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PubMed1989

Modelling sibship environment in the regressive logistic model for familial disease.

Recently analytical models for pedigree disease data have been developed that combine genetic and epidemiological modelling techniques. The regressive logistic model [Bonney, Biometrics 42: 611-625; 1986] relies on decomposing the likelihood of a pedigree into the product of conditional probabilities, one for each individual, by imposing a (natural) order on pedigree members. In addition to modelling measured epidemiological variables, vertical transmission, transmission of unmeasured ousiotypes (a special case being genotypes), and some modelling of sibship dependencies have been proposed. In this paper the model is extended to include an unmeasured sibship environment factor using a log-linear model for binary pedigree traits [Hopper et al., Genet Epidemiol 1: 183-188; 1984], which breaks the pedigree into conditionally independent groups. Statistical issues, such as designs for which these factors will be discernible and tests of fit, are discussed.

Environment↗

PubMed1988

Logistics of automated plasma collection.

Plasmapheresis is widely carried out to produce plasma for fractionation. Production of Factor VIII and albumin, two proteins in plasma, drives the plasma industry. The Canadian Red Cross would like to achieve national self-sufficiency in plasma production to meet the rising demands for these proteins in the next 4 years. In order to achieve this goal we must make efficient use of our automated plasmapheresis machines and available human resources to produce a cost-effective plasma product. An assessment was undertaken at the Ottawa Centre to evaluate the number of procedures which could be performed on each machine per day and the staff required to operate these machines safely and efficiently. Donor availability, reliability and reasons for donating plasma were recorded to determine if our population could support such an escalated programme. Donor/staff interest and acceptance of automated equipment was determined. The results showed that four automated plasmapheresis devices could be operated by one nurse and one clinic assistant processing 32 donors a day with an average time of 27 min required for a 500-ml donation of plasma. The donor population was available and extremely interested; however, the logistics of scheduling proved to be an area for concern requiring special attention.

Autoanalysis↗

PubMed2007

The CADEUS study: methods and logistics.

PURPOSE: At the request of the French Health authorities, a study called CADEUS (COX-2 inhibitors and NSAIDs: description of users) aimed to describe the users of cyclo-oxygenase (COX)-2 inhibitors and traditional non-selective non-steroidal anti-inflammatory drugs (tNSAIDs). We report here the methodology, logistics and study design performances. METHODS: CADEUS is a cohort study designed to include 40,000 patients randomly sampled monthly in the French National Healthcare Insurance database, who received at least one dispensation of celecoxib, rofecoxib or tNSAIDs (1:1:2), from September 2003 to August 2004. Patients and prescribers were asked to fill a questionnaire on indication, medical history, risk factors and hospitalizations since drug acquisition. There was no reminder. For each respondent, healthcare resources used for the 6 months before and after inclusion were extracted from the database. Response rate, response delay, responders and non-responders characteristics were assessed. RESULTS: Of the 222,879 patients and their prescribers contacted, 20.8% patients and 32.6% prescribers responded. Median response delay was 16 days for patients and 17 days for physicians. Factors associated with patient response were age, cohort, type of prescriber and period of inclusion. CONCLUSION: This is the first study of this design in France, combining data from a claims database and direct patient and prescriber questionnaires.

Adult↗

PubMedDate not supplied

The logistic function as an aid in the detection of acute coronary disease in emergency patients (a case study).

We empirically developed a logistic function to predict acute coronary heart disease (ACHD) and then tested it to determine its usefulness to emergency room physicians in diagnosis and admission of patients with suspected ACHD to the coronary care unit. The function was based on nine clinical, historical and electrocardiographic variables from a set of 105 variables collected on 643 patients with suspected ACHD. In the second phase of the study, we provide the probabilities generated by the function to emergency room physicians during alternate months as a supplement to existing diagnostic modalities. Use of the probability of ACHD (401 patients with probabilities versus 455 patients without probabilities) resulted in statistically significant improvement in diagnostic rates and reduction in the number of inappropriate admissions to the coronary care unit.

Acute Disease↗

PubMedDate not supplied

Bias correction in maximum likelihood logistic regression.

This paper provides an expression for bias of the maximum likelihood logistic regression estimates for use with small sample sizes. This bias correction is based on an expansion of the maximum likelihood equation. Simulation results show these corrections to be highly effective in small samples.

Probability↗

PubMedDate not supplied

The performance of logistic discrimination on myocardial infarction data, in comparison with some other discriminant analysis methods.

The Imminent Myocardial Infarction study Rotterdam (IMIR), concerns patients who visit their general practitioners and have complaints suspected to be of cardiac origin. The study aims to develop a protocol for diagnosing myocardial infarction without laboratory assistance. We use the IMIR data, consisting of continuous and binary variables, to compare the diagnostic performance of logistic discrimination with some other discriminant analysis techniques. We discuss which characterizations of mixed data sets may give indications for an appropriate choice from among the alternative methods for discrimination.

Humans↗

PubMedDate not supplied

The logistic analysis of epidemiologic prospective studies: investigation by simulation.

We performed a Monte Carlo computer simulation of the Walker-Duncan logistic regression technique in a typical epidemiologic prospective setting and analysed the results with respect to the accuracy and reliability of the regression estimates and the associated statistical significance tests (Z-tests). The results strongly suggest that the estimates were neither accurate nor reliable. The magnitude of the difference between the average estimated regression coefficient and its true population value did not necessarily decrease as the sample size increased. The average estimated standard deviation of the estimate of the regression coefficient either overestimated or underestimated the actual standard deviation, the former occurring most, but not all, of the time. The significance tests (a two-tailed Z-test with a significance level of 0.05) had actual type I errors ranging from 0.00 to 0.24 for different samples. This approach is therefore inadequate as an epidemiologic tool for analysis of a Framingham-type prospective study. Further simulation studies are indicated.

Coronary Disease↗

PubMedDate not supplied

On the use of the logistic model in prospective studies.

We describe the usual statistical concepts and consequent appropriate simulations of a prospective study for the simple case of a single risk variable and an assumed logistic model. We examine the simulations of Lilienfeld and Pyne, and show that they are seriously flawed. Contrary to those authors' claims, the estimates of parameters by the Walker-Duncan technique are both accurate and reliable.

Biometry↗

PubMedDate not supplied

Assessing the adequacy of the logistic regression model for matched case-control studies.

We apply results for the detection of outliers in logistic regression to the analysis of matched pairs data and illustrate the techniques with data from a matched pairs study of the relationship between iron-binding proteins and mortality in the Solomon Islands. Although the study includes 90 matched pairs, the conclusions change substantially with deletion of one or two pairs. Thus, even with reasonably large data sets, single observations may have a great impact on the results of an analysis. The identification of influential observations should constitute part of the analysis of matched case-control data.

Clinical Trials as Topic↗

PubMedDate not supplied

Small sample estimation of log odds ratios from logistic regression and fourfold tables.

Schaefer has proposed a method to correct the maximum likelihood logistic regression coefficients for bias in small samples. We show here that this reduces, in the special case of a single dichotomous regression variable, to an earlier result of Haldane, for the estimation of a single log odds ratio. This paper reviews various estimators for the log odds ratio in a fourfold table, and compares the properties of two by complete enumeration in sets of tables with small sample sizes. The first of these, also due to Haldane, is based on the addition of 1/2 to each cell of the table; the second is where 1/2 is added to all cells of the table only if a zero frequency arises. We evaluate the bias and mean squared error of both of these estimators in sets of tables with various sample sizes and odds ratios. Haldane's estimator usually has lower bias and MSE, and so we do not in general recommend the practice of adding 1/2 only as necessary. Exceptions might occur if one has good a priori estimates of the outcome probabilities in the two samples under comparison.

Regression Analysis↗

PubMed1988

Evaluating the discriminatory power of a multiple logistic regression model.

Various measures for estimating the goodness-of-fit of the multiple logistic regression (MLR) model have been suggested, although there is no clear consensus as to which measure is most suitable. In this paper, a simple measure of the discriminatory power of the fitted MLR model, based on maximization of Youden's J index (J*), is proposed and compared with several goodness-of-fit statistics described previously. The relative effectiveness of the measure is illustrated using data from the Lipid Research Clinics Prevalence Study. It is suggested that J* may be a useful alternative index of goodness-of-fit of an MLR model, with the added advantage of having a simple practical interpretation.

Adult↗

PubMed1988

An uncertainty measure in logistic discrimination.

The effect of sampling variation on individual decisions and error rates in logistic discriminant analysis is discussed. The concept of the beta-confidence allocation rule is introduced, which allows testing of whether observations are (in)correctly assigned at a given significance level. The procedure applied to sample data adds valuable information on the sharpness and the stability of the estimated classification rule. The method also suggests that individual posterior probabilities should be associated with a credibility measure. The potential of the approach is illustrated by an example from patients with liver disease.

Chronic Disease↗

PubMed1988

Correspondence analysis and logistic modelling: complementary use in the analysis of a health survey among nurses.

Logistic or other modelling approaches are often appropriate for studying epidemiological data. However, health surveys may be more complex. With numerous variables, there is a need for exploratory analysis. Parsimonious description of the data is also a useful complement to modelling. Correspondence analysis may be useful in such exploratory phases. An example of the complementary use of the two approaches is presented in the context of a health survey among nurses, which focused on the relationship between shift and night work, and declared health problems.

Adult↗

PubMed1989

Sample size tables for logistic regression.

Sample size tables are presented for epidemiologic studies which extend the use of Whittemore's formula. The tables are easy to use for both simple and multiple logistic regressions. Monte Carlo simulations are performed which show three important results. Firstly, the sample size tables are suitable for studies with either high or low event proportions. Secondly, although the tables can be inaccurate for risk factors having double exponential distributions, they are reasonably adequate for normal distributions and exponential distributions. Finally, the power of a study varies both with the number of events and the number of individuals at risk.

Adult↗

PubMed1990

Practical Bayesian analysis of a simple logistic regression: predicting corneal transplants.

The Bayesian analysis of a logistic regression model is described using an example of predicting the need for a corneal transplant in keratoconus. Controversy over the use of subjective prior information in Bayesian methods is avoided by a formulation representing negligible prior information. Simple computational procedures are described, and it is argued that the results are more accurate, clearer and make fuller use of the information contained in the data. Analysis of more complex models is considered. In particular, it is argued that classical methods as implemented in the computer package GLIM can be used as approximations to Bayesian methods, particularly at the initial stage of model selection.

Bayes Theorem↗

PubMed1995

Risk scores from logistic regression: unbiased estimates of relative and attributable risk.

In epidemiology, the risk of disease in terms of a set of covariates is often modelled by logistic regression. The resulting linear predictor can be used to define the extent of risk between extremes, and to calculate an attributable risk for the covariates taken together. As is well known, straightforward use of the linear predictor, on the sample from which it was derived, to obtain estimates the relative and attributable risk will be biased, often seriously. Use of the jack-knife technique is extended to produce asymptotically unbiased estimates of relative and attributable risks. The asymptotic variances associated with these estimates are derived by using the formulae of conditional variances. They are applied to the results of a case-control study of stomach cancer.

Bias↗

PubMed2002

A case study of logistic QSAR modeling methods and robustness tests.

Acute toxicity (15-min EC(50)) determination of 16 substituted naphthalene compounds to Photobacterium phosphoreum was undertaken according to the standard procedures, while the effects of molecular structures of selected compounds on their toxicity to test microorganisms were logistically conducted using the quantitative structure-activity relationship (QSAR) technique. The relationship was developed as -log EC(50)=5.5916 (+/-0.1189)-7.4893(+/-0.4900)qH(+)+0.7771(+/-0.0619E(lumo)+0.0088(+/-0.0009)alpha(N=16, R(2)(adj)=0.9698, SE=0.0892, P=0.0000). The cluster analyses of individual structure descriptors as well as the quality control chart and Monte Carlo simulation test indicated that the prediction model was reasonable and robust even if tested with several different methods. Furthermore, the quantum chemical parameters entering into the QSAR model were used to discuss the possible toxicity pathways, and the results revealed that the selected compounds were reactive and their toxicity behaviors were complex processes containing physical partition stages as well as biochemical reaction stages.

Lethal Dose 50↗

PubMed1998

Spatial structure, environmental heterogeneity, and population dynamics: analysis of the coupled logistic map.

Spatial extent can have two important consequences for population dynamics: It can generate spatial structure, in which individuals interact more intensely with neighbors than with more distant conspecifics, and it allows for environmental heterogeneity, in which habitat quality varies spatially. Studies of these features are difficult to interpret because the models are complex and sometimes idiosyncratic. Here we analyze one of the simplest possible spatial population models, to understand the mathematical basis for the observed patterns: two patches coupled by dispersal, with dynamics in each patch governed by the logistic map. With suitable choices of parameters, this model can represent spatial structure, environmental heterogeneity, or both in combination. We synthesize previous work and new analyses on this model, with two goals: to provide a comprehensive baseline to aid our understanding of more complex spatial models, and to generate predictions about the effects of spatial structure and environmental heterogeneity on population dynamics. Spatial structure alone can generate positive, negative, or zero spatial correlations between patches when dispersal rates are high, medium, or low relative to the complexity of the local dynamics. It can also lead to quasiperiodicity and hyperchaos, which are not present in the nonspatial model. With density-independent dispersal, spatial structure cannot destabilize equilibria or periodic orbits that would be stable in the absence of space. When densities in the two patches are uncorrelated, the probability that the population in a patch reaches extreme low densities is reduced relative to the same patch in isolation; this "rescue effect" would reduce the probability of metapopulation extinction beyond the simple effect of spreading of risk. Pure environmental heterogeneity always produces positive spatial correlations. The dynamics of the entire population is approximated by a nonspatial model with mean patch characteristics. This approximation worsens as the difference between the patches increases and the dispersal rate decreases: Under extreme conditions, destabilization of equilibria and periodic orbits occurs at mean parameter values lower than those predicted by the mean parameters. Apparent within-patch dynamics are distorted: The local population appears to have the wrong growth parameter and a constant number of immigrants (or emigrants) per generation. Adding environmental heterogeneity to spatial structure increases the occurrence of spatially correlated population dynamics, but the resulting temporal dynamics are more complex than would be predicted by the mean parameter values. The three classes of spatial pattern (positive, negative, and zero correlation), while still mathematically distinct, become increasingly similar phenomenologically.

Animals↗