No abstract

We investigated whether familial factors influence 1) the incidence of prostatic cancer and 2) the plasma content of sex steroids. A 4-fold higher relative risk for the development of prostatic cancer was observed for brothers (n = 257) of prostatic cancer cases (n = 150) compared to their brothers-in-law (n = 202) and males in the general population of the state Utah. The intraclass correlation for plasma testosterone content [intraclass correlation coefficient (r1) = 0.51; P less than 0.01] and the apparent free testosterone concentration (r1 = 0.54; P less than 0.01) were highly significant in nonendocrinologically treated cases and their brothers. Further, sons and their fathers had significant intraclass correlations for both plasma dihydrotesterone (r1 = 0.83; P less than 0.01) and the ratio of testosterone to dihydrotestosterone (r1 = 0.46; P less than 0.05). Probands and their brothers, and sons of the patients with the disease had significantly lower plasma testosterone levels than controls of comparable age. This is the first documentation indicating that familial (possibly genetic) factors are potent risk factors for predisposing men to the development of prostatic cancer and in regulating the plasma content of androgens. Our results indicate that plasma androgen levels in families with prostatic cancer are clustered in the lower range of the normal population. They also suggest that plasma androgen content is more similar within each family with the cancer than among the families without cancer.

GENCAT is a computer program which implements an extremely general methodology for the analysis of multivariate categorical data. This approach essentially involves the construction of test statistics for hypotheses involving functions of the observed proportions which are directed at the relationships under investigation and the estimation of corresponding model parameters via weighted least squares computations. Any compounded function of the observed proportions which can be formulated as a sequence of the following transformations of the data vector--linear, logarithmic, exponential, or the addition of a vector of constants--can be analyzed within this general framework. This algorithm produces minimum modified chi-square statistics which are obtained by partitioning the sums of squares as in ANOVA. The input data can be either: (a) frequencies from a multidimentional contingency table; (b) a victor of functions with its estimated covariance matrix; and (c) raw data in the form of integer-valued variables associated with each subject. The input format is completely flexible for the data as well as for the matrices.

No abstract

No abstract