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Results for “Regression Analysis”

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At least 451 records · Page 25Linked to original sources

PubMed2004

Partial Cox regression analysis for high-dimensional microarray gene expression data.

MOTIVATION: An important application of microarray technology is to predict various clinical phenotypes based on the gene expression profile. Success has been demonstrated in molecular classification of cancer in which different types of cancer serve as categorical outcome variable. However, there has been less research in linking gene expression profile to censored survival outcome such as patients' overall survival time or time to cancer relapse. In this paper, we develop a partial Cox regression method for constructing mutually uncorrelated components based on microarray gene expression data for predicting the survival of future patients. RESULTS: The proposed partial Cox regression method involves constructing predictive components by repeated least square fitting of residuals and Cox regression fitting. The key difference from the standard principal components of Cox regression analysis is that in constructing the predictive components, our method utilizes the observed survival/censoring information. We also propose to apply the time-dependent receiver operating characteristic curve analysis to evaluate the results. We applied our methods to a publicly available dataset of diffuse large B-cell lymphoma. The outcomes indicated that combining the partial Cox regression method with principal components analysis results in parsimonious model with fewer components and better predictive performance. We conclude that the proposed partial Cox regression method can be very useful in building a parsimonious predictive model that can accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. AVAILABILITY: R codes are available upon request.

Biomarkers, Tumor↗

PubMed1996

Mammographic features after conservation therapy for malignant breast disease: serial findings standardized by regression analysis.

OBJECTIVE: The purpose of this study was to determine the incidence and natural history of mammographic changes in patients within 5 years of conservation therapy for malignant breast disease. MATERIALS AND METHODS: We reviewed the records of 164 consecutive patients with a history of conservation therapy for malignant disease. We recorded mammographic changes related to treatment for each year after surgery. Linear regression analysis was applied to determine trends for progression, regression, or stability of findings. RESULTS: We evaluated 158 patients with 162 lesions for which initial mammographic evaluation had occurred within 5 years of surgery. Of these 158 patients, 121 (77%) underwent serial studies. A total of 152 patients (96%) showed changes on mammograms that represented scarring, usually in multiple locations. Findings at initial evaluation included architectural distortion (n = 110; 82%), increased regional density or scarring (n = 106; 79%), skin thickening (n = 73; 54%), masses (n = 16; 12%), and calcifications (n = 4; 3%). All findings except calcifications showed partial resolution over time, with architectural distortion showing the most significant resolution (p = .05). CONCLUSION: Mammographic features after conservation therapy for breast cancer are common at 1 year after treatment. With the exception of calcifications, we found that all changes showed decreased prominence during the next 5 years. Recognition of such trends during routine surveillance should facilitate the early identification of changes that represent recurrence or do novo malignancy.

Adult↗

PubMed2004

[Logistic regression analysis of factors influencing clinical therapeutic effect on acute tetramine poisoning].

OBJECTIVE: To investigate the factors affecting clinical therapeutic effect on acute tetramine poisoning. METHODS: Using Logistic regression to analyze the relationships among the degree of tetramine poisoning, time of onset, time of admission, exposure history, sex, age, unithol, gastric lavage, etc with the death of poisonded patients. RESULTS: The fatality rate of patient with tetramine poisoning who got gastric lavage was less than that who did not (5.85% vs 38.00%, P < 0.01). In patients who got gastric lavage, the fatality rates were increased with the degree of tetramine poisoning (control: 0%, mild poisoning: 3.07%, severe poisoning: 9.14%, P < 0.05). There was no significant difference in fatality between using unithol and not using patients (7.22% vs 8.25%, P > 0.05). CONCLUSION: Unithol has no significant influence of clinical therapeutic effect on tetramine poisoning patients and dose not reduce the fatality rate of patient with tetramine poisoning, but gastric lavage and the degree of tetramine poisoning do. Logistic regression analysis showed that gastric lavage is the main factor affecting the therapeutic effect on tetramine poisoning.

Acute Disease↗

PubMedDate not supplied

[Influence of secretory otitis media on mastoid pneumatization. Multiple regression analysis].

The influence of secretory otitis media (SOM) on mastoid pneumatization was studied in 122 ears of 61 children under 15 who were seen at our clinic two times. Age, tympanometry, mastoid size, and time between visits were analyzed. Multiple regression analysis yielded and equation relating age and SOM with mastoid size at first examination. Another regression equation explained the contribution of SOM resolution or persistence and age on mastoid size at the time of the second examination. This equation explained 76.14% of the variability in mastoid size and showed that normal ears had better mastoid growth than children with SOM. Even thought inflammation resolved with medical or surgical treatment, mastoid pneumatization was more developed in normal subjects.

Acoustic Impedance Tests↗

PubMed1999

Logistic regression analysis for more than one characteristic of exposure.

When more than one characteristic of an exposure is under study, it is easy to misinterpret the results of a logistic regression analysis that incorporates terms for each characteristic. For example, in a study of the risk of endometrial cancer in relation to the duration and the recency of use of estrogen replacement therapy (ERT), simultaneously including terms for duration and recency of exposure to ERT in a logistic model may leave the mistaken impression that it is possible to adjust for recency when a given duration of ERT use is compared with no use. In this article, the authors show why such an adjusted comparison is impossible, and they discuss several pitfalls in the interpretation of logistic regression coefficients when two or more characteristics of exposure are under study. They also suggest a method for avoiding these pitfalls.

Confounding Factors, Epidemiologic↗

PubMed1995

Some recent developments for regression analysis of multivariate failure time data.

Cox's seminal 1972 paper on regression methods for possibly censored failure time data popularized the use of time to an event as a primary response in prospective studies. But one key assumption of this and other regression methods is that observations are independent of one another. In many problems, failure times are clustered into small groups where outcomes within a group are correlated. Examples include failure times for two eyes from one person or for members of the same family. This paper presents a survey of models for multivariate failure time data. Two distinct classes of models are considered: frailty and marginal models. In a frailty model, the correlation is assumed to derive from latent variables ("frailties") common to observations from the same cluster. Regression models are formulated for the conditional failure time distribution given the frailties. Alternatively, marginal models describe the marginal failure time distribution of each response while separately modelling the association among responses from the same cluster. We focus on recent extensions of the proportional hazards model for multivariate failure time data. Model formulation, parameter interpretation and estimation procedures are considered.

Clinical Trials as Topic↗

PubMed2001

Melanin and blood concentration in a human skin model studied by multiple regression analysis: assessment by Monte Carlo simulation.

Measurement of melanin and blood concentration in human skin is needed in the medical and the cosmetic fields because human skin colour is mainly determined by the colours of melanin and blood. It is difficult to measure these concentrations in human skin because skin has a multi-layered structure and scatters light strongly throughout the visible spectrum. The Monte Carlo simulation currently used for the analysis of skin colour requires long calculation times and knowledge of the specific optical properties of each skin layer. A regression analysis based on the modified Beer-Lambert law is presented as a method of measuring melanin and blood concentration in human skin in a shorter period of time and with fewer calculations. The accuracy of this method is assessed using Monte Carlo simulations.

Biophysical Phenomena↗

PubMed2000

Survival regression analysis: a powerful tool for evaluating fighting and assessment.

Theoretical models of animal contests frequently generate predictions about how asymmetries (e.g. differences in size, residence status) between contestants affect fight duration. Linear regression and nonparametric correlation analyses are commonly used to test the fit of data to such models. We show how survival regression analysis (SRA) is a powerful technique for studying the effect of asymmetries on the duration of contests. SRA, which is under-utilized by students of animal behaviour, offers several advantages over more frequently used procedures. It provides unbiased parameter estimates even when including censored data (i.e. results of contests that have not ended at the time when observations are stopped). The analysis of hazard functions, which is a component of SRA, is an easy way to test for consistency with predictions of the sequential assessment game model. These and other advantages of SRA are illustrated by using SRA and more conventional methods to analyse the effect of asymmetries on contest duration for encounters between female Mediterranean tarantulas, Lycosa tarentula (L.). It is hoped that this example of the advantages of SRA will encourage more widespread use of this powerful technique. Copyright 2000 The Association for the Study of Animal Behaviour.

Journal Article↗

PubMed2000

Reactivity of six antibodies in effusions of mesothelioma, adenocarcinoma and mesotheliosis: stepwise logistic regression analysis.

Anti-CEA, anti-vimentin, CAM5.2, BerEp4, Leu-M1 and anti-EMA were applied to effusions from 36 mesotheliomas, 53 adenocarcinomas and 24 reactive mesothelial proliferations. Stepwise logistic regression analysis selected three criteria of major importance for distinguishing between adenocarcinoma and mesothelioma: BerEp4, CEA and EMA accentuated at the cell membrane (mEMA), these three being of similar diagnostic value. The pattern BerEp4-, CEA- and mEMA+ was fully predictive for mesothelioma (sensitivity 47%), whereas the opposite pattern was fully predictive for adenocarcinoma (sensitivity 80%). Only EMA seemed to distinguish between mesotheliosis and mesothelioma. Comparison of reactivity in cytological and histological material from the same mesotheliomas showed similar staining frequencies for CEA and CAM5.2, with some random variation for Leu-M1 and EMA, whereas vimentin and BerEp4 reactivity was more frequent in cytological specimens.

Adenocarcinoma↗

PubMed1974