Technical note: two programs for performing multigroup longitudinal data analyses.
Explore the source record for details and available documents.
Analysis of Variance↗
Biomedical subjects
T R Ten Have
Publications and source records attributed to T R Ten Have.
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↗
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↗
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↗