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PubMedDate not supplied

Analysis of the methacholine dose-response curve: usefulness of a simplified log-logistic model in epidemiological studies.

The object of the study was to find a model to summarize all the information from dose-response curves, by determining the coefficients to be used to compare groups of subjects. Three coefficients were calculated from the following model: F(d)/F(o) = ONE - k(d-delta)alpha+, where F(d)/F(o) was the ratio between FEV1 at dose (d) of methacholine and prechallenge FEV1, 'k' the slope of the relative variation of FEV1 with the dose, 'delta' the threshold dose and 'alpha' a shape factor. The model was applied to the study of hyperresponsiveness in a population of 317 men. The results illustrated the interest of this model which was applicable to 91% of the population and permitted fine discrimination of the groups studied.

Adult↗
PubMed1997

Prediction of low bone mineral density in postmenopausal women by artificial neural network model compared to logistic regression model.

Measuring bone mineral density (BMD) is currently the best modality to diagnose osteoporosis and predict future fractures. The use of risk factors to predict BMD and fracture risk has been considered to be inadequate for precise diagnostic purpose, but it may be helpful as a screening tool to determine who actually needs BMD assessment. Recently, artificial neural network (ANN), a nonlinear computational model, has been used in clinical diagnosis and classification. In the present study, we evaluated the risk factors associated with low BMD in Thai postmenopausal women and assessed the prediction of low BMD using an ANN model compared to a logistic regression model. The subjects consisted of 129 Thai postmenopausal women divided into 2 groups, 100 subjects in the training set and the remaining 29 subjects in the validation set. The subjects were classified as having either low BMD or normal BMD by using BMD value 1 SD lower than the mean value of young adults as the cutoff point. Decreased body weight, decreased hip circumference and increased years since menopause were found to be associated with low BMD at the lumbar spine by logistic regression. For the femoral neck, increased age and decreased urinary calcium were associated with low BMD. The models had a sensitivity of 85.0 per cent, a specificity of 11.1 per cent and an accuracy of 62.0 per cent for the diagnosis of low BMD at the lumbar spine when tested in the validation group. For the femoral neck, the sensitivity, specificity and accuracy were 90.5 per cent, 12.5 per cent, and 69.0 per cent, respectively. Models based on ANN correctly classified 65.5 per cent of the subjects in the validation group according to BMD at the lumbar spine with a sensitivity of 80.0 per cent and a specificity of 33.3 per cent while it correctly classified 58.6 per cent of the subjects at the femoral neck with a sensitivity of 76.2 per cent and a specificity of 12.5 per cent. There was no significant difference in terms of accuracy, sensitivity and specificity in the prediction of low BMD at the lumbar spine or the femoral neck between ANN model and logistic regression model. We concluded that ANN does not perform better than convention statistical methods in the prediction of low BMD. The less than perfect performance of the prediction rules used in the prediction of low BMD may be due to the lack of adequate association between the commonly used risk factors and BMD rather than the nature of the computational models.

Aged↗
PubMed2001

Application of a sigmapolycyclic aromatic hydrocarbon model and a logistic regression model to sediment toxicity data based on a species-specific, water-only LC50 toxic unit for Hyalella azteca.

Two models, a sigmapolycyclic aromatic hydrocarbon (PAH) model based on equilibrium partitioning theory and a logistic-regression model, were developed and evaluated to predict sediment-associated PAH toxicity to Hyalella azteca. A sigmaPAH model was applied to freshwater sediments. This study is the first attempt to use a sigmaPAH model based on water-only, median lethal concentration (LC50) toxic unit (TU) values for sediment-associated PAH mixtures and its application to freshwater sediments. To predict the toxicity (i.e., mortality) from contaminated sediments to H. azteca, an interstitial water TU, calculated as the ambient interstitial water concentration divided by the water-only LC50 in which the interstitial water concentrations were predicted by equilibrium partitioning theory, was used. Assuming additive toxicity for PAH, the sum of TUs was calculated to predict the total toxicity of PAH mixtures in sediments. The sigmaPAH model was developed from 10- and 14-d H. azteca water-only LC50 values. To obtain estimates of LC50 values for a wide range of PAHs, a quantitative structure-activity relationship (QSAR) model (log LC50 - log Kow) with a constant slope was derived using the time-variable LC50 values for four PAH congeners. The logistic-regression model was derived to assess the concentration-response relationship for field sediments, which showed that 1.3 (0.6-3.9) TU were required for a 50% probability that a sediment was toxic. The logistic-regression model reflects both the effects of co-occurring contaminants (i.e., nonmeasured PAH and unknown pollutants) and the overestimation of exposure to sediment-associated PAH. An apparent site-specific bioavailability limitation of sediment-associated PAH was found for a site contaminated by creosote. At this site, no toxic samples were less than 3.9 TU. Finally, the predictability of the sigmaPAH model can be affected by species-specific responses (Hyalella vs Rhepoxynius); chemical specific (PAH vs DDT in H. azteca) biases, which are not incorporated in the equilibrium partitioning model; and the uncertainty from site-specific effects (creosote vs other sources of PAH contamination) on the bioavailability of sediment-associated PAH mixtures.

Animals↗
PubMed2003

Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a simulation study.

Case-control studies are typically analysed using the conventional logistic model, which does not directly account for changes in the covariate values over time. Yet, many exposures may vary over time. The most natural alternative to handle such exposures would be to use the Cox model with time-dependent covariates. However, its application to case-control data opens the question of how to manipulate the risk sets. Through a simulation study, we investigate how the accuracy of the estimates of Cox's model depends on the operational definition of risk sets and/or on some aspects of the time-varying exposure. We also assess the estimates obtained from conventional logistic regression. The lifetime experience of a hypothetical population is first generated, and a matched case-control study is then simulated from this population. We control the frequency, the age at initiation, and the total duration of exposure, as well as the strengths of their effects. All models considered include a fixed-in-time covariate and one or two time-dependent covariate(s): the indicator of current exposure and/or the exposure duration. Simulation results show that none of the models always performs well. The discrepancies between the odds ratios yielded by logistic regression and the 'true' hazard ratio depend on both the type of the covariate and the strength of its effect. In addition, it seems that logistic regression has difficulty separating the effects of inter-correlated time-dependent covariates. By contrast, each of the two versions of Cox's model systematically induces either a serious under-estimation or a moderate over-estimation bias. The magnitude of the latter bias is proportional to the true effect, suggesting that an improved manipulation of the risk sets may eliminate, or at least reduce, the bias.

Canada↗
PubMed1989

Empirical comparisons of proportional hazards and logistic regression models.

We compare parameter estimates from the proportional hazards model, the cumulative logistic model and a new modified logistic model (referred to as the person-time logistic model), with the use of simulated data sets and with the following quantities varied: disease incidence, risk factor strength, length of follow-up, the proportion censored, non-proportional hazards, and sample size. Parameter estimates from the person-time logistic regression model closely approximated those from the Cox model when the survival time distribution was close to exponential, but could differ substantially in other situations. We found parameter estimates from the cumulative logistic model similar to those from the Cox and person-time logistic models when the disease was rare, the risk factor moderate, and censoring rates similar across the covariates. We also compare the models with analysis of a real data set that involves the relationship of age, race, sex, blood pressure, and smoking to subsequent mortality. In this example, the length of follow-up among survivors varied from 5 to 14 years and the Cox and person-time logistic approaches gave nearly identical results. The cumulative logistic results had somewhat larger p-values but were substantively similar for all but one coefficient (the age-race interaction). The latter difference reflects differential censoring rates by age, race and sex.

Adolescent↗
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↗
PubMed1995

Misclassification of a prognostic dichotomous variable: sample size and parameter estimate adjustment.

Under general conditions, Lagakos showed that for an explanatory variable observed with error, the asymptotic relative efficiency (ARE) when using the observed rather than the true values in linear models, logistic models and proportional hazards models for survival is the square of the correlation between the true and observed variables. The result is useful for sample size adjustment when this correlation is estimable. Often, one cannot observe correct values of the explanatory variable under any circumstances. We show, however, that under the models considered by Lagakos for a dichotomous explanatory variable, the ARE equals the kappa statistic in a read-reread protocol. Consequently, one need not know 'truth' in this situation to estimate the ARE and to adjust sample size to maintain desired power; divide the estimated sample size obtained with the assumption of no measurement error by the consistent estimate of the kappa statistic (which is unlikely to be zero or negative). We then develop heuristically an adjusted estimate of the beta parameter in a proportional hazards survival model. The work was motivated by analyses of the Childhood Brain Tumour Consortium database. Examples from this database illustrate the method.

Brain Neoplasms↗
PubMed1996

Lactitol enhances short-chain fatty acid and gas production by swine cecal microflora to a greater extent when fermenting low rather than high fiber diets.

The study was conducted to determine if the response of swine cecal microflora to lactitol (beta-D-galactopyranosyl-(1-->4)-D-sorbitol; 3 mmol/L) varies when fermenting low (LF) or high fiber (HF) predigested diets. The inoculum was collected from four sows fitted with cecal cannulas, pooled, buffered and dispensed in 27 vessels under anaerobic conditions. The LF or HF predigested diets were used as substrate in two separate experiments. In each trial nine vessels were used as controls (C) without feed addition, nine received predigested feed (LF or HF) and the remaining nine vessels received the same amount of feed with the supplementation of lactitol (LF+L or HF+L). Lactitol (L) significantly lowered pH and the acetic to propionic acid ratio in the first 8 h of fermentation in both experiments (P < 0.05). At 4 and 8 h, the addition of lactitol reduced ammonia by 100 and 84% in LF+L and by 56 and 38% in HF+L (P < 0.05). In addition, LF+L and HF+L diets gave higher short-chain fatty acid energy yields by 70 and 40% than LF and HF, respectively (P < 0.05). Two bacterial growth models (logistic and Gompertz) were tested to fit gas production data. The Gompertz equation provided a better fit than the logistic model to gas production data for both LF and HF experiments. Lactitol reduced culture lag time in both experiments by approximately 50%, but it increased gas production rate and maximum gas production by approximately 60% only when the microflora was fermenting the LF predigested diet (P < 0.05). No difference in the duration of the exponential phase due to lactitol was observed in either experiment. Our results indicate that lactitol may be an interesting additive to animal feeding. It controlled harmful fermentation processes and stimulated short-chain fatty acid production to a greater extent in low than in high fiber diets, suggesting an improved fermentation of low fiber feed carbohydrates and eventually an increased availability of short-chain fatty acids for the host.

Acetates↗
PubMed1996

Clinical performances of galactosyl hydroxylysine, pyridinoline, and deoxypyridinoline in postmenopausal osteoporosis.

We have previously shown that galactosyl hydroxylysine (GHYL), pyridinoline (PYD), and deoxypyridinoline (DPD) have a better accuracy and discriminate power than hydroxyproline in distinguishing postmenopausal osteoporotic women from premenopausal controls. In this study, we evaluated the clinical performances of GHYL, PYD, and DPD, alone or in combination, in distinguishing postmenopausal osteoporotic women (OPBD, n = 26) from age-matched controls (CBD, n = 19). The diagnosis of osteoporosis was based upon the bone density (BD) of the lumbar spine measured by quantitative computed tomography (CBD: BD > 108 mg/cm3; OPBD: BD < 70 mg/cm3). Urinary excretion of GHYL, PYD, and DPD were measured by HPLC, and all data were expressed as the molar ratio with the creatinine excretion (GHYL/CR, PYD/CR, and DPD/CR). The clinical performances were tested by: Z score analysis (Z), Receiver Operated Characteristic curve analysis (%Acc) and logistic-regression analysis of the posterior probabilities for prediction from a logistic model (LOGIST). GHYL/CR, PYD/CR, and DPD/CR were significantly increased in OPBD compared with CBD. The clinical performances were similar for the three assays, with slightly better performances for GHYL/CR (GHYL/CR: Z = 3.14, %Acc = 70 +/- 8, LOGIST P = 0.01; PYD/CR: Z = 2.19, %Acc = 67 +/- 8, LOGIST P = 0.051; DPD/CR: Z = 2.13, %Acc = 65 +/- 8, LOGIST P = 0.06). None of the possible combinations of the three assays yielded better clinical performances than GHYL/CR alone. In conclusion, this study further confirms the validity of GHYL, PYD, and DPD as markers of bone resorption.

Aged↗