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At least 595 records · Page 33Linked to original sources
PubMed2002

Association between blood pressure, ultrafiltration, and hemodialysis graft thrombosis: a multivariable logistic regression analysis.

Although a low blood flow (Q(a)) is the most important cause of graft thrombosis, several studies have shown that Q(a) measurements do not accurately predict thrombosis. This suggests that additional variables may influence thrombosis. Identification of such variables may be essential to designing surveillance protocols that accurately predict thrombosis. In this nested case-control study, we prospectively followed 105 patients for up to 2.5 years in order to test the association of a number of variables with thrombosis. These included Q(a) (monthly by ultrasound dilution), percentage stenosis (quarterly by duplex ultrasound), mean arterial pressure (MAP), percentage ultrafiltration (%UF) during dialysis (%UF = 100[liters]/[kilogram of weight]), and other variables that defined patient and graft characteristics. Patients were divided into patent (n = 53) and thrombosed groups (n = 52), and MAP and %UF from seven consecutive dialysis sessions were analyzed. In the thrombosed group, the last session was the final session before thrombosis. A multivariable logistic regression model showed that Q(a), MAP (the predialysis average of seven sessions), and %UF (from the last session) were independently associated with thrombosis, whereas all other variables were not. The model yielded the following odds ratios for thrombosis: for a single Q(a) value (reduction of 1,000 mL/min), 12.0 (P < 0.01); for %UF (increase of 4%), 5.3 (P < 0.01); for MAP (reduction of 30 mm Hg), 4.1 (P = 0.02); and for percentage decrease in Q(a) (> or =20% versus <20%), 2.4 (P = 0.12). We conclude that in addition to Q(a), both %UF at the last session before thrombosis and average predialysis MAP from seven sessions are independently associated with thrombosis. These results help explain why Q(a) alone does not accurately predict thrombosis. A prospective study is needed to determine whether %UF at each session and a moving average MAP from seven sessions improve the prediction of thrombosis. However, it should be recognized that a large %UF is a preterminal event that likely provides too short a warning for intervention before thrombosis.

Blood Pressure↗
PubMed1985

Identification of a mixture of two different monoclonal antibodies by nonlinear regression analysis after solid phase radioimmunoassay with labeled antigen.

The results of competitive solid phase radioimmunoassay with labeled antigen, when analyzed by nonlinear regression, may reveal the presence of two different monoclonal antibodies, occasionally produced in a hybridoma culture. In fact, a hybridoma-derived antihuman chorionic somatomammotropin antibody prepared in our laboratories showed an unsatisfactory curve fitting when the experimental data were elaborated on the basis of a monoclonal model. The antibody was subjected to recloning, and gave two separate homogeneous monoclonal antibodies with different affinity constants.

Antibodies, Monoclonal↗
PubMed1999

Exact simultaneous confidence bands for a collection of univariate polynomials in regression analysis.

We discuss a simple simulation method for construction of exact confidence bands, having a pre-assigned confidence level simultaneously for several regression functions which are univariate polynomials in the predictors. This is accomplished by combining and extending existing results in a manner that permits both finite and infinite ranges for individual predictor variables. We illustrate the method for a logistic regression model with both dichotomous and continuous predictors.

Animals↗
PubMed2001

A multiple imputation approach to regression analysis for doubly censored data with application to AIDS studies.

Sun, Liao, and Pagano (1999) proposed an interesting estimating equation approach to Cox regression with doubly censored data. Here we point out that a modification of their proposal leads to a multiple imputation approach, where the double censoring is reduced to single censoring by imputing for the censored initiating times. For each imputed data set one can take advantage of many existing techniques and software for singly censored data. Under the general framework of multiple imputation, the proposed method is simple to implement and can accommodate modeling issues such as model checking, which has not been adequately discussed previously in the literature for doubly censored data. Here we illustrate our method with an application to a formal goodness-of-fit test and a graphical check for the proportional hazards model for doubly censored data. We reanalyze a well-known AIDS data set.

Acquired Immunodeficiency Syndrome↗
PubMed1978

Regression analysis of grouped survival data with application to breast cancer data.

Use of the proportional hazards regression model (Cox 1972) substantially liberalized the analysis of censored survival data with covariates. Available procedures for estimation of the relative risk parameter, however, do not adequately handle grouped survival data, or large data sets with many tied failure times. The grouped data version of the proportional hazards model is proposed here for such estimation. Asymptotic likelihood results are given, both for the estimation of the regression coefficient and the survivor function. Some special results are given for testing the hypothesis of a zero regression coefficient which leads, for example, to a generalization of the log-rank test for the comparison of several survival curves. Application to breast cancer data, from the National Cancer Institute-sponsored End Results Group, indicates that previously noted race differences in breast cancer survival times are explained to a large extent by differences in disease extent and other demographic characteristics at diagnosis.

Black People↗
PubMed2001

Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis.

To assist the ultrasound (US) differential diagnosis of solid breast tumors by using stepwise logistic regression (SLR) analysis of tumor contour features, we retrospectively reviewed 111 medical records of digitized US images of breast pathologies. They were pathologically proved benign breast tumors from 40 patients (i.e., 40 fibroadenomas) and malignant breast tumors from 71 patients (i.e., 71 infiltrative ductal carcinomas). Radiologists, before analysis by the computer-aided diagnosis (CAD) system, segmented the tumors manually. The contour features were calculated by measuring the radial length of tumor boundaries. The features selection process was accomplished using a stepwise analysis procedure. Then, an SLR model with contour features was used to classify tumors as benign or malignant. In this experiment, cases were sampled with "leave-one-out" test methods to evaluate the SLR performance using a receiver operating characteristic (ROC) curve. The accuracy of our SLR model with contour features for classifying malignancies was 91.0% (101 of 111 tumors), the sensitivity was 97.2% (69 of 71), the specificity was 80.0% (32 of 40), the positive predictive value was 89.6% (69 of 77), and the negative predictive value was 94.1% (32 of 34). The CAD system using SLR can differentiate solid breast nodules with relatively high accuracy and its high negative predictive value could potentially help inexperienced operators to avoid misdiagnoses. Because the SLR model is trainable, it could be optimized if a larger set of tumor images were supplied.

Adolescent↗