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PubMed2000

Prediction of genetic structure in eukaryotic DNA using reference point logistic regression and sequence alignment.

MOTIVATION: Current software tools are moderately effective in predicting genetic structure (exons, introns, intergenic regions, and complete genes) from raw DNA sequence data. Improvements in accuracy and speed are needed to deal with the increasing volume of data from large scale sequencing projects. RESULTS: We present a two-stage computer program to predict genetic structure in eukaryotic DNA. The first stage makes use of a novel statistical technique, called reference point logistic (RPL) regression, to calculate scores for potential functional sites. These site scores are combined with interval content, length, and state scores, via a Generalized Hidden Markov Model, to determine a combined score for each possible parse of a given DNA sequence into exons, introns, and intergenic regions. An optimal parse is found using a dynamic programming algorithm. In the second stage, protein sequence alignment methods are applied to improve the accuracy of the initial parse. Computation in the first stage of the program is very fast (1 s on a 360 MHz CPU for a 16 kb sequence) and its predictive accuracy typically matches or exceeds the best results reported for other methods (Sensitivity = 0.93 and Specificity = 0.93 for the Burset/Guigótest set). Computation in the second stage is slower, but the final predictions are more accurate (Sn = 0.97, Sp = 0.97). The program (called GRPL) can handle partial, single, and multi-gene sequences. The program is also capable of predicting the genetic structure of vertebrate, invertebrate, and plant DNA with nearly equal accuracy. Statistical techniques have also been introduced to model the effects of varying C+G content in a continuous manner and to control overfitting of parameters for smaller training sets. AVAILABILITY: An academic implementation of GRPL, compiled for SUN workstations, is available by anonymous ftp from snipe.pharmacy. ualberta.ca/pub. The training and test sets used in this work, together with supplementary material, can be found at the same location. A commercial implementation is available as a component of GeneTool (BioTools Inc., http://biotools.com).

Algorithms↗
PubMed2004

Classification of gene microarrays by penalized logistic regression.

Classification of patient samples is an important aspect of cancer diagnosis and treatment. The support vector machine (SVM) has been successfully applied to microarray cancer diagnosis problems. However, one weakness of the SVM is that given a tumor sample, it only predicts a cancer class label but does not provide any estimate of the underlying probability. We propose penalized logistic regression (PLR) as an alternative to the SVM for the microarray cancer diagnosis problem. We show that when using the same set of genes, PLR and the SVM perform similarly in cancer classification, but PLR has the advantage of additionally providing an estimate of the underlying probability. Often a primary goal in microarray cancer diagnosis is to identify the genes responsible for the classification, rather than class prediction. We consider two gene selection methods in this paper, univariate ranking (UR) and recursive feature elimination (RFE). Empirical results indicate that PLR combined with RFE tends to select fewer genes than other methods and also performs well in both cross-validation and test samples. A fast algorithm for solving PLR is also described.

Algorithms↗
PubMed1996

Prediction of postoperative nausea and vomiting using a logistic regression model.

In a previous study, logistic regression analysis was used to determine the association of independent fixed patient factors with the incidence of postoperative nausea and vomiting (PONV). Female sex, previous history of PONV, use of postoperative opioids, previous history of motion sickness and an interaction between male sex and previous history of PONV were combined in an equation from which risk of PONV could be estimated. The present study was designed to test this equation in a group of patients with wide selection criteria. Data on 400 patients were collected in relation to pre-, per- and postoperative factors which may influence the incidence of PONV. The equation was used to predict PONV, and actual outcome was compared with that predicted. The overall incidence of PONV was 36%. The equation predicted an overall probability of PONV of 27.4%. If the model was used to define individual patients as predicted to have or not to have PONV, it was correct only 71% of the time. However, there was good agreement between the actual incidences of PONV and those predicted among the 16 risk groups created by the model.

Analgesics, Opioid↗
PubMed1998

A logistic model of the seasonal migration decision for elderly households in Arizona and Minnesota.

While most previous studies of elderly migration have investigated permanent migration using Census data, this paper employs household (micro) data from statewide surveys conducted in Arizona and Minnesota to analyze seasonal migration as an economic decision. Estimation of logistic regression models for each state indicate income and age are important determinants, whereas these variables have not proven significant in previous studies based on aggregate data. The results lead to the conclusion that seasonal migration may be an integral part of a life cycle of elderly migration.

Aged↗
PubMed2006

Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression.

BACKGROUND: As women present at earlier gestations to early pregnancy units (EPUs), the number of women diagnosed with a pregnancy of unknown location (PUL) increases. Some of these women will have an ectopic pregnancy (EP), and it is this group in the PUL population that poses the greatest concern. The aim of this study was to develop Bayesian networks to predict EPs in the PUL population. METHODS: Data were gathered in a single EPU from all women with a PUL. This data set was divided into a model-building (599 women with 44 EPs) and a validation (257 women with 22 EPs) data set and consisted of the following variables: vaginal bleeding, fluid in the pouch of Douglas, midline echo, lower abdominal pain, age, endometrial thickness, gestation days, the ratio of HCG at 48 and 0 h, progesterone levels (0 and 48 h) and the clinical outcome of the PUL. We developed Bayesian networks with expert information using this data set to predict EPs. RESULTS: The best Bayesian network used the gestational age, HCG ratio and the progesterone level at 48 h and had an area under the receiver operator characteristic curve (AUC) of 0.88 for predicting EPs when tested prospectively. CONCLUSIONS: Discrete-valued Bayesian networks are more complex to build than, for example, logistic regression. Nevertheless, we have demonstrated that such models can be used to predict EPs in a PUL population. Prospective interventional multicentre studies are needed to validate the use of such models in clinical practice.

Adolescent↗
PubMed2005

Functional responses of coccinellid predators: an illustration of a logistic approach.

The functional response parameters and patterns of three coccinellid predators, Cheilomenes sexmaculata, Propylea dissecta, and Coccinella transversalis (Coleoptera: Coccinellidae) were evaluated to find out how these predators respond at two different prey species across various prey densities levels. All three predators exhibited a decelerating curve Type II response determined by a logistic regression model. The linear reciprocal transformation of Holling's disc equation was used to further evaluate the parametric values. C. sexmaculata responded maximally, followed by C. transversalis and P. dissecta, in terms of consumption of the aphids, Aphis craccivora and Myzus persicae, with suitable values of coefficient of attack rates and handling times on these prey species. Differences in handling times were found to be significant within and between the predatory species on both prey species indicating that predators respond differentially to prey species. Differences in coefficients of attack rates, however, did not vary significantly in most of the treatments. The potential role of these predators in biocontrol of A. craccivora and M. persicae suggests that C. sexmaculata is the best predator for the management of both prey species, particularly A. craccivora. However, further field based studies are needed to confirm this hypothesis.

Animals↗
PubMed1991

Use of a four-parameter logistic equation to evaluate the response of growing rats to ten levels of each indispensable amino acid.

Over a 21-d period, 400 [four rats/level, 10 levels/amino acid, 10 indispensable amino acids (IAA)] male weanling rats (65.9 +/- 0.3 g; mean +/- SEM) were fed diets with one of 10 levels of each of the 10 IAA. In addition, four rats were fed an amino acid-free diet and 16 rats were killed on d 0 for individual body composition. With the exception of the limiting amino acid (LAA), an increment (35% of the requirement) of each IAA was added to the mixture to insure that the LAA remained first limiting. A four-parameter logistic equation was used to describe the nitrogen and weight gain responses of rats to each IAA. Conservation of nitrogen, defined as a predicted y-intercept value greater than the value observed for rats fed an amino acid-free diet (-0.304 +/- 0.023 g N/21 d), was seen when diets devoid of total aromatic amino acids or lysine (-0.062 +/- 0.013 g N/21 d) or histidine, leucine, tryptophan or valine (-0.115 +/- 0.011 g N/21 d) were fed. When total sulfur amino acids were first limiting, diminishing returns (a decrease in the first derivative) was evident from zero intake to Rmax (estimated asymptotic response maximum). In contrast, when other IAA were limiting, diminishing returns were apparent after approximately the first third of the full response. Based on the first derivative of the response curves, the efficiency of nitrogen gain depends on the LAA. The dietary LAA would be expected to influence the shape of the response curve and therefore influence the quantitative aspects of diminishing returns.

Amino Acids↗
PubMed1997

Measurement error correction for logistic regression models with an "alloyed gold standard".

Recently, some authors have questioned the validity of methods which correct relative risk estimates for measurement error and misclassification when the "gold standard" used to obtain information about the measurement error process is itself imperfect. When such an "alloyed" gold standard is used to validate the usual exposure measurement, the bias in the "regression calibration" (Rosner et al., Stat Med 1989; 8:1051-69) measurement-error correction factor for relative risks estimated from logistic regression models is derived. This quantity is a function of the correlations of the "alloyed" gold standard (X) and the usual exposure assessment method (Z) with the truth, of the ratio of the variances of X and Z, and of the correlation between the errors in the "alloyed" gold standard and the errors in the usual exposure assessment method. In this paper, it is proven that if the errors between Z and X are uncorrelated, the regression calibration method has no bias even when the gold standard is "alloyed." When a third method of exposure assessment is available and it is reasonable to assume that the errors in this method are uncorrelated with the errors in the other two exposure assessment methods, point and interval estimates of the correlation between the errors in X and Z are derived. These methods are illustrated here with data on the measurement of physical activity, vitamins A and E, and poly- and monounsaturated fat. In addition, when a third exposure assessment method is available, a modification of standard regression calibration is derived which can be used to calculate point and interval estimates of relative risk that are corrected for measurement error in both X and Z. This new method is illustrated here with data from the Health Professionals Follow-up Study, a study investigating the associations between physical activity and colon cancer incidence and between vitamin E intake and coronary heart disease. It is shown that in these examples, correlations of the errors in X and Z tended to be small. Even when moderate, estimates of relative risk corrected for error in both X and Z were not very different from the estimates which assumed that X was a true gold standard.

Bias↗
PubMed1995

Bivariate logistic regression analysis of childhood psychopathology ratings using multiple informants.

A central issue in studies of risk factors for childhood psychopathology is utilization of the information obtained about the child's mental health status from multiple informants. In this paper, the authors propose a new approach to the analysis of risk factor data when the outcomes are binary ratings (presence/absence of symptoms). This new approach has several attractive features in this setting. The strategy taken is to perform a single analysis using multivariate modeling, in which simultaneous logistic regressions are conducted for the outcomes given by each of several informants. The advantages of this approach include the following: 1) it retains the complete information about case status for each informant; 2) it permits assessment of informant-risk factor interactions as well as "overall" risk factor effects; 3) it provides measures of association between the multiple informants and adjusts for the association between responses in the analysis; and 4) missing data on a subset of respondents can be incorporated in a straightforward way, permitting all subjects with at least one informant to be used in the analysis. To illustrate the methods, the authors present findings on risk factors for measures of "Internalizing" and "Externalizing" behaviors from two surveys using parent and teacher ratings of 6- to 11-year-old children in Connecticut between 1986 and 1989.

Child↗
PubMed1995

The probability of pregnancy after embryo transfer is affected by the age of the patient, cause of infertility, number of embryos transferred and the average morphology score, as revealed by multiple logistic regression analysis.

Because the process of conception is affected by many variables, a multiple logistic regression analysis was performed to assess (i) the impact and relative weight of both patient and embryo variables and (ii) their possible effects on the probability of a vital pregnancy after embryo transfer. A statistical model was constructed predicting the probability of pregnancy after embryo transfer. The variables that contributed significantly to the predictive value of the model were the age of the patient, the cause of infertility, the number of embryos transferred and the average morphology score of the transferred embryos. Embryo variables appeared to have a significant but modest value in predicting the probability of pregnancy after embryo transfer. Other variables, such as the thickness of the endometrium, were found to have no prognostic value. Moreover, we found that their effect could be explained by the variables already included in the model.

Adult↗
PubMed1992

Distinction between early normal intrauterine pregnancies and pathological pregnancies by means of a logistic model.

The probability of an unclear very early pregnancy being a normal intrauterine pregnancy was estimated using a logistic model. Five diagnostic measures of prognostic value were identified in the model: (i) daily change in human chorionic gonadotrophin (HCG), (ii) results of transvaginal ultrasound, (iii) vaginal bleeding, (iv) serum progesterone level and (v) risk score for ectopic pregnancy. With the use of this model, the probability of a normal intrauterine pregnancy has been estimated as 96.7%.

Chorionic Gonadotropin↗
PubMed2003

Reliability of pharmacodynamic analysis by logistic regression: mixed-effects modeling.

BACKGROUND: Many pharmacologic studies record data as binary, yes-or-no, variables with analysis using logistic regression. In a previous study, it was shown that estimates of C50, the drug concentration associated with a 50% probability of drug effect, were unbiased, whereas estimates of gamma, the term describing the steepness of the concentration-effect relationship, were biased when sparse data were naively pooled for analysis. In this study, it was determined whether mixed-effects analysis improved the accuracy of parameter estimation. METHODS: Pharmacodynamic studies with binary, yes-or-no, responses were simulated and analyzed with NONMEM. The bias and coefficient of variation of C50 and gamma estimates were determined as a function of numbers of patients in the simulated study, the number of simulated data points per patient, and the "true" value of gamma. In addition, 100 sparse binary human data sets were generated from an evaluation of midazolam for postoperative sedation of adult patients undergoing cardiac surgery by random selection of a single data point (sedation score vs. midazolam plasma concentration) from each of the 30 patients in the study. C50 and gamma were estimated for each of these data sets by using NONMEM and were compared with the estimates from the complete data set of 656 observations. RESULTS: Estimates of C50 were unbiased, even for sparse data (one data point per patient) with coefficients of variation of 30-50%. Estimates of gamma were highly biased for sparse data for all values of gamma greater than 1, and the value of gamma was overestimated. Unbiased estimation of gamma required 10 data points per patient. The coefficient of variation of gamma estimates was greater than that of the C50 estimates. Clinical data for sedation with midazolam confirmed the simulation results, showing an overestimate of gamma with sparse data. CONCLUSION: Although accurate estimations of C50 from sparse binary data are possible, estimates of gamma are biased. Data with 10 or more observations per patient is necessary for accurate estimations of gamma.

Cardiac Surgical Procedures↗
PubMed2001

Predicting hospital mortality for patients in the intensive care unit: a comparison of artificial neural networks with logistic regression models.

OBJECTIVE: Logistic regression (LR), commonly used for hospital mortality prediction, has limitations. Artificial neural networks (ANNs) have been proposed as an alternative. We compared the performance of these approaches by using stepwise reductions in sample size. DESIGN: Prospective cohort study. SETTING: Seven intensive care units (ICU) at one tertiary care center. PATIENTS: Patients were 1,647 ICU admissions for whom first-day Acute Physiology and Chronic Health Evaluation III variables were collected. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We constructed LR and ANN models on a random set of 1,200 admissions (development set) and used the remaining 447 as the validation set. We repeated model construction on progressively smaller development sets (800, 400, and 200 admissions) and retested on the original validation set (n = 447). For each development set, we constructed models from two LR and two ANN architectures, organizing the independent variables differently. With the 1,200-admission development set, all models had good fit and discrimination on the validation set, where fit was assessed by the Hosmer-Lemeshow C statistic (range, 10.6-15.3; p > or = .05) and standardized mortality ratio (SMR) (range, 0.93 [95% confidence interval, 0.79-1.15] to 1.09 [95% confidence interval, 0.89-1.38]), and discrimination was assessed by the area under the receiver operating characteristic curve (range, 0.80-0.84). As development set sample size decreased, model performance on the validation set deteriorated rapidly, although the ANNs retained marginally better fit at 800 (best C statistic was 26.3 [p = .0009] and 13.1 [p = .11] for the LR and ANN models). Below 800, fit was poor with both approaches, with high C statistics (ranging from 22.8 [p <.004] to 633 [p <.0001]) and highly biased SMRs (seven of the eight models below 800 had SMRs of <0.85, with an upper confidence interval of <1). Discrimination ranged from 0.74 to 0.84 below 800. CONCLUSIONS: When sample size is adequate, LR and ANN models have similar performance. However, development sets of < or = 800 were generally inadequate. This is concerning, given typical sample sizes used for individual ICU mortality prediction.

APACHE↗
PubMed2002

Calibration and discrimination by daily Logistic Organ Dysfunction scoring comparatively with daily Sequential Organ Failure Assessment scoring for predicting hospital mortality in critically ill patients.

OBJECTIVE: The Logistic Organ Dysfunction (LOD) score has been proved effective in evaluating severity during the first day in an intensive care unit but has not been evaluated later. To evaluate attributable mortality related to nosocomial events, organ dysfunction scores that remain accurate throughout the intensive care unit stay are needed. The objective of this study was to evaluate how accurately daily LOD scoring predicts mortality comparatively with daily Sequential Organ Failure Assessment (SOFA) scoring. DESIGN: Prospective multicenter study. SETTING: Six intensive care units in France. PATIENTS: A total of 1685 patients with intensive care unit stays longer than 48 hrs were included in this study (511 hospital deaths). Median age was 66 yrs, and median Simplified Acute Physiology Score II at admission was 38. For each patient, a senior physician recorded the variables needed to compute organ dysfunction scores daily throughout the intensive care unit stay. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: SOFA and LOD scores were computed daily during the first 7 days. Calibration was evaluated based on goodness-of-fit by the Hosmer-Lemeshow chi-square statistic (lower chi-square values and higher values indicate better fit) and discrimination based on the receiver operating characteristics (ROC) area under the curve (AUC; a ROC-AUC of 1 indicates faultless discrimination and a ROC-AUC of 0.5 indicates the effects of chance alone). Because calibration of both scores was poor at all time points ( p<.001), customization was performed using the total score (model 1) or separate introduction of each dysfunction (model 2). The performance of customized LOD and SOFA scores on a given day in predicting mortality was assessed in those patients who spent at least one more calendar day in the intensive care unit. The original LOD and SOFA scores had satisfactory ROC-AUC values (0.720 to 0.766). Internal consistency of both scores was acceptable ( p< 10(-4) for each organ dysfunction). After customization, the original scores calibrated well between days 1 and 7. Discrimination by both scores was better with model 2 (AUC-ROC, 0.729-0.784). CONCLUSION: Daily LOD and SOFA scores showed good accuracy and internal consistency, and they could be used to adjust severity for events occurring in the intensive care unit.

Aged↗
PubMed1998

Focal calvarial bone lesions. Comparison of logistic regression and neural network models.

RATIONALE AND OBJECTIVES: To assess the accuracy of logistic regression (LR) and artificial neural networks (NN) in the diagnosis of calvarial lesions using computed tomography (CT) and to establish the importance of the different features needed for the diagnosis. METHODS: One hundred sixty-seven patients with calvarial lesions as the only known disease were enrolled. The clinical and CT data were used for LR and NN models. Both models were tested with the leave-one-out method. The final results of each model were compared using the area under receiver operating characteristic curves (Az). RESULTS: Of the lesions, 73.1%, were benign and 26.9% were malignant. There was no statistically significant difference between LR and NN in differentiating malignancy. In characterizing the histologic diagnoses, NN was statistically superior to LR. Important NN features needed for malignancy classification were age and edge definition, and for the histologic diagnoses matrix, marginal sclerosis, and age. CONCLUSION: NNs offer wide possibilities over statistics for the study of calvarial lesions other than their superior diagnostic performance.

Adolescent↗
PubMed1999

Validation procedures in radiologic diagnostic models. Neural network and logistic regression.

OBJECTIVE: To compare the performance of two predictive radiologic models, logistic regression (LR) and neural network (NN), with five different resampling methods. METHODS: One hundred sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross-validation, leave-one-out, and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). RESULTS: The NN obtained statistically higher Az values than LR with cross-validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. CONCLUSIONS: The NN classifier performs better than the one based on LR. This advantage is well detected by three-fold cross-validation but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Adolescent↗
PubMedDate not supplied

Adjusting a prognostic score for burned children with logistic regression.

This study was aimed at the adjustment and cross-validation of a prognostic score for burned children on hospital admission. DEMI score assesses seven risk factors related to four main topics: depth (percent of partial-thickness and full-thickness burn), extension (percent of total body surface area burned and injury to dorso-gluteal area), morbidity on admission (preexisting illness and complications caused by inadequate transfer), and presence of Inhalation syndrome. These items were selected in a previous case control study with bivariate analysis on an estimation sample of 168 children with acute burns admitted to the burn unit of the Hospital de Pediatria Juan P. Garrahan between July 1991 and October 1993. This study uses the same retrospective sample to adjust this score by means of multivariate analysis. Only four items were included in the logistic regression formula: percentage total body surface area burned, inadequate transfer, inhalation syndrome, and dorso-gluteal affection. Resulting abbreviation of DEMI score was then cross-validated on a prospective validation sample of 137 patients, yielding high correlation with the outcome (R = 0.78) and high sensitivity (80.95%), specificity (96.55%), and positive (80.95%) and negative (96.55%) predictive values. We conclude that this new DEMI score is a simple and accurate tool to predict mortality risk in burned children.

Adolescent↗
PubMed1990

A stepwise logistic regression analysis of factors affecting morbidity and mortality after thoracic trauma: effect of epidural analgesia.

Rib fractures and other chest wall injuries can lead to weak ventilation, atelectasis, and even death. Whereas such injuries in young patients are usually well tolerated, relatively minor chest wall trauma can be serious in elderly patients. Epidural analgesia, by improving pain control and ventilatory function, might improve morbidity and mortality rates compared to other forms of analgesia. Stepwise logistic regression was used to compare thoracic trauma patients more than 60 years of age treated with either epidural or parenteral (IV/IM) analgesia. In spite of more severe thoracic trauma in epidural patients as measured by the Abbreviated Injury Score for the chest (epidural = 3.3 +/- 0.1, IV/IM = 2.8 +/- 0.1; p less than 0.05) the use of epidural analgesia was an independent predictor of both decreased mortality (p = 0.0035) and a decreased incidence of pulmonary complications (p = 0.0088). Epidural analgesia has a positive effect on outcome in elderly trauma victims with chest wall injury and is useful in high-risk patients. Increased costs associated with epidural analgesia are minimal and are justified by improvements in outcome.

Age Factors↗