---
title: "NLSY: Regression demonstration with Flu Vaccination and SES data"
author: Jonathan Trattner
output: rmarkdown::html_vignette
bibliography: references.bib
vignette: >
  %\VignetteIndexEntry{Regression}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)
```


# Introduction

This vignette shows how to run a **discordant-kinship regression** with the `{discord}` package and the `{NLSYLinks}` package. The example uses data on flu vaccination and socioeconomic status (SES) from the National Longitudinal Survey of Youth 1979 (NLSY79). The goal of this analysis is to examine whether SES at age 40 predicts the number of flu vaccinations received between 2006 and 2016, while controlling for genetic and shared environmental factors by leveraging a discordant-kinship design.

## Data Description

We build on Trattner et al. (2020) [@jonathantrattner2020], originally motivated by reports of health disparities among ethnic minority groups during the COVID-19 pandemic [@hooper2020]. 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](https://www.nlsinfo.org/investigator/pages/login) and are available [here](https://github.com/R-Computing-Lab/discord/blob/main/data-raw/flu_shot.dat). SES at age 40 values can be found [here](https://github.com/R-Computing-Lab/discord/blob/main/data-raw/nlsy-ses.csv). For clarity and to emphasize the functionality of {discord}, the data has been pre-processed using [this script](https://github.com/R-Computing-Lab/discord/blob/main/data-raw/preprocess-discord-flu.R). This analysis is enabled by recent work that inferred genetic relatedness for approximately 95% of kin pairs in the NLSY79 cohort [@rodgers2016]. These kinship links are provided in the [{NlsyLinks}](https://nlsy-links.github.io/NlsyLinks/index.html) R package [@beasley2016], and can be easily utilized with the {discord} package.

```{r setup-discord-data, include = FALSE, cache = FALSE, eval=FALSE}
```

## Data Cleaning

### Prepare person-level data

To perform a discordant-kinship regression, we first need to preprocess the data. For this example, we will load the following packages:

```{r discord-setup, message = FALSE}
# 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.

```{r preview-pre-processed-data, echo = FALSE, eval = knitr::is_html_output(),error=FALSE}
data_flu_ses %>%
  select(CASEID, RACE, SEX, FLU_total, S00_H40) %>%
  filter(!is.na(S00_H40)) %>%
  slice(1:500) %>%
  slice_sample(n = 6) %>%
  kableExtra::kbl("html", align = "c") %>%
  kableExtra::kable_styling(full_width = FALSE) # %>%
#    kableExtra::column_spec(1:11, extra_css = "text-align: center;")
```

```{r preview-pre-processed-data-latex, echo = FALSE, eval = knitr::is_latex_output()}
data_flu_ses %>%
  select(CASEID, RACE, SEX, FLU_total, S00_H40) %>%
  filter(!is.na(S00_H40)) %>%
  slice(1:500) %>%
  slice_sample(n = 6) %>%
  kableExtra::kbl(format = "latex", booktabs = TRUE, align = "c") %>%
  kableExtra::kable_styling(latex_options = c("striped", "hold_position"), position = "center")
```

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.

```{r set-kinship-link-vars}
# 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"
)
```


### Create kinship-linked data

We now link the subjects by the specified variables using `CreatePairLinksSingleEntered()`, from the {NlsyLinks} package. We filter full siblings (RFull == .5) and use RelationshipPath == "Gen1Housemates"; see {NlsyLinks} docs for other kin types.

```{r create-linked-data}
# 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:[^discord-2]

```{r preview-linked-dat, echo = FALSE, eval = knitr::is_html_output(),error=FALSE}
df_link %>%
  select(
    ExtendedID,
    SubjectTag_S1, SubjectTag_S2,
    FLU_total_S1, FLU_total_S2,
    S00_H40_S1, S00_H40_S2
  ) %>%
  filter(!is.na(S00_H40_S1) & !is.na(S00_H40_S2)) %>%
  slice(1:500) %>%
  slice_sample(n = 6) %>%
  kableExtra::kbl("html", align = "c") %>%
  kableExtra::kable_styling(full_width = FALSE) # %>%
#  kableExtra::column_spec(1:11, extra_css = "text-align: center;")
```

```{r preview-linked-dat-latex, echo = FALSE, eval = knitr::is_latex_output()}
df_link %>%
  select(
    ExtendedID,
    SubjectTag_S1, SubjectTag_S2,
    FLU_total_S1, FLU_total_S2,
    S00_H40_S1, S00_H40_S2
  ) %>%
  filter(!is.na(S00_H40_S1) & !is.na(S00_H40_S2)) %>%
  slice(1:500) %>%
  slice_sample(n = 6) %>%
  kableExtra::kbl(format = "latex", booktabs = TRUE, align = "c") %>%
  kableExtra::kable_styling(latex_options = c("striped", "hold_position", "scale_down"))
```

[^discord-2]: 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. 

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.

```{r consistent-kin-data}
# 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).

```{r finalize-flu-modeling-data, cache = FALSE}
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:

```{r preview-flu-modeling-data, echo = FALSE, eval = knitr::is_html_output()}
flu_modeling_data %>%
  select(contains(c("extended_id", "subject_tag", "flu_total", "race", "sex", "s00_h40"))) %>%
  rename(
    ses_age_40_s1 = s00_h40_s1,
    ses_age_40_s2 = s00_h40_s2
  ) %>%
  slice(1:10) %>%
  kableExtra::kbl("html", align = "c") %>%
  kableExtra::kable_styling() %>%
  kableExtra::column_spec(1:11, extra_css = "text-align: center;")
```

```{r preview-flu-modeling-data-latex, eval = knitr::is_latex_output(), echo = FALSE}
flu_modeling_data %>%
  select(contains(c("extended_id", "subject_tag", "flu_total", "race", "sex", "s00_h40"))) %>%
  rename(
    ses_age_40_s1 = s00_h40_s1,
    ses_age_40_s2 = s00_h40_s2
  ) %>%
  slice(1:10) %>%
  kableExtra::kbl(format = "latex", booktabs = TRUE, align = "c") %>%
  kableExtra::kable_styling(latex_options = c("striped", "hold_position", "scale_down"))
```

## 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).[^discord-3] 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](https://github.com/R-Computing-Lab/discord/blob/main/data-raw/preprocess-discord-flu.R), these columns contain dummy variables where the reference group for race is "non-Black, non-Hispanic" and the reference group for sex is female. 

[^discord-3]: Note these ids were previously "\_S1" and "\_S2", however, we used the `clean_names()` function which coerced the column names to lowercase.

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

```{r run-regression, cache = FALSE}
# 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")
)
```

```{r broom-reg, echo = FALSE}
flu_model_output %<>%
  broom::tidy()
```

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

```{r summarize-model-html, echo = FALSE, eval = knitr::is_html_output(), error=FALSE}
flu_model_output %>%
  mutate(
    p.value = scales::pvalue(p.value, add_p = TRUE),
    across(.cols = where(is.numeric), ~ round(.x, 3))
  ) %>%
  rename(
    "Standard Error" = std.error,
    "T Statistic" = statistic
  ) %>%
  rename_with(~ snakecase::to_title_case(.x)) %>%
  kableExtra::kbl("html", align = "c") %>%
  kableExtra::kable_styling() %>%
  kableExtra::column_spec(1:5, extra_css = "text-align: center;")
```

```{r summarize-model-latex, echo = FALSE, eval = knitr::is_latex_output(), error=FALSE}
flu_model_output %>%
  mutate(
    p.value = scales::pvalue(p.value, add_p = TRUE),
    across(.cols = where(is.numeric), ~ round(.x, 3))
  ) %>%
  rename(
    "Standard Error" = std.error,
    "T Statistic" = statistic
  ) %>%
  rename_with(~ snakecase::to_title_case(.x)) %>%
  kableExtra::kbl(format = "latex", booktabs = TRUE, align = "c") %>%
  kableExtra::kable_styling(latex_options = c("striped", "hold_position"), position = "center")
```

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 estimates the expected difference for two sisters of a non-minority ethnic background (the reference groups for sex and race), when SES and all other predictors are equal -- approximately `r round(flu_model_output$estimate[1], 1)`. 
The term `flu_total_mean` is essentially an extra component of the intercept that adjusts for between-family differences in the outcome variable.  It allows the model to capture how the expected difference between siblings changes as a function of their average level of flu vaccinations, helping to avoid misattributing between-family differences to within-family effects.

The next term, `s00_h40_mean`, represents the mean socioeconomic status for the siblings. Like `flu_total_mean`, it adjusts the expected difference to reflect between-family variation in SES.  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 `r flu_model_output %>% filter(term == "s00_h40_diff") %>% pull(estimate) * 10 %>% round(3)` 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 demographic covariates (sex and race) 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.


# Session Info

```{r session-info, echo = FALSE}
sessioninfo::session_info()
```

# References
