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Likelihood Score (likelihood + score)
Selected AbstractsBlack, Hispanic, and White Women's Knowledge of the Symptoms of Acute Myocardial InfarctionJOURNAL OF OBSTETRIC, GYNECOLOGIC & NEONATAL NURSING, Issue 4 2005Cynthia Arslanian-Engoren Objective: To examine Black, Hispanic, and White women's knowledge of the symptoms of acute myocardial infarction. Design: Descriptive, nonexperimental design. Setting: Detroit, Michigan, and San Antonio, Texas, metropolitan areas. Participants: A convenience sample of 78 ethnically diverse women. Hispanics (n = 26) were recruited from San Antonio, Texas; Blacks (n = 26) were recruited from Detroit, Michigan; and Whites were recruited from San Antonio, Texas (n = 13), and Detroit, Michigan (n = 13). Main Outcome Measures: Participants ranked 10 acute symptoms they believed represented a myocardial infarction: anxiety, arms ache, change in thinking, chest pain, cough, fatigue, decreased appetite, headache, indigestion, and shortness of breath. Next, participants assigned a likelihood score for each acute symptom as representing a myocardial infarction. Results: Hispanic women were more likely than Black women to perceive the symptom of headache as indicative of a myocardial infarction. Women older than age 45 were more likely to assign a higher likelihood score to the symptom of shortness of breath than were women age 45 or younger. Conclusions: Age and ethnic differences were noted in women's perception of the signs and symptoms indicative of a myocardial infarction. [source] Linkage analysis of schizophrenia to chromosome 15AMERICAN JOURNAL OF MEDICAL GENETICS, Issue 8 2001Dr. Pablo V. Gejman Abstract We have mapped a sample of 68 families consisting of one or more affected sibling pairs with schizophrenia or schizoaffective disorder with 20 markers spanning all of chromosome 15 to investigate whether there is a locus on chromosome 15 that confers an increased susceptibility to schizophrenia using parametric and nonparametric linkage analyses. Allele sharing identical by descent and multipoint maximum likelihood score (MLS) statistics were employed. Results show excess allele sharing for multiple markers in 15q11.2,q25, a chromosomal region previously found linked to a decrease in the normal inhibition of the P50 auditory-evoked response to the second of paired stimuli, a decrease associated with schizophrenia. Excess allele sharing was found for markers spanning about 48 cM in 15q11.2,q25 (D15S1002,D15S1023). The greatest single point allele sharing was found at D15S659 (62.6%). The multipoint MLS scores were greater than 1.0 in the 30,52 cM interval delimited by ACTC and D15S150, with a maximum value of 2.0 with GENEHUNTER PLUS near D15S1039. © 2001 Wiley-Liss, Inc. [source] Design and Inference for Cancer Biomarker Study with an Outcome and Auxiliary-Dependent SubsamplingBIOMETRICS, Issue 2 2010Xiaofei Wang Summary In cancer research, it is important to evaluate the performance of a biomarker (e.g., molecular, genetic, or imaging) that correlates patients' prognosis or predicts patients' response to treatment in a large prospective study. Due to overall budget constraint and high cost associated with bioassays, investigators often have to select a subset from all registered patients for biomarker assessment. To detect a potentially moderate association between the biomarker and the outcome, investigators need to decide how to select the subset of a fixed size such that the study efficiency can be enhanced. We show that, instead of drawing a simple random sample from the study cohort, greater efficiency can be achieved by allowing the selection probability to depend on the outcome and an auxiliary variable; we refer to such a sampling scheme as,outcome and auxiliary-dependent subsampling,(OADS). This article is motivated by the need to analyze data from a lung cancer biomarker study that adopts the OADS design to assess epidermal growth factor receptor (EGFR) mutations as a predictive biomarker for whether a subject responds to a greater extent to EGFR inhibitor drugs. We propose an estimated maximum-likelihood method that accommodates the OADS design and utilizes all observed information, especially those contained in the likelihood score of EGFR mutations (an auxiliary variable of EGFR mutations) that is available to all patients. We derive the asymptotic properties of the proposed estimator and evaluate its finite sample properties via simulation. We illustrate the proposed method with a data example. [source] ESTIMATING A GEOGRAPHICALLY EXPLICIT MODEL OF POPULATION DIVERGENCEEVOLUTION, Issue 3 2007L. Lacey Knowles Patterns of genetic variation can provide valuable insights for deciphering the relative roles of different evolutionary processes in species differentiation. However, population-genetic models for studying divergence in geographically structured species are generally lacking. Since these are the biogeographic settings where genetic drift is expected to predominate, not only are population-genetic tests of hypotheses in geographically structured species constrained, but generalizations about the evolutionary processes that promote species divergence may also be potentially biased. Here we estimate a population-divergence model in montane grasshoppers from the sky islands of the Rocky Mountains. Because this region was directly impacted by Pleistocene glaciation, both the displacement into glacial refugia and recolonization of montane habitats may contribute to differentiation. Building on the tradition of using information from the genealogical relationships of alleles to infer the geography of divergence, here the additional consideration of the process of gene-lineage sorting is used to obtain a quantitative estimate of population relationships and historical associations (i.e., a population tree) from the gene trees of five anonymous nuclear loci and one mitochondrial locus in the broadly distributed species Melanoplus oregonensis. Three different approaches are used to estimate a model of population divergence; this comparison allows us to evaluate specific methodological assumptions that influence the estimated history of divergence. A model of population divergence was identified that significantly fits the data better compared to the other approaches, based on per-site likelihood scores of the multiple loci, and that provides clues about how divergence proceeded in M. oregonensis during the dynamic Pleistocene. Unlike the approaches that either considered only the most recent coalescence (i.e., information from a single individual per population) or did not consider the pattern of coalescence in the gene genealogies, the population-divergence model that best fits the data was estimated by considering the pattern of gene lineage coalescence across multiple individuals, as well as loci. These results indicate that sampling of multiple individuals per population is critical to obtaining an accurate estimate of the history of divergence so that the signal of common ancestry can be separated from the confounding influence of gene flow,even though estimates suggest that gene flow is not a predominant factor structuring patterns of genetic variation across these sky island populations. They also suggest that the gene genealogies contain information about population relationships, despite the lack of complete sorting of gene lineages. What emerges from the analyses is a model of population divergence that incorporates both contemporary distributions and historical associations, and shows a latitudinal and regional structuring of populations reminiscent of population displacements into multiple glacial refugia. Because the population-divergence model itself is built upon the specific events shaping the history of M. oregonensis, it provides a framework for estimating additional population-genetic parameters relevant to understanding the processes governing differentiation in geographically structured species and avoids the problems of relying on overly simplified and inaccurate divergence models. The utility of these approaches, as well as the caveats and future improvements, for estimating population relationships and historical associations relevant to genetic analyses of geographically structured species are discussed. [source] | ||||||||||