Overview of Two Co-Primary Endpoints Analysis • twoCoprimary

Introduction

The twoCoprimary package provides comprehensive tools for sample size calculation and power analysis in clinical trials with two co-primary endpoints. This package implements methodologies from multiple publications and supports various endpoint types.

library(twoCoprimary)

What are Co-Primary Endpoints?

In clinical trials, co-primary endpoints are multiple primary endpoints that must all show statistically significant treatment effects for the trial to be considered successful. This is in contrast to multiple primary endpoints where demonstrating an effect on any one endpoint is sufficient.

Statistical Properties

No multiplicity adjustment needed for Type I error control
The overall Type I error rate is controlled at level $\alpha$ without Bonferroni correction
The overall power is the joint probability: Power = $\Pr(\text{Reject } \text{H}_{01} \text{ and Reject } \text{H}_{02})$
Accounting for correlation between endpoints can improve efficiency

Hypotheses Structure

For two co-primary endpoints, we test:

Null hypothesis: $\text{H}_{0} = \text{H}_{01} \cup \text{H}_{02}$ (at least one null hypothesis is true)

Alternative hypothesis: $\text{H}_{1} = \text{H}_{11} \cap \text{H}_{12}$ (both alternative hypotheses are true)

We reject $\text{H}_{0}$ only if both $\text{H}_{01}$ and $\text{H}_{02}$ are rejected at level $\alpha$ .

Statistical Framework

Intersection-Union Test (IUT)

The co-primary endpoint framework is based on the intersection-union test principle. Let $Z_{1}$ and $Z_{2}$ be the test statistics for endpoints 1 and 2, respectively.

Decision rule: Reject $\text{H}_{0}$ if and only if: $Z_{1} > z_{1-\alpha} \text{ and } Z_{2} > z_{1-\alpha}$

where $z_{1-\alpha}$ is the $(1-\alpha)$ -th quantile of the standard normal distribution.

Type I Error Control

The overall Type I error rate is:

$\alpha_{\text{overall}} = \Pr(\text{Reject } \text{H}_{0} \mid \text{H}_{0} \text{ true})$

Under the intersection-union test, this is automatically controlled at level $\alpha$ without adjustment:

$\alpha_{\text{overall}} \leq \alpha$

This is because rejecting when both statistics exceed the threshold is more conservative than rejecting when either statistic exceeds the threshold.

Overall Power

Under the alternative hypothesis $\text{H}_{1}$ , the overall power is:

$1 - \beta = \Pr(Z_{1} > z_{1-\alpha} \text{ and } Z_{2} > z_{1-\alpha} \mid \text{H}_{1})$

When $(Z_{1}, Z_{2})$ follow a bivariate normal distribution with correlation $\rho$ :

$1 - \beta = \Phi_{2}(-z_{1-\alpha} + \omega_{1}, -z_{1-\alpha} + \omega_{2} \mid \rho)$

where:

$\Phi_{2}(\cdot, \cdot \mid \rho)$ is the bivariate normal cumulative distribution function with correlation $\rho$
$\omega_{1}$ and $\omega_{2}$ are the non-centrality parameters under $\text{H}_{1}$

Impact of Correlation

The correlation $\rho$ between test statistics affects the overall power:

Positive correlation ( $\rho > 0$ ): Increases power and reduces required sample size
Zero correlation ( $\rho = 0$ ): Test statistics are independent
Negative correlation ( $\rho < 0$ ): Decreases power and increases required sample size

Key insight: Accounting for positive correlation between endpoints can lead to substantial sample size reductions (typically 5-15% for $\rho = 0.5$ -0.8) compared to assuming independence.

Supported Endpoint Types

The package supports five combinations of co-primary endpoints:

1. Two Continuous Endpoints

Use case: Trials measuring two continuous outcomes (e.g., systolic and diastolic blood pressure)

Statistical model: Both endpoints follow normal distributions

Key functions:

ss2Continuous(): Sample size calculation
power2Continuous(): Power calculation
twoCoprimary2Continuous(): Unified interface for both

Reference: Sozu et al. (2011)

# Example: Two continuous endpoints with correlation rho = 0.5
ss2Continuous(
  delta1 = 0.5,  # Standardized effect size for endpoint 1
  delta2 = 0.5,  # Standardized effect size for endpoint 2
  sd1 = 1,       # Standard deviation for endpoint 1
  sd2 = 1,       # Standard deviation for endpoint 2
  rho = 0.5,     # Correlation between endpoints
  r = 1,         # Balanced allocation
  alpha = 0.025,
  beta = 0.2,
  known_var = TRUE
)
#> 
#> Sample size calculation for two continuous co-primary endpoints
#> 
#>              n1 = 79
#>              n2 = 79
#>               N = 158
#>           delta = 0.5, 0.5
#>              sd = 1, 1
#>             rho = 0.5
#>      allocation = 1
#>           alpha = 0.025
#>            beta = 0.2
#>       known_var = TRUE

2. Two Binary Endpoints (Asymptotic Approximation)

Use case: Trials with two binary outcomes using normal approximation (large sample)

Statistical model: Binary endpoints with asymptotic normal approximation

Key functions:

ss2BinaryApprox(): Sample size calculation
power2BinaryApprox(): Power calculation
twoCoprimary2BinaryApprox(): Unified interface for both

Reference: Sozu et al. (2010)

Supported test methods:

AN: Asymptotic normal test without continuity correction
ANc: Asymptotic normal test with continuity correction
AS: Arcsine transformation without continuity correction
ASc: Arcsine transformation with continuity correction

When to use: Large sample sizes (typically $N > 200$ ) and probabilities not too extreme ( $0.1 < p < 0.9$ )

# Example: Two binary endpoints
ss2BinaryApprox(
  p11 = 0.7, p12 = 0.6,  # Endpoint 1 and 2 in treatment group
  p21 = 0.4, p22 = 0.3,  # Endpoint 1 and 2 in control group
  rho1 = 0.5,            # Correlation in treatment group
  rho2 = 0.5,            # Correlation in control group
  r = 1,                 # Balanced allocation
  alpha = 0.025,
  beta = 0.2,
  Test = "AN"
)
#> 
#> Sample size calculation for two binary co-primary endpoints
#> 
#>              n1 = 52
#>              n2 = 52
#>               N = 104
#>     p (group 1) = 0.7, 0.6
#>     p (group 2) = 0.4, 0.3
#>             rho = 0.5, 0.5
#>      allocation = 1
#>           alpha = 0.025
#>            beta = 0.2
#>            Test = AN

3. Two Binary Endpoints (Exact Methods)

Use case: Small to medium sample sizes requiring exact inference

Statistical model: Binary endpoints with exact tests

Key functions:

ss2BinaryExact(): Sample size calculation using exact tests
power2BinaryExact(): Exact power calculation
twoCoprimary2BinaryExact(): Unified interface for both

Reference: Homma and Yoshida (2025)

Supported tests:

Chisq: Chi-squared test
Fisher: Fisher’s exact test (conditional test)
Fisher-midP: Fisher’s mid-p test
Z-pool: Z-pooled exact unconditional test
Boschloo: Boschloo’s exact unconditional test

When to use: Small/medium samples ( $N < 200$ ), extreme probabilities ( $p < 0.1$ or $p > 0.9$ ), or when strict Type I error control is required

# Example: Exact methods for small samples
ss2BinaryExact(
  p11 = 0.7, p12 = 0.6,
  p21 = 0.4, p22 = 0.3,
  rho1 = 0.5, rho2 = 0.5,
  r = 1,
  alpha = 0.025,
  beta = 0.2,
  Test = "Fisher"  # or "Chisq", "Fisher-midP", "Z-pool", "Boschloo"
)
#> 
#> Sample size calculation for two binary co-primary endpoints
#> 
#>              n1 = 59
#>              n2 = 59
#>               N = 118
#>     p (group 1) = 0.7, 0.6
#>     p (group 2) = 0.4, 0.3
#>             rho = 0.5, 0.5
#>      allocation = 1
#>           alpha = 0.025
#>            beta = 0.2
#>            Test = Fisher

4. Mixed Continuous and Binary Endpoints

Use case: Trials with one continuous and one binary outcome

Statistical model: Normal distribution for continuous endpoint, Bernoulli for binary endpoint, with biserial correlation

Key functions:

ss2MixedContinuousBinary(): Sample size calculation
power2MixedContinuousBinary(): Power calculation
twoCoprimary2MixedContinuousBinary(): Unified interface for both

Reference: Sozu et al. (2012)

Supported test methods for binary endpoint:

AN: Asymptotic normal test without continuity correction
ANc: Asymptotic normal test with continuity correction
AS: Arcsine transformation without continuity correction
ASc: Arcsine transformation with continuity correction
Fisher: Fisher’s exact test (simulation-based)

Correlation structure: Uses biserial correlation between observed continuous variable and latent continuous variable underlying the binary outcome

# Example: Continuous + Binary endpoints
ss2MixedContinuousBinary(
  delta = 0.5,           # Effect size for continuous endpoint
  sd = 1,                # Standard deviation
  p1 = 0.7,              # Success probability in treatment group
  p2 = 0.4,              # Success probability in control group
  rho = 0.5,             # Biserial correlation
  r = 1,
  alpha = 0.025,
  beta = 0.2,
  Test = "AN"
)
#> 
#> Sample size calculation for mixed continuous and binary co-primary endpoints
#> 
#>              n1 = 68
#>              n2 = 68
#>               N = 136
#>           delta = 0.5
#>              sd = 1
#>               p = 0.7, 0.4
#>             rho = 0.5
#>      allocation = 1
#>           alpha = 0.025
#>            beta = 0.2
#>            Test = AN

5. Mixed Count and Continuous Endpoints

Use case: Trials with overdispersed count data (e.g., exacerbations) and continuous outcomes (e.g., lung function)

Statistical model: Negative binomial distribution for count endpoint, normal distribution for continuous endpoint

Key functions:

ss2MixedCountContinuous(): Sample size calculation
power2MixedCountContinuous(): Power calculation
twoCoprimary2MixedCountContinuous(): Unified interface for both
corrbound2MixedCountContinuous(): Calculate valid correlation bounds

Reference: Homma and Yoshida (2024)

Special considerations:

The negative binomial distribution accommodates overdispersion (variance $>$ mean) common in count data
Treatment effects must be in the negative direction for both endpoints: Lower event rate (count endpoint) and lower/better continuous values indicate treatment benefit. For example, reduction in exacerbation rate (r1 < r2) and improvement in lung function (e.g., mu1 < mu2 when lower is better, or larger negative change from baseline)

# Example: Count (exacerbations) + Continuous (FEV1)
ss2MixedCountContinuous(
  r1 = 1.0, r2 = 1.25,   # Count rates (events per unit time)
  nu = 0.8,              # Dispersion parameter
  t = 1,                 # Follow-up time
  mu1 = -50, mu2 = 0,    # Continuous means
  sd = 250,              # Standard deviation
  rho1 = 0.5, rho2 = 0.5, # Correlations
  r = 1,
  alpha = 0.025,
  beta = 0.2
)
#> 
#> Sample size calculation for mixed count and continuous co-primary endpoints
#> 
#>              n1 = 705
#>              n2 = 705
#>               N = 1410
#>              sd = 250
#>            rate = 1, 1.25
#>              nu = 0.8
#>               t = 1
#>              mu = -50, 0
#>             rho = 0.5, 0.5
#>      allocation = 1
#>           alpha = 0.025
#>            beta = 0.2

Impact of Correlation

A key advantage of accounting for correlation between co-primary endpoints is the potential for sample size reduction.

The table below illustrates this for two continuous endpoints:

# Sample size at different correlation levels
correlations <- c(0, 0.3, 0.5, 0.8)
results <- sapply(correlations, function(rho) {
  ss2Continuous(
    delta1 = 0.5, delta2 = 0.5,
    sd1 = 1, sd2 = 1,
    rho = rho, r = 1,
    alpha = 0.025, beta = 0.2,
    known_var = TRUE
  )$N
})

data.frame(
  Correlation = correlations,
  Total_N = results,
  Reduction = paste0(round((1 - results/results[1]) * 100, 1), "%")
)
#>   Correlation Total_N Reduction
#> 1         0.0     166        0%
#> 2         0.3     162      2.4%
#> 3         0.5     158      4.8%
#> 4         0.8     148     10.8%

As correlation increases, the required sample size decreases. At $\rho = 0.8$ , approximately 11% reduction in sample size can be achieved compared to $\rho = 0$ .

Why Does Correlation Matter?

The correlation between endpoints affects the joint distribution of test statistics. When endpoints are positively correlated:

Test statistics tend to move together: If $Z_{1}$ is large, $Z_{2}$ is also likely to be large
Higher probability of rejecting both nulls: $\Pr(Z_{1} > c, Z_{2} > c)$ increases with $\rho$
Sample size reduction: Fewer subjects needed to achieve target power

Mathematically, for bivariate normal $(Z_{1}, Z_{2})$ with correlation $\rho$ :

$\Pr(Z_{1} > c, Z_{2} > c \mid \rho) > \Pr(Z_{1} > c, Z_{2} > c \mid \rho = 0)$

when $\rho > 0$ and both endpoints have positive treatment effects.

Choosing the Right Method

Endpoint Types	Sample Size	Method	Key Considerations
Both continuous	Any	Asymptotic	Simple, well-established
Both binary	Large ( $N > 200$ )	Asymptotic	Fast computation, probabilities moderate
Both binary	Small/Medium	Exact	Better Type I error control, exact inference
1 Continuous + 1 Binary	Any	Asymptotic	Handles mixed types, biserial correlation
1 Count + 1 Continuous	Any	Asymptotic	Accounts for overdispersion

Decision Guidelines

For binary endpoints:

Use asymptotic methods when: $N > 200$ , $0.1 < p < 0.9$ , computational efficiency important
Use exact methods when: $N < 200$ , extreme probabilities ( $p < 0.1$ or $p > 0.9$ ), regulatory requirements for exact tests

For mixed endpoint types:

Ensure correlation structure is appropriate (e.g., biserial for continuous-binary)
Consider clinical plausibility of correlation magnitude
Use conservative estimates if correlation is uncertain

Sample Size Calculation Approach

All methods in this package follow a similar computational approach:

Specify design parameters: Effect sizes, probabilities, standard deviations, etc.
Specify correlation: Between endpoints
Specify error rates: Type I error $\alpha$ (typically 0.025 for one-sided) and Type II error $\beta$ (typically 0.2 for 80% power)
Calculate sample size: Using iterative algorithms
Verify power: Confirm that calculated sample size achieves target power

Detailed Vignettes

For detailed methodology, examples, and validation against published results, please see:

vignette("two-continuous-endpoints"): Two continuous endpoints
vignette("two-binary-endpoints-approx"): Two binary endpoints (asymptotic)
vignette("two-binary-endpoints-exact"): Two binary endpoints (exact)
vignette("mixed-continuous-binary"): Mixed continuous and binary
vignette("mixed-count-continuous"): Mixed count and continuous

References

Homma, G., & Yoshida, T. (2024). Sample size calculation in clinical trials with two co-primary endpoints including overdispersed count and continuous outcomes. Pharmaceutical Statistics, 23(1), 46-59.

Homma, G., & Yoshida, T. (2025). Exact power and sample size in clinical trials with two co-primary binary endpoints. Statistical Methods in Medical Research, 34(1), 1-19.

Sozu, T., Sugimoto, T., & Hamasaki, T. (2010). Sample size determination in clinical trials with multiple co-primary binary endpoints. Statistics in Medicine, 29(21), 2169-2179.

Sozu, T., Sugimoto, T., & Hamasaki, T. (2011). Sample size determination in superiority clinical trials with multiple co-primary correlated endpoints. Journal of Biopharmaceutical Statistics, 21(4), 650-668.

Sozu, T., Sugimoto, T., & Hamasaki, T. (2012). Sample size determination in clinical trials with multiple co-primary endpoints including mixed continuous and binary variables. Biometrical Journal, 54(5), 716-729.