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Biomedical subjects

T R Ten Have

Publications and source records attributed to T R Ten Have.

32 records · Page 2Linked to original sources
PubMed1996

Take-home informed consent for intravenous contrast media: do patients learn more?

RATIONALE AND OBJECTIVES: In 1978, Morrow et al published the results of a simple technique for raising radiation oncology patients' levels of awareness about medical options. They reported that providing written information at least 24 hours in advance was an effective tool for increasing the baseline knowledge in this patient group. However, Morrow's cohort consisted exclusively of cancer patients receiving radiation therapy. The authors of this article are concerned with whether the encouraging results reported by Morrow are reproducible when applied to patients awaiting invasive radiologic procedures. METHODS: One hundred sixty consecutive outpatients awaiting contrast were block randomized into one of eight groups based on age, sex, and previous contrast exposure. For each group, half were given their consent form at least 24 hours prior, and the other half at the time of their procedure. All patients were tested at the time of their procedure to evaluate knowledge retention. RESULTS: Comparison between the two study groups showed no overall statistically significant differences either in knowledge (experimental group 4.7 +/- 1.32 versus 4.38 +/- 1.30 control group) or level of satisfaction. Additionally, individuals experienced with contrast failed to outperform those who had never previously been given contrast. There is no significant difference in the performance between the two sexes regardless of group. CONCLUSIONS: The providing of information 24 to 72 hours in advance of an invasive procedure does not have a beneficial effect over just providing the same information at the time of the study.

Age Factors↗
PubMed1996

A mixed effects model for multivariate ordinal response data including correlated discrete failure times with ordinal responses.

The mixed effects model for binary responses due to Conaway (1990, A Random Effects Model for Binary Data) is extended to accommodate ordinal responses in general and discrete time survival data with ordinal responses in particular. Given a multinomial likelihood, cumulative complementary log-log link function, and log-gamma random effects distribution, the resulting marginal likelihood has a closed form. As a result, a Newton-Raphson estimation procedure is feasible without resorting to numerical, approximation-based, or Monte Carlo integration techniques. The parameters in the model have a proportional hazards interpretation in terms of multivariate discrete time data with ordinal responses. Using data from a psychological example, the proposed method is compared with other mixed effects approaches as well as population-averaged models.

Biometry↗
PubMed1995

Association models for periodontal disease progression: a comparison of methods for clustered binary data.

We investigate population-averaged (PA) and cluster-specific (CS) associations for clustered binary logistic regression in the context of a longitudinal clinical trial that investigated the association between tooth-specific visual elastase kit results and periodontal disease progression within 26 weeks of follow-up. We address estimation of population-averaged logistic regression models with generalized estimating equations (GEE), and conditional likelihood (CL) and mixed effects (ME) estimation of CS logistic regression models. Of particular interest is the impact of clusters that do not provide information for conditional likelihood methods (non-informative clusters) on inferences based upon the various methodologies. The empirical and analytical results indicate that CL methods yield smaller test statistics than ME methods when non-informative clusters exist, and that CL estimates are less efficient than ME estimates under certain conditions. Moreover, previously reported relationships between population-averaged and cluster-specific parameters appear to hold for the corresponding estimates in the presence of these clusters.

Cluster Analysis↗
PubMed1995

Comparison of two approaches to analyzing correlated binary data in developmental toxicity studies.

Recently developed statistical methodology is presented for analyzing correlated developmental toxicity data where multiple measures are obtained on individual units such as rat pups. Of particular interest is the assessment of the homogeneity of effects from chemical exposure across different outcomes such as malformation types or non-ossification of forepaw digits. Such contrasts are referred to as within-cluster effects, as they represent differences between observations within a cluster (e.g., pup). Between-cluster effects correspond to comparison of observations from different clusters, e.g., pups exposed to varied doses of chemicals or carcinogens. Two classes of statistical models are available for estimation of these distinct types of effects: (1) cluster-specific models in which regression adjustments are made for cluster effects, and hence all parameters are interpreted as within-cluster effects; and (2) population-averaged models in which no explicit adjustments are made for cluster, and thus all parameters are interpreted as group differences without regard to whether they represent between- or within-cluster effects. Many models in the developmental toxicity literature are population-averaged logistic regression models ignoring variation among clusters which may be significant under strong influences of genetic and environmental factors. Under such conditions, confidence interval-based inference for effect homogeneity will depend upon the class of models. These issues are illustrated with data from two developmental toxicity studies: (1) a National Toxicology Program study of the effects of in utero exposure to di(2ethylhexyl)phthalate among three malformation outcomes in mouse pups; and (2) a study conducted by Hartsfield to investigate the effects of anticonvulsant phenytoin on the risk of non-ossification among digits of the left and right forepaws in rat pups.

Abnormalities, Drug-Induced↗
PubMed1995

Multivariate contingency tables and the analysis of exchangeability.

There are settings in the social and health sciences where it is natural to question whether a collection of discrete random variables is exchangeable. In this paper the inter-relationships between parameter symmetry, parameter invariance, and exchangeable discrete random variables are investigated within the log-linear models framework. We demonstrate how log-linear models can be used to formulate and test hypotheses of various forms of exchangeability, and to characterize departures from exchangeability. Conditions under which the observed cross-classification collapses into a lower dimensional cross-classification, while preserving the essential probability structure of the higher dimensional cross-classification, and the model structure of this lower dimensional cross-classification are presented. The development is sufficiently general to allow for subsetting the variables into classes, which is important for some applications. For example, in studying the spatial clustering of periodontal disease, there is interest in studying differences among disease patterns between the upper and lower arches in terms of parameter symmetry, parameter invariance, and exchangeability. Cross-sectional periodontal disease data from a study of Pima Indians residing in the Gila River Indian Community are used to illustrate how log-linear models may be used to examine for exchangeability, and for specific departures from exchangeability.

Adolescent↗
PubMed1994

Bayesian hierarchical analysis of within-units variances in repeated measures experiments.

We develop hierarchical Bayesian models for biomedical data that consist of multiple measurements on each individual under each of several conditions. The focus is on investigating differences in within-subject variation between conditions. We present both population-level and individual-level comparisons. We extend the partial likelihood models of Chinchilli et al. with a unique Bayesian hierarchical framework for variance components and associated degrees of freedom. We use the Gibbs sampler to estimate posterior marginal distributions for the parameters of the Bayesian hierarchical models. The application involves a comparison of two cholesterol analysers each applied repeatedly to a sample of subjects. Both the partial likelihood and Bayesian approaches yield similar results, although confidence limits tend to be wider under the Bayesian models.

Algorithms↗
PubMed1994

A PC program for computing McMahan's tracking indices from one-sample longitudinal data sets.

Tracking refers to regular, predictable growth behavior. Most definitions of the term are related to the idea that tracking occurs to the extent that individuals' repeated measurements maintain their relative positions in the distribution of the measurement whose growth is being monitored as that distribution changes over time. In this paper we describe four indices of tracking, all due to McMahan (Biometrics, 37 (1981) 447-455), which may be used to measure the extent to which a given longitudinal sample exhibits this 'regular behavior,' and we provide a program, written in GAUSS386i, for performing the associated computations. An example is given, and the values of McMahan's indices are compared to several alternative measures of tracking.

Algorithms↗
PubMed1993

Longer-term growth prediction using GAUSS.

In several areas of biomedicine, one needs to predict future measurements for a growing individual on the basis of longitudinal data. Here we consider the problem of estimating the values of a given measurement for a particular individual at T-T* points in time, given T* observations on that individual, and all T values for a sample of N "similar" individuals. This extends our previous discussion [Schneiderman et al., Comput. Biol. Med. 22, 181-188 (1992)], which was limited to the case T* = T-1, to longer-term predictions. We again make a user-friendly GAUSS program available to perform the associated computations. Examples illustrating the use of the program and the accuracy of the predictions it provides are included.

Algorithms↗
PubMed1993

PC program extending the two-stage polynomial growth curve model to allow missing data.

A stand-alone, menu-driven PC program, written in GAUSS386i, extending the analysis of one-sample longitudinal data sets satisfying the two-stage polynomial growth curve model (Ten Have et al., Am J Hum Biol, 3 (1991) 269-279) to allow missing data is described, illustrated and made available to interested readers. The method and the program are illustrated using data previously analyzed by the authors (Schneiderman and Kowalski, Am J Phys Anthropol, 67 (1985) 323-333) but with several randomly chosen data points discarded and treated as missing.

Animals↗
PubMed1992

A PC program for growth prediction in the context of Rao's polynomial growth curve model.

We consider the problem of growth prediction in the context of Rao's [1] one-sample polynomial growth curve model and provide a PC program, written in GAUSS, to perform the associated computations. Specifically, the problem considered is that of estimating the value of the measurement under consideration for a "new" individual at the Tth time point given measurements on that individual at T-1 previous points in time and the values of the measurement on N "similar" individuals at all T time points. The times of measurement t1, t2, . . ., tT need not be equally spaced, but we assume that each of the N individuals comprising the normative sample were measured at these times. The method and the program are illustrated using the leave-one-out method on a sample of N = 12 male rhesus monkeys whose mandibular ramus height was measured five times at yearly intervals.

Animals↗
PubMed1991

Rao's polynomial growth curve model for unequal-time intervals: a menu-driven GAUSS program.

For lack of alternatives, longitudinal data are often analyzed with cross-sectional statistical methods, for instance, t-tests, ANOVA and ordinary least-squares regression. Appropriate statistical software has been generally unavailable to investigators using serial records to study growth and development or treatment effects. In an earlier paper (Schneiderman and Kowalski, Am. J. Phys. Anthropol., 67 (1985) 323-333.) we described a suitable method, Rao's polynomial growth curve model (Rao, Biometrika, 46 (1959) 49-58), and provided an SAS computer program for the analysis of a single sample of complete longitudinal data. This method included the computation of an average polynomial growth curve, its 95% confidence band, its coefficients and corresponding confidence intervals. The present paper extends this method to accommodate a sample with observations made at unequal time-intervals. Significant improvements in the accessibility, operation and user-friendliness of the program have been made, facilitated by recent advances in microcomputer technology. This stand-alone GAUSS program (no compiler necessary) runs on PC-compatibles and is available at a nominal cost. In this report we provide an overview of the statistical model, the general structure of the program, and give an example in which a developmental variable (human upper incisor angulation) is analyzed. Ease of installation and use, speed of execution and color graphic displays of growth curves and confidence bands, and most importantly, suitability to longitudinal data, make this method/program a potentially valuable tool for those interested in growth, development, and treatment effects in humans and other species. Some areas in which this method will have immediate applications are orthodontics, maxillofacial surgery and pediatrics.

Adolescent↗
PubMed1989

A multivariate approach to analyzing the relation between occlusion and craniofacial morphology.

This study examined the association between occlusion and craniofacial morphology using univariate and multivariate statistical methods. Data were obtained from study casts and lateral cephalometric radiographs of 164 children in the early permanent dentition. The following multiple features of occlusion were assessed: molar relation, overjet, overbite, and anterior crowding. Angular skeletal measures assessed cranial base flexure, maxillary horizontal and vertical positions, mandibular horizontal and vertical positions, horizontal and vertical maxillary-mandibular relations, and positions of the incisors. The relation between the Occlusal Index, which is a malocclusion severity index, and skeletal morphology was also investigated. Associations were examined by use of linear correlation, stepwise multiple regression, and canonical correlation analyses. Individually and in combination, occlusal features were poorly associated with individual skeletal measures (r2 less than or equal to 0.35). The strongest association occurred between a linear combination of occlusal features and a linear combination of skeletal measures (R2 = 0.66, p = 0.0001). A malocclusion severity index did not aid in the identification of craniofacial morphology. The results suggested that combinations of certain occlusal characteristics may be associated with specific skeletal types; however, a generalized statement of this concept could not be supported.

Adolescent↗
PubMed1987

Changes in soft tissue profile during the treatment of Class III malocclusion.

The changes in soft tissue profile for 66 Angle's Class III malocclusion subjects treated either with the Function Regulator, or Upper Removable Appliances, or the Edgewise Appliance were assessed by comparing cephalometric radiographs taken before and after correction of the anterior occlusion. While the mean soft tissue profile in all groups improved in relation to a Control Group, the FR.III Group demonstrated the most profound change.

Activator Appliances↗