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Outcome Distributions (outcome + distribution)

Distribution by Scientific Domains

Mathematics and Statistics	37%

Selected Abstracts

Pregnancy outcome distribution and prenatal diagnosis of autosomal abnormalities, Hawaii, 1986-1999

BIRTH DEFECTS RESEARCH, Issue S1 2002
Mathias B. Forrester
Background Approximately 10% of birth defects result from chromosomal abnormalities. This study investigated the pregnancy outcome distribution of autosomal abnormalities and impact of prenatal diagnosis on autosomal abnormalities. Methods Data were obtained from a population-based birth defects registry and included all autosomal abnormalities delivered in Hawaii during 1986-1999. Results There were 1,015 autosomal abnormality cases, consisting of 523 (52%) live births, 38 (4%) late fetal deaths, 187 (18%) early fetal deaths, 265 (26%) elective terminations, and 2 unknown pregnancy outcome. Live births comprised the majority of translocations (81%), inversions (93%), and deletions (84%) but a smaller proportion of trisomies (42%). Autosomal abnormalities were prenatally diagnosed in 489 (48%) of the cases, of which 243 (50%) were subsequently electively terminated. By type of autosomal abnormality, prenatal diagnosis rates were trisomy (44%), translocation (68%), inversion (91%), deletion (29%), and subsequent elective termination rates were trisomy (73%), translocation (11%), inversion (4%), deletion (50%). The prenatal diagnosis rate was higher for maternal age 35 years or greater than for maternal age less than 35 years (relative risk (RR) 1.8, 95% confidence interval (CI) 1.6-2.0), as was the elective termination rate (RR 1.3, 95% CI 1.1-1.6). The prenatal diagnosis rate was higher in 1993-1999 than in 1986-1992 (RR 1.2, 95% CI 1.1-1.4), although there was no statistically significant difference between the two time periods for subsequent elective termination rate (RR 0.9, 95% CI 0.8-1.1). Conclusions Pregnancy outcome distribution, prenatal diagnosis rates, and subsequent elective terminations rates vary by type of autosomal abnormality. Teratology 66:S7,S11, 2002. © 2002 Wiley-Liss, Inc. [source]

Ideological beliefs as determinants of discrimination in positive and negative outcome distributions

EUROPEAN JOURNAL OF SOCIAL PSYCHOLOGY, Issue 5 2005
Catherine E. Amiot
Social identity theory proposes that discrimination contributes favourably to group members' social identity. In minimal group paradigm (MGP) studies involving positive outcome distributions (e.g. money), discrimination is associated with a more positive social identity. But studies on the positive-negative asymmetry effect show that categorization leads to less discrimination when negative (salary cuts) than when positive outcomes (salary increases) are distributed. Using structural equation modelling, this study (N,=,279) tested whether discrimination involving negative outcome distributions could contribute as much to group members' positive social identity as discrimination on positive outcomes. The study also tested if ideological beliefs (i.e. social dominance orientation, authoritarianism), measured one month before the MGP experiment, could predict positive and negative outcome discrimination. While the fit of the hypothesized model was adequate, only social dominance orientation predicted both positive and negative outcome discrimination. Also, discrimination on positive outcomes but not on negative ones contributed to positive social identity. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Integration of VaR and expected utility under departures from normality

AGRICULTURAL ECONOMICS, Issue 6 2009
Peter J. Barry
Cornish,Fisher expansion; Expected utility; Risk aversion; Value-at-Risk Abstract This article identifies the level of the expected utility (EU) risk aversion and Value-at-Risk (VaR) confidence level that yield the same choice from a given distribution of outcomes, and thus allow for consistent application of the two criteria. The result for a given distribution is an explicit mapping between risk aversion under EU and VaR, for both normal and nonnormal distributions. The Cornish,Fisher expansion is used to establish adjusted mean-deviates for nonnormal outcome distributions and the investor's preference function is expanded to include elements for variance, skewness, and excess kurtosis. A farm-level application with nonnormal revenue distribution illustrates these approaches. [source]

Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome

BIOMETRICS, Issue 1 2009
Jing Cheng
Summary This article considers the analysis of two-arm randomized trials with noncompliance, which have a multinomial outcome. We first define the causal effect in these trials as some function of outcome distributions of compliers with and without treatment (e.g., the complier average causal effect, the measure of stochastic superiority of treatment over control for compliers), then estimate the causal effect with the likelihood method. Next, based on the likelihood-ratio (LR) statistic, we test those functions of or the equality of the outcome distributions of compliers with and without treatment. Although the corresponding LR statistic follows a chi-squared (,2) distribution asymptotically when the true values of parameters are in the interior of the parameter space under the null, its asymptotic distribution is not ,2 when the true values of parameters are on the boundary of the parameter space under the null. Therefore, we propose a bootstrap/double bootstrap version of a LR test for the causal effect in these trials. The methods are illustrated by an analysis of data from a randomized trial of an encouragement intervention to improve adherence to prescribed depression treatments among depressed elderly patients in primary care practices. [source]

Bayesian Covariance Selection in Generalized Linear Mixed Models

BIOMETRICS, Issue 2 2006
Bo Cai
Summary The generalized linear mixed model (GLMM), which extends the generalized linear model (GLM) to incorporate random effects characterizing heterogeneity among subjects, is widely used in analyzing correlated and longitudinal data. Although there is often interest in identifying the subset of predictors that have random effects, random effects selection can be challenging, particularly when outcome distributions are nonnormal. This article proposes a fully Bayesian approach to the problem of simultaneous selection of fixed and random effects in GLMMs. Integrating out the random effects induces a covariance structure on the multivariate outcome data, and an important problem that we also consider is that of covariance selection. Our approach relies on variable selection-type mixture priors for the components in a special Cholesky decomposition of the random effects covariance. A stochastic search MCMC algorithm is developed, which relies on Gibbs sampling, with Taylor series expansions used to approximate intractable integrals. Simulated data examples are presented for different exponential family distributions, and the approach is applied to discrete survival data from a time-to-pregnancy study. [source]

A hierarchical modelling approach to analysing longitudinal data with drop-out and non-compliance, with application to an equivalence trial in paediatric acquired immune deficiency syndrome

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2002
Joseph W Hogan
Longitudinal clinical trials with long follow-up periods almost invariably suffer from a loss to follow-up and non-compliance with the assigned therapy. An example is protocol 128 of the AIDS Clinical Trials Group, a 5-year equivalency trial comparing reduced dose zidovudine with the standard dose for treatment of paediatric acquired immune deficiency syndrome patients. This study compared responses to treatment by using both clinical and cognitive outcomes. The cognitive outcomes are of particular interest because the effects of human immunodeficiency virus infection of the central nervous system can be more acute in children than in adults. We formulate and apply a Bayesian hierarchical model to estimate both the intent-to-treat effect and the average causal effect of reducing the prescribed dose of zidovudine by 50%. The intent-to-treat effect quantifies the causal effect of assigning the lower dose, whereas the average causal effect represents the causal effect of actually taking the lower dose. We adopt a potential outcomes framework where, for each individual, we assume the existence of a different potential outcomes process at each level of time spent on treatment. The joint distribution of the potential outcomes and the time spent on assigned treatment is formulated using a hierarchical model: the potential outcomes distribution is given at the first level, and dependence between the outcomes and time on treatment is specified at the second level by linking the time on treatment to subject-specific effects that characterize the potential outcomes processes. Several distributional and structural assumptions are used to identify the model from observed data, and these are described in detail. A detailed analysis of AIDS Clinical Trials Group protocol 128 is given; inference about both the intent-to-treat effect and average causal effect indicate a high probability of dose equivalence with respect to cognitive functioning. [source]