Logistic Regression Equation (logistic + regression_equation)

Distribution by Scientific Domains


Selected Abstracts


Factors related to gizzard shad and the threadfin shad occurrence and abundance in Florida lakes

JOURNAL OF FISH BIOLOGY, Issue 2 2000
M. S. Allen
Gizzard shad Dorosoma cepedianum were collected in 23 and threadfin shad D. petenense were collected in 22 of the 60 Florida lakes sampled. Logistic regression equations were 94% effective for predicting gizzard shad occurrence from chlorophyll and lake surface area, and 84% effective for predicting threadfin shad occurrence from lake surface area and lake volume inhabited (PVI). Occurrence of both shad species was related positively to lake size. In lakes where gizzard shad or threadfin shad were collected, shad density and biomass of both shad species were related positively to chlorophyll. Gizzard shad populations were generally vulnerable to predation in lakes, with the per cent of gizzard shad ,200mm LT values exceeding 60% with few exceptions. Effects of gizzard shad and threadfin shad on fish community dynamics may be confined to relatively large (>100 ha) and fertile (chlorophyll >20,30,g l,1) Florida lakes. [source]


A multivariate logistic regression equation to screen for dysglycaemia: development and validation

DIABETIC MEDICINE, Issue 5 2005
B. P. Tabaei
Abstract Aims To develop and validate an empirical equation to screen for dysglycaemia [impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and undiagnosed diabetes]. Methods A predictive equation was developed using multiple logistic regression analysis and data collected from 1032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, body mass index (BMI), post-prandial time (self-reported number of hours since last food or drink other than water), systolic blood pressure, high-density lipoprotein (HDL) cholesterol and random capillary plasma glucose as independent covariates for prediction of dysglycaemia based on fasting plasma glucose (FPG) , 6.1 mmol/l and/or plasma glucose 2 h after a 75-g oral glucose load (2-h PG) , 7.8 mmol/l. The equation was validated using a cross-validation procedure. Its performance was also compared with static plasma glucose cut-points for dysglycaemia screening. Results The predictive equation was calculated with the following logistic regression parameters: P = 1 + 1/(1 + e,X) = where X = ,8.3390 + 0.0214 (age in years) + 0.6764 (if female) + 0.0335 (BMI in kg/m2) + 0.0934 (post-prandial time in hours) + 0.0141 (systolic blood pressure in mmHg) , 0.0110 (HDL in mmol/l) + 0.0243 (random capillary plasma glucose in mmol/l). The cut-point for the prediction of dysglycaemia was defined as a probability , 0.38. The equation's sensitivity was 55%, specificity 90% and positive predictive value (PPV) 65%. When applied to a new sample, the equation's sensitivity was 53%, specificity 89% and PPV 63%. Conclusions This multivariate logistic equation improves on currently recommended methods of screening for dysglycaemia and can be easily implemented in a clinical setting using readily available clinical and non-fasting laboratory data and an inexpensive hand-held programmable calculator. [source]


Nonlinear mixed effects pharmacokinetic/pharmacodynamic analysis of the anticonvulsant ameltolide (LY201116) in a canine seizure model

JOURNAL OF VETERINARY PHARMACOLOGY & THERAPEUTICS, Issue 6 2008
P. R. TERRITO
The anticonvulsant ameltolide (LY201116) is a novel potential therapy for the treatment of canine epilepsy. Eight dogs were administered five different oral doses of ameltolide and clinical scoring of the maximal electroshock (MES) induced seizures at 3 and 24 h postdosing were determined in two separate crossover design studies. Plasma ameltolide concentrations were determined at the time of seizures in all dogs and complete plasma concentration-time profiles were also determined in a separate study. A nonlinear mixed effects PK/PD model was fit to the resulting data. A one compartment open model with first order absorption was determined to best fit the ameltolide pharmacokinetics. An effect compartment with a cumulative logistic regression equation was used to establish the PK/PD relationship. The mean bioavailability normalized volume of distribution and the elimination half-life were estimated at 1.20 L/kg and 5.46 h, respectively. The fitted model estimated that from 2 to 15 h following a single 3 mg/kg oral ameltolide dose the mean probability of obtaining a 1 unit reduction in the seizure clinical score severity was greater than 0.80. The utilized PK/PD analysis combined with the canine MES model allowed for the rapid and efficient determination of the plasma ameltolide concentration-anticonvulsant relationship preclinically in dogs. [source]


Risk prediction for Down's syndrome in young pregnant women using maternal serum biomarkers: determination of cut-off risk from receiver operating characteristic curve analysis

JOURNAL OF EVALUATION IN CLINICAL PRACTICE, Issue 2 2007
Hsiao-Lin Hwa MD PhD
Abstract Objective, The aim of this study was to establish a predictive model for Down's syndrome using maternal age as well as maternal serum levels of alpha-fetoprotein (AFP) and human chorionic gonadotropin (hCG), and to identify an optimal cut-off risk in women under the age of 35 years to improve sensitivity. Methods, Logistic regression models were utilized to predict fetal Down's syndrome as a function of maternal age and logarithm of levels of AFP as well as hCG using training data of 20 pregnancies with fetal Down's syndrome and 9730 unaffected pregnancies. Validation was performed using data of another nine affected pregnancies and 3496 unaffected pregnancies. Receiver operating characteristic (ROC) curves were plotted. Results, Based on the newly established logistic regression equations, the optimal cut-off risk from the ROC curve analysis was at 1:499, with a 17.8% false-positive rate and a 90.0% sensitivity. A suboptimal cut-off risk was estimated at 1:332, with a 12.0% false-positive rate and an 80% sensitivity. Conclusion, A predictive model for Down's syndrome was developed using logistic regression. By ROC curve analysis and clinical consideration, the cut-off risk for young pregnant women could be determined. [source]