Response transformations in repeated measures and growth curve models

This article describes estimation and inference procedures for the parameters of the Box-Cox and foided-power transformations in repeated measures and growth curve models. Procedures for computing maximum likelihood estimates of the transformation and covariance parameters under several covanance structures (omnibus sphericity, local sphericity, and unstructured) are described. Lack of fit statistics and hypothesis tests for comparing these structures also are described. The procedures are illustrated on three data sets. Software for performing the analyses in the SAS System is described and is available from the authors.     

Comment: The Future of Administrative Records in the Economic Programs of the Census Bureau

Using recently developed statistical tools for analyzing cointegrated 1(2) data, this article models money, income, prices, and interest rates in Denmark. The final model describes the dynamic adjustment to short-run changes of the process, to deviations from long-run steady states, and to several political interventions. It provides new insights about the effects of the liberalization of trade and capital in a small open European economy.     

Contradictions of commitment in the new economy: Insecurity, time, and technology

The new flexible, 24/7 economy is characterized by employment insecurity, time pressure, and technologies that increase employees’ accessibility to their workplace. Accompanying these changes is a changing social contract between employees and their employers. Labor process and organizational theories suggest that these employment characteristics will undermine the attachment component of organizational commitment that remains an important feature of contemporary workplaces. We use an unpublished dataset of a nationally representative sample of employed individuals from the Families and Work Life Institute to test hypotheses linking the attachment component of organizational commitment to organizational insecurity, time pressure, and technologically created accessibility. Our expectation is that these changes will be particularly important for decreasing older cohorts of workers’ attachment to the organization since they have experienced a change in the social contract. The data are largely consistent with these expectations.     

How useful are recoveries of North American passerines for survival analyses?

Of 17 000 000 passerines ringed in North America from 1955 to 1984, only 0.4% were ever recovered. Only 62 species had more than 100 recoveries, of which only 26 had more than 500 recoveries. Preliminary analyses using stochastic recovery models for four of the species with more than 500 recoveries suggest estimates of mean annual adult survival can be fairly precise (CV < 4%), but estimates of immature survival are much less precise (CV > 8%). Comparable estimates may be possible for other species with more than 100 recoveries, depending on the temporal and geographic distribution of ringings and recoveries. Further analyses are required to determine whether these estimates may be biased by heterogeneity in survival or recovery rates.     

Dose‐Response Models Incorporating Aerosol Size Dependency for Francisella tularensis

The effect of bioaerosol size was incorporated into predictive dose‐response models for the effects of inhaled aerosols of Francisella tularensis (the causative agent of tularemia) on rhesus monkeys and guinea pigs with bioaerosol diameters ranging between 1.0 and 24 μm. Aerosol‐size‐dependent models were formulated as modification of the exponential and β‐Poisson dose‐response models and model parameters were estimated using maximum likelihood methods and multiple data sets of quantal dose‐response data for which aerosol sizes of inhaled doses were known. Analysis of F. tularensis dose‐response data was best fit by an exponential dose‐response model with a power function including the particle diameter size substituting for the rate parameter k scaling the applied dose. There were differences in the pathogen's aerosol‐size‐dependence equation and models that better represent the observed dose‐response results than the estimate derived from applying the model developed by the International Commission on Radiological Protection (ICRP, 1994) that relies on differential regional lung deposition for human particle exposure.     

Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing

Abstract

A central objective of empirical research on treatment response is to inform treatment choice. Unfortunately, researchers commonly use concepts of statistical inference whose foundations are distant from the problem of treatment choice. It has been particularly common to use hypothesis tests to compare treatments. Wald’s development of statistical decision theory provides a coherent frequentist framework for use of sample data on treatment response to make treatment decisions. A body of recent research applies statistical decision theory to characterize uniformly satisfactory treatment choices, in the sense of maximum loss relative to optimal decisions (also known as maximum regret). This article describes the basic ideas and findings, which provide an appealing practical alternative to use of hypothesis tests. For simplicity, the article focuses on medical treatment with evidence from classical randomized clinical trials. The ideas apply generally, encompassing use of observational data and treatment choice in nonmedical contexts.     

The exponential distribution analog of the Grubbs–Weaver method

Abstract

Grubbs and Weaver (1947 Grubbs, F. E., and C. L. Weaver. 1947. The best unbiased estimate of population standard deviation based on group ranges. Journal of the American Statistical Association 42 (238):224–41. doi: 10.2307/2280652.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) suggest a minimum-variance unbiased estimator for the population standard deviation of a normal random variable, where a random sample is drawn and a weighted sum of the ranges of subsamples is calculated. The optimal choice involves using as many subsamples of size eight as possible. They verified their results numerically for samples of size up to 100, and conjectured that their “rule of eights” is valid for all sample sizes. Here we examine the analogous problem where the underlying distribution is exponential and find that a “rule of fours” yields optimality and prove the result rigorously.     

Evaluating the Use of Spatial Covariance Structures in the Analysis of Cardiovascular Imaging Data

This article investigates the choice of working covariance structures in the analysis of spatially correlated observations motivated by cardiac imaging data. Through Monte Carlo simulations, we found that the choice of covariance structure affects the efficiency of the estimator and power of the test. Choosing the popular unstructured working covariance structure results in an over-inflated Type I error possibly due to a sample size not large enough relative to the number of parameters being estimated. With regard to model fit indices, Bayesian Information Criterion outperforms Akaike Information Criterion in choosing the correct covariance structure used to generate data.