A profile of smaller hospitals: planning for a novel, statewide surveillance program, Victoria, Australia.
Explore the source record for details and available documents.
Cross Infection↗
Biomedical subjects
Lyle C Gurrin
Publications and source records attributed to Lyle C Gurrin.
Using bivariate models to understand between- and within-cluster regression coefficients, with application to twin data.
In the regression analysis of clustered data it is important to allow for the possibility of distinct between- and within-cluster exposure effects on the outcome measure, represented, respectively, by regression coefficients for the cluster mean and the deviation of the individual-level exposure value from this mean. In twin data, the within-pair regression effect represents association conditional on exposures shared within pairs, including any common genetic or environmental influences on the outcome measure. It has therefore been proposed that a comparison of the within-pair regression effects between monozygous (MZ) and dizygous (DZ) twins can be used to examine whether the association between exposure and outcome has a genetic origin. We address this issue by proposing a bivariate model for exposure and outcome measurements in twin-pair data. The between- and within-pair regression coefficients are shown to be weighted averages of ratios of the exposure and outcome variances and covariances, from which it is straightforward to determine the conditions under which the within-pair regression effect in MZ pairs will be different from that in DZ pairs. In particular, we show that a correlation structure in twin pairs for exposure and outcome that appears to be due to genetic factors will not necessarily be reflected in distinct MZ and DZ values for the within-pair regression coefficients. We illustrate these results in a study of female twin pairs from Australia and North America relating mammographic breast density to weight and body mass index.
Australia↗
Tutorial in biostatistics: spline smoothing with linear mixed models.
The semi-parametric regression achieved via penalized spline smoothing can be expressed in a linear mixed models framework. This allows such models to be fitted using standard mixed models software routines with which many biostatisticians are familiar. Moreover, the analysis of complex correlated data structures that are a hallmark of biostatistics, and which are typically analysed using mixed models, can now incorporate directly smoothing of the relationship between an outcome and covariates. In this paper we provide an introduction to both linear mixed models and penalized spline smoothing, and describe the connection between the two. This is illustrated with three examples, the first using birth data from the U.K., the second relating mammographic density to age in a study of female twin-pairs and the third modelling the relationship between age and bronchial hyperresponsiveness in families. The models are fitted in R (a clone of S-plus) and using Markov chain Monte Carlo (MCMC) implemented in the package WinBUGS.
Adolescent↗
Regression models for twin studies: a critical review.
Twin studies have long been recognized for their value in learning about the aetiology of disease and specifically for their potential for separating genetic effects from environmental effects. The recent upsurge of interest in life-course epidemiology and the study of developmental influences on later health has provided a new impetus to study twins as a source of unique insights. Twins are of special interest because they provide naturally matched pairs where the confounding effects of a large number of potentially causal factors (such as maternal nutrition or gestation length) may be removed by comparisons between twins who share them. The traditional tool of epidemiological 'risk factor analysis' is the regression model, but it is not straightforward to transfer standard regression methods to twin data, because the analysis needs to reflect the paired structure of the data, which induces correlation between twins. This paper reviews the use of more specialized regression methods for twin data, based on generalized least squares or linear mixed models, and explains the relationship between these methods and the commonly used approach of analysing within-twin-pair difference values. Methods and issues of interpretation are illustrated using an example from a recent study of the association between birth weight and cord blood erythropoietin. We focus on the analysis of continuous outcome measures but review additional complexities that arise with binary outcomes. We recommend the use of a general model that includes separate regression coefficients for within-twin-pair and between-pair effects, and provide guidelines for the interpretation of estimates obtained under this model.
Birth Weight↗
A note on genetic variance components in mixed models.
Burton et al. ([1999] Genet. Epidemiol. 17:118-140) proposed a series of generalized linear mixed models for pedigree data that account for residual correlation between related individuals. These models may be fitted using Markov chain Monte Carlo methods, but the posterior mean for small variance components can exhibit marked positive bias. Burton et al. ([1999] Genet. Epidemiol. 17:118-140) suggested that this problem could be overcome by allowing the variance components to take negative values. We examine this idea in depth, and show that it can be interpreted as a computational device for locating the posterior mode without necessarily implying that the original random effects structure is incorrect. We illustrate the application of this technique to mixed models for familial data.
Data Interpretation, Statistical↗
Infant intake of fatty acids from human milk over the first year of lactation.
Despite the importance of human milk fatty acids for infant growth and development, there are few reports describing infant intakes of individual fatty acids. We have measured volume, fat content and fatty acid composition of milk from each breast at each feed over a 24 h period to determine the mean daily amounts of each fatty acid delivered to the infant from breast milk at 1, 2, 4, 6, 9 and 12 months of lactation in five women. Daily (24 h) milk production was 336.60 (SEM 26.21) and 414.49 (SEM 28.39) ml and milk fat content was 36.06 (SEM 1.37) and 34.97 (SEM 1.50) g/l for left and right breasts respectively over the course of the first year of lactation. Fatty acid composition varied over the course of the day (mean CV 14.3 (SD 7.7) %), but did not follow a circadian rhythm. The proportions (g/100 g total fatty acids) of fatty acids differed significantly between mothers (P<0.05) and over the first year of lactation (P<0.05). However, amounts (g) of most fatty acids delivered to the infant over 24 h did not differ during the first year of lactation and only the amounts of 18:3n-3, 22:5n-3 and 22:6n-3 delivered differed between mothers (P<0.05). Mean amounts of 18:2n-6, 18:3n-3, 20:4n-6 and 22:6n-3 delivered to the infant per 24 h over the first year of lactation were 2.380 (SD 0.980), 0.194 (SD 0.074), 0.093 (SD 0.031) and 0.049 (SD 0.021) g respectively. These results suggest that variation in proportions of fatty acids may not translate to variation in the amount delivered and that milk production and fat content need to be considered.
Adult↗
Uising WinBUGS to fit nonlinear mixed models with an application to pharmacokinetic modelling of insulin response to glucose challenge in sheep exposed antenatally to glucocorticoids.
Many chronic diseases of adulthood, such as hypertension and diabetes, are now believed to have at least some of their origins before birth. Extensive studies in animal models have identified antenatal exposure to excess glucocorticoids as a leading candidate for the physiological cause of fetal compromise. The resulting adverse intra-uterine environment appears to "program" the individual for higher risk of subsequent disease. We present an analysis of blood glucose and insulin concentrations collected during glucose tolerance tests at 6 and 12 months postnatal age in a cohort of sheep that were treated antenatally with injections of betamethasone (a synthetic glucocorticoid) which, when injected into the mother, cross the placenta to the fetus. A simple pharmacokinetic model, essentially a modification of the single compartment model with first-order absorption and elimination, is developed to describe the time course of glucose concentration and the associated insulin response. The resulting nonlinear mixed model is implemented in a Bayesian framework using the Markov chain Monte Carlo technique Gibbs Sampling via the software package BUGS. This sampling process allows inferences to be made directly about derived quantities with an immediate physical interpretation, such as the maximum insulin concentration in response to glucose challenge. At 6 months postnatal age, sheep treated with antenatal injections of synthetic glucocorticoids had raised insulin concentration in comparison to controls after bolus administration of glucose. This effect persisted to 12 months postnatal age only in the sheep that received multiple doses of glucocorticoids. Moreover, the raised insulin concentration in sheep that received direct injections of synthetic glucocorticoid as fetuses is accompanied by better glucose clearance than in those sheep that received only saline injections, a phenomenon that is not observed in the animals that received maternal injections. It is argued that the fitting of an appropriate statistical model to complex physiological data does not necessarily proclude a result that has a clear interpretation for clinical scientists.
Animals↗
Using imprecise probabilities to address the questions of inference and decision in randomized clinical trials.
Randomized controlled clinical trials play an important role in the development of new medical therapies. There is, however, an ethical issue surrounding the use of randomized treatment allocation when the patient is suffering from a life threatening condition and requires immediate treatment. Such patients can only benefit from the treatment they actually receive and not from the alternative therapy, even if it ultimately proves to be superior. We discuss a novel new way to analyse data from such clinical trials based on the use of the recently developed theory of imprecise probabilities. This work draws an explicit distinction between the related but nevertheless distinct questions of inference and decision in clinical trials. The traditional question of scientific interest asks 'Which treatment offers the greater chance of success?' and is the primary reason for conducting the clinical trial. The question of decision concerns the welfare of the patients in the clinical trial, asking whether the accumulated evidence favours one treatment over the other to such an extent that the next patient should decline randomization and instead express a preference for one treatment. Consideration of the decision question within the framework of imprecise probabilities leads to a mathematical definition of equipoise and a method for governing the randomization protocol of a clinical trial. This paper describes in detail the protocol for the conduct of clinical trials based on this new method of analysis, which is illustrated in a retrospective analysis of data from a clinical trial comparing the anti-emetic drugs ondansetron and droperidol in the treatment of postoperative nausea and vomiting. The proposed methodology is compared quantitatively using computer simulation studies with conventional clinical trial designs and is shown to maintain high statistical power with reduced sample sizes, at the expense of a high type I error rate that we argue is irrelevant in some specific circumstances. Particular emphasis is placed on describing the type of medical conditions and treatment comparisons where the new methodology is expected to provide the greatest benefit.
Antiemetics↗
A randomized, double-blinded trial of subarachnoid bupivacaine and fentanyl, with or without clonidine, for combined spinal/epidural analgesia during labor.
UNLABELLED: Subarachnoid clonidine may increase the duration of spinal opioid and local anesthetic analgesia during labor, but it may also increase hypotension and sedation, and the therapeutic range is unclear. We studied 110 term parturients of mixed parity having combined spinal/epidural analgesia during labor in this randomized, double-blinded trial. All received subarachnoid fentanyl 20 micro g and bupivacaine 2.5 mg, plus either saline or clonidine (15, 30, or 45 micro g). Of 101 per-protocol parturients (n = 25, 24, 26, and 26 in Groups C0, C15, C30, and C45, respectively), 22 delivered before the cessation of spinal analgesia. Group demographics and pain scores from Time 0 to 120 min were similar. There was no significant difference among groups in the duration of spinal analgesia (P = 0.09) or in the duration of clonidine groups combined compared with control (median, 120 min [interquartile range, 96-139 min] versus 98 min [80-120 min]; P = 0.07). Systolic blood pressure was significantly lower in all clonidine groups between 40 and 90 min (P = 0.001). Hypotension (P = 0.05) and the requirement for ephedrine (P = 0.02) were dose dependent, but groups had a similar incidence of hypotension. The addition of clonidine 15-45 micro g to subarachnoid fentanyl and bupivacaine reduced blood pressure and did not significantly increase the duration of spinal analgesia. IMPLICATIONS: The addition of 15-45 micro g of clonidine to subarachnoid fentanyl plus bupivacaine did not significantly increase the duration of spinal analgesia but did decrease maternal blood pressure. The results of this study do not support the use of subarachnoid clonidine to prolong the action of spinal labor analgesia when fentanyl plus bupivacaine are administered.
Adjuvants, Anesthesia↗
Efficacy of breast milk expression using an electric breast pump.
The authors compared breastfeeding and expression characteristics in 30 mothers of exclusively breastfeeding, healthy term infants. Mean (+/- SD) volume per breastfeed from one breast was 71.8 +/- 26.3 mL, and mean duration per breastfeed for one breast was 16.6 +/- 10.5 minutes. Mean volume of milk expressed in 5 minutes from one breast was 60.6 +/- 39.0 mL and corresponded to the expression of 99.4 +/- 82.6% of the milk stored in the breast. The rate of milk expression differed greatly between mothers (P = .0001) but remained constant for the first 2.5 minutes before decreasing with time (P = .0001). These results show the mean breastfeed volume was similar to the volume of milk expressed in a 5-minute period. Furthermore, this study is the first to establish protocols that allow for the objective determination of breast pump efficacy.
Adult↗
Effect of vacuum profile on breast milk expression using an electric breast pump.
The authors compared milk expression using 5 experimental vacuum patterns and a commercially available vacuum pattern ranging in cycle times (20 to 78 cycles/min) and vacuum curve dynamics in 30 mothers using an experimental, software-controlled electric breast pump. The volume of milk removed over 5 minutes differed (P = .0072) between patterns (range = 62.8 +/- 6.6 mL to 47.2 +/- 5.1 mL). However, there was no difference in the percentage of available milk removed (range = 99.4% +/- 15.1% to 70.6% +/- 8.6%). The rate of milk removal differed between patterns at both the beginning (1 minute) and end (1.5 minutes) of the expression period (P < .05). Peak vacuum chosen differed between patterns (P = .0085) but was not related to either the volume or percentage of available milk expressed. Breastfeeding characteristics did not differ between poor and successful expressers. These results show that breast milk expression by an electric breast pump can be influenced by the vacuum pattern.
Adult↗