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R J Carroll

Publications and source records attributed to R J Carroll.

81 records · Page 5Linked to original sources
PubMed1975

Temporal heterogeneity of myocardial blood flow in anesthetized dogs.

Temporal variation in perfusion to small segments of the myocardium was studied in 19 opened-chest dogs. In six control dogs, three or four differently labeled 7-10 mu microspheres were injected simultaneously into the left atrium to assess the variability in measured myocardial perfusion due to the microsphere technique. In 13 other dogs, microspheres were injected four times at 5 minute intervals while various hemodynamics parameters (mean aortic pressure, peak systolic pressure, heart rate, left ventricular end-diastolic pressure, and Vmax) were stable (less than 10% variation in any one parameter). The left ventricles were divided into 96 segments, the mean weight+/- SD of each segment was 0.95+/-0.17 grams. The flow to each segment was expressed as a percent of the mean flow of the three or four measured flows to that segment, and the difference between the largest and the smallest percent of each segment was taken as a measure of the variability of flow to that segment. The average variability of segmental flow (mean +/-SD) when the three to four differently labeled microspheres were injected was 14.0+/-4.7; and the variability when differently labeled microspheres injected sequentially was 31.0+/-10.8% (P less than 0.001). Furthermore, in the sequentially injected animals the magnitude of temporal variation was similar in various subdivisions of the ventricle (layers, walls, apex to base). The mean and standard deviation of the variability of flow to the endo, mid, and epicardial layers were 28.7+/-10.2, 30.0+/-11.3 and 34.5+/-12.4%, respectively. These changes may reflect either spontaneous or local autoregulatory changes in precapillary sphincters or arterioles.

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PubMedDate not supplied

Segmented regression with errors in predictors: semi-parametric and parametric methods.

We consider the estimation of parameters in a particular segmented generalized linear model with additive measurement error in predictors, with a focus on linear and logistic regression. In epidemiologic studies segmented regression models often occur as threshold models, where it is assumed that the exposure has no influence on the response up to a possibly unknown threshold. Furthermore, in occupational and environmental studies the exposure typically cannot be measured exactly. Ignoring this measurement error leads to asymptotically biased estimators of the threshold. It is shown that this asymptotic bias is different from that observed for estimating standard generalized linear model parameters in the presence of measurement error, being both larger and in different directions than expected. In most cases considered the threshold is asymptotically underestimated. Two standard general methods for correcting for this bias are considered; regression calibration and simulation extrapolation (simex). In ordinary logistic and linear regression these procedures behave similarly, but in the threshold segmented regression model they operate quite differently. The regression calibration estimator usually has more bias but less variance than the simex estimator. Regression calibration and simex are typically thought of as functional methods, also known as semi-parametric methods, because they make no assumptions about the distribution of the unobservable covariate X. The contrasting structural, parametric maximum likelihood estimate assumes a parametric distributional form for X. In ordinary linear regression there is typically little difference between structural and functional methods. One of the major, surprising findings of our study is that in threshold regression, the functional and structural methods differ substantially in their performance. In one of our simulations, approximately consistent functional estimates can be as much as 25 times more variable than the maximum likelihood estimate for a properly specified parametric model. Structural (parametric) modelling ought not be a neglected tool in measurement error models. An example involving dust concentration and bronchitis in a mechanical engineering plant in Munich is used to illustrate the results.

Bias↗
PubMedDate not supplied

The partial questionnaire design for case-control studies.

We propose an alternative to a long questionnaire that may increase quality while reducing the cost and effort of participants and researchers. In the 'partial questionnaire design', information about the exposure of interest is obtained from all subjects, while zero, one, or more disjoint subsets of questions about possible confounders are asked to randomly selected subgroups. The proposed analyses exploit the fact that the uncollected data can be considered to be missing at random. We show that it is possible to obtain high efficiency for estimating the effect of exposure of interest, adjusted for confounding, while substantially shortening average questionnaire length.

Bias↗