Comparative effects of neonatal and prepubertal castration on craniofacial growth in rats

Testosterone suppression impacts craniofacial growth structures during puberty : An animal study

AIM

Hormones play a crucial role in growth development; however, the impact of testosterone suppression (TS) on craniofacial growth during puberty remains inconclusive. This study aimed to evaluate the impact of TS during puberty on cephalometric measurements and histological characteristics of facial growth centers.

MATERIALS AND METHODS

Thirty-six heterogenic Wistar male rats were randomly allocated into experimental orchiectomy (ORX) and control (sham) groups. At an age of 23 days (prepubertal stage), orchiectomy and placebo surgery were performed. Cephalometric measurements were performed via lateral cephalograms during and after puberty. The animals were euthanized at an age of 45 days (pubertal stage) and 73 days (postpubertal stage). Histological slices of the growth centers (condyle, premaxilla, and median palatine suture) were stained with hematoxylin and eosin, and sirius red. Student's t or Mann-Whitney U tests were used to compare linear and angular cephalometric measurements across groups (α error = 5%).

RESULTS

Linear and angular measurements were statistically different in ORX animals (cranial bones, maxilla, and mandible) at 45 days and 73 days. Condylar histology showed a decrease in prechondroblast differentiation and a delay of mineralization in ORX animals. Vascularization of the medium palatine suture was lower in the ORX group at 45 days. Type I and III collagen fiber synthesis was lower in the ORX groups. In the premaxillary suture, collagen fibers were better organized in the sham groups.

CONCLUSIONS

Our results suggest that testosterone suppression affects craniofacial growth during puberty.

A review on linear mixed models for longitudinal data, possibly subject to dropout

Many approaches are available for the analysis of continuous longitudinal data. Over the last couple of decades, a lot of emphasis has been put on the linear mixed model. The current paper is dedicated to an overview of this approach, with emphasis on model formulation, interpretation and inference. Advantages as well as drawbacks are discussed, and guidelines are given for general statistical practice. Special attention is given to the problem of missing data, i.e., the case where not all data are present as planned in the original design of the study.

Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data

In problems with missing or latent data, a standard approach is to first impute the unobserved data, then perform all statistical analyses on the completed dataset--corresponding to the observed data and imputed unobserved data--using standard procedures for complete-data inference. Here, we extend this approach to model checking by demonstrating the advantages of the use of completed-data model diagnostics on imputed completed datasets. The approach is set in the theoretical framework of Bayesian posterior predictive checks (but, as with missing-data imputation, our methods of missing-data model checking can also be interpreted as "predictive inference" in a non-Bayesian context). We consider the graphical diagnostics within this framework. Advantages of the completed-data approach include: (1) One can often check model fit in terms of quantities that are of key substantive interest in a natural way, which is not always possible using observed data alone. (2) In problems with missing data, checks may be devised that do not require to model the missingness or inclusion mechanism; the latter is useful for the analysis of ignorable but unknown data collection mechanisms, such as are often assumed in the analysis of sample surveys and observational studies. (3) In many problems with latent data, it is possible to check qualitative features of the model (for example, independence of two variables) that can be naturally formalized with the help of the latent data. We illustrate with several applied examples.

The use of score tests for inference on variance components

Whenever inference for variance components is required, the choice between one-sided and two-sided tests is crucial. This choice is usually driven by whether or not negative variance components are permitted. For two-sided tests, classical inferential procedures can be followed, based on likelihood ratios, score statistics, or Wald statistics. For one-sided tests, however, one-sided test statistics need to be developed, and their null distribution derived. While this has received considerable attention in the context of the likelihood ratio test, there appears to be much confusion about the related problem for the score test. The aim of this paper is to illustrate that classical (two-sided) score test statistics, frequently advocated in practice, cannot be used in this context, but that well-chosen one-sided counterparts could be used instead. The relation with likelihood ratio tests will be established, and all results are illustrated in an analysis of continuous longitudinal data using linear mixed models.

Sensitivity analysis for nonrandom dropout: a local influence approach

Diggle and Kenward (1994, Applied Statistics 43, 49-93) proposed a selection model for continuous longitudinal data subject to nonrandom dropout. It has provoked a large debate about the role for such models. The original enthusiasm was followed by skepticism about the strong but untestable assumptions on which this type of model invariably rests. Since then, the view has emerged that these models should ideally be made part of a sensitivity analysis. This paper presents a formal and flexible approach to such a sensitivity assessment based on local influence (Cook, 1986, Journal of the Royal Statistical Society, Series B 48, 133-169). The influence of perturbing a missing-at-random dropout model in the direction of nonrandom dropout is explored. The method is applied to data from a randomized experiment on the inhibition of testosterone production in rats.