Subgroup Analysis with Multivariate Binary Outcomes in Multilevel Data

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Introduction

When data are clustered (e.g., students within schools, patients within hospitals), observations are not independent. Ignoring this clustering can lead to:

Underestimated standard errors
Inflated Type I error rates
Invalid inference
Under- or overpowered decisions

The bglmm() function extends bglm() to handle clustered data by incorporating random effects.

When to Use Multilevel Models

Use bglmm() when:

Observations are grouped into clusters (schools, hospitals, families, etc.);
Cluster variation affects outcomes
Clustering is theoretically meaningful

Use bglm() when:

Data are not clustered; AND
Clustering is not theoretically meaningful

Example: Educational Intervention Study

We’ll analyze a study comparing two teaching methods across 20 schools on reading and math ability, differentiating on student baseline ability.

Generate Example Data

library(bmco)
set.seed(2020)

n_schools <- 20 
n_per_school <- 15
n_total <- n_schools * n_per_school

# Generate random intercepts
school_intercepts_math <- rnorm(n_schools, mean = 0, sd = 1.50)
school_intercepts_reading <- rnorm(n_schools, mean = 0, sd = 1.50)

# Cluster randomization: whole schools receive the same method.
school_method <- rep(c("traditional", "new_method"), each = n_schools/2)

study_data <- data.frame(
  school_id = factor(rep(1:n_schools, each = n_per_school)),
  method = rep(school_method, each = n_per_school),
  baseline_ability = rnorm(n_total, mean = 50, sd = 10),
  stringsAsFactors = FALSE
)

# Standardize covariate
study_data$baseline_ability_std <- scale(study_data$baseline_ability)[, 1]

study_data$math_pass <- NA
study_data$reading_pass <- NA

p_math <- p_reading <- rep(NA, nrow(study_data))

for (i in 1:nrow(study_data)) {
  school <- as.numeric(study_data$school_id[i])
  is_new <- ifelse(study_data$method[i] == "new_method", 1, 0)

  p_math[i]    <- plogis(-0.50 + 0.75 * is_new + 0.10 * study_data$baseline_ability_std[i] + 
                           0.20 * is_new * study_data$baseline_ability_std[i] + 
                           school_intercepts_math[school])
  p_reading[i] <- plogis(-0.50 + 0.80 * is_new + 0.05 * study_data$baseline_ability_std[i] + 
                           0.15 * is_new * study_data$baseline_ability_std[i] + 
                           school_intercepts_reading[school])
  
  study_data$math_pass[i]    <- rbinom(1, 1, p_math[i])
  study_data$reading_pass[i] <- rbinom(1, 1, p_reading[i])
}

Fit Multilevel Model

set.seed(2025)
result <- bglmm(
  data = study_data,
  grp = "method",
  grp_a = "traditional",
  grp_b = "new_method",
  id_var = "school_id",
  x_var = "baseline_ability",
  y_vars = c("math_pass", "reading_pass"),
  x_method = "Empirical",
  x_def = c(-Inf, Inf),
  fixed = c("method", "baseline_ability", "method_baseline_ability"),
  random = c("Intercept"),
  test = "right_sided",
  rule = "All",
  n_burn = 200,
  n_it = 500,
  n_thin = 1
)

print(result)
#> 
#> Bayesian Multilevel Multivariate Logistic Regression
#> ======================================================
#> 
#>        Group mean y1 mean y2
#>  traditional   0.416   0.546
#>   new_method   0.537   0.634
#>   J = 20 clusters    n(traditional) = 150    n(new_method) = 150
#> 
#> Posterior probability P(new_method > traditional) [All rule]: 0.980
#> Marginalization: Empirical over [-Inf, Inf]
#> MPSRF: fixed = 10.9836    random = 1.3948    variance = 5.4158
#> 
#> Use summary() for full coefficient tables, priors and MCMC diagnostics.
summary(result)
#> 
#> Bayesian Multilevel Multivariate Logistic Regression
#> ======================================================
#> 
#> Multilevel Structure:  J = 20 clusters    n(traditional) = 150    n(new_method) = 150
#> 
#> Group Estimates:
#>        Group mean y1 sd y1 mean y2 sd y2
#>  traditional   0.416 0.030   0.546 0.033
#>   new_method   0.537 0.031   0.634 0.032
#> 
#> Treatment Effect:
#>   Delta mean (y1, y2): 0.121, 0.088
#>   Delta SE   (y1, y2): 0.002, 0.002
#>   Posterior probability P(new_method > traditional): 0.980
#> 
#> Fixed Effects (mean [SD]):
#>                                   b11           b10            b01
#> method                  3.693 [5.041] 2.346 [3.905]  3.596 [4.233]
#> baseline_ability        0.021 [0.032] 0.045 [0.024] -0.005 [0.027]
#> method_baseline_ability 0.233 [0.205] 0.155 [0.194]  0.216 [0.191]
#> 
#> Random Effects (population mean [SD]):
#>                      g11            g10           g01
#> Intercept -0.241 [2.605] -1.000 [2.675] 1.010 [2.475]
#> 
#> Variance Components (posterior mean):
#>   y1=1, y2=1 (b11):
#>           Intercept
#> Intercept   1029.96
#>   y1=1, y2=0 (b10):
#>           Intercept
#> Intercept   544.973
#>   y1=0, y2=1 (b01):
#>           Intercept
#> Intercept   712.445
#> 
#> Prior Specification:
#>   Fixed effects -- Normal prior:
#>     Mean:      0, 0, 0 
#>     Variance:  10, 10, 10 
#>   Random effects -- Normal prior:
#>     Mean:      0 
#>     Variance:  10 
#>   Covariance -- Inverse-Wishart: df = 1
#> 
#> Marginalization:
#>   Method: Empirical    (Sub)population: [-Inf, Inf]
#>   Decision rule: All
#> 
#> MCMC Convergence Diagnostics:
#>   Fixed effects -- MPSRF: 10.9836
#>                       Parameter  ESS   Rhat
#>                   b11_method[1] 36.9 7.0948
#>         b11_baseline_ability[2] 10.7 1.2712
#>  b11_method_baseline_ability[3] 12.9 5.4743
#>                   b10_method[1] 41.2 3.5012
#>         b10_baseline_ability[2] 17.5 1.1125
#>  b10_method_baseline_ability[3] 19.8 5.4723
#>                   b01_method[1] 19.1 2.4033
#>         b01_baseline_ability[2] 11.4 1.6787
#>  b01_method_baseline_ability[3] 10.6 5.5842
#> 
#>   Random effects -- MPSRF: 1.3948
#>         Parameter   ESS   Rhat
#>  g11_Intercept[1] 565.3 1.1999
#>  g10_Intercept[1] 515.1 1.5451
#>  g01_Intercept[1] 512.2 1.2905
#> 
#>   Variance components -- MPSRF: 5.4158
#>               Parameter   ESS   Rhat
#>  g11_InterceptIntercept  89.5 4.4046
#>  g10_InterceptIntercept 106.7 4.9981
#>  g01_InterceptIntercept 305.3 4.5160

Interpretation

The posterior probability indicates the probability that the new teaching method improves both math and reading outcomes (rule = “All”), accounting for:

Fixed effects: Overall method effect, baseline ability effect, and their interaction
Random effects: School-specific deviations from the overall pattern
Clustering: Within-school correlation of student outcomes

Specifying Population of Interest

Like bglm(), you can define subpopulations:

Specific Ability Level

set.seed(2027)
result_value <- bglmm(
  # ... other arguments ...
  x_method = "Value",
  x_def = 50,  # Average ability
)

cat("Effect for students with baseline ability = 50:\n")
cat("  Treatment difference:", result_value$delta$mean_delta, "\n")
cat("  Posterior probability:", result_value$delta$pop, "\n")

Ability Range (Empirical)

set.seed(2028)
result_empirical <- bglmm(
  # ... other arguments ...
  x_method = "Empirical",
  x_def = c(40,60) # Ability range 40-60
)

Ability Range (Analytical)

set.seed(2029)
result_analytical <- bglmm(
  # ... other arguments ...
    x_method = "Analytical",
    x_def = c(40,60)
 )

Decision Rules with Multilevel Data

All Rule (Conjunctive)

Both outcomes must favor the new method:

result_all <- bglmm(
  data = study_data,
  # ... other arguments ...
  rule = "All"  # P(new_method better on BOTH outcomes)
)

Any Rule (Disjunctive)

At least one outcome must favor the new method:

result_any <- bglmm(
  data = study_data,
  # ... other arguments ...
  rule = "Any"  # P(new_method better on AT LEAST ONE outcome)
)

Compensatory Rule

Weighted combination (e.g., math weighted 60%, reading 40%):

result_comp <- bglmm(
  data = study_data,
  # ... other arguments ...
  rule = "Comp",
  w = c(0.6, 0.4)  # Weights for math and reading
)

Specifying Prior Distributions

bglmm() uses weakly informative priors by default. You can customize these priors for:

Fixed effects regression coefficients (b_mu0, b_sigma0)
Random effects means (g_mu0, g_sigma0)
Random effects covariance (nu0, tau0)

Fixed Effects Priors (bglm and bglmm)

Fixed effects have a multivariate normal prior:

\[\beta \sim N(\mu_0, \Sigma_0)\]

Default Priors

# Default
p_fixed <- 2
b_mu0 <- rep(0, p_fixed)                 # Zero prior mean
b_sigma0 <- solve(diag(1e1, p_fixed))    # Small prior precision (large prior variance on the log-odds scale). 
                                         # Weakly informative. 
                                         # solve() generates precision matrix needed as input

Where p_fixed is the number of fixed effects. In our example, we use: - Covariate effect(s) - Interaction term(s)

Custom Fixed Effects Priors

# Assume treatment improves outcomes (positive coefficient on group)

custom_b_mu0 <- c(0, 0.5)                 # Expect positive group effect 
custom_b_sigma0 <- solve(diag(c(1e1, 5))) # More certainty on group effect

Random Effects Priors

Population-Level Random Effects

Random effects means have a multivariate normal prior:

\[\gamma \sim N(\mu_g, \Sigma_g)\]

Default:

p_random <- 2
g_mu0 <- rep(0, p_random)               # Prior mean
g_sigma0 <- solve(diag(1e1, p_random))  # Weakly informative prior variance; 
                                        # solve() transforms variance matrix to precision matrix needed as input

Where p_random is the number of fixed effects. In our example, we use: - Intercept - Group effect

Random Effects Covariance

The covariance matrix has an inverse-Wishart prior:

\[\Sigma \sim \text{InvWishart}(\nu_0, \mathbf{T}_0)\]

Default:

nu0 <- p_random                # Degrees of freedom (dimension)
tau0 <- diag(1e-1, p_random)   # Scale matrix

Important: - nu0 must be ≥ p_random - tau0 must be positive definite (never use diag(0, p_random)) - Smaller nu0 = less informative - Larger tau0 diagonal values = expect more between-cluster variation

Prior Sensitivity Analysis

Compare results under different priors:

# -----------------------------------------------------------------
# Three priors that pull in clearly different directions.
# -----------------------------------------------------------------

# 1. Weakly informative (neutral starting point)
#    Diffuse on fixed effects, diffuse IW for random effects.
result_weak <- bglmm(
  # ... other arguments ...
  b_mu0    = rep(0, p_fixed),
  b_sigma0 = solve(diag(10, p_fixed)),
  g_mu0    = rep(0, p_random),
  g_sigma0 = solve(diag(10, p_random)),
  nu0  = p_random,
  tau0 = diag(0.1, p_random),
)

# 2. Skeptical prior
#    Tight variance so the prior resists positive or negative evidence. 
#    Also a small tau0, implying homogeneity between schools.
result_skeptical <- bglmm(
  # ... other arguments ...
  b_mu0    = rep(0, p_fixed),               
  b_sigma0 = solve(diag(c(1, 1), p_fixed)),  # Tight — hard(er) to overcome
  g_mu0    = rep(0, p_random),
  g_sigma0 = solve(diag(c(1, 1), p_random)), # Tight — hard(er) to overcome
  nu0  = p_random + 4,                 # More informative IW
  tau0 = diag(0.05, p_random),         # Expects small school variation
)

Interpretation:

If results are similar → data dominate, prior has little influence
If results differ substantially → prior is influential

Guidelines for Choosing Priors

Default priors: Use for exploratory analysis or when no prior information exists
Informative priors: Use when:
- You have strong theoretical expectations
- Previous studies provide evidence
- You want to regularize extreme estimates
- Sample size is very small
Skeptical priors: Use for:
- Conservative hypothesis testing
- Replication studies (favor null)
- High-stakes decisions requiring strong evidence
Prior sensitivity: Check:

   # Run with default priors
   # Run with informative priors
   # Compare posterior probabilities and effect sizes
   # If similar → robust; if different → prior-dependent

Common Mistakes to Avoid

Don’t: Use tau0 = diag(0, p) (singular matrix)
Do: Use tau0 = diag(1e-1, p) or diag(0.5, p)
Don’t: Set nu0 < p (too few degrees of freedom)
Do: Use `nu0 =\(\geq p\)
Don’t: Use extremely small prior variances without justification
Do: Use weakly informative priors unless you have strong evidence
Don’t: Ignore prior sensitivity when sample size is small
Do: Report results under multiple priors for transparency

MCMC Diagnostics

The function internally checks the multivariate potential scale reduction factor (MPSRF) and warns if > 1.1. Additional MCMC diagnostics will be returned When return_diagnostics = TRUE. When return_diagnostic_plots = TRUE, bglmm() returns diagnostic plots as well.

set.seed(2030)
result_diag <- bglmm(
  data = study_data,
  grp = "method",
  grp_a = "traditional",
  grp_b = "new_method",
  id_var = "school_id",
  x_var = "baseline_ability",
  y_vars = c("math_pass", "reading_pass"),
  x_method = "Value",
  x_def = 50,
  n_burn = 200,
  n_it = 500,
  n_thin = 1,
  start = c(0.5, 1),
  return_diagnostics = TRUE
)

Key diagnostics:

MPSRF (Multivariate PSRF): Ideally < 1.1
Effective sample size: Should be > 100 per parameter
Rhat: Ideally < 1.1 for all parameters

# Check convergence
cat("Random effects convergence:\n")
#> Random effects convergence:
print(result_diag$diags$g$convergence)
#> $mpsrf
#> [1] 6.27427
#> 
#> $psrf
#>                  Point est. Upper C.I.
#> g11_Intercept[1]   1.368093   2.748530
#> g11_method[2]      1.146185   1.151254
#> g10_Intercept[1]   1.135531   1.218255
#> g10_method[2]      2.271145   7.825439
#> g01_Intercept[1]   1.683002   3.983673
#> g01_method[2]      3.646306   8.278269

cat("\nRandom effects covariance convergence:\n")
#> 
#> Random effects covariance convergence:
print(result_diag$diags$tau$convergence)
#> $mpsrf
#> [1] 5.167838
#> 
#> $psrf
#>                        Point est. Upper C.I.
#> g11_InterceptIntercept   4.983014  29.190062
#> g11_methodIntercept      5.274581  21.553906
#> g11_methodmethod         1.201419   1.731316
#> g10_InterceptIntercept   2.539835  13.514819
#> g10_methodIntercept      2.659497  14.379082
#> g10_methodmethod         2.672124  14.473397
#> g01_InterceptIntercept   4.175515  20.831837
#> g01_methodIntercept      4.298845  20.442173
#> g01_methodmethod         4.247503  19.116352

Available plots:

Autocorrelation: Is ideally high at lag 0, then drops off quickly toward 0 within a few lags.
Traceplot: Chains should fluctuate randomly around a stable mean, with no clear trends, drifts, or long flat stretches. Good mixing.
Density: Posterior density of individual parameters (regression coefficients and variance parameters) if preferably smooth, unimodal (unless theory suggests otherwise), and without irregular spikes.

plot(result_diag)                                    # All
plot(result_diag, type = "trace")                    # Trace only
plot(result_diag, type = "density")                  # Density only

plot(result_diag, which = "fixed")                   # Fixed effects
plot(result_diag, which = "random")                  # Random effects
plot(result_diag, which = "variance")                # Variance components
plot(result_diag, which = "all")                     # All

# Combine type + which
plot(result_diag, type = "trace", which = "all")          # Trace for all parameters
plot(result_diag, type = "density", which = "variance")   # Variance densities

If convergence issues occur:

Increase n_burn (more warmup)
Increase n_it (more samples)
Check for very few clusters
Check for very small cluster sizes
Check range of covariate and consider standardizing
Check for problems and warnings in data (perfect separation, low variation, etc.)

If autocorrelation is high, usually the effective sample size is much lower than n_it:

Consider thinning
Consider increasing n_it

For more information on MCMC diagnostics, consult

Brooks, S. P. & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434–455.
Gelman, A. & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472.

Extracting Posterior Samples

set.seed(2031)
result_samples <- bglmm(
# ... other arguments ...
  return_samples = TRUE
)

# Treatment effect samples
str(result_samples$samples$delta)

# Compute custom summaries
cat("Treatment effect quantiles:\n")
print(apply(result_samples$samples$delta, 2, quantile, probs = c(0.025, 0.5, 0.975)))

# Probability that effect on math > 0.1
cat("\nP(delta_math > 0.1):", mean(result_samples$samples$delta[, 1] > 0.1), "\n")

Data Requirements

For reliable multilevel modeling:

Number of clusters: Ideally there are sufficient clusters
- More clusters → Better random effect estimates
Cluster sizes:
- Very small clusters provide little information about random effects
- Unbalanced cluster sizes are acceptable
Group balance within clusters: Ideally, each cluster has both treatment groups
- If some clusters have only one group, the model can still fit but with a warning
Missing data: Handled automatically
- Rows with missing outcomes or covariates are removed per cluster
- Complete-case analysis within each cluster

Common Issues and Solutions

Warning: “Very few clusters (J < 5)”

Problem: Too few clusters might hinder reliable random effect estimation

Solutions: - Collect more clusters if possible - Use bglm() instead (ignores clustering), but only if appropriate

Warning: “MCMC chains may not have converged”

Problem: MPSRF > 1.1

Solutions: 1. Increase burn-in: n_burn = 2000 or higher 2. Increase iterations: n_it = 5000 or higher 3. Reduce thinning: n_thin = 1 (keeps more samples) 4. Check data quality (outliers, sufficient variation)

Slow computation

Problem: Large number of clusters or long chains

Solutions: - Start with small n_it to verify model runs - Increase n_thin to reduce stored samples: n_thin = 10 - Run overnight for production analyses - Typical times: 5-10 minutes for 10 clusters, 10k iterations

Summary

bglmm() extends Bayesian multivariate analysis to clustered data by:

Accounting for clustering via random effects
Allowing for subgroup effects via logistic regression
Flexible population definitions (Value, Empirical, Analytical)
Multivariate decision rules (All, Any, Compensatory)

Choose your analysis:

bmvb(): Full sample, no clustering
bglm(): Subgroup analysis, no clustering
bglmm(): Subgroup analysis + clustering

All three functions support multivariate binary outcomes and provide posterior probabilities for treatment effects.

References

For more details on statistical methodology or when using the bglmm()-function, please cite:

Kavelaars, X., J. Mulder, and M. Kaptein (2023). “Bayesian multilevel, multivariate logistic regression for superiority decision-making under, observable treatment heterogeneity”. In: BMC Medical Research, Methodology 23.1. DOI:, 10.1186/s12874-023-02034-z.

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Subgroup Analysis with Multivariate Binary Outcomes in Multilevel Data

Introduction

When to Use Multilevel Models

Example: Educational Intervention Study

Generate Example Data

Fit Multilevel Model

Interpretation

Specifying Population of Interest

Specific Ability Level

Ability Range (Empirical)

Ability Range (Analytical)

Decision Rules with Multilevel Data

All Rule (Conjunctive)

Any Rule (Disjunctive)

Compensatory Rule

Specifying Prior Distributions

Fixed Effects Priors (bglm and bglmm)

Default Priors

Custom Fixed Effects Priors

Random Effects Priors

Population-Level Random Effects

Random Effects Covariance

Prior Sensitivity Analysis

Guidelines for Choosing Priors

Common Mistakes to Avoid

Further Reading

MCMC Diagnostics

Extracting Posterior Samples

Data Requirements

Common Issues and Solutions

Warning: “Very few clusters (J < 5)”

Warning: “MCMC chains may not have converged”

Slow computation

Summary

References