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At least 613 records · Page 34Linked to original sources
PubMed1996

Measuring habituation in infants: an approach using regression analysis.

The effectiveness of different habituation criteria was examined by means of computer simulations. A criterion based on fitting a second-order polynomial regression function to the looking time data was described. This criterion produced more accurate estimation of looking times as well as higher experimental power for detecting novelty effects, compared to the traditional windowed running average criterion or to a criterion based on linear regression. The polynomial regression approach probably has this advantage because it utilizes all of the available looking time data, rather than just the data in the current windowed average, and because it is sensitive to nonlinear trends in looking time. This new habituation criterion is easy to implement on a laboratory computer, and it should increase session lengths by no more than one trial or so, compared to windowed average criteria. With regard to test-retest reliability, all of the habituation criteria that were evaluated appear to have low reliability on average, with high sample-to-sample variability. These undesirable reliability characteristics are attributable to the high variability of infants' attentional behavior.

Attention↗
PubMed1994

Hierarchical regression analysis applied to a study of multiple dietary exposures and breast cancer.

Hierarchical regression attempts to improve standard regression estimates by adding a second-stage "prior" regression to an ordinary model. Here, we use hierarchical regression to analyze case-control data on diet and breast cancer. This regression yields semi-Bayes relative risk estimates for dietary items by using a second-stage model to pull estimates toward each other when the corresponding variables have similar levels of nutrients. Unlike classical Bayesian analysis, however, no use is made of previous studies on nutrient effects. Compared with results obtained with one-stage conditional maximum-likelihood logistic regression, our hierarchical regression model gives more stable and plausible estimates. In particular, certain effects with implausible maximum-likelihood estimates have more reasonable semi-Bayes estimates.

Bayes Theorem↗
PubMed1999

Characterization of breast masses by dynamic enhanced MR imaging. A logistic regression analysis.

PURPOSE: To identify features useful for differentiation between malignant and benign breast neoplasms using multivariate analysis of findings by MR imaging. MATERIAL AND METHODS: In a retrospective analysis, 61 patients with 64 breast masses underwent MR imaging and the time-signal intensity curves for precontrast dynamic postcontrast images were quantitatively analyzed. Statistical analysis was performed using a logistic regression model, which was prospectively tested in another 34 patients with suspected breast masses. RESULTS: Univariate analysis revealed that the reliable indicators for malignancy were first the appearance of the tumor border, followed by the washout ratio, internal architecture after contrast enhancement, and peak time. The factors significantly associated with malignancy were irregular tumor border, followed by washout ratio, internal architecture, and peak time. For differentiation between benignity and malignancy, the maximum cut-off point was to be found between 0.47 and 0.51. In a prospective application of this model, 91% of the lesions were accurately discriminated as benign or malignant lesions. CONCLUSION: Combination of contrast-enhanced dynamic and postcontrast-enhanced MR imaging provided accurate data for the diagnosis of malignant neoplasms of the breast. The model had an accuracy of 91% (sensitivity 90%, specificity 93%).

Adult↗
PubMed2000

Atypical antipsychotics in the treatment of schizophrenia: systematic overview and meta-regression analysis.

OBJECTIVE: To develop an evidence base for recommendations on the use of atypical antipsychotics for patients with schizophrenia. DESIGN: Systematic overview and meta-regression analyses of randomised controlled trials, as a basis for formal development of guidelines. SUBJECTS: 12 649 patients in 52 randomised trials comparing atypical antipsychotics (amisulpride, clozapine, olanzapine, quetiapine, risperidone, and sertindole) with conventional antipsychotics (usually haloperidol or chlorpromazine) or alternative atypical antipsychotics. MAIN OUTCOME MEASURES: Overall symptom scores. Rate of drop out (as a proxy for tolerability) and of side effects, notably extrapyramidal side effects. RESULTS: For both symptom reduction and drop out, there was substantial heterogeneity between the results of trials, including those evaluating the same atypical antipsychotic and comparator drugs. Meta-regression suggested that dose of conventional antipsychotic explained the heterogeneity. When the dose was </=12 mg/day of haloperidol (or equivalent), atypical antipsychotics had no benefits in terms of efficacy or overall tolerability, but they still caused fewer extrapyramidal side effects. CONCLUSIONS: There is no clear evidence that atypical antipsychotics are more effective or are better tolerated than conventional antipsychotics. Conventional antipsychotics should usually be used in the initial treatment of an episode of schizophrenia unless the patient has previously not responded to these drugs or has unacceptable extrapyramidal side effects.

Amisulpride↗
PubMed1999

Multiple regression analysis of octanol/water partition coefficients of non-ionic monomeric radiographic contrast media with the combination of three molecular descriptors.

A multiple non-linear regression equation is calculated for the correlation of the octanol/water partition coefficient of nine non-ionic monomeric radiographic contrast media molecules with a combination of three molecule describing parameters (heat of formation, dipole and ovality). Good correlation is obtained (proportion of variance explained, R2, is 0.9093). This simple equation can be used prospectively in the design of new compounds analogous to the used series. It was used to calculate prospectively the partition coefficient of another compound of the same family, iomeprol.

Chemical Phenomena↗
PubMed2003

Weighted estimating equations for linear regression analysis of clustered failure time data.

Estimation of regression parameters in linear survival models is considered in the clustered data setting. One step updates from an initial consistent estimator are proposed. The updates are based on scores that are functions of ranks of the residuals, and that incorporate weight matrices to improve efficiency. Optimal weights are approximated as the solution to a quadratic programming problem, and asymptotic relative efficiencies to various other weights computed. Except under strong dependence, simpler methods are found to be nearly as efficient as the optimal weights. The performance of several practical estimators based on exchangeable and independence working models is explored in simulations.

Cluster Analysis↗
PubMedDate not supplied

Testing the harvesting hypothesis by time-domain regression analysis. II: covariate effects.

This article extends the previous work of Fung et al. (2004) investigating the ability of the time-scale log-linear regression model, proposed by Dominici et al. (2003), to detect mortality displacement (sometimes known as harvesting) in time-series data relating air pollution to excess mortality. We conducted a simulation study based on two different compartment models of the death process: pure frailty model and mixed frailty model. We assume that nonaccidental death only affects frail population in a pure frailty model and affects both frail people and other individuals in the mixed frailty model. With a pure frailty model and a moderate-size pollution effect, we identified a characteristic mortality displacement pattern in the different time-scale coefficients of log relative risk. However, once a covariate like temperature was introduced into the model, such a mortality displacement pattern disappeared. Furthermore, a false mortality displacement effect was present in the incorrectly specified model, when temperature was not taken into account. We believe that time-scale regression has limited value for detecting mortality displacement in time-series data.

Air Pollution↗
PubMed1993

Sample size and power determination for a binary outcome and an ordinal exposure when logistic regression analysis is planned.

General methods of sample size determination for logistic regression analyses are now available, but these will often require substantial information for their application. The author presents methods useful in the special case of a binary outcome and a three-level quantitative exposure, which includes application to a three-level ordinal exposure for a specified scaling. The computationally simple methods were developed in planning an investigation of the prognostic value of multidrug resistance gene (mdr1) expression in sarcoma. Because logistic regression was planned for the analysis, calculations were based on the ability to detect a linear trend in the log odds of tumor response to chemotherapy associated with increases in the level of mdr1 expression from negative to low positive to high positive. Closed form expressions were used to assess sensitivity to the ordinal scaling and the distribution of the mdr1 levels, and to the assumption of a linear trend in the log odds versus a linear trend in the proportions.

Case-Control Studies↗