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PubMedDate not supplied

Regression analysis of digital ridge counts on pattern intensity index: a developmental interpretation.

Regression analyses of digital ridge counts on pattern intensity index (PII) are performed on data from three populations. The results for two Papua New Guinean populations (608 subjects from Karkar Island and 397 from Lufa sub District) are compared with those for a dermatoglyphically very different population from the Faroe Islands (296 subjects). Digital relationships are examined within each population. Equating the statistical regression of digital ridge counts with the embryological regression of apical volar pads, some biological interpretations of the findings are suggested. The results indicate that developmental fields across the embryological hand interact with genotype to produce digital differences in dermatoglyphic phenotype.

Denmark↗
PubMed1986

An insight on the use of multiple logistic regression analysis to estimate association between risk factor and disease occurrence.

Multiple logistic regression is an accepted statistical method for assessing association between an anticedant characteristic (risk factor) and a quantal outcome (probability of disease occurrence), statistically adjusting for potential confounding effects of other covariates. Yet the method has potential drawbacks which are not generally recognized. This article considers one important drawback of logistic regression. Specifically the so-called main effect logistic model assumes that the probability of developing disease is linearly and additively related to the risk factors on the logistic scale. This assumption stipulates that for each risk factor, the odds ratio is constant over all reference exposure levels, and that the odds ratio exposed to two or more factors is equal to the product of individual risk factor odds ratios. If the observed odds ratios in the data follow this pattern, the model-predicted odds ratios will be accurate, and the meaning of the odds ratio for each risk factor will be straightforward. But if the observed odds ratios deviate from the model assumption, the model will not fit the data accurately, and the model-predicted odds ratios will not reflect those in the data. Although satisfactory fit can always be achieved by adding to the model polynomial and product terms derived from the original risk factors, the odds ratios estimated by such an interaction logistic model are difficult to interpret, viz., the odds ratio for each risk factor depends not only on the reference exposure levels of that factor, but also on the exposure level in other factors.(ABSTRACT TRUNCATED AT 250 WORDS)

Environmental Exposure↗
PubMed2000

Random-effects regression analysis of correlated grouped-time survival data.

Random-effects regression modelling is proposed for analysis of correlated grouped-time survival data. Two analysis approaches are considered. The first treats survival time as an ordinal outcome, which is either right-censored or not. The second approach treats survival time as a set of dichotomous indicators of whether the event occurred for time periods up to the period of the event or censor. For either approach both proportional hazards and proportional odds versions of the random-effects model are developed, while partial proportional hazards and odds generalizations are described for the latter approach. For estimation, a full-information maximum marginal likelihood solution is implemented using numerical quadrature to integrate over the distribution of multiple random effects. The quadrature solution allows some flexibility in the choice of distributions for the random effects; both normal and rectangular distributions are considered in this article. An analysis of a dataset where students are clustered within schools is used to illustrate features of random-effects analysis of clustered grouped-time survival data.

Biometry↗
PubMed1990

The role of hippocampal commissures in the interhemispheric transfer of epileptiform afterdischarges in the rat: a study using linear and non-linear regression analysis.

The role of the forebrain commissures and the septal area in the interhemispheric transfer of hippocampal afterdischarges (ADs) was investigated in the rat under halothane anesthesia. Electrical seizures were elicited from the dorsal hippocampus before and after commissurotomy. The degree of relatedness between EEG signals recorded from homologous sites of both hippocampi was quantified using two approaches: (i) a time domain analysis considering an AD as a succession of discrete bursts; the onset times of such bursts were measured and used to estimate interhemispheric onset delays; (ii) using signal analysis the linear (r2) and non-linear (h2) regression coefficients between pairs of EEG signals were computed as a function of time shift between the two signals. In this way the values of association (linear and non-linear) and the corresponding time delays were measured. In general a tetanus applied unilaterally to the dorsal CA3 field resulted in bilaterally synchronous ADs. The estimated interhemispheric time delay was in most cases zero. This bilateral synchrony disappeared after section of a specific part of the ventral hippocampal commissure (VHC), the dorso-caudal third, but was not affected by section of other commissural fibers or by a lesion of the septal area. This study also allowed evaluation of different methods of quantification of the association between EEG signals, namely the linear (r2) and the non-linear (h2) regression coefficients. The latter was shown to be a more robust measure than the former and to yield values of association even in cases in which r2 was at noise level. The experimental findings allow the conclusion that ADs elicited from an epileptogenic focus spread to homologous sites in the contralateral hemisphere following commissural systems that may be strong enough to ensure the forming of one bilateral oscillating system.

Animals↗
PubMed1988

Regression analysis of prognostic factors in colorectal cancer after curative resections.

The clinical, laboratory, and pathologic data of 310 patients who had curative resections were prospectively collected and analyzed in a multiple stepwise regression model. Although several factors (i.e., venous invasion) were of importance in univariate analysis, the following conclusions reflect the outcome and relative importance of the regression analysis only. Blood loss as an initial symptom and duration of symptoms were associated with a better prognosis. Location of the primary tumor, age, and sex did not appear to have prognostic value. Observations during operation such as palpable lymph nodes, fixity to adjacent organs, and tumor spill were related to a diminished tumor-free survival. Laboratory data (hemoglobin, leukocytes, ESR, GGTP, SGOT, SGPT, LDH, total protein, CEA) were tested for their potential prognostic values. Only a preoperative low protein level or an elevated CEA level were associated with an increased risk of death due to recurrent tumor. The histopathologic features (stage and grade), with the exception of venous invasion, were of relative importance in the determination of prognosis. The aforementioned variables can be included in a prognostic index on the base of which high-risk groups suitable for adjuvant studies can be identified.

Adenocarcinoma↗
PubMed1998

[Epidemiologic and statistical interaction models and consequences for regression analysis].

Different ways to define interaction between exposition factors in epidemiological studies as well as the choice between additive and multiplicative no interaction leads frequently to confusion during data analysis. In their standard form methods of event data analysis such as Poisson or logistic regression assume a multiplicative parameterization of no interaction. However, evidence from empirical investigations as well as causal models of disease etiology, e.g. the simple independent action model of Finney or the sufficient-component-causes model of Rothman, suggest additive or other kinds of non-multiplicative concepts of no interaction. For additive structured data we illustrate the asymptotic bias ("interaction-bias") of main effect estimates which are based on inappropriate data analysis using multiplicative models and omitting significant or non-significant interaction terms. We show that both the epidemiological study design as well as the underlying causal model are determinants of the interaction structure of the data and should be considered in the model selection process. The definition of interaction should distinguish between risk, rate and odds if risks are not very small. Using generalized linear models with parametrical link functions we are able to analyze non-multiplicative interaction structures.

Adult↗
PubMedDate not supplied

A free derivative program for non-linear regression analysis of enzyme kinetics to be used on small computers.

A non-linear regression program, written in BASIC, is described. The program uses the Marquardt 's algorithm as modified by Reich et al. (Eur. J. Biochem., 26 (1972) pp. 368-379). The user only supplies the expression to fit, since the program uses numerical differentiation. It is possible to fit models of 1 substrate, 2 substrates, 1 substrate and 1 inhibitor, and 2 substrates and 1 inhibitor. Likewise, several weighting patterns, as well as a simple or robust regression, can be selected.

Computers↗
PubMed1989

Linear regression analysis for enzyme kinetic studies in toxicant interactions.

A new method based on linear regression of per cent modulation in enzyme activity over substrate concentration is proposed to give an interpretative base to the influence of more than one modulator/inhibitor/toxicant, a situation commonly met with in toxicological studies-toxicant interactions. Advantages of this method over the classical kinetic methods are discussed in detail using brain Mg2+ ATPase activity of the fish Tilapia mossambica under in vitro regimes of hexachlorocyclohexane and malathion.

Acetylcholinesterase↗
PubMed1992

Rumen protein degradation rates estimated by non-linear regression analysis of Michaelis-Menten in vitro data.

An in vitro method applying Michaelis-Menten saturation kinetics was developed as an alternative approach for estimating protein degradation rates in the rumen. Non-linear regression (NLR) analysis of the integrated Michaelis-Menten equation yielded fractional degradation rates, kd, from direct estimates of the maximum velocity: Michaelis constant ratio (kd = Vmax:Km). Degradation rates obtained using data from a series of 2 h inhibitor in vitro incubations were respectively 0.989, 0.134, and 0.037/h for casein, solvent soya-bean meal (SSBM) and expeller soya-bean meal (ESBM). Degradation rates obtained from 2 h incubations had lower standard errors than those obtained using 1 h incubations; 2 h rates were not significantly different from 1 h rates, suggesting end-product inhibition was not significant at 2 h. The NLR Michaelis-Menten method was used to determine degradation rates for twelve protein sources: casein, bovine serum albumin, two samples of lucerne (Medicago sativa) hay, and four samples each of SSBM and ESBM. Statistical analysis of NLR results revealed significant differences among the twelve protein sources. Casein was degraded most rapidly (0.827/h), and the four ESBM samples most slowly (0.050-0.098/h). Degradation rate for serum albumin was 0.135/h; rates for SSBM and lucerne hays ranged from 0.160 to 0.208/h. Degradation rates estimated using the NLR method were more rapid than those obtained with a limited substrate approach; NLR rates were more consistent with in vivo estimates of rumen protein escape. Greater concentrations of slowly degraded proteins were needed with the NLR method to define curvilinearity of the degradation curve more accurately.

Animals↗
PubMed2002

Random effects logistic regression analysis with auxiliary covariates.

We study a semiparametric estimation method for the random effects logistic regression when there is auxiliary covariate information about the main exposure variable. We extend the semiparametric estimator of Pepe and Fleming (1991, Journal of the American Statistical Association 86, 108-113) to the random effects model using the best linear unbiased prediction approach of Henderson (1975, Biometrics 31, 423-448). The method can be used to handle the missing covariate or mismeasured covariate data problems in a variety of real applications. Simulation study results show that the proposed method outperforms the existing methods. We analyzed a data set from the Collaborative Perinatal Project using the proposed method and found that the use of DDT increases the risk of preterm births among U.S. children.

Biometry↗
PubMed1984

Linear regression analysis with repeated measurements.

The statistical aspects of repeated measures linear regression, in which each subject contributes several pairs of measurements to the analysis, are discussed. It is shown that the standard error of a regression coefficient computed from the ordinary least squares analysis can either underestimate or overestimate the true standard error, depending on how values of the independent variable are selected. The application of generalized least squares analysis to repeated measures data is also discussed. Two examples are given.

Adolescent↗