A Test for Instrument Validity

This paper develops a specification test for instrument validity in the heterogeneous treatment effect model with a binary treatment and a discrete instrument. The strongest testable implication for instrument validity is given by the condition for nonnegativity of point‐identifiable compliers' outcome densities. Our specification test infers this testable implication using a variance‐weighted Kolmogorov–Smirnov test statistic. The test can be applied to both discrete and continuous outcome cases, and an extension of the test to settings with conditioning covariates is provided.     

Kullback-leibler information approach to the optimum measurement point for bayesian estimation

When an appropriate parametric model and a prior distribution of its parameters are given to describe clinical time courses of a dynamic biological process, Bayesian approaches allow us to estimate the entire profiles from a few or even a single observation per subject. The goodness of the estimation depends on the measurement points at which the observations were made. The number of measurement points per subject is generally limited to one or two. The limited measurement points have to be selected carefully. This paper proposes an approach to the selection of the optimum measurement point for Bayesian estimations of clinical time courses. The selection is made among given candidates, based on the goodness of estimation evaluated by the Kullback-Leibler information. This information measures the discrepancy of an estimated time course from the true one specified by a given appropriate model. The proposed approach is applied to a pharmacokinetic analysis, which is a typical clinical example where the selection is required. The results of the present study strongly suggest that the proposed approach is applicable to pharmacokinetic data and has a wide range of clinical applications.     

Information criteria for the predictive evaluation of bayesian models

As a natural successor of the information criteria AIC and ABIC, information criteria for the Bayes models were developed by evaluating the bias of the log likelihood of the predictive distribution as an estimate of its expected log-likelihood. Considering two specific situations for the true distribution, two information criteria, PIC₁ and PIC₂ are derived. Linear Gaussian cases are considered in details and the evaluation of the maximum a posteriori estimator is also considered. By a simple example of estimating the signal to noise ratio, it was shown that the PIC₂ is a good approximation to the expected log-likelihood in the entire region of the signal to noise ratio. On the other hand, PIC₁ performs good only for the smaller values of the variance ratio. For illustration, the problems of trend estimation and seasonal adjustment are considered. Examples show that the hyper-parameters estimated by the new criteria are usually closer to the best ones than those by the ABIC.     

The Prediction of Time Series With Trends and Seasonalities

A maximization of the expected entropy of the predictive distribution interpretation of Akaike's minimum AIC procedure is exploited for the modeling and prediction of time series with trend and seasonal mean value functions and stationary covariances. The AIC criterion best one-step-ahead and best twelve-step-ahead prediction models can be different. The different models exhibit the relative optimality properties for which they were designed. The results are related to open questions on optimal trend estimation and optimal seasonal adjustment of time series.     

Effects of dutasteride on lower urinary tract symptoms: a prospective analysis based on changes in testosterone/dihydrotestosterone levels and total prostatic volume reduction

This study analyzed the effects of dutasteride on lower urinary tract symptoms based on the association between changes in the total testosterone (TT)/dihydrotestosterone (DHT) levels and total prostate volume (TPV) reduction. Sixty participants diagnosed with benign prostatic hyperplasia were given 0.5?mg of dutasteride daily for 52 weeks. Measures of TT and DHT levels, TPV and uroflowmetry were obtained before and after dutasteride treatment. Forty-three patients demonstrated a TPV reduction of ≥5% (Group 1), whereas the remaining 17 patients demonstrated a TPV reduction of <5% (Group 2). DHT suppression and DHT/TT ratio at baseline were significantly higher in Group 1 than Group 2. International Prostate Symptom Scores (IPSS) and uroflowmetry were significantly improved in both groups. In Group 2, nine patients demonstrated some improvement in IPSS (Group 2A), whereas eight did not (Group 2B). The rate of TT increase and improvement in voiding symptoms were significantly higher in Group 2A than Group 2B. Dutasteride-induced TPV reduction is dependent on individual 5-α reductase inhibitor activity. Some patients demonstrating smaller dutasteride-induced TPV reduction may experience an improvement in voiding symptoms owing to an increased level of testosterone.