2 results
Latent profile analysis of dietary intake in a community-dwelling sample of older Americans
- Nicholas J Bishop, Krystle E Zuniga, Christina M Ramirez
-
- Journal:
- Public Health Nutrition / Volume 23 / Issue 2 / February 2020
- Published online by Cambridge University Press:
- 28 June 2019, pp. 243-253
-
- Article
-
- You have access Access
- HTML
- Export citation
-
Objective:
To estimate latent dietary profiles in a community-dwelling sample of older Americans and identify associations between dietary profile membership and individual demographic, socio-economic and health characteristics.
Design:Secondary analysis of the 2012 Health and Retirement Study (HRS) and linked 2013 Health Care and Nutrition Study (HCNS). Latent profile analysis identified mutually exclusive subgroups of dietary intake and bivariate analyses examined associations between dietary profile membership, participant characteristics and nutrient intakes.
Setting:USA.
Participants:An analytic sample of 3558 adults aged 65 years or older.
Results:Four dietary profiles were identified with 15·5 % of the sample having a ‘Healthy’ diet, 42·0 % consuming a ‘Western’ diet, 29·7 % having a diet consisting of high intake of all food groups and 12·7 % reporting relatively low intake of all food groups. Members of the ‘Healthy’ profile reported the greatest socio-economic resources and health, and members of the ‘Low Intake’ profile had the fewest resources and worst health outcomes. Macronutrient and micronutrient intakes varied across profile although inadequate and excessive intakes of selected nutrients were observed for all profiles.
Conclusions:We identified dietary patterns among older Americans typified by either selective intake of foods or overall quantity of foods consumed, with those described as ‘Low Intake’ reporting the fewest socio-economic resources, greatest risk of food insecurity and the worst health outcomes. Limitations including the presence of measurement error in dietary questionnaires are discussed. The causes and consequences of limited dietary intake among older Americans require further study and can be facilitated by the HRS and HCNS.
6 - Random Forests and Fuzzy Forests in Biomedical Research
- from PART 1 - COMPUTATIONAL SOCIAL SCIENCE TOOLS
-
- By Daniel Conn, Department of Biostatistics, UCLA Fielding School of Public Health, Christina M. Ramirez, Department of Biostatistics, UCLA Fielding School of Public Health
- Edited by R. Michael Alvarez, California Institute of Technology
-
- Book:
- Computational Social Science
- Published online:
- 05 March 2016
- Print publication:
- 07 March 2016, pp 168-196
-
- Chapter
- Export citation
-
Summary
INTRODUCTION
With the advent of high-throughput technologies such as multicolor flow cytometry and next-generation sequencing, high-dimensional data has become increasingly common in biomedical research. In many applications such as proteomics, genomics, and immunology, the data has become increasingly wide. That is, we know a great deal about a small number of subjects. In these applications, the number of features greatly exceeds the number of observations. This “large p, small n” problem gives rise to a number of well-known statistical issues.
For example, a researcher may be interested in discovering a certain single nucleotide polymorphism (SNP) that is associated with a particular disease outcome. Note that a nucleotide is a subunit of the DNA molecule, and each nucleotide consists of either an A (adenine), T/U (thymine/uracil), G (guanine), or C (cytosine), with base pairs formed between the former and latter two nucleotides. A SNP is a single base pair such that the nucleotides differ between members of a population.
For example, 99% of patients may carry two A alleles, AA. One percent of the population may have different nucleotides on one or more alleles, say AG or GG at this particular base pair. In this case, A would be the common allele. The feature corresponding to this SNP may count the number of rare alleles (0, 1, or 2). Alternatively, the features may record whether an observation has alleles AA, AG, or GG. This is achieved with two dummy variables. In this case, there are two features corresponding to each SNP.
In many cases, there may be hundreds of thousands of potentially important SNPs and only a few hundred subjects. To further complicate matters, these SNPs may be highly correlated with one another. The SNPs may be more or less variable across members of the population. Some SNPs may be missing for a significant portion of the observations. Additional demographic features, such as gender or race, may confound the relationship between a SNP and a disease outcome. In many of these applications, prediction may be secondary to feature selection. That is, a researcher may want to know the top variables of interest for further study.
Classical regression methods such as linear regression or logistic regression are highly unstable if p is comparable to n, and they altogether fail to yield a result if p is larger than n.