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At least 1,261 records · Page 70Linked to original sources
PubMed1996

Pathologic models to predict outcome for women with endometrial adenocarcinoma: the importance of the distinction between surgical stage and clinical stage--a Gynecologic Oncology Group study.

BACKGROUND: Numerous pathologic factors have been identified as important in predicting outcome for women with endometrial adenocarcinoma. However, most patients have a mixture of good and bad factors. For these women, the prognosis is uncertain, and it is often unclear whether postoperative therapy is indicated. METHODS: Using univariate and multivariate analysis, we investigated the pathologic factors commonly reported to be of prognostic significance, using data from 819 patients with clinical Stages I and II endometrial adenocarcinoma from a Gynecologic Oncology Group study. Since the clinical stage frequently underestimated the surgical stage, models that designate the relative risk associated with each of the variables were created for both clinical and surgical Stage I and 11 patients. RESULTS: We confirmed the importance of age, depth of myometrial invasion, and to a lesser degree, histologic grade, and cell type, as independent prognostic variables. CONCLUSIONS: The relative risk of death can be determined using a simple multiplicative calculation, and the absolute risk can be estimated by inspection of the accompanying figures. These data can be used to provide patients with prognostic information and to help determine the need for postoperative adjuvant therapy.

Adenocarcinoma↗
PubMed1997

Regression analysis of interval-censored survival data with covariates using log-linear models.

We considered the regression analysis of the event time data with left-, right-, or interval-censored observations. We extended life-table techniques for censored survival data using log-linear models to incorporate interval-censored failures. The EM algorithm was used to calculate maximum likelihood estimates for the parameters. We assumed that the hazard function was a stepwise function over disjoint intervals of time; thus, the nonparametric model, the parametric exponential model, and the semiparametric Cox proportional hazard model were easily implemented as special cases. We adapted the restricted EM algorithm to test hypotheses and to construct confidence intervals for the parameters. These methods were applied in an analysis of the recurrence time for treated melanoma patients.

Adult↗
PubMed1983

A cohort mortality study of petrochemical workers.

A historical prospective cohort mortality study was conducted for a cohort of 6,588 white male employees of a Texas petrochemical plant because of a suspected increased incidence of malignant brain tumors. Mortality experience from 1941 to 1977 was determined and compared with that of the general U.S. white male population adjusting for age and time period. Overall and cause-specific standardized mortality ratios were calculated for various subgroups of the population defined by length of employment, latency and payroll status. Significant deficits in total cohort mortality were found for all causes of death, all circulatory diseases, all respiratory diseases and all digestive diseases. Although not statistically significant, fewer deaths were observed (O) than expected (E) for all malignant neoplasms. No statistically significant excess of malignant brain tumors was found in the overall plant population (O/E = 12/7.42 = 1.62). A borderline significant excess of brain cancer deaths was found among hourly employees with more than six months' employment based on 10 observed and five expected deaths. This excess was observed to occur among elderly employees (over 55 years) and in later follow-up years (post-1970). Risk did not appear to be related to length of employment. Because of the nature of the problem that prompted this study, the small number of cases involved and the lack of a suspect agent in the plant that could have produced this excess, insufficient evidence was found to conclude that these tumors were occupationally related.

Age Factors↗
PubMed1997

Frailty modelling for the excess hazard.

Long-term excess hazards for cancer survival sometimes tend to zero or become negative even though we expect them to be positive. This may be explained by selection at diagnosis; individuals with certain cancers may have an increased risk of dying of other diseases in general. Then comparing with population mortality rates is not correct. Alternatively, we may have a continuous selection of the most robust individuals after diagnosis. When there are unobserved heterogeneity, and those with highest risk of dying of cancer also have the highest risk of dying of other diseases, this will cause selection after diagnosis. This may be modelled by multivariate frailty variables, and a corrected excess hazard may be estimated. In two examples, these corrected excess hazards give a better estimate when comparing to the cause-specific cancer mortality. Actually, this study questions the usefulness of long-term excess hazard rates.

Adult↗
PubMed1998

Assessing uncertainty in microsimulation modelling with application to cancer screening interventions.

Microsimulation is fast becoming the approach of choice for modelling and analysing complex processes in the absence of mathematical tractability. While this approach has been developed and promoted in engineering contexts for some time, it has more recently found a place in the mainstream of the study of chronic disease interventions such as cancer screening. The construction of a simulation model requires the specification of a model structure and sets of parameter values, both of which may have a considerable amount of uncertainty associated with them. This uncertainty is rarely quantified when reporting micro-simulation results. We suggest a Bayesian approach and assume a parametric probability distribution to mathematically express the uncertainty related to model parameters. First, we design a simulation experiment to achieve good coverage of the parameter space. Second, we model a response surface for the outcome of interest as a function of the model parameters using the simulation results. Third, we summarize the variability in the outcome of interest, including variation due to parameter uncertainty, using the response surface in combination with parameter probability distributions. We illustrate the proposed method with an application of a microsimulator designed to investigate the effect of prostate specific antigen (PSA) screening on prostate cancer mortality rates.

Adult↗
PubMed2002

Selection models and pattern-mixture models to analyse longitudinal quality of life data subject to drop-out.

Longitudinally observed quality of life data with large amounts of drop-out are analysed. First we used the selection modelling framework, frequently used with incomplete studies. An alternative method consists of using pattern-mixture models. These are also straightforward to implement, but result in a different set of parameters for the measurement and drop-out mechanisms. Since selection models and pattern-mixture models are based upon different factorizations of the joint distribution of measurement and drop-out mechanisms, comparing both models concerning, for example, treatment effect, is a useful form of a sensitivity analysis.

Aged↗
PubMed2007

Nested, non-parametric, correlative analysis of microarrays for heterogenous phenotype characterization.

We present a non-parametric approach for qualitatively selecting candidate genes to characterize several criteria that are nested among genes selected on the basis of their individual, similar effects upon an array-wide closeness measure. In this setting, a goal is to obtain a reliable characterization of phenotypes, based on very high-dimensional data from a few samples. As opposed to a distance-based approach, the proposed measure defines closeness based on gene signal profiles (functionals) rather than on isolated (numerical) differences in each gene between samples. By using such a measure to characterize intensity differences, we effectively separate biological from artifactual variation in expression, due to tissue effects or signal calibration. Based on this measure, we successively examine the significance of the following: a set of similarly behaved genes relative to all arrayed genes, a set of candidate genes relative to similarly behaved genes, individual candidate genes relative to non-candidates, and the direction, as over- or under-expressed, of candidate genes. In each setting, sample pairs are the units of analysis, with U-statistics the theoretical framework. We illustrate the method on a microarray experiment, where the goal is to select sets of genes that characterize a type of skin cancer and its histological subtypes.

Genetic Heterogeneity↗
PubMed1990

Prognosis in patients with metastatic non-seminomatous testicular cancer.

155 patients with metastatic non-seminomatous testicular cancer were treated with cisplatin-based chemotherapy which in most cases was combined with surgery. The 5 year crude survival was 90% for all patients (98 patients with small volume disease: 97%; 32 patients with large volume disease: 91%; 25 patients with very large volume disease: 64%). High pre-chemotherapy serum tumour marker levels (AFP greater than 500 micrograms/l; and/or HCG greater than 1000 U/l) decreased the survival rates in all groups. Only 4 of 17 relapsing patients were rendered tumour-free by salvage chemotherapy. In a multivariate analysis, a pre-chemotherapy alpha-foetoprotein (AFP) level greater than 500 micrograms/l was associated with poor survival as was the presence of a retroperitoneal tumour greater than 10 cm, lung metastases greater than 3 cm and/or extrapulmonary hematogenous metastases. It is concluded that easily assessable clinical pre-treatment variables can be used to define high risk or low risk patients with metastatic testicular cancer. Treatment intensity should be adjusted in accordance to such prognostic factors.

Adolescent↗
PubMed2005

Gene selection for classification of cancers using probabilistic model building genetic algorithm.

Recently, DNA microarray-based gene expression profiles have been used to correlate the clinical behavior of cancers with the differential gene expression levels in cancerous and normal tissues. To this end, after selection of some predictive genes based on signal-to-noise (S2N) ratio, unsupervised learning like clustering and supervised learning like k-nearest neighbor (k NN) classifier are widely used. Instead of S2N ratio, adaptive searches like Probabilistic Model Building Genetic Algorithm (PMBGA) can be applied for selection of a smaller size gene subset that would classify patient samples more accurately. In this paper, we propose a new PMBGA-based method for identification of informative genes from microarray data. By applying our proposed method to classification of three microarray data sets of binary and multi-type tumors, we demonstrate that the gene subsets selected with our technique yield better classification accuracy.

Algorithms↗