The interest is generally focused on some specific causes of failure. Since
only one type can be actually observed on each patient, competing risks methodology is appropriate. In this context, the sub-distribution hazard model is applied
to infer on the difference among crude cumulative incidences. However, inference on sub-distribution hazards are not directly interpretable from a clinical perspective. To assess treatment or covariate effects, measures of clinical impact based on crude cumulative
incidence should be considered. In particular relative risks, excess of risks, relative risk reduction and number of patients needed to be treated are known to
be useful to clinical practitioners. The aim of this work is to provide a straightforward approach to obtain point and interval estimates of the above measures, by using
transformation models, through suitable link functions in presence of competing risks.
In order to make the technique readily applied the proposal of Klein and Andersen, based on pseudo-values, was considered as starting point. The baseline was estimated using regression spline functions with respect to time. Time-varying effects of covariates were tested through interaction with time functions. A published data set from a controlled clinical trial on prostate cancer, using causes of death as end-points, was used for illustration
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