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At least 649 records · Page 36Linked to original sources
PubMed1995

Variations in the utilization of coronary angiography for elderly patients with an acute myocardial infarction. An analysis using hierarchical logistic regression.

This article reports a study of variations in the utilization of angiography for Medicare recipients who had an acute myocardial infarction. The study cohort consisted of 1987 Medicare beneficiaries who had a recent acute myocardial infarction. Variations were examined from three perspectives: patient characteristics, regional practice patterns, and on-site availability of the procedure. Factors associated with variation within and among states were incorporated into the analysis using hierarchical logistic regression models. The probability of angiography during the first 90 days after an acute myocardial infarction was estimated as a function of patient age, gender, race, and comorbidity for patients in 51 states (including the District of Columbia). Interstate differences were examined in relation to geographic region and on-site availability of angiography. Observed rates of angiography ranged between 13.8% and 38.3% (median, 24.7%). Variation was nearly threefold based on estimated state probabilities of angiography for a patient with characteristics set at the national average. Observed and estimated rates were lower in northeastern states than in other parts of the United States. States with more extensive onsite availability of angiography tended to have higher angiography rates after adjusting for patient characteristics and geographic region. Adjusted angiography rates were on average higher for younger patients, males, and nonblacks. There was substantial interstate variation in race differences, with states in the Southeast generally having the largest differences. The adjusted black-to-nonblack odds ratio ranged from a low of 0.41 to a high of 0.94. Interstate variation in age and gender differences was moderate. The work reported in this article illustrates the potential of hierarchical regression modeling as a framework for the analysis of variations and some methodologic issues connected with its implementation. Our results show that large variations in the utilization of procedures can exist, despite uniform insurance coverage and a relatively homogeneous patient cohort. Aggressive use of angiography was highly variable across states as was the degree of access to the procedure for blacks and nonblacks. The state rate of on-site availability of angiography facilities was an important predictor of utilization. Increased on-site availability of angiography, however, was not associated with a reduction of differences in access to the procedure.

Age Factors↗
PubMedDate not supplied

Logistic regression and Bayesian networks to study outcomes using large data sets.

BACKGROUND: In nursing research, the interest in using large health care databases to predict nursing sensitive outcomes is growing rapidly. Traditionally, one of the most frequently used methods is logistic regression (LR), which, although powerful and familiar, has several limitations when used in the analysis of large databases. As a result, innovative approaches are required. APPROACH: To (a) introduce an innovative/alternative data analysis approach (Bayesian network), (b) discuss the constraints of LR and the complementary advantages of Bayesian networks (BNs) in working with large and multidimensional health care data, and (c) provide a fundamental understanding of the use of BNs in the nursing/health care domain. RESULTS: Studies have shown that BNs have several advantages over LR in analyzing complex and large data: (a) statistical assumptions, such as linearity and additivity, are relaxed; (b) handling of a larger number of predictors and identification of interactions among predictors is less complex; and (c) the discovery of structure, pattern, and knowledge, for example, of unknown, complex, and nonlinear relationships, in data is facilitated. CONCLUSION: Outcome studies, such as those undertaken by nurse researchers, may benefit from the examination and use of innovative approaches such as BNs to the analysis of very large and complex health care data sets.

Bayes Theorem↗
PubMed2001

Differentiation between pigmented Spitz naevus and melanoma by digital dermoscopy and stepwise logistic discriminant analysis.

Epiluminescence light microscopy (ELM) has proven useful in the diagnosis of pigmented skin lesions (PSLs). However, in some cases this technique does not sufficiently increase the diagnostic accuracy in distinguishing pigmented Spitz naevi (PSNs) from melanoma. With the aim of obviating these problems of qualitative interpretation, methods based on the mathematical analysis of PSLs, such as digital dermoscopy analysis (DDA), have recently been developed. In the present study we used a digital dermoscope (DBDermo-MIPS, Dell'Eva-Burroni) to analyse PSNs and melanomas with similar clinical and dermoscopic features for any correlation between variables and to determine its discriminating power with respect to histological diagnosis. The 100 lesions underwent histological examination by three experienced dermatopathologists and were identified as PSNs (43) or melanomas (57). Thirty-six parameters were identified as possible discriminating variables and were grouped in four categories: geometry, colour, texture, and islands of colour. Statistical analysis was used to identify the variables with the highest discriminating power. Stepwise discriminant analysis selected only four variables: entropy, minimum diameter, red lesion value and peripheral dark (the means of these variables were higher in melanomas than in PSNs). Thus the combined use of digital dermoscopy and stepwise logistic discriminant analysis made it possible to single out the best objective variables for distinguishing PSN and melanoma.

Dermatology↗
PubMed2005

A novel logistic model based on clinicopathological features predicts microsatellite instability in colorectal carcinomas.

High-frequency microsatellite instability has been reported to be associated with good prognosis in colorectal adenocarcinoma. However, methods to assess microsatellite instability (MIN) are based on genetic assays and are not ideally suited to most histopathology laboratories. The aim of the present study was to develop a model for prediction of MIN status in colorectal cancer based on phenotypic characteristics. Clinicopathological features of a cohort of 204 patients with primary colon cancer were retrospectively reviewed following predetermined criteria. Genetic assessment of MIN status was performed on DNA extracted from sections of formalin-fixed, paraffin-embedded specimens by testing a panel of 11 microsatellite markers. Logistic regression analysis generated a mathematical tool capable of identifying colorectal tumors displaying MIN status with a sensitivity of 77.8% and a specificity of 96.8%. Features associated with instability included the proximal location of the lesions, occurrence of solid and/or mucinous differentiation, absence of cribriform structures, presence of peritumoral Crohn-like reaction, expansive growth pattern, high Ki67 proliferative index, and p53-negative phenotype. This approach predicts microsatellite instability in colorectal carcinoma with an overall assigned accuracy of 95.1% and a negative predictive value of 97.8%. Implementation of this tool to routine histopathological studies could improve the management of patients with colorectal cancer, especially those presenting with stage II and III of the disease. It will also assist in identifying a subset of patients likely to benefit from adjuvant chemotherapy.

Aged↗
PubMed2005

Logistic regression protects against population structure in genetic association studies.

We conduct an extensive simulation study to compare the merits of several methods for using null (unlinked) markers to protect against false positives due to cryptic substructure in population-based genetic association studies. The more sophisticated "structured association" methods perform well but are computationally demanding and rely on estimating the correct number of subpopulations. The simple and fast "genomic control" approach can lose power in certain scenarios. We find that procedures based on logistic regression that are flexible, computationally fast, and easy to implement also provide good protection against the effects of cryptic substructure, even though they do not explicitly model the population structure.

Animals↗
PubMed2004

Pattern formation and nonlocal logistic growth.

Logistic growth process with nonlocal interactions is considered in one dimension. Spontaneous breakdown of translational invariance is shown to take place at some parameter region, and the bifurcation regime is identified for short and long-range interactions. Domain walls between regions of different order parameter are expressed as soliton solutions of the reduced dynamics for nearest-neighbor interactions. The analytic results are confirmed by numerical simulations.

Animals↗
PubMed2000

Adaptation to the edge of chaos in the self-adjusting logistic map.

Self-adjusting, or adaptive, systems have gathered much recent interest. We present a model for self-adjusting systems which treats the control parameters of the system as slowly varying, rather than constant. The dynamics of these parameters is governed by a low-pass filtered feedback from the dynamical variables of the system. We apply this model to the logistic map and examine the behavior of the control parameter. We find that the parameter leaves the chaotic regime. We observe a high probability of finding the parameter at the boundary between periodicity and chaos. We therefore find that this system exhibits adaptation to the edge of chaos.

Adaptation, Biological↗
PubMed2004

Association between maternal age and the likelihood of a cesarean section: a population-based multivariate logistic regression analysis.

BACKGROUND: A majority of studies examining the relationship between advancing maternal age and the likelihood of cesarean section (CS) use data from regional samples or from a limited number of medical institutions. This study uses population-based data from Taiwan to explore the relationship between maternal age and the likelihood of a CS. METHODS: The National Health Insurance Research Database (NHIRD) on registries of medical facilities and board-certified physicians and monthly claim summaries for inpatients were used. In total, 502 524 singleton deliveries were included in the study. Multivariate logistic regressions were performed with the presence of CS as the dependent variable and maternal age (<20, 20-29, 30-34 and >34 years) as the independent variable. The study controlled for maternal indications, institution characteristics, maternal requests and attending physician characteristics. RESULTS: CS rates for the age groups <20, 20-29, 30-34 and >34 years were 17.7, 27.4, 37.4 and 47.5%, respectively. The regression analyses consistently showed that the likelihood of a CS significantly increased with advancing maternal age within each category of complication after adjusting for medical institution characteristics and characteristics of the attending physician. CONCLUSIONS: This study found that, after adjusting for maternal indications, and healthcare institution and physician characteristics, there was a significant relationship between advancing maternal age and an increased likelihood of a CS. This finding, together with the high CS rate of 32.1% in Taiwan, one of the highest reported in the world today, highlights an imperative need to devise interventions to reduce the frequency of CSs.

Adult↗
PubMed1999

Marginally specified logistic-normal models for longitudinal binary data.

Likelihood-based inference for longitudinal binary data can be obtained using a generalized linear mixed model (Breslow, N. and Clayton, D. G., 1993, Journal of the American Statistical Association 88, 9-25; Wolfinger, R. and O'Connell, M., 1993, Journal of Statistical Computation and Simulation 48, 233-243), given the recent improvements in computational approaches. Alternatively, Fitzmaurice and Laird (1993, Biometrika 80, 141-151), Molenberghs and Lesaffre (1994, Journal of the American Statistical Association 89, 633-644), and Heagerty and Zeger (1996, Journal of the American Statistical Association 91, 1024-1036) have developed a likelihood-based inference that adopts a marginal mean regression parameter and completes full specification of the joint multivariate distribution through either canonical and/or marginal higher moment assumptions. Each of these marginal approaches is computationally intense and currently limited to small cluster sizes. In this manuscript, an alternative parameterization of the logistic-normal random effects model is adopted, and both likelihood and estimating equation approaches to parameter estimation are studied. A key feature of the proposed approach is that marginal regression parameters are adopted that still permit individual-level predictions or contrasts. An example is presented where scientific interest is in both the mean response and the covariance among repeated measurements.

Biometry↗
PubMed1999

Empirical Bayes estimation of random effects parameters in mixed effects logistic regression models.

We extend an approach for estimating random effects parameters under a random intercept and slope logistic regression model to include standard errors, thereby including confidence intervals. The procedure entails numerical integration to yield posterior empirical Bayes (EB) estimates of random effects parameters and their corresponding posterior standard errors. We incorporate an adjustment of the standard error due to Kass and Steffey (KS; 1989, Journal of the American Statistical Association 84, 717-726) to account for the variability in estimating the variance component of the random effects distribution. In assessing health care providers with respect to adult pneumonia mortality, comparisons are made with the penalized quasi-likelihood (PQL) approximation approach of Breslow and Clayton (1993, Journal of the American Statistical Association 88, 9-25) and a Bayesian approach. To make comparisons with an EB method previously reported in the literature, we apply these approaches to crossover trials data previously analyzed with the estimating equations EB approach of Waclawiw and Liang (1994, Statistics in Medicine 13, 541-551). We also perform simulations to compare the proposed KS and PQL approaches. These two approaches lead to EB estimates of random effects parameters with similar asymptotic bias. However, for many clusters with small cluster size, the proposed KS approach does better than the PQL procedures in terms of coverage of nominal 95% confidence intervals for random effects estimates. For large cluster sizes and a few clusters, the PQL approach performs better than the KS adjustment. These simulation results agree somewhat with those of the data analyses.

Adult↗
PubMed2000

Estimation and testing with overdispersed proportions using the beta-logistic regression model of Heckman and Willis.

Methods are presented for modeling dose-related effects in proportion data when extra-binomial variability is a concern. Motivation is taken from experiments in developmental toxicology, where similarity among conceptuses within a litter leads to intralitter correlations and to overdispersion in the observed proportions. Appeal is made to the well-known beta-binomial distribution to represent the overdispersion. From this, an exponential function of the linear predictor is used to model the dose-response relationship. The specification was introduced previously for econometric applications by Heckman and Willis; it induces a form of logistic regression for the mean response, together with a reciprocal biexponential model for the intralitter correlation. Large-sample, likelihood-based methods for estimating and testing the joint proportion-correlation response are studied. A developmental toxicity data set illustrates the methods.

Animals↗
PubMed2000

Mixed effects logistic regression models for longitudinal ordinal functional response data with multiple-cause drop-out from the longitudinal study of aging.

In the context of analyzing ordinal functional limitation responses from the Longitudinal Study of Aging, we investigate the association between current functional limitation and previous year's limitation and its modification by physical activity and multiple causes of drop-out. We accommodate the longitudinal nature of the multiple causes of informative drop-out (death and unknown loss-to-follow-up) with a mixed effects logistic model. Under the proposed model with a random intercept and slope, the ordinal functional outcome and multiple discrete time survival profiles share a common random effect structure. This shared parameter selection model assumes that the multiple causes of drop-out are conditionally independent of the functional limitation outcome given the underlying random effect representing an individual's trajectory of general health status across time. Although it is not possible to fully assess the adequacy of this assumption, we assess the robustness of the approach by varying the assumptions underlying the proposed model, such as the random effects distribution and the drop-out component. It appears that between-subject differences in initial functional limitation are strongly associated with future functional limitation and that this association is stronger for those who do not have physical activity regardless of the random effects and informative drop-out specifications. In contrast, the association between current functional limitation and previous trajectory of functional status within an individual is weaker and more sensitive to changes in the random effects and drop-out assumptions.

Aged↗
PubMed2001

Maximum likelihood analysis of logistic regression models with incomplete covariate data and auxiliary information.

This article presents a new method for maximum likelihood estimation of logistic regression models with incomplete covariate data where auxiliary information is available. This auxiliary information is extraneous to the regression model of interest but predictive of the covariate with missing data. Ibrahim (1990, Journal of the American Statistical Association 85, 765-769) provides a general method for estimating generalized linear regression models with missing covariates using the EM algorithm that is easily implemented when there is no auxiliary data. Vach (1997, Statistics in Medicine 16, 57-72) describes how the method can be extended when the outcome and auxiliary data are conditionally independent given the covariates in the model. The method allows the incorporation of auxiliary data without making the conditional independence assumption. We suggest tests of conditional independence and compare the performance of several estimators in an example concerning mental health service utilization in children. Using an artificial dataset, we compare the performance of several estimators when auxiliary data are available.

Analysis of Variance↗
PubMed2002

Mixed effects logistic regression models for multiple longitudinal binary functional limitation responses with informative drop-out and confounding by baseline outcomes.

In the context of analyzing multiple functional limitation responses collected longitudinally from the Longitudinal Study of Aging (LSOA), we investigate the heterogeneity of these outcomes with respect to their associations with previous functional status and other risk factors in the presence of informative drop-out and confounding by baseline outcomes. We accommodate the longitudinal nature of the multiple outcomes with a unique extension of the nested random effects logistic model with an autoregressive structure to include drop-out and baseline outcome components with shared random effects. Estimation of fixed effects and variance components is by maximum likelihood with numerical integration. This shared parameter selection model assumes that drop-out is conditionally independent of the multiple functional limitation outcomes given the underlying random effect representing an individual's trajectory of functional status across time. Whereas it is not possible to fully assess the adequacy of this assumption, we assess the robustness of this approach by varying the assumptions underlying the proposed model such as the random effects structure, the drop-out component, and omission of baseline functional outcomes as dependent variables in the model. Heterogeneity among the associations between each functional limitation outcome and a set of risk factors for functional limitation, such as previous functional limitation and physical activity, exists for the LSOA data of interest. Less heterogeneity is observed among the estimates of time-level random effects variance components that are allowed to vary across functional outcomes and time. We also note that. under an autoregressive structure, bias results from omitting the baseline outcome component linked to the follow-up outcome component by subject-level random effects.

Aged↗
PubMed2002

Random effects logistic regression analysis with auxiliary covariates.

We study a semiparametric estimation method for the random effects logistic regression when there is auxiliary covariate information about the main exposure variable. We extend the semiparametric estimator of Pepe and Fleming (1991, Journal of the American Statistical Association 86, 108-113) to the random effects model using the best linear unbiased prediction approach of Henderson (1975, Biometrics 31, 423-448). The method can be used to handle the missing covariate or mismeasured covariate data problems in a variety of real applications. Simulation study results show that the proposed method outperforms the existing methods. We analyzed a data set from the Collaborative Perinatal Project using the proposed method and found that the use of DDT increases the risk of preterm births among U.S. children.

Biometry↗
PubMed2003

Prognostic models in patients with non-small-cell lung cancer using artificial neural networks in comparison with logistic regression.

It is difficult to precisely predict the outcome of each individual patient with non-small-cell lung cancer (NSCLC) by using conventional statistical methods and ordinary clinico-pathological variables. We applied artificial neural networks (ANN) for this purpose. We constructed a prognostic model for 125 NSCLC patients with 17 potential input variables, including 12 clinico-pathological variables (age, sex, smoking index, tumor size, p factor, pT, pN, stage, histology) and 5 immunohistochemical variables (p27 percentage, p27 intensity, p53, cyclin D1, retinoblastoma (RB)), by using the parameter-increasing method (PIM). Using the resultant ANN model, prediction was possible in 104 of 125 patients (83%, judgment ratio (JR)) and accuracy for prediction of survival at 5 years was 87%. On the other hand, JR and survival prediction accuracy in the logistic regression (LR) model were 37% and 78%, respectively. In addition, ANN outperformed LR for prediction of survival at 1 or 3 years. In these cases, PIM selected p27 intensity and cyclin D1 for the 3-year survival model and p53 for the 1-year survival model in addition to clinico-pathological variables. Finally, even in an independent validation data set of 48 patients, who underwent surgery 10 years later, the present ANN model could predict outcome of patients at 5 years with the JR and accuracy of 81% and 77%, respectively. This study demonstrates that ANN is a potentially more useful tool than conventional statistical methods for predicting survival of patients with NSCLC and that inclusion of relevant molecular markers as input variables enhances its predictive ability.

Adenocarcinoma↗
PubMed1992

A multiple logistic model for predicting the occurrence of Campylobacter jejuni and Campylobacter coli in water.

A multiple logistic regression model was established to predict the occurrence of Campylobacter jejuni/coli, related to index bacteria such as faecal coliforms, faecal streptococci, and sulphite-reducing clostridia, in a water source in southern Norway. The fitted model indicated that faecal coliforms were strong predictors for C. jejuni/coli, although the water temperature also had a strong influence on results. Sulphite-reducing clostridia, faecal streptococci, and season of the year had no significant influence on the results, in spite of their apparent predictor value as separate variables. The model employed offers a new approach to the relationship between index bacteria and the occurrence of pathogenic bacteria in water. Similar models can also be established in general food microbiology.

Campylobacter↗
PubMed1996

The application of a log-logistic model to describe the thermal inactivation of Clostridium botulinum 213B at temperatures below 121.1 degrees C.

In this work, the death of Clostridium botulinum 213B was measured at temperatures between 101 degrees C and 121 degrees C. It was found that at all temperatures tested, survivor curves deviated from log-linearity which prevented their description using traditional first order kinetics. The survivor curves were better described using a vitalistic approach and the log-logistic transformation proposed by Cole et al. (1993). A single equation was derived to describe all survivor curves over the temperature range tested and a comparison of predicted and measured data showed good correlation. The implications of the use of the vitalistic approach to the validity of the 'minimum botulinum cook' is discussed.

Clostridium botulinum↗