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At least 973 records · Page 54Linked to original sources
PubMed2006

Improving the identification of patients at risk of postoperative renal failure after cardiac surgery.

BACKGROUND: Preoperative renal insufficiency is an important predictor of the need for postoperative renal replacement therapy (RRT). Serum creatinine (sCr) has a limited ability to identify patients with preoperative renal insufficiency because it varies with age, sex, and muscle mass. Calculated creatinine clearance (CrCl) is an alternative measure of renal function that may allow better estimation of renal reserve. METHODS: Data were prospectively collected for consecutive patients who underwent cardiac surgery requiring cardiopulmonary bypass at a tertiary care center. The relation between CrCl (Cockcroft-Gault equation) and RRT was initially described using descriptive statistics, logistic regression, and receiver operating curve analysis. Based on these analyses, preoperative renal insufficiency was defined as CrCl of 60 ml/min or less. Preoperative renal function was classified as moderate insufficiency (sCr > 133 microM), mild insufficiency (100 microM < sCr < or = 133 microM), occult insufficiency (sCr < or = 100 microM and CrCl < or = 60 ml/min), or normal function (sCr < or = 100 microM and CrCl > 60 ml/min). The independent association of preoperative renal function with RRT was subsequently determined using multiple logistic regression. RESULTS: Of the 10,751 patients in the sample, 137 (1.2%) required postoperative RRT. Approximately 13% of patients with normal sCr had occult renal insufficiency. Occult renal insufficiency was independently associated with RRT (odds ratio, 2.80; 95% confidence interval, 1.39-5.33). The magnitude of this risk was similar to patients with mild renal insufficiency (P = 0.73). CONCLUSIONS: The inclusion of a simple CrCl-based criterion in preoperative assessments may improve identification of patients at risk of needing postoperative RRT.

Acute Kidney Injury↗
PubMed2003

Using local correlation in kernel-based smoothers for dependent data.

We consider the general problem of smoothing correlated data to estimate the nonparametric mean function when a random, but bounded, number of measurements is available for each independent subject. We propose a simple extension to the local polynomial regression smoother that retains the asymptotic properties of the working independence estimator, while typically reducing both the conditional bias and variance for practical sample sizes, as demonstrated by exact calculations for some particular models. We illustrate our method by smoothing longitudinal functional decline data for 100 patients with Huntington's disease. The class of local polynomial kernel-based estimating equations previously considered in the literature is shown to use the global correlation structure in an apparently detrimental way, which explains why some previous attempts to incorporate correlation were found to be asymptotically inferior to the working independence estimator.

Biometry↗
PubMed1991

Attributable risk estimation from case-control data via logistic regression.

By fitting an unconditional logistic regression model to unmatched case-control data, an estimate of the joint population attributable risk for the factor included is obtained. This estimate and its asymptotic variance can easily be computed from the intercept parameter and its asymptotic variance. A generalization to the analysis of stratified data with large strata enables the calculation of stratum-specific attributable risks and their variances via stratum-specific intercept parameters. If sampling of cases is independent of strata, an estimate of the summary attributable risk and its asymptotic variance may be obtained as a weighted sum of the stratum-specific attributable risks.

Biometry↗
PubMed2004

Regression trees for regulatory element identification.

MOTIVATION: The transcription of a gene is largely determined by short sequence motifs that serve as binding sites for transcription factors. Recent findings suggest direct relationships between the motifs and gene expression levels. In this work, we present a method for identifying regulatory motifs. Our method makes use of tree-based techniques for recovering the relationships between motifs and gene expression levels. RESULTS: We treat regulatory motifs and gene expression levels as predictor variables and responses, respectively, and use a regression tree model to identify the structural relationships between them. The regression tree methodology is extended to handle responses from multiple experiments by modifying the split function. The significance of regulatory elements is determined by analyzing tree structures and using a variable importance measure. When applied to two data sets of the yeast Saccharomyces cerevisiae, the method successfully identifies most of the regulatory motifs that are known to control gene transcription under the given experimental conditions, and suggests several new putative motifs. Analysis of the tree structures also reconfirms several pairs of motifs that are known to regulate gene transcription in combination. AVAILABILITY: http://if.kaist.ac.kr/~phuong/RegTree

Algorithms↗
PubMed2001

Multinomial analysis of smoothed HIV back-calculation models incorporating uncertainty in the AIDS incidence.

Back-calculation models, developed to reconstruct the past trend of human immunodeficiency virus (HIV) and to project future acquired immunodeficiency syndrome incidence (AIDS), are usually and unrealistically based on the assumption that the observed AIDS counts are independently distributed according to a Poisson process. In contrast, we argue that a multinomial framework is more suitable to this situation, leading to a natural covariance structure. The ill-conditioned nature of the problem is solved by modelling the HIV parameters according to a cubic spline function to reduce the dimensionality of the parameter space and obtain smoother parameter estimates. We applied a regression spline technique which yields to a computationally stable basis incorporating the incubation period in the new design matrix. We directly incorporate the reporting delay distribution in the AIDS incidence data, leading to a more complex formulation of the variance and covariance model that is adapted to the iteratively reweighted least square (IRLS) algorithm. In this case we obtain more accurate estimates of the standard error of the HIV incidence, especially in the most recent time. Our model, which uses a cubic spline reparameterization based on a multinomial probability distribution, is applied to the AIDS epidemic data in Italy.

Acquired Immunodeficiency Syndrome↗
PubMed2003

Genetic evaluation of growth in nellore cattle by multiple-trait and random regression models.

The objective of this study was to identify issues in genetic evaluation of beef cattle for growth by a random regression model (RRM). Genetic evaluation data included 2,946,847 records of up to nine sequential weights of 812,393 Nellore cattle measured at ages ranging from birth to 733 d. Models considered were a five-trait multiple-trait model (MTM) and a cubic RRM. The MTM included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Both additive effects were assumed correlated. The RRM included the same effects as MTM, with the addition of permanent and random error effects. The purpose of the random error effect, which was in addition to a residual effect with constant variance, was to model heterogeneous residual variances. All effects in RRM were modeled as cubic Legendre polynomials. Expected progeny differences (EPD) were obtained iteratively using a preconditioned conjugate gradient algorithm. Numerically accurate solutions with RRM were not obtained until the random regressions were orthogonalized. Computing requirements of RRM were reduced by more than 50%, without affecting the accuracy by removing regressions corresponding to very low eigen-values and by replacing the random error effects with weights. Afterward, the correlations between EPD from RRM and from MTM for EPD on selected weights were between 0.84 and 0.89. For sires with at least 50 progeny, these correlations increased to 0.92 to 0.97. Low correlations were caused by differences in parameters. The RRM applied to growth i s prone to numerical problems. Estimates of EPD with RRM may be more accurate than those with MTM only if accurate parameters are applied.

Age Factors↗
PubMed1995

Risk scores from logistic regression: unbiased estimates of relative and attributable risk.

In epidemiology, the risk of disease in terms of a set of covariates is often modelled by logistic regression. The resulting linear predictor can be used to define the extent of risk between extremes, and to calculate an attributable risk for the covariates taken together. As is well known, straightforward use of the linear predictor, on the sample from which it was derived, to obtain estimates the relative and attributable risk will be biased, often seriously. Use of the jack-knife technique is extended to produce asymptotically unbiased estimates of relative and attributable risks. The asymptotic variances associated with these estimates are derived by using the formulae of conditional variances. They are applied to the results of a case-control study of stomach cancer.

Bias↗
PubMed1988

Correlated binary regression with covariates specific to each binary observation.

Regression methods are considered for the analysis of correlated binary data when each binary observation may have its own covariates. It is argued that binary response models that condition on some or all binary responses in a given "block" are useful for studying certain types of dependencies, but not for the estimation of marginal response probabilities or pairwise correlations. Fully parametric approaches to these latter problems appear to be unduly complicated except in such special cases as the analysis of paired binary data. Hence, a generalized estimating equation approach is advocated for inference on response probabilities and correlations. Illustrations involving both small and large block sizes are provided.

Adolescent↗
PubMed1990

Social and behavioral factors associated with high-risk sexual behavior among adolescents.

Relationships among risky sexual behaviors, other problem behaviors, and the family and peer context were examined for two samples of adolescents. Many adolescents reported behaviors (e.g., promiscuity or nonuse of condoms) which risked HIV or other sexually transmitted disease infection. Such risky behaviors were significantly intercorrelated. Consistent condom use was rare among those whose behavior otherwise entailed the greatest risk of infection. In both samples, an index of high-risk sexual behavior was significantly related to antisocial behavior, cigarette smoking, and illicit drug or alcohol use. Social context variables, including family structure, parenting practices, and friends' engagement in problem behaviors, were associated with high-risk sexual behavior. Finally, for sexually active adolescents, problem behaviors and social context variables were predictive of nonuse of condoms. Results were consistent across the two studies and regression weights held up well under cross-validation.

Adolescent↗
PubMed1996

Influence of apolipoprotein E genotype on the transmission of Alzheimer disease in a community-based sample.

The epsilon 4 allele of the apolipoprotein E locus (APOE) has been found to be an important predictor of Alzheimer disease (AD). However, linkage analysis has not clarified the role of APOE in the transmission of AD. The results of the current study provide evidence that the pattern of transmission of memory disorders differs in nuclear families in which the AD-affected proband did carry an epsilon 4 allele versus those families in which the AD-affected proband did not carry an epsilon allele. Further, risk of AD due to APOE genotype in the probands is modified by family history of memory disorders, suggesting gene-by-gene interactions. Family history remained a significant predictor of AD for affected probands with some, but not all, APOE genotypes in a logistic regression analysis. Though nonadditive in the prediction of AD, APOE genotype and family history acted additively in the prediction of age at AD onset. The results of complex segregation analysis were inconsistent with Mendelian segregation of memory disorders both in families of affected probands who did or did not carry an epsilon 4 allele, yet these two groups had significantly different parameter estimates for their transmission models. These results are consistent with gene-by-gene interactions, but also could result from common elements in the familial environment.

Age of Onset↗
PubMed1992

Predictors of adolescent female decision making regarding contraceptive usage.

The relationship of cognitive capacity, cognitive egocentrism, and experience factors to decision making in a contraceptive usage problem was examined. Fifty sexually active, unmarried females, ages 14-19, served as subjects. Using correlational, regression, and canonical correlational analyses, cognitive capacity and cognitive egocentrism variables, not experience with contraceptives, were found to be significantly related to, and predictive of, five of seven decision-making variables. Forty-one percent of the variance was accounted for in predicting the canonical decision-making variable. The implications of these results for future research are discussed.

Adolescent↗
PubMed1998

A more flexible regression-to-the-mean model with possible stratification.

We consider a regression-to-the-mean model that includes both additive and multiplicative treatment effects. We allow either or both of these treatment effects to be stratified by ranges of the first measurement. We focus on the situation where there is a very large sample on the first measurement and a relatively small subsample for the second measurement is selected, which often occurs in screening trials. We propose some asymptotically efficient estimators for the parameters of the model that are very simple to compute. We begin with a discussion of the full model, and more on tests and estimation for reduced models follows. An example from a large screening trial is discussed.

Biometry↗
PubMed1992

Within-subject diastolic blood pressure variability: implications for risk assessment and screening.

Because of variability in diastolic blood pressure within an individual, repeated measurements increase precision in assessing an individual's underlying mean pressure and so also aid risk classification. Data from a cohort of 11,299 middle-aged men is used to model the variability in diastolic pressure between annual measurements. A simple model with pressure normally distributed about an underlying mean with standard deviation increasing with level fits the data very well. In modelling risk of cardiovascular mortality, a strong association is found with observed diastolic pressure level but not to trends in or variability between observed values. The effect of regression dilution is clear with the risk relationship appearing greater as one uses the mean of an increasing number of measurements. A method of adjusting for this regression dilution is described so giving an estimate of the relationship with underlying mean diastolic pressure. Using this survival model and the model for blood pressure variability, a method is presented for estimating both underlying mean pressure and absolute risk of cardiovascular disease given a sequence of blood pressure measurements from screening. This allows a sequential strategy for determining whether (a) antihypertensive intervention is desirable, (b) no further screening is necessary, or (c) further screening would aid the assessment, and emphasizes the need to consider blood pressure in the context of multiple risk factors.

Adult↗
PubMedDate not supplied

Pancreatic liver metastases after curative resection combined with intraoperative radiation for pancreatic cancer.

BACKGROUND/AIMS: A high proportion of patients even after curative resection for pancreatic cancer suffer from hepatic metastases. The aim of this study was to identify clinicopathological predictors of liver metastases after surgery, retrospectively. METHODOLOGY: Forty-one patients underwent extended radical pancreatectomy combined with intraoperative radiotherapy, which is one of the best local control methods for ductal cell carcinoma of the pancreas. Of the 41 patients, twenty-one patients regarded as being in a cancer free state after this combined therapy were studied to analyze clinicopathological predictors of hepatic metastases. Odds ratios and their 95% confidence intervals were calculated from data using logistic regression analysis. Statistical difference was considered significant at p<0.05. RESULTS: Liver metastases after curative resection occurred in 11 patients. Preoperative biliary drainage, jaundice, elevated preoperative serum tumor-associated carbohydrate antigens levels, microscopic distal bile duct invasion, duodenal wall invasion, extrapancreatic nerve plexuses invasion were factors influencing postoperative liver metastases. CONCLUSIONS: We found clinicopathological predictors of postoperative liver metastases. Patients with these factors require consideration in careful follow-up and perioperative adjuvant therapy for prevention of postoperative liver metastases.

Adult↗
PubMed2002

Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head.

When performing a meta-analysis, interest often centres on finding explanations for heterogeneity in the data, rather than on producing a single summary estimate. Such exploratory analyses are frequently undertaken with published, study-level data, using techniques of meta-analytic regression. Our goal was to explore a real-world example for which both published, group-level and individual patient-level data were available, and to compare the substantive conclusions reached by both methods. We studied the benefits of anti-lymphocyte antibody induction therapy among renal transplant patients in five randomized trials, focusing on whether there are subgroups of patients in whom therapy might prove particularly beneficial. Allograft failure within 5 years was the endpoint studied. We used a variety of analytic approaches to the group-level data, including weighted least-squares regression (N=5 studies), logistic regression (N=628, the total number of subjects), and a hierarchical Bayesian approach. We fit logistic regression models to the patient-level data. In the patient-level analysis, we found that treatment was significantly more effective among patients with elevated (20 per cent or more) panel reactive antibodies (PRA) than among patients without elevated PRA. These patients comprise a small (about 15 per cent of patients) subgroup of patients that benefited from therapy. The group-level analyses failed to detect this interaction. We recommend using individual patient data, when feasible, to study patient characteristics, in order to avoid the potential for ecological bias introduced by group-level analyses.

Bias↗
PubMed1991

Oral contraceptive use and coronary risk factors in women.

The relationship between oral contraceptive use and other coronary artery disease risk factors was examined in 215 nonsmoking women grouped as never, current, or previous users. Current oral contraceptive users had higher triglyceride levels (p less than or equal to 0.001) than other groups, higher systolic blood pressure, and lower plasma HDL-cholesterol levels (p less than or equal to 0.05) than previous users. The effect of oral contraceptive use on plasma triglyceride values persists on multivariate regression analysis independently of age, body mass index, dietary sodium and cholesterol intake, cigarette smoking, and level of physical activity. Oral contraceptive use also has an independent relationship to the plasma total cholesterol/HDL- cholesterol ratio. These findings indicate that oral contraceptive use is adversely associated with plasma lipid and lipoprotein values.

Adult↗
PubMed1997

Modeling of time trends and interactions in vital rates using restricted regression splines.

For the analysis of time trends in incidence and mortality rates, the age-period-cohort (apc) model has became a widely accepted method. The considered data are arranged in a two-way table by age group and calendar period, which are mostly subdivided into 5- or 10-year intervals. The disadvantage of this approach is the loss of information by data aggregation and the problems of estimating interactions in the two-way layout without replications. In this article we show how splines can be useful when yearly data, i.e., 1-year age groups and 1-year periods, are given. The estimated spline curves are still smooth and represent yearly changes in the time trends. Further, it is straightforward to include interaction terms by the tensor product of the spline functions. If the data are given in a nonrectangular table, e.g., 5-year age groups and 1-year periods, the period and cohort variables can be parameterized by splines, while the age variable is parameterized as fixed effect levels, which leads to a semiparametric apc model. An important methodological issue in developing the nonparametric and semiparametric models is stability of the estimated spline curve at the boundaries. Here cubic regression splines will be used, which are constrained to be linear in the tails. Another point of importance is the nonidentifiability problem due to the linear dependency of the three time variables. This will be handled by decomposing the basis of each spline by orthogonal projection into constant, linear, and nonlinear terms, as suggested by Holford (1983, Biometrics 39, 311-324) for the traditional apc model. The advantage of using splines for yearly data compared to the traditional approach for aggregated data is the more accurate curve estimation for the nonlinear trend changes and the simple way of modeling interactions between the time variables. The method will be demonstrated with hypothetical data as well as with cancer mortality data.

Age Factors↗
PubMedDate not supplied

Risk of HIV infection among Dutch expatriates in sub-Saharan Africa.

In order to study the prevalence of human immunodeficiency virus (HIV) infections and related risk factors, Dutch expatriates returning from sub-Saharan Africa were asked to complete a questionnaire on sexual, occupational and other risk factors, and to donate a sample of blood to test for antibodies against HIV. The 1968 participants were workers of various professions and their family members over 16 years of age posted in sub-Saharan African countries by Dutch governmental, non-governmental and commercial organizations for at least 6 months cumulative time between 1 January 1979 and 1 January 1990. Antibodies against HIV-1 were found among 4 of 1122 men (0.4%) and 1 of 846 women (0.1%). The woman and 3 of the men had had sexual contact with African partners and had been treated for sexually transmitted diseases, 2 of these 3 men also had an African life partner. One man reported occupational exposure only. Of the 1968 participants 89 men (7.9%) and 18 women (2.1%) lived with an African partner; 344 men (30.7%) and 111 women (13.1%) had heterosexual contact with other African partners. Only 22.3% (men) and 18.6% (women) of casual sexual contacts with African partners were always protected by a condom. Two hundred and thirty-two of 408 (56.9%) (para)medics reported needlesticks. Groups at risk of HIV infection through sexual exposure were identified using logistic regression models. In conclusion, the observed prevalence of HIV-1 is low. However, unprotected sexual contact with African partners and needlestick accidents were common. This study underscores the continuous need for health education of expatriates on the risks of transmission of HIV in Africa.

Adolescent↗