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At least 1,117 records · Page 62Linked to original sources
PubMed2006

Estimation and prediction in a multi-state model for breast cancer.

An important aim in clinical studies in oncology is to study how treatment and prognostic factors influence the course of disease of a patient. Typically in these trials, besides overall survival, also other endpoints such as locoregional recurrence or distant metastasis are of interest. Most commonly in these situations, Cox regression models are applied for each of these endpoints separately or to composite endpoints such as disease-free survival. These approaches however fail to give insight into what happens to a patient after a first event. We re-analyzed data of 2795 patients from a breast cancer trial (EORTC 10854) by applying a multi-state model, with local recurrence, distant metastasis, and both local recurrence and distant metastasis as transient states and death as absorbing state. We used an approach where the clock is reset on entry of a new state. The influence of prognostic factors on each of the transition rates is studied, as well as the influence of the time at which intermediate events occur. The estimated transition rates between the states in the model are used to obtain predictions for patients with a given history. Formulas are developed and illustrated for these prediction probabilities for the clock reset approach.

Biometry↗
PubMed2006

The model for end-stage liver disease-based Japan Integrated Scoring system may have a better predictive ability for patients with hepatocellular carcinoma undergoing locoregional therapy.

BACKGROUND: The Japan Integrated Scoring (JIS) system was revealed as a better model for outcome prediction compared with the Cancer of Liver Italian Program system for hepatocellular carcinoma (HCC), and the Model for End-Stage Liver Disease (MELD) was better as a prognostic predictor for patients with cirrhosis compared with the Child-Turcotte-Pugh (CTP) system, which is a parameter used in the JIS system. The objective of the current study was to investigate the performance of the modified MELD-based JIS system. METHODS: In the modified JIS system, the CTP class in the original JIS was replaced with MELD cut-off scores of <10, 10 to 14, and >14. The modified JIS system was compared with the original system in 276 patients with HCC who underwent locoregional therapy (transarterial chemoembolization or percutaneous injection). RESULTS: The mean +/- standard error original JIS score was 1.8 +/- 1.0 (range, 0-4), compared with 2.0 +/- 1.1 (range, 0-5) for the modified JIS system (P < .001). Using mortality as the endpoint, the area under receiver operating characteristic curve (AUC) for the modified JIS system was 0.804 compared with 0.741 for the original JIS system (P = .008) at 12 months, and the AUC was 0.853 and 0.765, respectively (P < .001), at 24 months. Survival analysis showed that the modified JIS system had a better discriminatory ability for patients in different score groups and was more accurate for outcome prediction in the Cox multivariate model. CONCLUSIONS: The current results indicated that the MELD-based, modified JIS system has improved predictive ability compared with the original system and is a more feasible model for clinical staging in patients with HCC who are undergoing locoregional therapy.

Aged↗
PubMed2005

Testing treatment effects in the presence of competing risks.

Competing risks are often encountered in clinical research. In the presence of multiple failure types, the time to the first failure of any type is typically used as an overall measure of the clinical impact for the patients. On the other hand, use of endpoints based on the type of failure directly related to the treatment mechanism of action allows one to focus on the aspect of the disease targeted by treatment. We review the methodology commonly used for testing failure specific treatment effects. Simulation results demonstrate that the cause-specific log-rank test is robust (in the sense of preserving the nominal level of the test) and has good power properties for testing for differences in the marginal latent failure-time distributions, whereas the use of a popular cumulative incidence based approach may be problematic for this aim.

Computer Simulation↗
PubMedDate not supplied

Age, period and cohort models applied to cancer mortality rates.

We develop the application of age, period and cohort models to the representation of tables of age- and period-specific rates. A derivation is given by way of a familiar graphical technique. The identifiability problem is discussed, identification techniques are reviewed and a new approach is recommended that is based upon the success of the three two-variable submodels. Other constraints are introduced that enhance interpretation. Examples are given for two sites of cancer. This approach is contrasted with other methods designed to demonstrate trends. Finally, standard errors of the parameters and tests of goodness of fit are discussed.

Adult↗
PubMed1994

Estimating age, period and cohort effects using the multistage model for cancer.

To understand cancer aetiology better, epidemiologists often try to investigate the time trends in disease incidence with year of diagnosis (period) and birth cohort. Unfortunately, one cannot identify these factors uniquely in the usual regression model owing to a linear dependence between age, period and cohort, so that one requires additional information about the underlying biology of the disease. Carcinogenesis models provide one type of information that can result in a unique set of parameters for the effects of age, period and cohort. We use the multistage carcinogenesis model and its extensions to obtain a unique set of parameters for an age-period-cohort model of lung cancer trends of Connecticut males and females from 1935 to 1988. Some of these models do not seem to provide a reasonable set of model parameters, but we found that a model that included second-order terms and a multistage mixture model both gave a good fit to the data and realistic parameter estimates.

Age Factors↗
PubMed1991

Lung cancer and alpha radiation.

The considerable radiosensitivity of the human lung together with the highly localized alpha-doses in the bronchial and pulmonary regions from naturally occurring and man-enhanced radon decay products make the respiratory tract the most critical organ for cancer from exposure to ionizing radiation in our environment. From indoor radon, the tracheobronchial region of the lung generally receives radiation doses which are at least an order of magnitude above the total dose to any other organ. Excess lung cancer deaths found in epidemiological studies on heavily exposed populations of miners can be fitted reasonably well to a relative risk model, when declines in relative risk with both age at risk evaluation, and time since exposure, are incorporated. Smoking seems to act synergistically. A comparison of the major radon risk projections shows considerable discrepancies in the best estimates of risk, indicating that the uncertainties remain large.

Adolescent↗
PubMed2006

Chromosome aberrations in solid tumors have a stochastic nature.

An important question nowadays is whether chromosome aberrations are random events or arise from an internal deterministic mechanism, which leads to the delicate task of quantifying the degree of randomness. For this purpose, we have defined several Shannon information functions to evaluate disorder inside a tumor and between tumors of the same kind. We have considered 79 different kinds of solid tumors with 30 or more karyotypes retrieved from the Mitelman Database of Chromosome Aberrations in Cancer. The Kaplan-Meier cumulative survival was also obtained for each solid tumor type in order to correlate data with tumor malignance. The results here show that aberration spread is specific for each tumor type, with high degree of diversity for those tumor types with worst survival indices. Those tumor types with preferential variants (e.g. high proportion of a given karyotype) have shown better survival statistics, indicating that aberration recurrence is a good prognosis. Indeed, global spread of both numerical and structural abnormalities demonstrates the stochastic nature of chromosome aberrations by setting a signature of randomness associated to the production of disorder. These results also indicate that tumor malignancy correlates not only with karyotypic diversity taken from different tumor types but also taken from single tumors. Therefore, by quantifying aberration spread, we could confront diverse models and verify which of them points to the most likely outcome. Our results suggest that the generating process of chromosome aberrations is neither deterministic nor totally random, but produces variations that are distributed between these two boundaries.

Chromosome Aberrations↗