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Likelihood Procedure (likelihood + procedure)

Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Application of REML procedure to estimate the genetic parameters of weekly liveweights in one-to-one sire and dam pedigree recorded Japanese quail

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 1 2003
M. Saatci
Summary Residual maximum likelihood procedure was applied to analyse data from a one-to-one pedigreed Japanese quail population, using an animal model to estimate the genetic parameters of weekly liveweights. Records of 1108 animals from 113 sires and 152 dams were analysed. An individual animal model was used. The heritabilities of weights for hatching (HW) with their standard errors (SE), week 1 (W1), week 2 (W2), week 3 (W3), week 4 (W4), week 5 (W5) and week 6 (W6) were 0.51 ± 0.05, 0.32 ± 0.06, 0.20 ± 0.05, 0.21 ± 0.06, 0.20 ± 0.05, 0.15 ± 0.04 and 0.14 ± 0.04, respectively. The strongest genetic correlations were found between W1 and W3 (0.98 ± 0.11). Generally, genetic correlations were higher than the phenotypic correlations. The highest phenotypic correlation (0.85) was between the W4 and W5 weights. Strong genetic correlations among the weekly weights suggest that selection for W5 or W6 weight may be based on weights recorded earlier. Zusammenfassung Die Anwendung der REML-Methode zur Schätzung von genetischen Parametern für Wochengewichte bei japanischen Wachteln mit Abstammungsinformationen Die REML-Methode zur Varianzkomponentenschätzung mittels eines Tiermodells wurde für eine Population von japanischen Wachteln mit Abstammungsinformationen für Wochengewichte angewandt. Daten von 1108 Tieren von 113 Vätern und 152 Müttern wurden mit einem Tiermodell analysiert. Folgende Heritabilitäten wurden geschätzt: 0,51 ± 0,05 für Schlupfgewicht (HW), 0,32 ± 0,06 für Gewicht 1. Woche (W1), 0,20 ± 0,05 für Gewicht 2. Woche (W2), 0,21 ± 0,06 für Gewicht 3. Woche (W3), 0,20 ± 0,05 für Gewicht 4. Woche (W4), 0,15 ± 0,04 für Gewicht 5. Woche (W5) und 0,14 ± 0,04 für Gewicht 6. Woche (W6). Die höchste genetischen Korrelation wurde zwischen W1 und W3 gefunden (0,98 ± 0,11). Generell waren die genetischen Korrelationen höher als die phänotypischen. Die höchste phänotypische Korrelation (0,85) wurde zwischen den Gewichten in der 4. und 5. Woche beobachtet. Die hohen genetischen Korrelationen führen zu der Konsequenz, dass auf die Gewichte der 5. und 6. Woche selektiert werden kann unter Nutzung der früheren Wochengewichte. [source]

Heritability of anti-predatory traits: vigilance and locomotor performance in marmots

JOURNAL OF EVOLUTIONARY BIOLOGY, Issue 5 2010
D. T. BLUMSTEIN
Abstract Animals must allocate some proportion of their time to detecting predators. In birds and mammals, such anti-predator vigilance has been well studied, and we know that it may be influenced by a variety of intrinsic and extrinsic factors. Despite hundreds of studies focusing on vigilance and suggestions that there are individual differences in vigilance, there have been no prior studies examining its heritability in the field. Here, we present one of the first reports of (additive) genetic variation in vigilance. Using a restricted maximum likelihood procedure, we found that, in yellow-bellied marmots (Marmota flaviventris), the heritability of locomotor ability (h2 = 0.21), and especially vigilance (h2 = 0.08), is low. These modest heritability estimates suggest great environmental variation or a history of directional selection eliminating genetic variation in these traits. We also found a significant phenotypic (rP = ,0.09 ± 0.04, P = 0.024) and a substantial, but not significant, genetic correlation (rA = ,0.57 ± 0.28, P = 0.082) between the two traits (slower animals are less vigilant while foraging). We found no evidence of differential survival or longevity associated with particular phenotypes of either trait. The genetic correlation may persist because of environmental heterogeneity and genotype-by-environment interactions maintaining the correlation, or because there are two ways to solve the problem of foraging in exposed areas: be very vigilant and rely on early detection coupled with speed to escape, or reduce vigilance to minimize time spent in an exposed location. Both strategies seem to be equally successful, and this ,locomotor ability-wariness' syndrome may therefore allow slow animals to compensate behaviourally for their impaired locomotor ability. [source]

Likelihood Methods for Treatment Noncompliance and Subsequent Nonresponse in Randomized Trials

BIOMETRICS, Issue 2 2005
A. James O'Malley
Summary While several new methods that account for noncompliance or missing data in randomized trials have been proposed, the dual effects of noncompliance and nonresponse are rarely dealt with simultaneously. We construct a maximum likelihood estimator (MLE) of the causal effect of treatment assignment for a two-armed randomized trial assuming all-or-none treatment noncompliance and allowing for subsequent nonresponse. The EM algorithm is used for parameter estimation. Our likelihood procedure relies on a latent compliance state covariate that describes the behavior of a subject under all possible treatment assignments and characterizes the missing data mechanism as in Frangakis and Rubin (1999, Biometrika86, 365,379). Using simulated data, we show that the MLE for normal outcomes compares favorably to the method-of-moments (MOM) and the standard intention-to-treat (ITT) estimators under (1) both normal and nonnormal data, and (2) departures from the latent ignorability and compound exclusion restriction assumptions. We illustrate methods using data from a trial to compare the efficacy of two antipsychotics for adults with refractory schizophrenia. [source]

Use of resampling to select among alternative error structure specifications for GLMM analyses of repeated measurements,

INTERNATIONAL JOURNAL OF METHODS IN PSYCHIATRIC RESEARCH, Issue 1 2004
Scott Tonidandel
Abstract Autocorrelated error and missing data due to dropouts have fostered interest in the flexible general linear mixed model (GLMM) procedures for analysis of data from controlled clinical trials. The user of these adaptable statistical tools must, however, choose among alternative structural models to represent the correlated repeated measurements. The fit of the error structure model specification is important for validity of tests for differences in patterns of treatment effects across time, particularly when maximum likelihood procedures are relied upon. Results can be affected significantly by the error specification that is selected, so a principled basis for selecting the specification is important. As no theoretical grounds are usually available to guide this decision, empirical criteria have been developed that focus on model fit. The current report proposes alternative empirical criteria that focus on bootstrap estimates of actual type I error and power of tests for treatment effects. Results for model selection before and after the blind is broken are compared. Goodness-of-fit statistics also compare favourably for models fitted to the blinded or unblinded data, although the correspondence to actual type I error and power depends on the particular fit statistic that is considered. Copyright © 2004 Whurr Publishers Ltd. [source]