Association of electronic-health record (EHR)-derived race with BRCA testing in patients (pts) with breast cancer (BC) with similar genetic ancestry (GA) in a clinicogenomic database (CGDB).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6524-6524
Author(s):  
Yanling Jin ◽  
Charlotta Fruchtenicht ◽  
Sylvia Hu ◽  
Janis Allen ◽  
Anne-Marie Meyer ◽  
...  
Keyword(s):  
Health Disparities ◽  
Sample Size ◽  
South Asian ◽  
Racial Disparity ◽  
Brca Testing ◽  
Analysis Workflow ◽  
Comprehensive Genomic Profiling ◽  
Testing Patterns ◽  
Increase Sample Size ◽  
Selection Of

6524 Background: Disparities in health outcomes can be affected by biological factors associated with GA and social determinants of health. These factors can be teased apart using GA data from comprehensive genomic profiling (CGP) in pts with cancer. CGDBs that link EHR data with CGP enable the selection of pts with similar GA. Holding GA constant provides an opportunity to directly study the effects of reported race in health disparities. This study evaluated a published racial disparity (BRCA testing rates in African American [AA] vs White pts with BC) in a population with fixed, similar GA. Methods: The nationwide (US-based) deidentified Flatiron Health and Foundation Medicine (FMI) BC CGDB (Q3 2020) was used. For each pt, GA fractions from 5 geographic ancestry groups (African [AFR]; Admixed American; East Asian; European [EUR]; South Asian) were derived by FMI using an admixture analysis workflow using genes captured in the CGP assay. To focus on BRCA testing in AA vs White pts and find a sufficient population with similar GA but AA or White race, pts with admixture of both EUR and AFR ancestry were selected. The chosen fractions were: Cohort 1=35%-65% AFR and EUR each; Cohort 2=25%-60% AFR and EUR each; Cohort 3=30%-60% AFR. Cohorts overlap but were chosen to increase sample size. In each cohort, documented BRCA testing prevalence, time from diagnosis to BRCA test date, age at BRCA test and overall survival (OS) were compared between races. Other race (OR) and missing race (MR) were also reported. Results: Most pts (4130/6903) in the BC CGDB had ≥75% EUR ancestry; 129 pts had AFR ancestry fractions ≥25% with EUR ancestry >0%. AA pts had the lowest BRCA testing rates (39%, 43%, 44% for Cohorts 1-3, respectively), which were 18%, 10% and 17% lower compared with White pts, respectively (Table). In Cohorts 1-3, AA pts experienced a longer median time between diagnosis and testing (399, 668, 900 days) compared with White pts (93, 667, 106 days). The median age at BRCA test was 16, 9 and 8 years younger in AA pts (49, 47 and 50 years) compared with White pts. Although pts with MR data had the lowest OS compared with the other races within each cohort, the sample size of each arm for all cohorts was too small to make conclusions. Conclusions: This study demonstrated that when holding GA constant, racial disparities persist in BRCA testing patterns and outcome in pts with BC from a CGDB. With increasing availability of linked clinical and genomic data, further exploration of disparities in genetically similar cohorts can provide deeper insight for cancer outcomes and health disparities research.[Table: see text]

data a; * significance level; a=0.05; * variance of difference of two observations on the log scale; * sigmaW = within-subjects standard deviation; sigmaW=0.355; s=sqrt(2)*sigmaW; * total number of subjects (needs to be a multiple of 2); n=58; * error degrees of freedom for AB/BA cross-over with n subjects in total; n2=n-2; * ratio = mu_T/mu_R; ratio=1.00; run; data b; set a; * calculate power; t1=tinv(1-a,n-2); t2=-t1; nc1=(sqrt(n))*((log(ratio)-log(0.8))/s); nc2=(sqrt(n))*((log(ratio)-log(1.25))/s); df=(sqrt(n-2))*((nc1-nc2)/(2*t1)); prob1=probt(t1,df,nc1); prob2=probt(t2,df,nc2); answer=prob2-prob1; power=answer*100; run; proc print data=b; run; As an example of using this SAS code, suppose µ = 1, σ = 0.355, α = 0.05 and n = 58. The power (as a percentage) is calculated as 90.4. The required number of subjects to achieve a given power can easily be obtained by trial and error using a selection of values of n. An alternative approach is to use trial and error directly on the sample size n for a given power. For more on this see Phillips (1990) and Diletti et al. (1991), for example. 7.4 Individual bioequivalence As noted in Section 7.1, individual bioequivalence (IBE) is a criterion for deciding if a patient who is currently being treated with R can be

2003 ◽  
pp. 362-362
Keyword(s):  
Standard Deviation ◽  
Sample Size ◽  
Degrees Of Freedom ◽  
Trial And Error ◽  
Significance Level ◽  
Individual Bioequivalence ◽  
Alternative Approach ◽  
Within Subjects ◽  
Log Ratio ◽  
Selection Of

scholarly journals Shifting Positionalities: Empirical Reflections on a Queer/Trans of Colour Methodology

2008 ◽  
Vol 13 (1) ◽  
pp. 162-173 ◽  
Author(s):  
Jin Haritaworn
Keyword(s):  
South Asian ◽  
Coming Out ◽  
Role Model ◽  
Social Differences ◽  
Interracial Families ◽  
Trans People ◽  
Cultural Texts ◽  
Alternative Methodology ◽  
Selection Of

How can we study ‘Queer’, or indeed, should we? Drawing on fieldwork with people raised in interracial families in Britain and Germany, and reflecting on my own coming out as transgendered/genderqueer during the research, I reflect on the role of difference, similarity, and change in the production of queer knowledges. My entry point is a queer diasporic one. Queers of colour, I argue, have a particular stake in queering racialised heterosexualities; yet differences within diasporic spaces clearly matter. While ‘Queer’ can open up an alternative methodology of redefining and reframing social differences, the directionality of our queering - ‘up’ rather than ‘down’ - is clearly relevant. I suggest the anti-racist feminist principle of positionality as fruitful for such a queer methodology of change. This is explored with regard to a selection of empirical and cultural texts, including the debate around Paris is Burning, Jenny Livingston's film about the Harlem house/ball scene; the appeal that a non-white heterosexual artist such as South-Asian pop singer MIA can have for queers of colour; the camp role model which Thai sex work femininity can represent for queer and trans people from the second generation of Thai migration; and the solidarity of a Southeast Asian butch with feminine women in her diasporic collectivity.

scholarly journals Resampled dimensional reduction for feature representation in machine learning

2021 ◽  
Author(s):  
Herdiantri Sufriyana ◽  
Yu Wei Wu ◽  
Emily Chia-Yu Su
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Prediction Model ◽  
Sample Size ◽  
Dimensional Reduction ◽  
Latent Variables ◽  
Feature Representation ◽  
Estimated Parameters ◽  
Representation Technique ◽  
Selection Of

Abstract We aimed to provide a resampling protocol for dimensional reduction resulting a few latent variables. The applicability focuses on but not limited for developing a machine learning prediction model in order to improve the number of sample size in relative to the number of candidate predictors. By this feature representation technique, one can improve generalization by preventing latent variables to overfit data used to conduct the dimensional reduction. However, this technique may warrant more computational capacity and time to conduct the procedure. The key stages consisted of derivation of latent variables from multiple resampling subsets, parameter estimation of latent variables in population, and selection of latent variables transformed by the estimated parameters.

Construction and selection of quick switching double-sampling system: sample size tightening

1995 ◽  
Vol 22 (1) ◽  
pp. 105-119 ◽  
Author(s):  
S. Devaraj Arumainayagam ◽  
V. Soundararajan
Keyword(s):  
Sample Size ◽  
Double Sampling ◽  
Sampling System ◽  
Selection Of

scholarly journals Assessing terpene content variability of whitebark pine in order to estimate representative sample size

2013 ◽  
pp. 193-204
Author(s):  
Milena Stefanovic ◽  
Slobodanka Mitrovic ◽  
Dragica Obratov-Petkovic ◽  
Vera Vidakovic ◽  
Zorica Popovic ◽  
...  
Keyword(s):  
Sample Size ◽  
Representative Sample ◽  
Mean Value ◽  
Whitebark Pine ◽  
Initial Sample ◽  
Basic Set ◽  
Chemical Content ◽  
The Mean ◽  
Selection Of

In studies of population variability, particular attention has to be paid to the selection of a representative sample. The aim of this study was to assess the size of the new representative sample on the basis of the variability of chemical content of the initial sample on the example of a whitebark pine population. Statistical analysis included the content of 19 characteristics (terpene hydrocarbons and their derivates) of the initial sample of 10 elements (trees). It was determined that the new sample should contain 20 trees so that the mean value calculated from it represents a basic set with a probability higher than 95 %. Determination of the lower limit of the representative sample size that guarantees a satisfactory reliability of generalization proved to be very important in order to achieve cost efficiency of the research.

scholarly journals Effect of plot size and plant spatial arrangement on the efficiency of family selection in sugarcane

2021 ◽  
Vol 53 (2) ◽  
pp. 1-10
Author(s):  
Aparecido De Moraes ◽  
Matheus Henrique Silveira Mendes ◽  
Mauro Sérgio de Oliveira Leite ◽  
Regis De Castro Carvalho ◽  
Flávia Maria Avelar Gonçalves
Keyword(s):  
Sample Size ◽  
Bootstrap Method ◽  
Spatial Arrangement ◽  
Family Selection ◽  
Plot Size ◽  
Stalk Diameter ◽  
New Varieties ◽  
Selection Of ◽  
The Bootstrap Method ◽  
The Ideal

The purpose of this study was to identify the ideal sample size representing a family in its potential, to identify superior families and, in parallel, determine in which spatial arrangement they may have a better accuracy in the selection of new varieties of sugarcane. For such purpose, five families of full-sibs were evaluated, each with 360 individuals, in the randomized blocks design, with three replications in three different spacing among plants in the row (50 cm, 75 cm, and 100 cm) and 150 cm between the rows. To determine the ideal sample size, as well as the better spacing for evaluation, the bootstrap method was adopted. It was observed that 100 cm spacings provided the best average for the stalk numbers, stalk diameter and for estimated weight of stalks in the stool. The spacing of 75 cm between the plants allowed a better power of discrimination among the families for all characters evaluated. At this 75 cm spacing  was also possible to identify superior families with a sample of 30 plants each plot and 3 reps in the trial. Highlights The bootstrap method was efficient to determine the ideal sample size, as well as the best spacing for evaluation. The 75-cm spacing had the highest power of discrimination among families, indicating that this spacing is the most efficient in evaluating sugarcane families for selection purposes. From all the results and considering selective accuracy as the guiding parameter for decision making, the highest values obtained considering the number of stalks and weight of stalks in the stools were found at the 75-cm spacing.

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