ss_aipe_icc: Sample Size for AIPE on an Intraclass Correlation Coefficient

Description

Determines the sample size needed for a confidence interval on a population intraclass correlation coefficient (ICC) to have a desired width, using Bonett's (2002) Fisher-style variance-stabilizing transformation. The function inverts the asymptotic variance on the transformed scale (where the CI is symmetric and approximately normal), solves for the smallest $n$ that achieves the target half-width on the back-transformed (raw-ICC) scale, and optionally inflates the result by a chi squared assurance correction (Kelley & Maxwell, 2003).

Usage

ss_aipe_icc(
  rho,
  k,
  width,
  which_width = c("Full", "Lower", "Upper"),
  conf_level = 0.95,
  type = c("ICC(1,1)", "ICC(2,1)", "ICC(3,1)", "ICC(1,k)", "ICC(2,k)", "ICC(3,k)"),
  assurance = NULL
)

Value

A data.frame with rows for the recommended sample size (number of subjects), the expected back-transformed CI width, and the inputs echoed back. The Shrout-Fleiss form the plan targets is stored as the "icc_type" attribute so the

value column stays numeric.

Arguments

rho: Anticipated population ICC at the level matching type, in $[0, 1)$. This is the value the researcher expects the truth to be near, motivated by prior literature, a pilot, or substantive theory. Because the planning formula inverts the asymptotic variance at this value, a wrong guess inflates or deflates the realized confidence interval width relative to width; the function ss_aipe_icc_sensitivity quantifies the impact of misspecification by Monte Carlo.
k: Number of raters (or measurements per subject); must be at least 2.
width: Desired full width of the back-transformed CI on the ICC.
which_width: Whether width is the "Full" width of the interval (default) or a half-width: "Lower" and "Upper" both interpret width as half the full width, so they plan for a full width of twice width and return the same sample size. Because the interval is not generally symmetric about the estimate, its realized lower and upper half-widths can differ from each other and from half the full width; the planner does not target them separately. A genuinely one-sided width target is not currently offered.
conf_level: Desired confidence level (default 0.95).
type: Which Shrout-Fleiss (1979) ICC form is being planned. One of the single-rater forms "ICC(1,1)", "ICC(2,1)", "ICC(3,1)" (which share the planning variance) or the average-of-$k$ forms "ICC(1,k)", "ICC(2,k)", "ICC(3,k)"; default "ICC(1,1)". Both rho and width are interpreted on the scale of the requested form: for an average-of-$k$ form the planning value is mapped to the single-rater scale through the inverse Spearman-Brown relation and each candidate confidence limit is mapped back, so the returned sample size targets the width of the interval on the average-of-$k$ ICC itself (see Details).
assurance: Optional. Probability that the realized CI is no wider than width; when supplied, the sample size is inflated by the standard chi squared correction.

Author

Ken Kelley [email protected]

Details

Bonett's (2002) Fisher-style transform. Bonett (2002) showed that the transformation $$L(\rho) \;=\; \frac{1}{2} \log\!\left( \frac{1 + (k - 1)\rho}{1 - \rho}\right)$$ approximately variance-stabilizes the single-rater ICC, with $$\mathrm{Var}(L(\hat\rho)) \;\approx\; \frac{k}{2\,(k - 1)\,(n - 2)}.$$ A confidence interval is constructed by adding $\pm z_{1-\alpha/2}$ standard errors on the $L$ scale and back-transforming to the raw-ICC scale via $\rho = (e^{2L} - 1) / (e^{2L} - 1 + k)$. The minimum sample size is found by searching for the smallest $n$ whose back-transformed CI width is below the target.

Single-rater vs.\ average-of-$k$ ICC. The Bonett (2002) variance applies directly to the single-rater forms (ICC(1,1), ICC(2,1), ICC(3,1)). For the average-of-$k$ forms the planning value $\rho_k$ is first mapped to the single-rater scale through the inverse Spearman-Brown relation $\rho = \rho_k / [k - (k - 1)\rho_k]$, the interval is formed on the $L$ scale as above, and each candidate limit is mapped back to the average-of-$k$ scale (composing the inverse $L$ transform with the Spearman-Brown formula reduces to $\rho_k = 1 - e^{-2L}$). The width criterion therefore applies to the confidence interval on the average-of-$k$ ICC itself, following the convention used by var_icc. Because the two scales differ, an average-of-$k$ plan generally recommends a different sample size than a single-rater plan at the same numeric rho and width; at rho = 0.7, k = 3, and width = 0.20, planning for ICC(1,1) recommends $n = 69$ subjects while planning for ICC(1,k) recommends $n = 110$.

The assurance correction can fall slightly short. Monte Carlo evaluation with ss_aipe_icc_sensitivity shows that the chi squared inflation tends to deliver a little less assurance than requested. At the condition of the second example below (rho = 0.7, k = 3, width = 0.20, assurance = 0.80), the recommended $n = 79$ yields an empirical assurance of about .77 against the requested .80 (10,000 replications of the F-based interval computed by icc), and the smallest sample size whose empirical assurance reaches .80 is $n = 81$. The mechanism is that the realized interval widths on the raw-ICC scale have a heavier upper tail than the chi squared inflation on the transformed scale accounts for, so the buffer the correction adds is slightly too small. When meeting the assurance target matters, check the recommended sample size with ss_aipe_icc_sensitivity and increase $n$ until the empirical assurance reaches the target.

References

Bonett, D. G. (2002). Sample size requirements for estimating intraclass correlations with desired precision. Statistics in Medicine, 21(9), 1331--1335. tools:::Rd_expr_doi("10.1002/sim.1108")

Donner, A. (1986). A review of inference procedures for the intraclass correlation coefficient in the one-way random effects model. International Statistical Review, 54(1), 67--82.

Kelley, K., & Maxwell, S. E. (2003). Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8(3), 305--321. tools:::Rd_expr_doi("10.1037/1082-989X.8.3.305")

Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420--428.

Smith, C. A. B. (1956). On the estimation of intraclass correlation. Annals of Human Genetics, 21(4), 363--373.

Examples

Run this code

# 1. Plan n so the 95% CI on a single-rater ICC has full width <= 0.20
#        with k = 3 raters and an anticipated ICC of 0.7.
ss_aipe_icc(rho = 0.7, k = 3, width = 0.20)

# 2. With 80% assurance:
ss_aipe_icc(rho = 0.7, k = 3, width = 0.20, assurance = 0.80)

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