Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Statistical Inference on Health Disparity Indices for Complex Surveys.

ABSTRACT: The National Cancer Institute developed the Health Disparities Calculator (HD*Calc) to facilitate research on health disparities. HD*Calc calculates multiple measures of health disparities using data collected from population-based disease surveillance systems, such as cancer registries. In this paper, we extend the use of HD*Calc to complex survey data by developing plug-in point estimators and Taylor linearization variance estimators that consider complex designs: stratification, multistage clustering, and differential weighting. Our simulation indicates that the plug-in estimators are approximately unbiased and the Taylor linearization variance estimators are accurate. Using 2011-2016 data from the National Health and Nutrition Examination Survey, we demonstrate the use of these estimators in evaluating socioeconomic disparities in the prevalence of child and adolescent (ages 2-18 years) obesity in the United States. Statistical software has been developed for ease of disparity analyses using complex survey data.

SUBMITTER: Li Y

PROVIDER: S-EPMC6211244 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Statistical Inference on Health Disparity Indices for Complex Surveys.

Li Yan Y Yu Mandi M Zhang Jonathan J

American journal of epidemiology 20181101 11

The National Cancer Institute developed the Health Disparities Calculator (HD*Calc) to facilitate research on health disparities. HD*Calc calculates multiple measures of health disparities using data collected from population-based disease surveillance systems, such as cancer registries. In this paper, we extend the use of HD*Calc to complex survey data by developing plug-in point estimators and Taylor linearization variance estimators that consider complex designs: stratification, multistage cl ...[more]

PMID: 30383261

Similar Datasets

Differential Expression of miRNAs: Implication in Colon Cancer Racial Health Disparity

Project description:We have demonstrated that the miR-182 level, in addition to being significantly increased in colon cancer compared to adjacent normal colon tissue, is also significantly increased in African American(AA) compared to Caucasian American (CA) colon cancer. Since miR-182 has been previously associated with decreased survival in colon cancer patients and with increased liver metastases, this observation supports the concept that biological differences between AA and CA colon cancers may contribute to AA disparities in colon cancer survival. We aimed to identify miRNAs that were associated with effects of both tumor and race by generating Agilent miRNA expression profiles on paired colon cancer and adjacent normal colon collected from AA and CA colon cancer subjects. For the 30 paired Stony Brook University (SBU) colon cancer and adjacent normal colon samples, attempts were made to control for other covariates such as age, colon cancer location, stage, BMI and smoking in the selection of the CA samples . However no attempt was made to control for the other covariates in the 30 paired Washington University (WU) colon cancer and adjacent normal colon samples.

2015-06-25 | E-GEOD-48267 | biostudies-arrayexpress

"Magnitude-based inference": a statistical review.

Project description:We consider "magnitude-based inference" and its interpretation by examining in detail its use in the problem of comparing two means.We extract from the spreadsheets, which are provided to users of the analysis (http://www.sportsci.org/), a precise description of how "magnitude-based inference" is implemented. We compare the implemented version of the method with general descriptions of it and interpret the method in familiar statistical terms.We show that "magnitude-based inference" is not a progressive improvement on modern statistics. The additional probabilities introduced are not directly related to the confidence interval but, rather, are interpretable either as P values for two different nonstandard tests (for different null hypotheses) or as approximate Bayesian calculations, which also lead to a type of test. We also discuss sample size calculations associated with "magnitude-based inference" and show that the substantial reduction in sample sizes claimed for the method (30% of the sample size obtained from standard frequentist calculations) is not justifiable so the sample size calculations should not be used. Rather than using "magnitude-based inference," a better solution is to be realistic about the limitations of the data and use either confidence intervals or a fully Bayesian analysis.

| S-EPMC5642352 | biostudies-other

Multiply robust inference for statistical interactions.

Project description:A primary focus of an increasing number of scientific studies is to determine whether two exposures interact in the effect that they produce on an outcome of interest. Interaction is commonly assessed by fitting regression models in which the linear predictor includes the product between those exposures. When the main interest lies in the interaction, this approach is not entirely satisfactory because it is prone to (possibly severe) bias when the main exposure effects or the association between outcome and extraneous factors are misspecified. In this article, we therefore consider conditional mean models with identity or log link which postulate the statistical interaction in terms of a finite-dimensional parameter, but which are otherwise unspecified. We show that estimation of the interaction parameter is often not feasible in this model because it would require nonparametric estimation of auxiliary conditional expectations given high-dimensional variables. We thus consider 'multiply robust estimation' under a union model that assumes at least one of several working submodels holds. Our approach is novel in that it makes use of information on the joint distribution of the exposures conditional on the extraneous factors in making inferences about the interaction parameter of interest. In the special case of a randomized trial or a family-based genetic study in which the joint exposure distribution is known by design or by Mendelian inheritance, the resulting multiply robust procedure leads to asymptotically distribution-free tests of the null hypothesis of no interaction on an additive scale. We illustrate the methods via simulation and the analysis of a randomized follow-up study.

| S-EPMC3097121 | biostudies-literature

Simultaneous statistical inference for epigenetic data.

Project description:Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.

| S-EPMC4428829 | biostudies-literature

Statistical inference for multi-pathogen systems.

Project description:There is growing interest in understanding the nature and consequences of interactions among infectious agents. Pathogen interactions can be operational at different scales, either within a co-infected host or in host populations where they co-circulate, and can be either cooperative or competitive. The detection of interactions among pathogens has typically involved the study of synchrony in the oscillations of the protagonists, but as we show here, phase association provides an unreliable dynamical fingerprint for this task. We assess the capacity of a likelihood-based inference framework to accurately detect and quantify the presence and nature of pathogen interactions on the basis of realistic amounts and kinds of simulated data. We show that when epidemiological and demographic processes are well understood, noisy time series data can contain sufficient information to allow correct inference of interactions in multi-pathogen systems. The inference power is dependent on the strength and time-course of the underlying mechanism: stronger and longer-lasting interactions are more easily and more precisely quantified. We examine the limitations of our approach to stochastic temporal variation, under-reporting, and over-aggregation of data. We propose that likelihood shows promise as a basis for detection and quantification of the effects of pathogen interactions and the determination of their (competitive or cooperative) nature on the basis of population-level time-series data.

| S-EPMC3158042 | biostudies-literature

STatistical Inference Relief (STIR) feature selection.

Project description:MotivationRelief is a family of machine learning algorithms that uses nearest-neighbors to select features whose association with an outcome may be due to epistasis or statistical interactions with other features in high-dimensional data. Relief-based estimators are non-parametric in the statistical sense that they do not have a parameterized model with an underlying probability distribution for the estimator, making it difficult to determine the statistical significance of Relief-based attribute estimates. Thus, a statistical inferential formalism is needed to avoid imposing arbitrary thresholds to select the most important features. We reconceptualize the Relief-based feature selection algorithm to create a new family of STatistical Inference Relief (STIR) estimators that retains the ability to identify interactions while incorporating sample variance of the nearest neighbor distances into the attribute importance estimation. This variance permits the calculation of statistical significance of features and adjustment for multiple testing of Relief-based scores. Specifically, we develop a pseudo t-test version of Relief-based algorithms for case-control data.ResultsWe demonstrate the statistical power and control of type I error of the STIR family of feature selection methods on a panel of simulated data that exhibits properties reflected in real gene expression data, including main effects and network interaction effects. We compare the performance of STIR when the adaptive radius method is used as the nearest neighbor constructor with STIR when the fixed-k nearest neighbor constructor is used. We apply STIR to real RNA-Seq data from a study of major depressive disorder and discuss STIR's straightforward extension to genome-wide association studies.Availability and implementationCode and data available at http://insilico.utulsa.edu/software/STIR.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6477983 | biostudies-literature

Differential Expression of miRNAs: Implication in Colon Cancer Racial Health Disparity

2015-06-25 | GSE48267 | GEO

Understanding the gender disparity in HIV infection across countries in sub-Saharan Africa: evidence from the Demographic and Health Surveys.

Project description:Women in sub-Saharan Africa bear a disproportionate burden of human immunodeficiency virus (HIV) infections, which is exacerbated by their role in society and biological vulnerability. The specific objectives of this article are to (i) determine the extent of gender disparity in HIV infection; (ii) examine the role of HIV/acquired immune deficiency syndrome (AIDS) awareness and sexual behaviour factors on the gender disparity and (iii) establish how the gender disparity varies between individuals of different characteristics and across countries. The analysis involves multilevel logistic regression analysis applied to pooled Demographic and Health Surveys data from 20 countries in sub-Saharan Africa conducted during 2003-2008. The findings suggest that women in sub-Saharan Africa have on average a 60% higher risk of HIV infection than their male counterparts. The risk for women is 70% higher than their male counterparts of similar sexual behaviour, suggesting that the observed gender disparity cannot be attributed to sexual behaviour. The results suggest that the risk of HIV infection among women (compared to men) across countries in sub-Saharan Africa is further aggravated among those who are younger, in female-headed households, not in stable unions or marital partnerships or had an earlier sexual debut.

| S-EPMC3412216 | biostudies-literature

Evaluation and statistical inference for human connectomes.

Project description:Diffusion-weighted imaging coupled with tractography is currently the only method for in vivo mapping of human white-matter fascicles. Tractography takes diffusion measurements as input and produces the connectome, a large collection of white-matter fascicles, as output. We introduce a method to evaluate the evidence supporting connectomes. Linear fascicle evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to quantify the prediction error. We use the prediction error to evaluate the evidence that supports the properties of the connectome, to compare tractography algorithms and to test hypotheses about tracts and connections.

| S-EPMC4180802 | biostudies-literature

Applications of statistical experimental designs to improve statistical inference in weed management.

Project description:In a balanced design, researchers allocate the same number of units across all treatment groups. It has been believed as a rule of thumb among some researchers in agriculture. Sometimes, an unbalanced design outperforms a balanced design. Given a specific parameter of interest, researchers can design an experiment by unevenly distributing experimental units to increase statistical information about the parameter of interest. An additional way of improving an experiment is an adaptive design (e.g., spending the total sample size in multiple steps). It is helpful to have some knowledge about the parameter of interest to design an experiment. In the initial phase of an experiment, a researcher may spend a portion of the total sample size to learn about the parameter of interest. In the later phase, the remaining portion of the sample size can be distributed in order to gain more information about the parameter of interest. Though such ideas have existed in statistical literature, they have not been applied broadly in agricultural studies. In this article, we used simulations to demonstrate the superiority of the experimental designs over the balanced designs under three practical situations: comparing two groups, studying a dose-response relationship with right-censored data, and studying a synergetic effect of two treatments. The simulations showed that an objective-specific design provides smaller error in parameter estimation and higher statistical power in hypothesis testing when compared to a balanced design. We also conducted an adaptive experimental design applied to a dose-response study with right-censored data to quantify the effect of ethanol on weed control. Retrospective simulations supported the benefit of this adaptive design as well. All researchers face different practical situations, and appropriate experimental designs will help utilize available resources efficiently.

| S-EPMC8443065 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data