Joint Regression and Association Models for Repeated Categorical Responses

The focus of this study is on statistical analysis of categorical responses, where the response values are dependent of each other. The most typical example of this kind of dependence is when repeated responses have been obtained from the same study unit. For example, in Paper I, the response of interest is the pneumococcal nasopharengyal carriage (yes/no) on 329 children. For each child, the carriage is measured nine times during the first 18 months of life, and thus repeated respones on each child cannot be assumed independent of each other. In the case of the above example, the interest typically lies in the carriage prevalence, and whether different risk factors affect the prevalence. Regression analysis is the established method for studying the effects of risk factors. In order to make correct inferences from the regression model, the associations between repeated responses need to be taken into account. The analysis of repeated categorical responses typically focus on regression modelling. However, further insights can also be gained by investigating the structure of the association. The central theme in this study is on the development of joint regression and association models. The analysis of repeated, or otherwise clustered, categorical responses is computationally difficult. Likelihood-based inference is often feasible only when the number of repeated responses for each study unit is small. In Paper IV, an algorithm is presented, which substantially facilitates maximum likelihood fitting, especially when the number of repeated responses increase. In addition, a notable result arising from this work is the freely available software for likelihood-based estimation of clustered categorical responses.

Testicular function in adolescent boys with Klinefelter syndrome

Klinefelter syndrome (KS) is the most frequent karyotype disorder of male reproductive function. Since its original clinical description in 1942 and the identification of its chromosomal basis 47,XXY in 1959, the typical KS phenotype has become well recognized, but the mechanisms behind the testicular degeneration process have remained unrevealed. This prospective study was undertaken to increase knowledge about testicular function in adolescent KS boys. It comprised a longitudinal follow-up of growth, pubertal development, and serum reproductive hormone levels in 14 prepubertal and pubertal KS boys. Each boy had a testicular biopsy that was analyzed with histomorphometric and immunohistochemical methods. The KS boys had sufficient testosterone levels to allow normal onset and progression of puberty. Their serum testosterone levels remained within the low-normal range throughout puberty, but from midpuberty onwards, findings like a leveling-off in testosterone and insulin-like factor 3 (INSL3) concentrations, high gonadotropin levels, and exaggerated responses to gonadotropin-releasing hormone stimulation suggest diminished testosterone secretion. We also showed that the Leydig cell differentiation marker INSL3 may serve as a novel marker for onset and normal progression of puberty in boys. In the KS boys the number of germ cells was already markedly lower at the onset of puberty. The pubertal activation of the pituitary-testicular axis accelerated germ cell depletion, and germ cell differentiation was at least partly blocked at the spermatogonium or early primary spermatocyte stages. The presence of germ cells correlated with serum reproductive hormone levels. The immature Sertoli cells were incapable of transforming to the adult type, and during puberty the degeneration of Sertoli cells increased markedly. The older KS boys displayed an evident Leydig cell hyperplasia, as well as fibrosis and hyalinization of the interstitium and peritubular connective tissue. Altered immunoexpression of the androgen receptor (AR) suggested that in KS boys during puberty a relative androgen deficiency develops at testicular level. The impact of genetic features of the supernumerary X chromosome on the KS phenotype was also studied. The present study suggests that parental origin of the supernumerary X chromosome and the length of the CAG repeat of the AR gene influence pubertal development and testicular degeneration. The current study characterized by several means the testicular degeneration process in the testes of adolescent KS boys and confirmed that this process accelerates at the onset of puberty. Although serum reproductive hormone levels indicated no hypogonadism during early puberty, the histological analyses showed an already markedly reduced fertility potential in prepubertal KS boys. Genetic features of the X chromosome affect the KS phenotype.

Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.