# Full Data Workflow

#### S. Mason Garrison

# Introduction

This vignette provides a complete workflow for conducting discordant-kinship regression using the {discord} package. We encourage you to read the accompanying paper (Garrison et al., 2025) for a full explanation of the methods and their applications. We show how to transform data into a format suitable for analysis, then walk through standard OLS regression (for comparison) and discordant-kinship regression.

The tutorial covers three common data structures (wide, long, and pedigree formats) and uses simulated data for reproducibility. You can adapt our R-based analysis pipeline to your own data by substituting your variables and kinship links.

# Data Structures and Preparation

Your data likely exists in one of three common formats: wide, long, or pedigree. Regardless of format, the goal is to create wide-format data where each row represents a kinship pair. Variables for each member of the pair are distinguished by suffixes (e.g., `_1` and `_2`). Understanding your data structure determines which transformation steps you’ll need. Each subsection below outlines how to handle these formats.

First, load the necessary packages:

    # For easy data manipulation
    library(dplyr)
    library(tidyr)

    # For kinship linkages
    library(NlsyLinks)
    # For discordant-kinship regression
    library(discord)
    # To clean data frames
    library(janitor)
    library(broom)
    # For pipe
    library(magrittr)
    # For pedigree data manipulation
    library(BGmisc)
    # For pedigree plotting
    library(ggpedigree)
    library(ggplot2)

Once you load the necessary packages, feel free to skip to the relevant subsection for your data type.

## Wide Format Data

Wide format is the most direct structure for our analysis. Each row represents one kinship pair, with variables appearing twice—once for each member of the pair—using suffixes to distinguish the two individuals. For example, columns like `age_s1` and `age_s2`, where `_s1` and `_s2` identify which sibling’s data appears in each column.

The simulated sibling data below contains five sibling pairs with variables for ID, age, height, and weight for each sibling.

    df_wide <- data.frame(
      pid = 1:5,
      id_s1 = c(101, 201, 301, 401, 501),
      id_s2 = c(102, 202, 302, 402, 502),
      age_s1 = c(30, 27, 40, 36, 30),
      age_s2 = c(28, 25, 38, 35, 28),
      height_s1 = c(175, 160, 180, 170, 165),
      height_s2 = c(170, 162, 178, 172, 168),
      weight_s1 = c(70, 60, 80, 75, 65),
      weight_s2 = c(68, 62, 78, 74, 66)
    )

    df_wide %>%
      slice(1:5) %>%
      knitr::kable()

pid | id_s1 | id_s2 | age_s1 | age_s2 | height_s1 | height_s2 | weight_s1 | weight_s2
--- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 101 | 102 | 30 | 28 | 175 | 170 | 70 | 68
2 | 201 | 202 | 27 | 25 | 160 | 162 | 60 | 62
3 | 301 | 302 | 40 | 38 | 180 | 178 | 80 | 78
4 | 401 | 402 | 36 | 35 | 170 | 172 | 75 | 74
5 | 501 | 502 | 30 | 28 | 165 | 168 | 65 | 66

In this example, `pid` is a unique identifier for each sibling pair. Specifically, each row is anchored by a pair identifier. Variables ending in `_s1` refer to one sibling, while those ending in `_s2` refer to the other. In our case, the starting dataset is already arranged so that the older sibling is `_s1` and the younger is `_s2`. Nevertheless, this ordering will be adjusted later based on the outcome variable. If your data already has this structure, you can skip ahead to the “Ordering and Derived Variables” section.

## Long Format Data

Long format structures data with one row per individual. In other words, each person appears in their own row, with a pair-identifier column linking siblings. This format is common in many datasets (such as the NLSY), but it requires transformation for our analysis.

We can demonstrate long format by reshaping our wide data from above into long format using `pivot_longer()` from the {tidyr} package:

    df_long <- df_wide %>%
      tidyr::pivot_longer(
        cols = -pid, # keep the dyad identifier intact
        names_to = c(".value", "sibling"), # split base names and the sibling marker
        names_sep = "_s" # original suffix delimiter in column names
      )

    df_long %>%
      slice(1:10) %>%
      knitr::kable()

pid | sibling | id | age | height | weight
--- | --- | --- | --- | --- | ---
1 | 1 | 101 | 30 | 175 | 70
1 | 2 | 102 | 28 | 170 | 68
2 | 1 | 201 | 27 | 160 | 60
2 | 2 | 202 | 25 | 162 | 62
3 | 1 | 301 | 40 | 180 | 80
3 | 2 | 302 | 38 | 178 | 78
4 | 1 | 401 | 36 | 170 | 75
4 | 2 | 402 | 35 | 172 | 74
5 | 1 | 501 | 30 | 165 | 65
5 | 2 | 502 | 28 | 168 | 66

Notice how each individual now occupies their own row. The `sibling` column indicating whether each sibling was originally labeled `1` or `2`. The `pid` column still identifies which individuals form a pair.

Suppose your data does not already have a unique identifier for each kinship pair. In that case, you may need to construct one based on the available information or download one from a kinship linkage resource. Ideally, you will also have information about the type of kinship (e.g., full siblings, half-siblings, cousins) and whether they were raised together or apart. (More on how to do this below for pedigree data.)

### Converting Long to Wide

To convert long format data for discordant-kinship analysis, use `pivot_wider()` from the {tidyr} package:

    df_long2wide <- df_long %>%
      tidyr::pivot_wider(
        names_from = sibling, # the column that indicates the sibling number
        values_from = c(id, age, height, weight), # variables to spread into paired columns
        names_sep = "_s" # ensures id_s1, id_s2,etc
      )

    df_long2wide %>%
      slice(1:5) %>%
      knitr::kable()

pid | id_s1 | id_s2 | age_s1 | age_s2 | height_s1 | height_s2 | weight_s1 | weight_s2
--- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 101 | 102 | 30 | 28 | 175 | 170 | 70 | 68
2 | 201 | 202 | 27 | 25 | 160 | 162 | 60 | 62
3 | 301 | 302 | 40 | 38 | 180 | 178 | 80 | 78
4 | 401 | 402 | 36 | 35 | 170 | 172 | 75 | 74
5 | 501 | 502 | 30 | 28 | 165 | 168 | 65 | 66

The reshaped data now reproduces our original wide format. Each analytic variable appears twice with `_s1` and `_s2` suffixes, and the pair identifier `pid` anchors each row.

### Using existing kinship links

As mentioned before, if your long-format data lacks pair identifiers, you’ll need to create them using family IDs, household identifiers, or external kinship resources. This process can be straightforward if you have access to family or household identifiers, and are confident in the kinship relationships. However, it can be more complex if the relationships are not clearly defined or if there are multiple types of kinship (e.g., siblings, cousins, half-siblings) in your data.

For NLSY data specifically, the {NlsyLinks} package provides validated kinship links for the vast majority of dyads in the NLSY79 and NLSY97 cohorts. These links are provided in wide format, and can be used to left-join with the long-format data that the NLSY info website provides. See the {NlsyLinks} documentation for more details on how to access and use these datasets, or the reproducible source code for the analyses in the primary paper (Garrison et al., 2025).

    library(NlsyLinks)
    data(Links79PairExpanded)

    Links79PairExpanded %>%
      arrange(ExtendedID) %>%
      filter(RelationshipPath == "Gen1Housemates" & RFull == 0.5) %>%
      select(
        ExtendedID,
        SubjectTag_S1, SubjectTag_S2,
        RelationshipPath, RFull, IsMz,
        EverSharedHouse
      ) %>% # used to make the table
      slice_head(n = 5) %>%
      knitr::kable()

ExtendedID | SubjectTag_S1 | SubjectTag_S2 | RelationshipPath | RFull | IsMz | EverSharedHouse
--- | --- | --- | --- | --- | --- | ---
3 | 300 | 400 | Gen1Housemates | 0.5 | No | TRUE
5 | 500 | 600 | Gen1Housemates | 0.5 | No | TRUE
13 | 1300 | 1400 | Gen1Housemates | 0.5 | No | TRUE
17 | 1700 | 1800 | Gen1Housemates | 0.5 | No | TRUE
20 | 2000 | 2100 | Gen1Housemates | 0.5 | No | TRUE

As you can see, this dataset includes an extended family identifier (`ExtendedID`), individual IDs for each sibling in the pair (`R0000100` and `R0000200`), and their RelationshipPath (`RelationshipPath` = “Gen1Housemates” and genetic relatedness (`RFull` = 0.5). You can merge this dataset with your long-format data using the individual IDs.

At this stage the long‑origin data are indistinguishable from the wide-origin wide example. Each analytic variable now appears exactly twice—once for `_s1` and once for `_s2`—and the dyad identifier `pid` continues to anchor the row. You can proceed directly to ordering and construction of `*_mean` and `*_diff` with `discord_data()`.

## Pedigree Data

Pedigree data is common in genetic and family studies, where detailed family trees are available. It is long format data that provides rich information about familial relationships, such as (mother ID, father ID, spouse ID). Our research team has developed specialized tools in the {BGmisc} package to extract kinship links and transform these pedigree structures into a wide format suitable for analysis. Below is an example of how such a dataset might look in both tabular and graphical forms:

    data(potter)
    ggpedigree(potter, config = list(
      label_method = "geom_text",
      label_nudge_y = .25,
      focal_fill_personID = 7,
      focal_fill_include = TRUE,
      focal_fill_force_zero = TRUE,
      focal_fill_na_value = "grey50",
      focal_fill_low_color = "darkred",
      focal_fill_high_color = "gold",
      focal_fill_mid_color = "orange",
      focal_fill_scale_midpoint = .65,
      focal_fill_component = "additive",
      focal_fill_method = "steps", #
      # focal_fill_method = "viridis_c",
      focal_fill_use_log = FALSE,
      focal_fill_n_breaks = 10,
      sex_color_include = F,
      focal_fill_legend_title = "Genetic Relatives \nof Harry Potter"
    )) +
      labs(title = "Potter Pedigree Plot") +
      theme(legend.position = "right")

The pedigree tree above illustrates the family relationships among individuals in the Potter dataset. Each node represents an individual, and the lines connecting them indicate familial relationships such as parent-child and sibling connections.

To work with pedigree data, we first need to convert it into a long format data frame. The following code extracts relevant columns from the `potter` dataset and adds a synthetic variable (`x_var`) for demonstration purposes:

    data(potter)
    df_ped <- potter %>%
      as.data.frame() %>%
      select(
        personID, sex, famID, momID, dadID, spouseID,
        twinID, zygosity
      ) %>%
      mutate(x_var = round(rnorm(nrow(.), mean = 0, sd = 1), digits = 2))

    df_ped %>%
      slice(1:5) %>%
      knitr::kable(digits = 2)

personID | sex | famID | momID | dadID | spouseID | twinID | zygosity | x_var
--- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 1 | 1 | 101 | 102 | 3 | NA | NA | -0.08
2 | 0 | 1 | 101 | 102 | NA | NA | NA | -0.98
3 | 0 | 1 | 103 | 104 | 1 | NA | NA | -1.88
4 | 0 | 1 | 103 | 104 | 5 | NA | NA | -0.19
5 | 1 | 1 | NA | NA | 4 | NA | NA | -0.63

As you can see, each individual is represented in a separate row, with columns for their unique identifier, mother’s identifier, father’s identifier, and other relevant variables. The {BGmisc} package provides functions to compute kinship matrices from pedigree data. The package is available on CRAN and GitHub. The package ( and its documentation ) can be found at: https://CRAN.R-project.org/package=BGmisc as well as it’s accompanying Journal of Open Source Software paper: https://joss.theoj.org/papers/10.21105/joss.06203

### Computing Kinship Matrices

To transform this data into a wide format suitable for discordant-kinship regression, we need to create kinship links based on the pedigree information. To extract the necessary kinship information, we need to compute two matrices: the additive genetic relatedness matrix (add) and the shared environment matrix (cn). Other matrices, such as mitochondrial, can also be computed if needed.

    add <- ped2add(df_ped)
    cn <- ped2cn(df_ped)

The `ped2add()` function computes the additive genetic relatedness matrix, which quantifies the genetic similarity between individuals based on their pedigree information. The `ped2cn()` function computes the shared environment matrix, which indicates whether individuals were raised in the same environment (1) or different environments (0).

The resulting matrices are symmetric, with diagonal elements representing self-relatedness (1.0). The off-diagonal elements represent the relatedness between pairs of individuals, with values ranging from 0 (no relatedness) to 0.5 (full siblings) to 1 (themselves).

### Creating Wide-Format Kinship Pairs

We convert the component matrices into a wide-form dataframe of kin pairs using `com2links()`. Self-pairs and duplicate entries are removed.

    df_links <- com2links(
      writetodisk = FALSE,
      ad_ped_matrix = add,
      cn_ped_matrix = cn,
      drop_upper_triangular = TRUE
    ) %>%
      filter(ID1 != ID2)

    df_links %>%
      slice(1:5) %>%
      knitr::kable(digits = 3)

ID1 | ID2 | addRel | cnuRel
--- | --- | --- | ---
1 | 2 | 0.50 | 1
3 | 4 | 0.50 | 1
1 | 6 | 0.50 | 0
2 | 6 | 0.25 | 0
3 | 6 | 0.50 | 0

As you can see, the `df_links` data frame contains pairs of individuals (ID1 and ID2) along with their additive genetic relatedness (`addRel`) and shared environment status (`cnuRel`). These data are in wide format, with each row representing a unique pair of individuals.

Further, we can tally the number of pairs by relatedness and shared environment to understand the composition of the dataset.

    df_links %>%
      group_by(addRel, cnuRel) %>%
      tally() %>%
      knitr::kable()

addRel | cnuRel | n
--- | --- | ---
0.0625 | 0 | 3
0.1250 | 0 | 47
0.2500 | 0 | 104
0.5000 | 0 | 50
0.5000 | 1 | 32

This table shows the number of kinship pairs for each combination of genetic relatedness and shared environment status. Although the discord regression models can be used with any kin group, it is most interpretable when there is a single kinship group.

Feel free to merge this `df_links` data frame with your pedigree data to include additional variables for each individual in the pair.

The following optional subsection demonstrates how to simulate outcome and predictor variables for these kinship pairs.

### Simulating Outcome and Predictor Variables

To simulate outcome and predictor variables for our kinship pairs, we can use the `kinsim()` function from the {discord} package. This function allows us to generate synthetic data based on specified genetic and environmental parameters.

For demonstration, we’ll focus on first cousins raised in separate homes from our pedigree data.

    df_cousin <- df_links %>%
      filter(addRel == .125) %>% # only cousins %>%
      filter(cnuRel == 0) # only kin raised in separate homes

`kinsim` allows us to generate synthetic data based on specified genetic and environmental parameters or relatedness vectors. Here, we simulate data for first cousins raised apart (addRel = 0.125, cnuRel = 0) using the `df_links` data frame we created earlier.

    set.seed(2024)

    df_synthetic <- discord::kinsim(
      mu_all = c(1, 1), # means
      cov_a = .5,
      cov_c = .1, #
      cov_e = .3,
      c_vector = rep(df_cousin$cnuRel, 3),
      r_vector = rep(df_cousin$addRel, 3)
    )

I now have a synthetic dataset containing pairs of first cousins raised apart, with simulated values for weight and height. Each row represents a unique pair of cousins, with variables for each cousin distinguished by the `_1` and `_2` suffixes. This data is designed such that our weight and height variables have genetic (cov_a=.5) and environmental correlations (cov_c=.1, cov_e=.3). By default, univariate ACE values are 1/3 genetic, 1/3 shared environment, and 1/3 unique environment. Also by default, `kinsim()` generates variables named `y1` and `y2` for the first and second variables, respectively. We can rename these to more meaningful names like `weight` and `height` for clarity.

    df_synthetic <- df_synthetic %>%
      select(
        pid = id,
        r,
        weight_s1 = y1_1,
        weight_s2 = y1_2,
        height_s1 = y2_1,
        height_s2 = y2_2
      ) %>%
      mutate( # simulates age such that the 2nd sibling is between 1 and 5 years younger.
        age_s1 = round(rnorm(nrow(.), mean = 30, sd = 5), digits = 0),
        age_s2 = age_s1 - sample(1:5, nrow(.), replace = TRUE)
      )

    df_synthetic %>%
      slice(1:5) %>%
      knitr::kable(digits = 2)

pid | r | weight_s1 | weight_s2 | height_s1 | height_s2 | age_s1 | age_s2
--- | --- | --- | --- | --- | --- | --- | ---
1 | 0.12 | 2.09 | -1.30 | 4.54 | -0.66 | 31 | 30
2 | 0.12 | 1.21 | 0.40 | 1.20 | 0.26 | 32 | 28
3 | 0.12 | 1.03 | 2.53 | 2.76 | 0.58 | 29 | 27
4 | 0.12 | 1.46 | 4.92 | 0.93 | -0.67 | 30 | 29
5 | 0.12 | 2.69 | 2.78 | 0.95 | -0.54 | 23 | 18

This synthetic dataset now contains pairs of first cousins raised apart, with simulated values for weight and height, as well as age. Each row represents a unique pair of cousins, with variables for each cousin distinguished by the `_s1` and `_s2` suffixes.

# Ordering and Derived Variables

Now that we have our data in wide format, we can proceed to order the pairs and create the derived variables needed for discordant-kinship regression. The key steps are:

1. Ordering the pairs: We need to ensure that within each pair, the individual with the higher outcome value is consistently labeled as `_1` and the other as `_2`. This ordering is to ensure that we can extract meaningful difference scores.
2. Creating derived variables: We will create `*_mean` and `*_diff*` variables for both the outcome and predictor variables. The `*_mean` variable represents the average of the two individuals’ values, while the `*_diff` variable represents the difference between the two individuals’ values (i.e., `_1 - _2`).

These steps can be accomplished using the `discord_data()` function from the {discord} package. This function takes care of ordering the pairs and creating the derived variables, ensuring that the data is ready for analysis. When using `discord_data()`, you will need to specify the outcome variable, predictor variables, and the identifiers for the two members of each pair.

Below we default to pedigree data of cousins for reproducibility. Replace with your chosen source as needed.

    # CHOOSE ONE based on your path
    # source_wide <- df_wide
    # source_wide <- df_long2wide
    source_wide <- df_synthetic # if you followed the pedigree path

Now call `discord_data()` specifying the outcome and predictor present for both siblings. Here we use `weight` as the outcome and `height` and `age` as the predictors, with the `_s1` / `_s2` suffix convention.

    df_discord_weight <- discord::discord_data(
      data = source_wide,
      outcome = "weight",
      predictors = c("height", "age"),
      demographics = "none",
      pair_identifiers = c("_s1", "_s2"),
      id = "pid" # or "famID"
    )

    df_discord_weight %>%
      slice(1:5) %>%
      knitr::kable(digits = 2, caption = "Transformed data ready for discordant-kinship regression")

Transformed data ready for discordant-kinship regression
id | weight_1 | weight_2 | weight_diff | weight_mean | height_1 | height_2 | height_diff | height_mean | age_1 | age_2 | age_diff | age_mean
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 2.09 | -1.30 | 3.39 | 0.39 | 4.54 | -0.66 | 5.20 | 1.94 | 31 | 30 | 1 | 30.5
2 | 1.21 | 0.40 | 0.81 | 0.80 | 1.20 | 0.26 | 0.94 | 0.73 | 32 | 28 | 4 | 30.0
3 | 2.53 | 1.03 | 1.51 | 1.78 | 0.58 | 2.76 | -2.18 | 1.67 | 27 | 29 | -2 | 28.0
4 | 4.92 | 1.46 | 3.46 | 3.19 | -0.67 | 0.93 | -1.60 | 0.13 | 29 | 30 | -1 | 29.5
5 | 2.78 | 2.69 | 0.09 | 2.74 | -0.54 | 0.95 | -1.49 | 0.21 | 18 | 23 | -5 | 20.5

## Understanding the Transformation

Let’s examine what `discord_data()` did to our variables:

    # Show original data for first 3 pairs
    source_wide %>%
      slice(1:3) %>%
      select(pid, weight_s1, weight_s2, height_s1, height_s2) %>%
      knitr::kable(
        digits = 2,
        caption = "Original data: siblings not yet ordered by outcome"
      )

Original data: siblings not yet ordered by outcome
pid | weight_s1 | weight_s2 | height_s1 | height_s2
--- | --- | --- | --- | ---
1 | 2.09 | -1.30 | 4.54 | -0.66
2 | 1.21 | 0.40 | 1.20 | 0.26
3 | 1.03 | 2.53 | 2.76 | 0.58

    df_discord_weight %>%
      select(
        id,
        weight_1, weight_2, weight_mean, weight_diff,
        height_1, height_2, height_mean, height_diff
      ) %>%
      slice(1:3) %>%
      knitr::kable(
        digits = 2,
        caption = "After discord_data(): siblings ordered so weight_1 >= weight_2"
      )

After discord_data(): siblings ordered so weight_1 >= weight_2
id | weight_1 | weight_2 | weight_mean | weight_diff | height_1 | height_2 | height_mean | height_diff
--- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 2.09 | -1.30 | 0.39 | 3.39 | 4.54 | -0.66 | 1.94 | 5.20
2 | 1.21 | 0.40 | 0.80 | 0.81 | 1.20 | 0.26 | 0.73 | 0.94
3 | 2.53 | 1.03 | 1.78 | 1.51 | 0.58 | 2.76 | 1.67 | -2.18

Notice several important patterns in this output. First, the ordering changes depending on which sibling has the higher outcome value:

- If `weight_s1 > weight_s2` in the original data, sibling 1 becomes `_1` in the output
- If `weight_s2 > weight_s1` in the original data, the siblings are swapped: sibling 2 becomes `_1`
- The predictor values (height) are reordered accordingly to stay matched with the correct sibling

The sibling with higher weight becomes `_1` and the sibling with lower weight becomes `_2`. This ordering ensures that `weight_diff` (calculated as `weight_1 - weight_2`) is always non-negative. In the case of ties, discord data randomly assigned one sibling as `_1` and perserves that ordering throughout the dataset. Extremely motivated readers can dive into the discord source code for exactly how this calculation is implemented

Second, the mean scores represent each pair’s average weight. For example, `weight_mean` equals the average of `weight_1` and `weight_2`. These means capture between-family variation, reflecting differences across sibling pairs so that we can compare families to one another.

Third, the predictor differences can be positive or negative. Even though weight differences are always non-negative (by construction), height differences vary. The taller sibling might be the heavier one, or the shorter sibling might be heavier. This variation is what we’ll test in our regression.

This structure lets us ask a key question: within sibling pairs, does the sibling with more of the predictor also have more of the outcome? If the taller sibling is systematically heavier even after controlling for family background, that suggests height may causally influence weight. If not, the association likely reflects familial confounding.

## Data Selection for Standard OLS Regression

For comparison, we also need to prepare data for a standard OLS regression. When running a standard OLS regression, researchers typically select one sibling per family to avoid non-independence. Common approaches include:

- First-born sibling: The child born first in the family
- Oldest sibling: The sibling with the highest age at time of measurement
- Random selection: Randomly choosing one sibling from each pair

This selection is typically fixed based on a demographic characteristic (birth order, age) and remains constant regardless of which outcome variable you’re analyzing. In contrast, discordant-kinship regression dynamically orders siblings based on the outcome variable. Thus each analysis may involve different sibling orderings.

# Analyzing the Data

With our data prepared, we can now perform both standard OLS regression and discordant-kinship regression to compare results.

## Standard OLS Regression

First, let’s run a standard OLS regression using the original wide-format data. This will serve as a baseline for comparison with the family-based analyses.

For our baseline analysis, we select one sibling from each pair. In the original dataset, s1 was defined as the older sibling, so we can simply choose the sibling labeled `_s1`. But, another way to select the oldest sibling is to use the `discord_data()` function with the `outcome` argument set to the age variable. This approach will ensure that `_1` is always the oldest sibling within the pair. In the case of ties, discord data randomly assigned one sibling as `_1`. This assignment is preserved thru the entire data management process.

    df_for_ols <- df_synthetic %>%
      dplyr::select(
        id = pid,
        weight = weight_s1,
        height = height_s1,
        age = age_s1
      )

    df_discord_age <- discord::discord_data(
      data = source_wide,
      outcome = "age",
      predictors = c("height", "weight"),
      demographics = "none",
      pair_identifiers = c("_s1", "_s2"),
      id = "pid" # or "famID" if you followed the pedigree path
    )

The dataframes `df_for_ols` and `df_discord_age` now both contain one sibling per pair, selected based on age. We can verify that they contain the same individuals:

    df_discord_age %>%
      select(
        id, age_1, age_2,
        age_mean, age_diff, weight_1, height_1
      ) %>%
      slice(1:5) %>%
      knitr::kable(caption = "One sibling per pair for OLS regression, selecting by age", digits = 2)

One sibling per pair for OLS regression, selecting by age
id | age_1 | age_2 | age_mean | age_diff | weight_1 | height_1
--- | --- | --- | --- | --- | --- | ---
1 | 31 | 30 | 30.5 | 1 | 2.09 | 4.54
2 | 32 | 28 | 30.0 | 4 | 1.21 | 1.20
3 | 29 | 27 | 28.0 | 2 | 1.03 | 2.76
4 | 30 | 29 | 29.5 | 1 | 1.46 | 0.93
5 | 23 | 18 | 20.5 | 5 | 2.69 | 0.95

    df_for_ols %>%
      select(id, age, weight, height) %>%
      slice(1:5) %>%
      knitr::kable(caption = "One sibling per pair for OLS regression using original wide data", digits = 2)

One sibling per pair for OLS regression using original wide data
id | age | weight | height
--- | --- | --- | ---
1 | 31 | 2.09 | 4.54
2 | 32 | 1.21 | 1.20
3 | 29 | 1.03 | 2.76
4 | 30 | 1.46 | 0.93
5 | 23 | 2.69 | 0.95

As you can see, `df_for_ols` and `df_discord_age` both resulted in the same individuals being selected for analysis. The `df_discord_age` dataset was created by ordering siblings based on age, ensuring that `_1` is always the older sibling. The `df_for_ols` dataset directly selects the older sibling from the original wide data.

By selecting one sibling per pair, we’re analyzing the same number of observations as we have kinship pairs. This conrespondses with the standard individual-level approach used in most research:

\[ Y = \beta_0 + \beta_1 X1 + \beta_2 X2 + \epsilon \]

    ols_model <- lm(weight ~ height + age, data = df_for_ols)

    stargazer::stargazer(ols_model,
      type = "html", ci = TRUE,
      digits = 3, single.row = TRUE, title = "Standard OLS Regression Results"
    )

Standard OLS Regression Results
| Dependent variable:
| weight
height | 0.287*** (0.134, 0.440)
age | -0.073** (-0.129, -0.017)
Constant | 2.788*** (1.081, 4.495)
Observations | 141
R2 | 0.125
Adjusted R2 | 0.112
Residual Std. Error | 1.573 (df = 138)
F Statistic | 9.857*** (df = 2; 138)
Note: | p<0.1; p<0.05; p<0.01

    stargazer::stargazer(ols_model,
      type = "latex", ci = TRUE,
      digits = 3, single.row = TRUE, title = "Standard OLS Regression Results"
    )

This standard regression shows associations between our variables while controlling only for measured covariates.

## Discordant Between-Family Regression

Next, we run a between-family regression using the discordant data. This model regresses the outcome mean on predictor means, capturing between-family variation:

\[ Y_{mean} = \beta_0 + \beta_1 X1_{mean} + \beta_2 X2_{mean} + \epsilon \]

    between_model <- lm(
      weight_mean ~ height_mean + age_mean,
      data = df_discord_weight
    )

    stargazer::stargazer(between_model,
      type = "html", ci = TRUE,
      digits = 3, single.row = TRUE, title = "Between-Family Regression Results"
    )

Between-Family Regression Results
| Dependent variable:
| weight_mean
height_mean | 0.199** (0.049, 0.350)
age_mean | -0.044* (-0.089, 0.001)
Constant | 2.064*** (0.759, 3.368)
Observations | 141
R2 | 0.067
Adjusted R2 | 0.054
Residual Std. Error | 1.284 (df = 138)
F Statistic | 4.970*** (df = 2; 138)
Note: | p<0.1; p<0.05; p<0.01

This between-family regression captures how differences in average height and age across families relate to average weight. However, it does not account for within-family differences, which is where discordant-kinship regression comes in.

It is important to note that the between-family regression results are not directly comparable to the standard OLS regression results. The between-family model uses mean scores, which represent family-level averages, while the OLS model uses individual-level data. Therefore, the coefficients from these two models may differ in magnitude and interpretation. But, they both provide useful information about the relationships among the variables.

## Discordant-Kinship Regression

Now we return to our prepared data to run discordant-kinship regression. We can specify the models manually or use the `discord_regression()` convenience function. Both approaches produce identical results.

The discordant model regresses the outcome difference on outcome mean, predictor means, and predictor differences:

\[ Y_{diff} = \beta_0 + \beta_1 Y_{mean} + \beta_2 X1_{mean} + \beta_3 X1_{diff} + \beta_4 X2_{mean} + \beta_5 X2_{diff} + \epsilon \] When fitting the discordant regression model, the coefficient for each `*_mean` variable captures the between-family effect, while the coefficient for each `*_diff` variable captures the within-family effect. The within-family effects are of particular interest, as they provide insight into how differences between siblings relate to differences in the outcome, controlling for shared family background. Below you can see how this model is specified manually:

    discord_model_manual <- lm(
      weight_diff ~ weight_mean + height_mean + height_diff + age_mean + age_diff,
      data = df_discord_weight
    )

    tidy(discord_model_manual, conf.int = TRUE) %>%
      knitr::kable(digits = 3, caption = "Discordant Regression (Manual)")

Discordant Regression (Manual)
term | estimate | std.error | statistic | p.value | conf.low | conf.high
--- | --- | --- | --- | --- | --- | ---
(Intercept) | 0.500 | 0.528 | 0.948 | 0.345 | -0.544 | 1.544
weight_mean | -0.043 | 0.065 | -0.660 | 0.511 | -0.171 | 0.086
height_mean | 0.041 | 0.060 | 0.680 | 0.498 | -0.078 | 0.159
height_diff | 0.141 | 0.043 | 3.254 | 0.001 | 0.055 | 0.226
age_mean | 0.031 | 0.018 | 1.742 | 0.084 | -0.004 | 0.066
age_diff | -0.026 | 0.024 | -1.092 | 0.277 | -0.074 | 0.021

    stargazer::stargazer(between_model, discord_model_manual,
      type = "html", ci = TRUE,
      digits = 3, single.row = TRUE, title = "Between-Family and Discordant Regression Results"
    )

Between-Family and Discordant Regression Results
| Dependent variable:
| weight_mean | weight_diff
| (1) | (2)
weight_mean |  | -0.043 (-0.170, 0.084)
height_mean | 0.199** (0.049, 0.350) | 0.041 (-0.077, 0.158)
height_diff |  | 0.141*** (0.056, 0.225)
age_mean | -0.044* (-0.089, 0.001) | 0.031* (-0.004, 0.066)
age_diff |  | -0.026 (-0.074, 0.021)
Constant | 2.064*** (0.759, 3.368) | 0.500 (-0.534, 1.534)
Observations | 141 | 141
R2 | 0.067 | 0.102
Adjusted R2 | 0.054 | 0.069
Residual Std. Error | 1.284 (df = 138) | 0.978 (df = 135)
F Statistic | 4.970*** (df = 2; 138) | 3.060** (df = 5; 135)
Note: | p<0.1; p<0.05; p<0.01

Note that we are using the df_discord_weight dataset created earlier with `discord_data()`. This dataset is ordered such that within a pair, the sibling with the higher weight is always `_1`. This ordering ensures that `weight_diff` is always non-negative, which is a key feature of discordant-kinship regression.

### Using the Helper Function

Alternatively, we can use the `discord_regression()` function, which simplifies the process:

By default, `discord_regression()` orders siblings by the outcome variable, creates the necessary derived variables, and fits the discordant regression model in one step. This function also allows for easy inclusion of demographic covariates if needed. See the function documentation for more details on these options, as well as a vignette focused on demographic controls.

    discord_model <- discord_regression(
      data = source_wide,
      outcome = "weight",
      predictors = c("height", "age"),
      demographics = "none",
      sex = NULL,
      race = NULL,
      pair_identifiers = c("_s1", "_s2"),
      id = "pid"
    )

    tidy(discord_model, conf.int = TRUE) %>%
      knitr::kable(digits = 3, caption = "Discordant Regression Results")

Discordant Regression Results
term | estimate | std.error | statistic | p.value | conf.low | conf.high
--- | --- | --- | --- | --- | --- | ---
(Intercept) | 0.500 | 0.528 | 0.948 | 0.345 | -0.544 | 1.544
weight_mean | -0.043 | 0.065 | -0.660 | 0.511 | -0.171 | 0.086
height_diff | 0.141 | 0.043 | 3.254 | 0.001 | 0.055 | 0.226
age_diff | -0.026 | 0.024 | -1.092 | 0.277 | -0.074 | 0.021
height_mean | 0.041 | 0.060 | 0.680 | 0.498 | -0.078 | 0.159
age_mean | 0.031 | 0.018 | 1.742 | 0.084 | -0.004 | 0.066

    glance(discord_model) %>%
      select(r.squared, adj.r.squared, sigma, p.value, nobs) %>%
      knitr::kable(digits = 3)

r.squared | adj.r.squared | sigma | p.value | nobs
--- | --- | --- | --- | ---
0.102 | 0.069 | 0.978 | 0.012 | 141

    stargazer::stargazer(discord_model,
      discord_model_manual,
      type = "html", ci = TRUE,
      digits = 3, single.row = TRUE, title = "Discordant Regression Results Comparison"
    )

% Error: Unrecognized object type.

Both the manual and helper function approaches yield identical results.

# Comparing the Three Approaches

To fully understand the value of discordant-kinship regression, it’s helpful to compare all three models side by side. Each approach answers a different question about the data.

## Side-by-Side Model Comparison

    stargazer::stargazer(
      ols_model,
      between_model,
      discord_model_manual,
      type = "html", ci = TRUE,
      digits = 3,
      single.row = TRUE,
      title = "Comparison of OLS, Between-Family, and Discordant Regression Models",
      column.labels = c("Standard OLS", "Between-Family", "Discordant"),
      model.names = FALSE
    )

Comparison of OLS, Between-Family, and Discordant Regression Models
| Dependent variable:
| weight | weight_mean | weight_diff
| Standard OLS | Between-Family | Discordant
| (1) | (2) | (3)
height | 0.287*** (0.134, 0.440) |  |
age | -0.073** (-0.129, -0.017) |  |
weight_mean |  |  | -0.043 (-0.170, 0.084)
height_mean |  | 0.199** (0.049, 0.350) | 0.041 (-0.077, 0.158)
height_diff |  |  | 0.141*** (0.056, 0.225)
age_mean |  | -0.044* (-0.089, 0.001) | 0.031* (-0.004, 0.066)
age_diff |  |  | -0.026 (-0.074, 0.021)
Constant | 2.788*** (1.081, 4.495) | 2.064*** (0.759, 3.368) | 0.500 (-0.534, 1.534)
Observations | 141 | 141 | 141
R2 | 0.125 | 0.067 | 0.102
Adjusted R2 | 0.112 | 0.054 | 0.069
Residual Std. Error | 1.573 (df = 138) | 1.284 (df = 138) | 0.978 (df = 135)
F Statistic | 9.857*** (df = 2; 138) | 4.970*** (df = 2; 138) | 3.060** (df = 5; 135)
Note: | p<0.1; p<0.05; p<0.01

## Interpreting the Differences

For each model, we can summarize the key features, what they capture, and how to interpret the results:

Standard OLS Regression provides estimates of associations between variables at the individual level, controlling for measured covariates. However, it does not account for unmeasured familial confounding, which may bias the results. - Data: One sibling per family (e.g., first-born or older sibling)

- Level of analysis: Individual level

- What it captures: Total association between predictors and outcome, confounded by both genetic and environmental factors shared within families

- Interpretation: “Individuals with higher height and age tend to have higher weight”

Between-Family Regression compares kin from different families, effectively controlling for all family-level unobserved heterogeneity. While it provides a clearer picture of the effects of interest, it may miss important individual-level variation within families. - Data: Average values for each kin pair

- Level of analysis: Family level

- What it captures: How family-average predictors relate to family-average outcomes

- Interpretation: “Cousin pairs with higher average height and age tend to have higher average weight”

Discordant Regression focuses on within-family comparisons by examining kin with differing outcomes. It allows for a nuanced understanding of how individual-level factors interact above and beyond familial confounding. - Data: Differences between cousins within families

- Level of analysis: Within-family (controlling for between-family variation)

- What it captures: Within-family effects after controlling for shared genetic and environmental factors

- Key coefficients:

  - `*_mean`: Between-family effects (similar to between-family model)

  - `*_diff`: Within-family effects - the unique contribution of the predictor after controlling for familial confounding

- Interpretation: “The cousin with higher weight tends to have higher height and age”

- Advantage: Controls for unmeasured familial confounders, providing stronger evidence for (or against) causal effects # Conclusion

Discordant-kinship regression offers a powerful tool for disentangling within-family effects from between-family confounding. By leveraging sibling and cousin comparisons, researchers can gain insights into causal relationships that are often obscured in traditional analyses.

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