Regression demonstration

Jonathan Trattner

Introduction

This vignette presents a simple example of a discordant-kinship regression using the discord package.

The analysis draws from work presented by Trattner et al (2020) (Trattner, Kennon, and Garrison 2020), which was initially motivated by reports of health disparities among ethnic minority groups during the COVID-19 pandemic (Hooper, Nápoles, and Pérez-Stable 2020). The data come from the 1979 National Longitudinal Survey of Youth (NLSY79), a nationally-representative household probability sample jointly sponsored by the U.S. Bureau of Labor Statistics and the Department of Defense. Participants were surveyed annually from 1979 until 1994 at which point surveys occurred biennially. The data are publicly available at https://www.nlsinfo.org/ and include responses from a biennial flu vaccine survey administered between 2006 and 2016.

The original analysis examined whether socioeconomic status (SES) at age 40 predicted flu vaccination rates, using a discordant kinship design. For this vignette, the data were downloaded using the NLS Investigator and are available here. SES at age 40 values can be found here. For clarity and to emphasize the functionality of {discord}, the data has been pre-processed using this script. This analysis is enabled by recent work that inferred genetic relatedness for approximately 95% of kin pairs in the NLSY79 cohort (Rodgers et al. 2016). These kinship links are provided in the {NlsyLinks} R package (Beasley et al. 2016), and can be easily utilized with the {discord} package.

Data Cleaning

For this example, we will load the following packages.

# For easy data manipulation
library(dplyr)
# For kinship linkages
library(NlsyLinks)
# For discordant-kinship regression
library(discord)
# To clean data frame names
library(janitor)
# tidy up output
library(broom)
# pipe
library(magrittr)

data(data_flu_ses)

After preprocessing, we obtain a data frame that contain subject identifiers, demographic information such as race and sex, and behavioral variables such as flu vaccination totals and SES at age 40. A random slice of this dataset is shown below.

CASEID RACE SEX FLU_total S00_H40
591 1 0 0 48.92893
136 0 0 4 28.09980
306 1 1 0 46.09039
256 0 0 0 45.76613
224 1 0 5 28.75588
138 0 0 0 76.36864

Using kinship data from the NlsyLinks package, we restructure the dataset to better lend itself to discordant kinship analysis. For each kin pair, the function CreatePairLinksSingleEntered() takes a data set like the one above, [a specification of the NLSY database and the kin’s relatedness], and the variables of interest. It returns a data frame where each row represents a kin pair, and each variable appears twice—once for each sibling—using suffixes to distinguish individuals.

In this example, we examine the relationship between total flu vaccinations (received between 2006 and 2016) and SES at age 40, focusing on full siblings. The variable names used in this linkage are drawn from the preprocessed data previewed above.

# Get kinship links for individuals with the following variables:
link_vars <- c(
  "FLU_total", "FLU_2008", "FLU_2010",
  "FLU_2012", "FLU_2014", "FLU_2016",
  "S00_H40", "RACE", "SEX"
)

We now link the subjects by the specified variables using CreatePairLinksSingleEntered(), from the {NlsyLinks} package.

# Specify NLSY database and kin relatedness
link_pairs <- Links79PairExpanded %>%
  filter(RelationshipPath == "Gen1Housemates" & RFull == 0.5)

df_link <- CreatePairLinksSingleEntered(
  outcomeDataset = data_flu_ses,
  linksPairDataset = link_pairs,
  outcomeNames = link_vars
)

We have saved this data frame as df_link. A random subset of this data is shown below::1

ExtendedID SubjectTag_S1 SubjectTag_S2 FLU_total_S1 FLU_total_S2 S00_H40_S1 S00_H40_S2
1030 103000 103400 4 3 8.908928 42.35215
969 97000 97100 0 3 53.581989 30.70186
575 57500 57600 0 3 57.948751 92.75390
309 30900 31000 2 0 48.006737 48.54822
859 85900 86100 1 1 46.923766 30.82954
1649 164900 165000 0 3 8.675655 62.73942

This data is almost ready for analysis, but we want to ensure that the data are representative of actual trends. The FLU_total column is simply a sum of the biennial survey responses. So for a given sibling-pair, one or both individuals may not have responded to the survey indicating their vaccination status. If that’s the case, we want to exclude those siblings to reduce [non-response bias], by examining the biennial responses and removing any rows with missingness.

# Take the linked data, group by the sibling pairs and
# count the number of responses for flu each year. If there is an NA,
# then data is missing for one of the years, and we omit it.
consistent_kin <- df_link %>%
  group_by(SubjectTag_S1, SubjectTag_S2) %>%
  count(
    FLU_2008_S1, FLU_2010_S1,
    FLU_2012_S1, FLU_2014_S1,
    FLU_2016_S1, FLU_2008_S2,
    FLU_2010_S2, FLU_2012_S2,
    FLU_2014_S2, FLU_2016_S2
  ) %>%
  na.omit()

# Create the flu_modeling_data object with only consistent responders.
# Clean the column names with the {janitor} package.
flu_modeling_data <- semi_join(df_link,
  consistent_kin,
  by = c(
    "SubjectTag_S1",
    "SubjectTag_S2"
  )
) %>%
  clean_names()

To avoid violating assumptions of independence, in our analysis we specify that the kin-pairs should be from unique households (i.e. we randomly select one sibling pair per household).

flu_modeling_data <- flu_modeling_data %>%
  group_by(extended_id) %>%
  slice_sample() %>%
  ungroup()

The data we will use for modeling now contains additional information for each member of the kin pair, including sex and race of each individual, flu vaccination status for the biennial survey between 2006-2016, and a total flu vaccination count for that period. The total vaccination count ranges from 0 - 5, where 0 indicates that the individual did not get a vaccine in any year between 2006-2016 and 5 indicates that an individual got at least 5 vaccines between 2006-2016. Although our data set has individual years, we focused on the aggregate as we felt that was a measure of general tendency. A subset of the data to use in this regression looks like:

extended_id subject_tag_s1 subject_tag_s2 flu_total_s1 flu_total_s2 race_s1 race_s2 sex_s1 sex_s2 ses_age_40_s1 ses_age_40_s2
17 1700 1800 0 0 0 0 1 1 49.26537 74.92440
29 2900 3000 2 0 0 0 0 0 56.80481 32.05423
37 3700 3800 1 5 0 0 0 0 58.55547 50.45408
40 4000 4100 2 0 0 0 1 1 78.19220 73.41860
58 5800 5900 5 0 0 0 0 1 80.56835 49.68414
61 6100 6200 3 4 0 0 0 0 74.43720 50.56920
67 6700 6800 4 4 0 0 1 0 89.67767 82.68649
74 7500 7600 0 0 0 0 0 1 88.15524 61.54234
83 8300 8400 0 3 1 1 1 1 46.41507 64.12765
85 8500 8700 0 4 1 1 1 1 40.12748 64.14045

Modeling and Interpretation

To perform the regression using the {discord} package, we supply the data frame and specify the outcome and predictors. It also requires a kinship pair id, extended_id in our case, as well as pair identifiers – the column name suffixes that identify to which kin a column’s values correspond (“_s1” and “_s2” in our case).2 Optional, although recommended, are columns containing sex and race information to control for as additional covariates. In our case, these columns are prefixed “race” and “sex”. Per the pre-processing script, these columns contain dummy variables where the reference group for race is “non-Black, non-Hispanic” and the reference group for sex is female.

By entering this information into the discord_regression() function, we can run the model as such:

# Setting a seed for reproducibility
set.seed(18)
flu_model_output <- discord_regression(
  data = flu_modeling_data,
  outcome = "flu_total",
  predictors = "s00_h40",
  id = "extended_id",
  sex = "sex",
  race = "race",
  pair_identifiers = c("_s1", "_s2")
)

The default output of discord_regression() is an lm object. The metrics for our regression can be summarized as follows:

Term Estimate Standard Error T Statistic P Value
(Intercept) 1.544 0.190 8.109 p<0.001
flu_total_mean 0.186 0.033 5.571 p<0.001
s00_h40_diff 0.006 0.002 2.747 p=0.006
s00_h40_mean 0.000 0.003 0.154 p=0.878
sex_1 -0.143 0.098 -1.462 p=0.144
race_1 -0.092 0.103 -0.893 p=0.372
sex_2 0.069 0.098 0.703 p=0.482

Looking at this output, the intercept can be thought of as the average difference in outcomes between siblings, controlling for all other variables. That is, it looks like the average difference for two sisters of a non-minority ethnic background (the reference groups for sex and race) is approximately 1.5. The term flu_total_mean is essentially an extra component of the intercept that captures some non-linear trends and allows the difference score to change as a function of the average predictors. Here, this is the mean socioeconomic status for the siblings, s00_h40_mean. We also accounted for sex and race, neither of which have a statistically significant effect on the differences in flu vaccine shots between siblings (different families) or within a sibling pair (same family).

The most important metric from the output, though, is the difference score, s00_h40_diff. Here, it is statistically significant. An interpretation of this result might be, “the difference in socioeconomic status between siblings at age 40 is positively associated with the difference in the number of flu vaccinations received between 2006-2016.” This finding means that a sibling with 10% higher SES is expected to have 0.0553768 average in flu shots.

The goal of performing a discordant-kinship regression is to see whether there is a significant difference in some behavioral measure while controlling for as much gene-and-environmental variance as possible. In this section, we walked through an analysis showing a statistically significant difference in the number of flu shots a sibling received and their socioeconomic status. From this finding, we could not claim the relationship is causal. However, we cannot eliminate causality because there are statistically significant within- and between-family differences in our predictors and outcomes.

Conclusion

In its current implementation, the {discord} package encourages best practices for performing discordant-kinship regressions. For example, the main function has the default expectation that sex and race indicators will be supplied. These measures are both important covariates when testing for causality between familial background and psychological characteristics.

This, and other design choices, are crucial to facilitating transparent and reproducible results. Software ever-evolves, however, and to further support reproducible research we plan to provide improved documentation and allow for easier inspection of the underlying model implementation and results.

Acknowledgments

We acknowledge contributions from Cermet Ream, Joe Rodgers, and support from Lucy D’Agostino McGowan on this project.

References

Beasley, Will, Joe Rodgers, David Bard, Michael Hunter, S. Mason Garrison, and Kelly Meredith. 2016. NlsyLinks: Utilities and Kinship Information for Research with the NLSY. https://CRAN.R-project.org/package=NlsyLinks.
Hooper, Monica Webb, Anna María Nápoles, and Eliseo J. Pérez-Stable. 2020. “COVID-19 and Racial/Ethnic Disparities.” JAMA, May. https://doi.org/10.1001/jama.2020.8598.
Rodgers, Joseph Lee, William H. Beasley, David E. Bard, Kelly M. Meredith, Michael D. Hunter, Amber B. Johnson, Maury Buster, et al. 2016. “The NLSY Kinship Links: Using the NLSY79 and NLSY-Children Data to Conduct Genetically-Informed and Family-Oriented Research.” Behavior Genetics 46 (4): 538–51. https://doi.org/10.1007/s10519-016-9785-3.
Trattner, Jonathan, Later Kennon, and S. Mason Garrison. 2020. “Vaccine Willingness and Socioeconomic Status: A Biometrically Controlled Design.” Behavior Genetics, Behavior Genetics Association 50th Annual Meeting Abstracts, 50 (6): 483–83. https://doi.org/10.1007/s10519-020-10018-8.

  1. Each variable name includes the suffix “_S1” and “_S2”, identifying the sibling to whom the value belongs. The only exception is the first column, which identifies the kin pair.↩︎

  2. Note these ids were previously “_S1” and “_S2”, however, we used the clean_names() function which coerced the column names to lowercase.↩︎