The analysis of incomplete data using stochastic covariates

In the development of many diseases there are often associated random variables which continuously reflect the progress of a subject towards the final expression of the disease (failure). At any given time these processes, which we call stochastic covariates, may provide information about the current hazard and the remaining time to failure. Likewise, in situations when the specific times of key prior events are not known, such as the time of onset of an occult tumour or the time of infection with HIV-1, it may be possible to identify a stochastic covariate which reveals, indirectly, when the event of interest occurred. The analysis of carcinogenicity trials which involve occult tumours is usually based on the time of death or sacrifice and an indicator of tumour presence for each animal in the experiment. However, the size of an occult tumour observed at the endpoint represents data concerning tumour development which may convey additional information concerning both the tumour incidence rate and the rate of death to which tumour-bearing animals are subject. We develop a stochastic model for tumour growth and suggest different ways in which the effect of this growth on the hazard of failure might be modelled. Using a combined model for tumour growth and additive competing risks of death, we show that if this tumour size information is used, assumptions concerning tumour lethality, the context of observation or multiple sacrifice times are no longer necessary in order to estimate the tumour incidence rate. Parametric estimation based on the method of maximum likelihood is outlined and is applied to simulated data from the combined model. The results of this limited study confirm that use of the stochastic covariate tumour size results in more precise estimation of the incidence rate for occult tumours.     

Estimating the Rayleigh distribution scale parameter from doubly censored samples—Some comments

In a recent paper in this journal, Lee, Kapadia and Brock (1980) developed maximum likelihood (ML) methods for estimating the scale parameter of the Rayleigh distribution from doubly censored samples. They reported convergence difficulties in attempting to solve numerically the nonlinear likelihood equation (LE). To mitigate these difficulties, they employed approximations to simplify the LE, but found that the solution of the resulting simplified equation can give rise to parameter estimates of erratic accuracy. We show that the use of approximations to simplify the LE is unnecessary. In fact, under suitable parametric transformation, the log-likelihood function is strictly concave, the ML estimate always exists, is unique and finite. Furthermore, the LE is easy to solve numerically. A numerical example is given to illustrate the computations involved.     

Benefits of preventive dentistry in children: Cost factors and public policy

Dental caries in children is now recognized as a preventable disease. The use of fluoride and sealants has produced a major reduction in caries prevalence among school aged children in the United States and other countries. A portion of the child population who are educationally and socioeconomically disadvantaged are not fully receiving these benefits. Public policy, insurance and medicaid groups must be made aware of these preventative measures and strategies developed to implement them.     

Construction and selection of tightened-normal-tightened sampling inspection scheme of type TNT-(n1, n2; c)

A table is presented for the construction and selection of tightened-normal-tightened sampling scheme of type TNT-(n1, n2;c). Efficiency of TNT-(n1, n2;c) scheme over conventional single and double sampling plans is discussed.     

Sociological computing: An opportunity missed?

Rapid advances in sociological computing are changing virtually every aspect of scholarly sociological work. These changes offer an opportunity for sociologists to improve the quality of their work and bring new insights and approaches to bear on important sociological problems. Nevertheless, sociologists display a profound ambivalence toward computer work by other sociologists. Left unchecked, this ambivalence threatens to turn sociological computing into an opportunity missed. Inadequate rewards, lack of training, and a general failure to understand the importance of computing in sociology threaten to stifle the development of quality soft-ware, push sociologists competent in computing into other fields, and jeopardize the quality of sociological scholarship. This article documents these ambivalent tendencies and proposes reasonable steps the discipline should take to assure that sociological computing does not become an opportunity missed.