A method for diagnosing and planning the treatment of adolescent drug abusers (the Adolescent Drug Abuse Diagnosis [ADAD] instrument)

There is a need for a diagnostic method and an instrument appropriate for adolescent drug abuse clients, that permits the assignment of clients to the most appropriate treatment setting, provides the basis for individualized treatment planning, and facilitates comparability across research studies. The development of the Adolescent Drug Abuse Diagnosis (ADAD), a 150-item instrument with a structured interview format, modeled after the Addiction Severity Index (ASI) (which is for adults), is described. The ADAD produces a broad-spectrum comprehensive evaluation of the client, the interviewer's ten-point severity ratings, and composite scores for each of nine life problem areas that are often relevant to the treatment needs of adolescent drug abuse clients. A series of validity and reliability tests are described. The characteristics of the standardization sample (N = 1,042), and the comparison of the characteristics of the three subsamples (outpatient, residential or non-hospital, and inpatient) are also presented.     

A Discrete Time Parametric Model for the Analysis of Failure Time Data

This paper introduces a parametric discrete failure time model which allows a variety of smooth hazard function shapes, including shapes which are not readily available with continuous failure time models. The model is easy to fit, and statistical inference is simple. Further, it is readily extended to allow for differences between subjects while retaining the ease of fit and simplicity of statistical inference. The performance of the discrete time analysis is demonstrated by application to several data sets.     

Estimation of a standard measuring error by repeated measurements and sortings

A sorting-and-measuring machine (SMM) measures and sorts (classifies) on-line produced items into several groups according to their size. The measuring devices of the SMM perceive the actual item size with a random error ε and classify the item as being smaller than b iff z+ε<b. Here ε is a normal zero-mean r.v. with unknown standard deviation σ which is the main parameter characterizing the precision and technical condition of an SMM. The paper gives the following method of estimating σ. N₀ items are measured and N₁ of them are recognized by the SMM as belonging to the group a<z≤b. These N₁ items are sorted again and N₂ of them return to this group, these are sorted again, and so on. The estimation of σ is based on the statistics N_m/N_n. Moments of the ratio statistics N_m/N_n and their distributional properties are investigated. It turns out that the expected value of N_m/N_n depends almost linearly on σ which allows us to construct ‘almost’ unbiased estimators of type $\text{σ}\text{?}{}_{\mathrm{mn}}\text{=A}\textcolor{red}{}\text{N}{}_{\mathrm{m}}\text{/N}{}_{\mathrm{n}}\text{+B}$ with good propert including robustness with respect to the distribution of item size. Convex combinations of $\text{σ}\text{?}{}_{\mathrm{mn}}$ statistics are considered to obtain an estimator with minimal variance.