In smasongarrison/discord: Functions for Discordant Kinship Modeling

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

Introduction

Objective

The aim of this vignette is to provide a comprehensive guide for incorporating categorical predictors in discordant-kinship regression analyses using the discord package. Furthermore, we will demonstrate how to interpret there different types of categorical variables—mixed variables and between-dyad variables—within the discordant-kinship framework.

Research Question

We explore whether sex significantly predicts SES at age 40 using different coding schemes for categorical variables.

Data Source

We use a subset of the 1979 National Longitudinal Survey of Youth (NLSY79).

Data Preparation

Package and Data Loading

# Loading necessary packages and data
# For easy data manipulation
library(dplyr)
# For kinship linkages
library(NlsyLinks)
# For discordant-kinship regression
library(discord)
# pipe
library(magrittr)

#data
data(data_flu_ses)

Data Cleaning and Preprocessing

# for reproducibility 
set.seed(2023)

link_vars <- c("S00_H40", "RACE", "SEX")

# 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 removed the pair when the Dependent Variable is missing. 

df_link <- df_link %>%
           filter(!is.na(S00_H40_S1) & !is.na(S00_H40_S2)) %>%
  mutate(SEX_S1 = case_when(SEX_S1 == 0 ~ "MALE", 
                            SEX_S1 == 1 ~ "FEMALE"),
         SEX_S2 = case_when(SEX_S2 == 0 ~ "MALE", 
                            SEX_S2 == 1 ~ "FEMALE"),
         RACE_S1 = case_when(RACE_S1 == 0 ~ "NONMINORITY", 
                            RACE_S1 == 1 ~ "MINORITY"),
         RACE_S2 = case_when(RACE_S2 == 0 ~ "NONMINORITY", 
                            RACE_S2 == 1 ~ "MINORITY"))

# we only include same-race pairs in this example for demonstration purpose
df_link <- df_link %>%
  dplyr::filter(RACE_S1==RACE_S2)

We only pick one pair per household to avoid the independence assumption violation.

df_link<-df_link%>% group_by(ExtendedID) %>% slice_sample() %>% ungroup()

# Methodology

## Discordant-Kinship Regression: General Logic


If the predictor is a continuous variable, the DV (the difference score of the outcome variable) is regressed on the mean score of the outcome variable, the mean score of the predictor, and the difference score of the predictor. However, it is impossible to calculate the mean score and difference score of the categorical variable. This vignette introduces how to deal with categorical predictors in discordant-kinship regressions. In this example, we use sex and race as categorical variables for demonstration. We referred to  @kenny2006  for an idea about handling categorical predictors in dyadic analysis, and @Hwang2022 for incorporating that idea into discordant-kinship regression.

## Handling Categorical Predictors


### Breif explanation for types of variable in dyadic analysis

In dyadic analysis, a variable can be at the within-dyads level, the between-dyad level, or a combination. This combination is called a "mixed variable", meaning that it varies both within and between dyads. 

Between-dyads variable refers to a variable that members in the same pair have the same score. For example, the length of the marriage is a between-dyads variable if the study consists of married-couple samples. Race can be a between-dyads variable if the study consists of same-race sibling pairs. 

Within-dyads variable refers to a variable where variance exists within dyads, but variance does not exist across dyads. For example, if two same-sex roommates report their division of house chores, which adds up to 100% for all pairs, that is a within-dyads variable. 

A mixed variable refers to a variable that variances exist both within- and between-dyads. For example, gender is a prototypical example of a mixed variable in that individuals in the pair can be male or female, regardless of their dyad. 

## Mixed Variables: Gender as an Example

As discussed above, sex is a mixed variable in this example.

We use the `discord_data()` function to prepare the data for analysis.


The `discord_data()` function restructures the paired data. 
In the pair, the one with the higher DV is assigned to be "_1" and the other one is assigned to be "_2".

```r
cat_sex <- discord_data(
            data = df_link,
            outcome = "S00_H40",
            sex= "SEX",
            race= "RACE",
            demographics = "sex",
            predictors=NULL,
            pair_identifiers = c("_S1","_S2"),
            coding_method = "both")

A random slice of this data looks as follows:

cat_sex <- cat_sex %>%
  dplyr::mutate(SEX_binarymatch = case_when(SEX_binarymatch == 0 ~ "mixed-sex", 
                                            SEX_binarymatch == 1 ~ "same-sex"))

cat_sex %>%
slice(1:500) %>%
    slice_sample(n = 6) %>%
    kableExtra::kbl('html', align = "c")%>%
    kableExtra::kable_styling(full_width = FALSE)

The sex compositions of the pairs in the data are as follows:

cat_sex %>%
  group_by(SEX_1, SEX_2) %>%
  summarize( n(),.groups = 'drop') %>%
  kableExtra::kbl('html', align = "c",col.names=c("SEX_1","SEX_2","sample_size"))%>%
  kableExtra::kable_styling(full_width = FALSE) 

The SEX_1 variable indicates the sex of the individual who has the higher DV in the pairs, and the SEX_2 variable indicates the sex of the other one in the pair.

In this example, the dependent variable (DV) is S00_H40_diff, the difference score of socio-economic status (SES) of the pair at age 40. Considering the DV (S00_H40_diff) is a between-dyads variable, we can make the sex variable a between-dyads variable as well.

If we put the individual's sex in the pair as a predictor in the regression model, the individual's sex is an individual-level predictor, while DV (S00_H40_diff) is not an individual-level variable. This means that variables are not at a comparable level, hindering meaningful interpretation. However, by forcing the sex variable to be a between-dyads variable by using a gender-composition variable, results can be more interpretable and meaningful. To sum up, using the sex composition of the pair as a predictor in the discordant-kinship regression model can yield more meaningful results, rather than using an individual's sex as a predictor in the regressions.

So, how to force the sex variable to be a sex-composition variable? The above table shows all the possible combinations of sex composition: 1) male-male, 2) male-female, 3) female-male, and 4) female-female. However, it is hard to believe that the distinction between "female-male" pairs and the "male-female" is meaningful unless there are strong theoretical reasons behind it because the "_S1" and "_S2" were assigned by the DV value of the participants. Specifically, the "female-male" pair means that the one with a higher DV in the pair is "female" and the other one was "male", and the "male-female" pair means that the one with a higher DV in the pair was "male" and the other one was "female".

Thus, we can utilize the following sex-composition variables. First, we can use sex-composition variables that have three factors: 1) female-female,2) "male-female" and "female-male", and 3) "male-male". Or, we can also use sex-composition variables that have two factors: 1) "same-sex", and 2) mixed sex. The discord_data() function and discord_regression() function utilize these two options; the binary match variable utilizes the former categorizations, and the multi-match variable utilizes the latter categorization.

As shown above, the discord_data() function generates SEX_binarymatch and SEX_multimatch for the sex variable. By doing so, the sex variable (which was initially a mixed variable) becomes a between-dyad variable as follows:

cat_sex %>%
  group_by(SEX_binarymatch,SEX_multimatch, SEX_1, SEX_2) %>%
  summarize( n(),.groups = 'drop') %>%
  kableExtra::kbl('html', align = "c",col.names=c("binary","multi", "SEX_1","SEX_2","sample_size"))%>%
  kableExtra::kable_styling(full_width = FALSE) 

Researchers can choose between two options based on their research question. For example, if the research question focuses on the difference between same-sex pairs and mixed-sex pairs, SEX_binarymatch can be a good option. If the research question focuses on the difference between male-male pairs, female-female pairs, and mixed-sex pairs, SEX_multimatch can be a good option.

In the regression model section, we demonstrated the regression analyses with these sex-composition variables.

Between-dyad variable: Race as an Example

As discussed above, a categorical predictor can be a between-dyads variable when two people in the same dyad share the same value. Race can be a between-dyads variable if the study consists of same-race sibling pairs.

For demonstration purposes, we force the race variable to be a between-dyad variable, meaning that we only include the same-race pairs in the data.

By using the discord_data() function, we can prepare data for the analysis as follows:

set.seed(2023) # for reproducibility 

cat_race <- discord_data(
            data = df_link,
            outcome = "S00_H40",
            predictors = NULL,
            sex= "SEX",
            race= "RACE",
            demographics = "race",
            pair_identifiers = c("_S1","_S2"),
            coding_method= "both") 

The race compositions in the data set are:

cat_race <- cat_race %>%
  dplyr::mutate(RACE_binarymatch = case_when(RACE_binarymatch == 0 ~ "mixed-race", 
                                             RACE_binarymatch == 1 ~ "same-race"))

cat_race %>%
  group_by(RACE_binarymatch, RACE_multimatch, RACE_1, RACE_2)%>%
  summarize( n(),.groups = 'drop')%>%
  kableExtra::kbl('html', align = "c",col.names=c("RACE_binarymatch", "RACE_multimatch", "RACE_1","RACE_2","sample_size"))%>%
  kableExtra::kable_styling(full_width = FALSE) 

The RACE_binarymatch variable indicates whether the pair is the same-race pair or mixed-race pair. Because the race variable is a between-dyads variable, all the pairs in this example are same-race pairs. The RACE_multimatch variable indicates whether the pair is minority-minority, nonminority-nonminority, or mixed-race.

By using the discord_data() function, users can prepare data that contains both race- and sex-composition variables.

# for reproducibility 

set.seed(2023)

cat_both <- discord_data(
            data = df_link,
            outcome = "S00_H40",
            predictors = NULL,
            sex= "SEX",
            race= "RACE",
            demographics = "both",
            pair_identifiers = c("_S1","_S2"),
            coding_method= "both") 

The race composition and sex composition of the pairs in the dataset are as such:

cat_both <- cat_both %>%
  dplyr::mutate(RACE_binarymatch = case_when(RACE_binarymatch == 0 ~ "mixed-race", 
                                             RACE_binarymatch == 1 ~ "same-race"),
                SEX_binarymatch = case_when(SEX_binarymatch == 0 ~ "mixed-sex", 
                                            SEX_binarymatch == 1 ~ "same-sex"))
cat_both %>%
  group_by(RACE_multimatch, RACE_1, RACE_2,SEX_binarymatch,SEX_multimatch,SEX_1,SEX_2)%>%
  summarize( n(),.groups = 'drop')%>%
  kableExtra::kbl('html', align = "c",col.names=c("RACE_multi", "RACE_1","RACE_2","SEX_binary","SEX_multi","SEX_1","SEX_2",
  "sample_size"))%>%
  kableExtra::kable_styling(full_width = FALSE) 

Results and Interpretation

Regression Analysis: Gender

SEX binary match

The regression model with the binary-match sex variable can be conducted as such:

discord_sex_binary <- discord_regression(
     data = df_link,
     outcome = "S00_H40",
     sex= "SEX",
     race= "RACE",
     demographics = "sex",
     predictors=NULL,
     pair_identifiers = c("_S1","_S2"),
     coding_method ="binary") 

discord_sex_binary%>%
  broom::tidy() %>%
  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;")

Looking at the results, the mean SES score for the siblings (S00_H40_diff) is a significant control variable (p =r round(summary(discord_sex_binary)[["coefficients"]]["S00_H40_mean", "Pr(>|t|)"],3)). S00_H40_mean is negatively associated with the difference in SES score between siblings at age 40, S00_H40_diff, controlling for another variable (in this case, SEX_binarymatch). It is estimated that for one unit increase of S00_H40_mean, S00_H40_diffis expected to decrease approximately r round(discord_sex_binary[["coefficients"]][["S00_H40_mean"]],3).

The term SEX_binarymatch is not a significant predictor (p = r round(summary(discord_sex_binary)[["coefficients"]]["SEX_binarymatch", "Pr(>|t|)"],3)) when controlling for S00_H40_mean. There is no significant differences between same-sex pairs and mixed-sex pairs in S00_H40_diff. This means that the difference between same-sex pairs and mixed-sex pairs does not significantly predict the S00_H40_diff in the pair when controlling for S00_H40_mean.

It is estimated that, compared to the reference group (the mixed-sex pairs), the same-sex pairs would have approximately r round(discord_sex_binary[["coefficients"]][["SEX_binarymatch"]],3) higher difference score of S00_H40_diff, when controlling for S00_H40_mean. However, this coefficient is not significant, so it is not advisable to interpret the coefficient.

SEX multi match

The regression model with the multi-match sex variable can be conducted as such:

discord_sex_multi <- discord_regression(
     data = df_link,
     outcome = "S00_H40",
     sex= "SEX",
     race= "RACE",
     demographics = "sex",
     predictors=NULL,
     pair_identifiers = c("_S1","_S2"),
     coding_method ="multi")

discord_sex_multi%>%
  broom::tidy() %>%
  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;")

The term S00_H40_mean was a significant control variable (p = r round(summary(discord_sex_multi)[["coefficients"]]["S00_H40_mean","Pr(>|t|)"],3)). This means that the mean SES score for the sibling pairs (S00_H40_mean) is negatively associated with the difference in SES between siblings (S00_H40_diff), controlling for other variables (in this case, SEX_multimatch). It is estimated that for one unit increase of S00_H40_mean, S00_H40_diff is expected to decrease approximately r abs(round(discord_sex_multi[["coefficients"]][["S00_H40_mean"]],3)).

There was no significant difference between female-female pairs and male-male pairs (p=r round(summary(discord_sex_multi)[["coefficients"]]["SEX_multimatchMALE", "Pr(>|t|)"],3)) to predict S00_H40_diff. Similarly, there were no significant differences between mixed-sex pairs and female-female pairs (p = r round(summary(discord_sex_multi)[["coefficients"]]["SEX_multimatchmixed", "Pr(>|t|)"],3)).

The coefficient r abs(round(discord_sex_multi[["coefficients"]][["SEX_multimatchMALE"]],3)) is the difference between the expected S00_H40_diff for the reference group (in this case, the female-female pairs) and the male-male pairs.

The coefficient r abs(round(discord_sex_multi[["coefficients"]][["SEX_multimatchmixed"]],3)) is the difference between the expected S00_H40_diff for the reference group (in this case, the female-female pairs) and the mixed-sex pairs. However, these coefficients are not significant, so it is not advisable to interpret the coefficients.

Mean model

if users are interested in predicting the mean SES score for the sibling pairs with predictors, regression analyses can be carried out as such::

discord_cat_mean <- lm(S00_H40_mean ~ SEX_binarymatch,
    data = cat_sex)

discord_cat_mean %>%
# for nicer regression output   
  broom::tidy() %>%
  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;")

In this regression model, the mean SES score for the siblings (S00_H40_mean) was regressed on the SEX-composition variable (SEX_binarymatch).

There is no significant difference between same-sex pairs and mixed-sex pairs in the mean SES score for the siblings (p=r round(summary(discord_cat_mean)[["coefficients"]]["SEX_binarymatchsame-sex", "Pr(>|t|)"],3))

It is estimated that compared to the mixed-sex pairs, the same-sex pairs would have approximately r abs(round(discord_cat_mean[["coefficients"]][["SEX_binarymatchsame-sex"]],3)) higher S00_H40_mean. However, this coefficient is not significant, so it is not advisable to interpret the coefficient.

discord_cat_mean2 <- lm( S00_H40_mean ~ SEX_multimatch,
    data = cat_sex) 

discord_cat_mean2%>%
# for nicer regression output   
  broom::tidy() %>%
  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;")

There is a significant difference between female-female pairs and male-male pairs (r round(summary(discord_cat_mean2)[["coefficients"]]["SEX_multimatchMALE", "Pr(>|t|)"],3)) to predict the S00_H40_mean. However, there is no significant difference between mixed-sex pairs and female-female pairs (p = r round(summary(discord_cat_mean2)[["coefficients"]]["SEX_multimatchmixed", "Pr(>|t|)"],3)).

The coefficient r abs(round(discord_cat_mean2[["coefficients"]]["SEX_multimatchMALE"],3)) is the difference between the expected S00_H40_mean (the mean SES score for the siblings) for the reference group (in this case, the female-female pairs) and the male-male pairs. It can be concluded that male-male pairs and female-female pair has significant differences in S00_H40_mean.

The coefficient r abs(round(discord_cat_mean2[["coefficients"]]["SEX_multimatchmixed"],3)) is the difference between the expected S00_H40_mean for the reference group (in this case, the female-female pairs) and the mixed-sex pairs. However, these coefficients are not significant, so it is not advisable to interpret the coefficients.

Regression Analysis: Race

The race multi-match variable indicates whether the race composition of the pair is 1)"minority-minority", 2) "nonminority-nonminority), and 3) mixed-race pairs. The race binary-match variable indicates whether the race composition of the pair is 1) same-race or 2) mixed-race pairs.

The regression model with a multi-match race variable as a predictor can be conducted as such:

RACE multi match

# perform kinship regressions 
cat_race_reg <-  discord_regression(
     data = df_link,
     outcome = "S00_H40",
     sex= "SEX",
     race= "RACE",
     demographics = "race",
     predictors=NULL,
     pair_identifiers = c("_S1","_S2"),
     coding_method ="multi") 

cat_race_reg%>%
# for nicer regression output   
  broom::tidy() %>%
  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;")

The mean SES score for the siblings (S00_H40_mean) is a significant control variable (p =r round(summary(cat_race_reg)[["coefficients"]]["S00_H40_mean","Pr(>|t|)"],3). The term S00_H40_mean is negatively associated with the difference score of SES between siblings (S00_H40_diff), controlling for another variable (in this case, RACE_multimatchNONMINORITY). It is estimated that for one unit increase of S00_H40_mean, the DV (S00_H40_diff) is expected to decrease by approximately r abs(round(cat_race_reg[["coefficients"]][["S00_H40_mean"]],3)).

The term RACE_multimatchNONMINORITY was a significant predictor of S00_H40_diff (p = r round(summary(cat_race_reg)[["coefficients"]]["RACE_multimatchNONMINORITY","Pr(>|t|)"],3)) after controlling for S00_H40_mean. This means that the difference between the "Minority-minority" sibling pairs and "nonminority-non-minority" sibling pairs significantly predicts S00_H40_diff. Specifically, compared to the reference group (the "minority" pairs), "nonminority" pairs are expected to have approximately r abs(round(cat_race_reg[["coefficients"]][["RACE_multimatchNONMINORITY"]],3)) lower S00_H40_diff.

Mean model

discord_cat_mean <- lm(S00_H40_mean ~ RACE_multimatch,
    data = cat_race)

discord_cat_mean%>%
# for nicer regression output   
  broom::tidy() %>%
  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;")

There is significant difference between "minority" pairs and "nonminority" pairs in S00_H40_mean (p =r round(summary(discord_cat_mean)[["coefficients"]]["RACE_multimatchNONMINORITY","Pr(>|t|)"],3)). It is estimated that, compared to the reference group (minority pairs), the nonminority pairs would have approximately r abs(round(discord_cat_mean[["coefficients"]][["RACE_multimatchNONMINORITY"]],3)) lower S00_H40_mean.

Regression Analysis: Gender and Race

We can include both sex and race as predictors as well.

First, we restructure the data for the kinship-discordant regression using the discord_data() function.

Sex and Race (Multi-match)

 both_multi <-discord_regression(
     data = df_link,
     outcome = "S00_H40",
     sex= "SEX",
     race= "RACE",
     demographics = "both",
     predictors=NULL,
     pair_identifiers = c("_S1","_S2"),
     coding_method ="multi")

both_multi%>%
# for nicer regression output   
  broom::tidy() %>%
  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;")

The mean SES score for the siblings (S00_H40_mean) is a significant control variable (p = r round(summary(both_multi)[["coefficients"]]["S00_H40_mean","Pr(>|t|)"],3) ). S00_H40_mean is negatively associated with the difference score of SES between the siblings (S00_H40_diff), controlling for other variables (in this case, the SEX_multimatchMALE, SEX_multimatchmixed and RACE_multimatchNONMINORITY). It is estimated that for one unit increase of the mean SES score for the sibling pairs( S00_H40_mean), the difference score of SES between siblings(S00_H40_diff) is expected to decrease approximately r round(both_multi[["coefficients"]][["S00_H40_mean"]],3).

The SEX_multimatchmixed and SEX_multimatchMALE are not significant predictors when controlling for other variables (i.e., S00_H40_mean and RACE_multimatchNONMINORITY). The coefficient r round(both_multi[["coefficients"]][["SEX_multimatchMALE"]],3) is the difference between the expected DV (S00_H40_diff) for the reference group (in this case, the "female-female" pairs) and the "male-male" pairs. The coefficient r round(both_multi[["coefficients"]][["SEX_multimatchmixed"]],3) is the difference between the expected DV (S00_H40_diff) for the female-female pairs and the mixed-sex pairs. However, these coefficients are not significant, so it is not advisable to interpret the coefficients.

The term RACE_multimatchNONMINORITY is a significant predictor (p = r round(summary(both_multi)[["coefficients"]]["RACE_multimatchNONMINORITY", "Pr(>|t|)"],3)) when controlling for other variables (i.e., SEX_multimatchMALE, SEX_multimatchmixed, and S00_H40_mean). This means that there is a significant difference between minority race pairs and nonminority race pairs in the difference score of SES between siblings (S00_H40_diff) when controlling for the model covariates (i.e., SEX_multimatchMALE, SEX_multimatchmixed, and S00_H40_mean). Specifically, compared to the minority race pairs, the nonminority race pairs were expected to have approximately r round(both_multi[["coefficients"]][["RACE_multimatchNONMINORITY"]],3) higher difference score of SES between siblings at age 40.

SEX binary match and Race multi match

Currently, the discord_regression() function can conduct regression with either a binary match or multi-match of sex and race. Thus, if user wants to use the binarymatch sex variable and multimatch variable, it can be done by using the lm() function.

We can perform regression using the binary-match sex variable and multi-match race variable as such:

discord_cat_diff <- lm( S00_H40_diff ~ S00_H40_mean + RACE_multimatch + SEX_binarymatch,
    data = cat_both)  

# for nicer regression output   

discord_cat_diff %>%
  broom::tidy() %>%
  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;")

The mean SES score for the siblings at 40 (S00_H40_mean) is a significant control variable (p = r round(summary(discord_cat_diff)[["coefficients"]]["S00_H40_mean","Pr(>|t|)"],3). The mean SES score for the siblings (S00_H40_mean) is negatively associated with the difference score of SES between the siblings (S00_H40_diff), controlling for other variables (in this case, SEX_binarymatchsame-sex and RACE_multimatchNONMINORITY). It is estimated that for one unit increase of S00_H40_mean, the DV (S00_H40_diff) is expected to decrease approximately r abs(round(discord_cat_diff[["coefficients"]][["S00_H40_mean"]],3)).

The term SEX_binarymatchsame-sex is not a significant predictor (p = r round(summary(discord_cat_diff)[["coefficients"]][["SEX_binarymatchsame-sex", "Pr(>|t|)"]],3)) when controlling for other variables (i.e., S00_H40_mean and RACE_multimatchNONMINORITY). This means that the difference between same-sex pairs and mixed-sex pairs does not significantly predict the difference score of SES between siblings (S00_H40_diff) when controlling for the mean SES score for the siblings (S00_H40_mean) and race-composition of the pair (RACE_multimatchNONMINORITY). Compared to the mixed-sex pairs, it is estimated that the same-sex pairs have approximately r abs(round(discord_cat_diff[["coefficients"]][["SEX_binarymatchsame-sex"]],3))higher difference score of SES between siblings (S00_H40_diff) when controlling for the mean SES score for the sibling pairs (S00_H40_mean) and race-composition of the pairs (RACE_multimatchNONMINORITY). However, this variable is not significant, so it is not advisable to interpret the coefficient.

The term RACE_multimatchNONMINORITYis a significant predictor (p = r round(summary(discord_cat_diff)[["coefficients"]][["RACE_multimatchNONMINORITY", "Pr(>|t|)"]],3)). This means that there is a significant difference between minority race pairs and nonminority race pairs to predict the difference score of SES between the siblings (S00_H40_diff) when controlling for the model covariates (i.e., SEX_binarymatchsame-sex and S00_H40_mean). Specifically, compared to the minority race pairs, nonminority race pairs were expected to have approximately r abs(round(discord_cat_diff[["coefficients"]][["RACE_multimatchNONMINORITY"]],3)) lower difference scores of SES between siblings (S00_H40_diff).

Mean models

The mean SES score for the sibling pairs can be regressed on SEX-composition and race-composition variables as follows:

discord_cat_mean <- lm( S00_H40_mean ~ RACE_multimatch + SEX_multimatch,
    data = cat_both) 

discord_cat_mean%>%
  broom::tidy() %>%
  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;")

The term SEX_multimatchMALE is a significant predictor (p = r round(summary(discord_cat_mean)[["coefficients"]]["SEX_multimatchMALE","Pr(>|t|)"],3)) when controlling for other variables (i.e., SEX_multimatchmixedand RACEe_multimatchNONMINORITY). This means that the difference between female-female pairs and male-male pairs significantly predicted the mean SES score for the siblings when controlling for race-composition of the pairs. Compared to the female-female pairs, it is estimated that the male-male pairs have approximately r abs(round(discord_cat_mean[["coefficients"]][["SEX_multimatchMALE"]],3)) higher mean SES score for the siblings when controlling for and race-composition of the pairs.

The term SEX_multimatchmixed was not a significant predictor (p = r round(summary(discord_cat_mean)[["coefficients"]]["SEX_multimatchmixed","Pr(>|t|)"],3)) when controlling for other variables (i.e., SEX_multimatchMALEand RACE_multimatchNONMINORITY). This means that the difference between female-female pairs and mixed-sex pairs does not significantly predict the mean SES score for the siblings when controlling for race-composition of the pairs. Compared to the female-female pairs, it is estimated that the mixed-sex pairs have approximately r abs(round(discord_cat_mean[["coefficients"]][["SEX_multimatchmixed"]],3)) higher mean SES score for the sibling pairs when controlling for and race-composition of the pairs. However, this variable is not significant, so it is not advisable to interpret the coefficient.

The term RACE_multimatchNONMINORITY is a significant predictor (p = r round(summary(discord_cat_mean)[["coefficients"]]["RACE_multimatchNONMINORITY","Pr(>|t|)"],3)). This means that there is a significant difference between minority race pairs and nonminority race pairs in the mean SES score for the sibling pairs (S00_H40_mean) when controlling for the other variables (i.e., SEX_multimatchmixed and SEX_multimatchMALE). Specifically, compared to the minority race pairs, nonminority race pairs were expected to have approximately r abs(round(discord_cat_mean[["coefficients"]][["RACE_multimatchNONMINORITY"]],3)) higher mean SES score for siblings

discord_cat_mean2 <- lm( S00_H40_mean ~ RACE_multimatch + SEX_binarymatch,
    data = cat_both)  

discord_cat_mean2 %>%
  broom::tidy() %>%
  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;")

The term SEX_binarymatchsame-sex is not a significant predictor (p = r round(summary(discord_cat_mean2)[["coefficients"]]["SEX_binarymatchsame-sex","Pr(>|t|)"],3)) when controlling for the race-composition variable (i.e., RACE_multimatchNONMINORITY). This means that the difference between mixed-sex pairs and same sex pairs does not significantly predict the mean SES score for the siblings when controlling for race-composition of the pairs. Compared to the mixed-sex pairs, it is estimated that the same-sex pairs have approximately r abs(round(discord_cat_mean2[["coefficients"]][["SEX_binarymatchsame-sex"]],3)) higher mean SES score for the sibling pairs (S00_H40_mean) when controlling for and race-composition of the pairs (RACE_multimatchNONMINORITY). However, this variable is not significant, so it is not advisable to interpret the coefficient.

The term RACE_multimatchNONMINORITY is a significant predictor (p = r round(summary(discord_cat_mean2)[["coefficients"]]["RACE_multimatchNONMINORITY","Pr(>|t|)"],3)). This means that there is a significant difference between minority race pairs and nonminority race pairs in the the mean SES score for the siblings when controlling for the sex-composition variable (i.e., SEX_binarymatchsame-sex). Specifically, compared to the minority race pairs, nonminority race pairs were expected to have approximately r abs(round(discord_cat_mean2[["coefficients"]][["RACE_multimatchNONMINORITY"]],3)) higher mean SES score for siblings

Conclusion

This vignette demonstrates how to effectively include categorical predictors in discordant-kinship regressions using the discord package. Depending on the research question, one can choose to use either mixed variables or between-dyad variables as predictors, as exemplified by gender and race.

References

smasongarrison/discord documentation built on March 4, 2024, 12:55 p.m.