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PubMed1991

[Multivariate analysis of prognostic factors in patients with prostatic cancer].

A multivariate statistical analysis of 9 potential prognostic factors was made, using data gathered on 107 patients admitted between 1975 and 1987. After each variable was assessed separately for its prognostic importance by means of univariate analysis, a multivariate analysis using Cox's proportional hazards regression model and multivariate logistic model was done. These tests identified Gleason's score as the most important factor for all patients followed by M category and alkaline phosphatase (ALP), while Gleason's score alkaline phosphatase and age at diagnosis were most important for stage D patients. For short-term survival, the significant prognostic factors were Gleason's score and M category while for long term survival they were Gleason's score and age. T. category, stage, acid phosphatase, prostatic acid phosphatase, and treatment were found to be significantly related to survival time when examined individually; they were not found to be significant in the multivariate analysis.

Age Factors↗
PubMed2005

Inactivation of Salmonella typhimurium DT 104 in UHT whole milk by high hydrostatic pressure.

Cell suspensions of Salmonella typhimurium DT 104 in ultra-high temperature (UHT) whole milk were exposed to high hydrostatic pressure at 350, 400, 450, 500, 550, and 600 MPa at ambient temperature (ca. 21 degrees C). Tailing was observed in all survival curves, and sigmoidal survival curves were observed at relatively high pressure (500-600 MPa). Four modeling methods (linear and nonlinear including Weibull, modified Gompertz, and log-logistic models) were fitted to these data at 500, 550, and 600 MPa. Performances of the modeling methods were compared using mean square error (MSE). The linear regression model at these three pressure levels had a mean square error (MSE) of 1.260-2.263. Nonlinear regressions using Weibull, modified Gompertz, and log-logistic models had MSE values in the range of 0.334-0.764, 0.601-1.479, and 0.359-0.523, respectively. Modeling results indicated that first-order kinetics could not accurately describe pressure inactivation of S. typhimurium DT 104 in UHT milk; the log-logistic model produced the best fit to data.

Animals↗
PubMed2004

Statistical assessment of mediational effects for logistic mediational models.

The concept of mediation has broad applications in medical health studies. Although the statistical assessment of a mediational effect under the normal assumption has been well established in linear structural equation models (SEM), it has not been extended to the general case where normality is not a usual assumption. In this paper, we propose to extend the definition of mediational effects through causal inference. The new definition is consistent with that in linear SEM and does not rely on the assumption of normality. Here, we focus our attention on the logistic mediation model, where all variables involved are binary. Three approaches to the estimation of mediational effects-Delta method, bootstrap, and Bayesian modelling via Monte Carlo simulation are investigated. Simulation studies are used to examine the behaviour of the three approaches. Measured by 95 per cent confidence interval (CI) coverage rate and root mean square error (RMSE) criteria, it was found that the Bayesian method using a non-informative prior outperformed both bootstrap and the Delta methods, particularly for small sample sizes. Case studies are presented to demonstrate the application of the proposed method to public health research using a nationally representative database. Extending the proposed method to other types of mediational model and to multiple mediators are also discussed.

Adolescent↗
PubMed1995

Measurement and kinetic analysis of the neutral detergent-soluble carbohydrate fraction of legumes and grasses.

The fermentation of the neutral detergent-soluble (NDS) fraction of two legumes (clover, alfalfa) and two grasses (timothy, guinea grass) was measured using a curve subtraction technique with in vitro gas production data from the whole forage and the isolated neutral detergent-extracted fiber. The NDF disappearance and VFA production also were measured. There were no significant differences between the VFA patterns from whole forage and NDF. There was a good linear correlation between the volume of gas produced and the mass of fiber digested in the NDF samples. Analysis of the gas curves with a dual-pool logistic model gave a lag value, digestion rates and pool sizes for the whole forage, the fiber component, and the NDS fraction. Rates for the NDS fraction ranged from .152 h-1 for clover to .191 h-1 for timothy. These rates are appreciably lower than values assumed in some models. Application of a triple-pool logistic model revealed the presence of faster-digesting material in the legumes. We discuss several different ways to measure the NDS pool size. The simplest method requires only a single gas measurement at the end of in vitro digestion of the whole forage coupled with an NDF disappearance measurement. The curve subtraction technique can provide information on the size and digestion kinetics of the NDS pool. This information is useful both for model studies and for agronomic research and may help us to understand the nutritional significance of this fast-digesting carbohydrate fraction.

Carbohydrate Metabolism↗
PubMed1987

Logistic risk model for the unique effects of inherent aerobic capacity on +Gz tolerance before and after simulated weightlessness.

Small sample size (n less than 10) and inappropriate analysis of multivariate data have hindered previous attempts to describe which physiologic and demographic variables are most important in determining how long humans can tolerate acceleration. Data from previous centrifuge studies conducted at NASA/Ames Research Center, utilizing a 7-14 d bed rest protocol to simulate weightlessness, were included in the current investigation. After review, data on 25 women and 22 men were available for analysis. Study variables included gender, age, weight, height, percent body fat, resting heart rate, mean arterial pressure, VO2max, and plasma volume. Since the dependent variable was time to greyout (failure), two contemporary biostatistical modeling procedures (proportional hazard and logistic discriminant function) were used to estimate risk, given a particular subject's profile. After adjusting for pre-bed-rest tolerance time, none of the profile variables remained in the risk equation for post-bed-rest tolerance greyout. However, prior to bed rest, risk of greyout could be predicted with 91% accuracy. All of the profile variables except weight, MAP, and those related to inherent aerobic capacity (VO2max, percent body fat, resting heart rate) entered the risk equation for pre-bed-rest greyout. A cross-validation using 24 new subjects indicated a very stable model for risk prediction, accurate within 5% of the original equation. The result for the inherent fitness variables is significant in that a consensus as to whether an increased aerobic capacity is beneficial or detrimental has not been satisfactorily established. We conclude that tolerance to +Gz acceleration before and after simulated weightlessness is independent of inherent aerobic fitness.

Acceleration↗
PubMed1990

Examination by logistic regression modelling of the variables which increase the relative risk of elderly women falling compared to elderly men.

In a community based, prospective study to determine risk factors for falls, 465 women and 296 men 70 years and over were followed for 1 year and 507 falls were documented. A greater proportion of women (32.7%) than men (23.0%) experienced at least one fall in which there was no or minimal external contribution. Using unconditional logistic regression models we investigated the effect of physical and sociological variables on the sex difference in fall rate. Controlling for the variables age, use of psychotropic drugs, inability to rise from a chair without using arms, going outdoors less than daily and living alone decreased the relative risk of women falling compared to men from 2.02 (95% CI, 1.40-2.92) to 1.55 (95% CI 1.04-2.31). Some of the increased risk of falling associated with being a women was able to be explained and is potentially correctable. But even after controlling for the physical and social variables which we had assessed, women compared to men still had a significantly increased relative risk of falling.

Accidental Falls↗
PubMed1982

A review of goodness of fit statistics for use in the development of logistic regression models.

Several statistics have recently been proposed for the purpose of assessing the goodness of fit of an estimated logistic regression model. These statistics are reviewed and compared to other, less formal, procedures in the context of applications in epidemiologic research. One statistic is recommended for use and its computation is illustrated using data from a recent study of mortality of intensive care unit patients.

Humans↗
PubMed1997

A nested approach to evaluating dose-response and trend.

PURPOSE: Conventional dose-response and trend analysis fits either a linear or categorical logistic model and tests the resulting coefficients. These analyses, however, are based on implausible assumptions. METHODS: We present an alternative approach that uses likelihood ratio tests to compare nested regression models and determine when a model is rich enough to capture the data trends. RESULTS: For illustration, we apply this approach to data on diet and colorectal polyps. CONCLUSIONS: Comparison of linear and quadratic spline logistic models indicates that the conventional approach of using only a linear logistic model would not appropriately describe the association between intake of fruits and vegetables and colorectal polyps in our data. Graphical checking further supports this conclusion.

Case-Control Studies↗
PubMed1993

Glycated haemoglobin, plasma glucose and diabetic retinopathy: cross-sectional and prospective analyses.

Among Pima Indians with Type 2 (non-insulin-dependent) diabetes mellitus the relationships between glycated haemoglobin (HbA1), fasting or 2-h post-load plasma glucose and diabetic retinopathy were examined by cross-sectional and prospective analyses, and the strengths of the associations were directly compared by receiver operating characteristic analysis. In the cross-sectional analysis, HbA1, fasting and 2-h plasma glucose were each significantly related to retinopathy among 789 diabetic subjects by separate logistic models. In a stepwise multiple logistic model in which HbA1, fasting and 2-h plasma glucose were included, HbA1 was selected as having the strongest association with retinopathy and neither fasting nor 2-h plasma glucose contributed significantly to the model once HbA1 was entered. Similarly, in the prospective analysis, HbA1, fasting and 2-h plasma glucose all predicted retinopathy in 227 diabetic subjects by separate proportional-hazards models. In a stepwise proportional-hazards model with HbA1, fasting and 2-h plasma glucose available to the model, HbA1 was again selected as having the strongest association with the incidence of retinopathy, and neither fasting nor 2-h plasma glucose significantly added to the prediction of retinopathy. A receiver operating characteristic analysis was used to determine if HbA1 was statistically significantly better than fasting or 2-h plasma glucose in assessing the risk for retinopathy. In neither the cross-sectional nor the prospective data did the area under the receiver operating characteristic curve for HbA1 differ significantly from that for fasting or 2-h plasma glucose (p > 0.05 for each).(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed2006

Model comparison for Escherichia coli growth in pouched food.

We recently studied the growth characteristics of Escherichia coli cells in pouched mashed potatoes (Fujikawa et al., J. Food Hyg. Soc. Japan, 47, 95-98 (2006)). Using those experimental data, in the present study, we compared a logistic model newly developed by us with the modified Gompertz and the Baranyi models, which are used as growth models worldwide. Bacterial growth curves at constant temperatures in the range of 12 to 34 degrees C were successfully described with the new logistic model, as well as with the other models. The Baranyi gave the least error in cell number and our model gave the least error in the rate constant and the lag period. For dynamic temperature, our model successfully predicted the bacterial growth, whereas the Baranyi model considerably overestimated it. Also, there was a discrepancy between the growth curves described with the differential equations of the Baranyi model and those obtained with DMfit, a software program for Baranyi model fitting. These results indicate that the new logistic model can be used to predict bacterial growth in pouched food.

Escherichia coli↗
PubMed1987

Analysis of the clinical findings used to diagnose coliform mastitis in dairy cows, and comparison to a prediction model.

Logistic regression was used to analyze the clinical findings (attributes) which predicted coliform mastitis in 113 dairy cattle, 36 of which had coliforms cultured from milk. Weakness of the cow, swelling of the udder, decreased body temperature and watery consistency of the milk were selected for the model. An analysis was then done to find the attributes which clinicians used when predicting that a cow would have a coliform cultured. Clinicians appeared to use water consistency of the milk, shivering, firmness of the udder, pulse rate, elevated body temperature, and respiratory rate. In a final analysis the clinicians' predictions were forced into the model to determine which attributes might be used by clinicians to increase diagnostic accuracy. Inclusion of weakness of the cow, swelling of the udder, decreased temperature of the cow, and duration of mastitis of less than 24 hours increased accuracy over clinical prediction alone. Accuracy of cowside diagnosis might be increased if more attention were paid to these attributes when making a diagnosis of coliform mastitis.

Animals↗
PubMed2003

[Logistic regression model analysis on Bolton ratio of orthodontic extraction model].

OBJECTIVE: The purpose of this study was to evaluate the role of Angle's class, overall ratio and anterior ratio in the creation of tooth size discrepancies, and to determine whether any tooth extraction combinations create more severe discrepancies. METHODS: 166 dental casts of orthodontic patients were selected randomly. These models were classified according to angle's criterion. Mesio-distal dimensions of mandibular and maxillary teeth were measured before treatment, and subjected to Bolton's analysis. Hypothetical tooth extraction by the following combinations: all the first premolars, all the second premolars, upper first and lower second premolars, and upper second and lower first premolars, was performed on each patient. The measurement results were again subjected to Bolton's analysis to see whether any tooth-size discrepancy had been created. The results were evaluated statistically by means of Logistic regression model. RESULTS: Overall ratio, anterior ratio and extraction models affected mesio-distal tooth size ratio of both maxillary and mandibular teeth in the final stage of orthodontic treatment, Whereas, the results showed no significant difference among these groups of malocclusion. CONCLUSION: The results suggested that dentists should always keep in mind that each patient should be treated individually and should be aware of that other factors also played important roles in determining what teeth, if any, should be removed and the Bolton analyses of all kinds of extraction models should be carried out, as well as the general Bolton analysis.

Adolescent↗
PubMedDate not supplied

Logistic regression model of the clinical response to 5-fluorouracil based chemotherapy for metastatic colorectal cancer patients.

Several genes have been involved in drug resistance but none are currently used in the drug decision process. To address this problem, mRNA levels were measured for the 5-fluorouracil metabolism-related genes, thymidylate synthase, thymidine phosphorylase and dihydropyrimidine dehydrogenase in tumor samples of 40 patients with synchronous metastatic colon cancer by quantitative RT-PCR. Drug response and overall survival were also obtained for each patient. A logistic regression model was defined to calculate a response predicting score (RPS) with gene expression levels. This RPS split responders from nonresponders as, at the best statistical threshold (0.35), the area of receiver operating characteristic (ROC) curve established with this method was 0.82 and sensitivity and specificity were respectively 100% and 65.4%. Furthermore patients with scores above 0.35 tended to have better overall survival than those with a score less than 0.35 (p = 0.09).

Aged↗
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↗
PubMed2006

Comparison of methods incorporating quantitative covariates into affected sib pair linkage analysis.

For complex traits, it may be possible to increase the power to detect linkage if one takes advantage of covariate information. Several statistics have been proposed that incorporate quantitative covariate information into affected sib pair (ASP) linkage analysis. However, it is not clear how these statistics perform under different gene-environment (G x E) interactions. We compare representative statistics to each other on simulated data under three biologically-plausible G x E models. We also compared their performance with a model-free method and with quantitative trait locus (QTL) linkage approaches. The statistics considered here are: (1) mixture model; (2) general conditional-logistic model (LODPAL); (3) multinomial logistic regression models (MLRM); (4) extension of the maximum-likelihood-binomial approach (MLB); (5) ordered-subset analysis (OSA); and (6) logistic regression modeling (COVLINK). In all three G x E models, most of these six statistics perform better when using the covariate C1 associated with a G x E interaction effect than when using the environmental risk factor C2 or the random noise covariate C3. Compared with a model-free method without covariates (S(all)), the mixture model performs the best when using C1, with the high-to-low OSA method also performing quite well. Generally, MLB is the least sensitive to covariate choice. However, most of these statistics do not provide better power than S(all). Thus, while inclusion of the "correct" covariate can lead to increased power, careful selection of appropriate covariates is vital for success.

Analysis of Variance↗