Assessing Uncertainty in Small Area Forecasts: State of the Practice and Implementation Strategy

Forecasts are needed for everyday decisions and must be in the form of numbers. Yet forecasts invariably turn out to be different than the numbers that actually occur. Yet, most producers of forecasts only present a deterministic view of the future in the form of point predictions. However, the presence of uncertainty is inherent in management or policy decisions and there is often concern that benefits are overstated and risks are understated. Such concerns are difficult to address by providing only point forecasts with no assessment of their uncertainty. Having a better understanding of uncertainty can enhance the usefulness of forecasts and make the work of forecasting agencies an even more valuable product for planners, policy makers, and the public. The purpose of this paper is twofold. First, it presents an overview of the current state-of-the-practice is assessing forecast uncertainty. Second, it offers a guidelines and options for implementing and building uncertainty into small area forecasting processes. There are options for assessing forecasting uncertainty that can and should be implemented by most, if not all, producers of forecasts.     

The optimum growth rate for population under critical-level utilitarianism

We characterize optimal consumption, capital, and population growth of a production economy under critical-level utilitarianism. First, we show that neither classical utilitarianism nor average utilitarianism can avoid a corner solution for the population growth rate, in that under the former, population grows at the maximum speed (the so-called repugnant conclusion) while under the latter, it grows at the minimum. Second, we show that critical level utilitarianism yields an interior solution for the population growth rate provided the critical level belongs to a positive, open interval. Finally, we characterize the transition to the steady state and perform comparative dynamics analysis.     

Life test sampling plans for Weibull distributed lifetimes under accelerated hybrid censoring

Life test sampling plans (LSPs) for the Weibull distribution are usually developed under the assumptions that the shape parameter is known and the life test is conducted at an accelerated condition for which the acceleration factor (AF) is known. However, the sensitivities of a plan to the assumed shape parameter and AF have been rarely investigated. This paper considers the case where the life test is hybrid censored and develops attributes LSPs under the above assumptions. Then, sensitivity analyses are conducted to assess the effects of the uncertainties in the assumed AF and shape parameter on the actual producer and consumer risks. A method is also developed for constructing LSPs that can accommodate these uncertainties.     

On a problem with singularity in comparison of linear experiments

For consistency, the parameter space in the Gauss-Markov model with singular covariance matrix is usually restricted by observation vector. This restriction arises some difficulties in comparison of linear experiments. To avoid it we reduce the problem of comparison from singular to nonsingular case.     

An information-theoretic approach to surrogate-marker evaluation with failure time endpoints

Over the last decades, the evaluation of potential surrogate endpoints in clinical trials has steadily been growing in importance, not only thanks to the availability of ever more potential markers and surrogate endpoints, also because more methodological development has become available. While early work has been devoted, to a large extent, to Gaussian, binary, and longitudinal endpoints, the case of time-to-event endpoints is in need of careful scrutiny as well, owing to the strong presence of such endpoints in oncology and beyond. While work had been done in the past, it was often cumbersome to use such tools in practice, because of the need for fitting copula or frailty models that were further embedded in a hierarchical or two-stage modeling approach. In this paper, we present a methodologically elegant and easy-to-use approach based on information theory. We resolve essential issues, including the quantification of “surrogacy” based on such an approach. Our results are put to the test in a simulation study and are applied to data from clinical trials in oncology. The methodology has been implemented in R.     

A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome

In some clinical trials and epidemiologic studies, investigators are interested in knowing whether the variability of a biomarker is independently predictive of clinical outcomes. This question is often addressed via a naïve approach where a sample-based estimate (e.g., standard deviation) is calculated as a surrogate for the “true” variability and then used in regression models as a covariate assumed to be free of measurement error. However, it is well known that the measurement error in covariates causes underestimation of the true association. The issue of underestimation can be substantial when the precision is low because of limited number of measures per subject. The joint analysis of survival data and longitudinal data enables one to account for the measurement error in longitudinal data and has received substantial attention in recent years. In this paper we propose a joint model to assess the predictive effect of biomarker variability. The joint model consists of two linked sub-models, a linear mixed model with patient-specific variance for longitudinal data and a full parametric Weibull distribution for survival data, and the association between two models is induced by a latent Gaussian process. Parameters in the joint model are estimated under Bayesian framework and implemented using Markov chain Monte Carlo (MCMC) methods with WinBUGS software. The method is illustrated in the Ocular Hypertension Treatment Study to assess whether the variability of intraocular pressure is an independent risk of primary open-angle glaucoma. The performance of the method is also assessed by simulation studies.     

What's the problem? A problem-based approach to the reform of the Norwegian drug rehabilitation sector

This article explores how the scope of solutions to complex problems is determined by the way in which the problem is formulated. We inquire into this by means of a theory-driven case study of the reform in the Norwegian drug rehabilitation sector and the Tyrili facility. We analyze divergent approaches to drug rehabilitation by means of a problem formulation framework, which distinguishes between (1) the value dimensions of given problems, and (2) the phases of problem solving activities. Based on this inquiry, we arrive at a set of propositions, in which we propose that the two approaches differ in their inclusion of various value dimensions. Moreover, we suggest that the reform pursued increased efficiency as a solution to a reductionist formulation of the problem, which challenged Tyrili's opportunity to solve the problem in the multidimensional manner they conceive of it. This is an argument against a one-dimensional maximization of efficiency as a pursuit of quality in social services.     

The story about theater organizations, the public's approval, and the actors' identity formation in Nordic disability theater

Nordic disability theater is a relatively new and interesting field of disability research. In this article, the authors provide an overview of the field of disability theater in a Nordic context. The article is based on a comparative analysis from 3 research projects conducted in Sweden and Norway. The projects used qualitative methods and were analyzed from different theoretical perspectives. Interviews were conducted at 4 different disability theaters involving actors with hearing impairments, intellectual disabilities, physical disabilities, and mental disabilities. The aim of this article is to illustrate how the organizational settings reflect different goals and aims at the political and artistic levels. The authors will also address the relationship between the theaters and the public opinion and media. Finally, they will illustrate in what way organization, recognition, and public approval play an important role for the actors' identity formation and sense of belonging.     

Reliability of reasons for early termination of breastfeeding: application of a bivariate probability model with sample selection to data from surveys in Malaysia in 1976-77 and 1988-89

Although extensively collected, data on people's reasons for their behaviour provided retrospectively have been met with some scepticism on the grounds that they may be subject to biases and errors that jeopardize their usefulness. This study investigates, for a sample of 1,327 births, the reliability with which women in Peninsular Malaysia recalled, at intervals 12 years apart, reasons for not initiating or for stopping breastfeeding less than 3 months after a birth. Overall, we find low to moderate reliability of recall. Levels of reliability are relatively high for some reasons (the child died and no or insufficient milk) but low for some others (child ill, breastfeeding inconvenient). Results from selection models show that reliability does not vary with the length of time since the child's birth but is inversely related to socio-economic status (proxied by education and employment). Social status, social norms, and health-related factors appear to be significant influences on women's consistency of reporting.