Effective Information for Binary Outcomes (Logistic)

Computes \(d_{\text{eff}} = n_H \bar{p} (1 - \bar{p})\) from the Fisher information of the logistic regression treatment coefficient.

Usage

d_eff_binary(n_sg, p_event)

Arguments

n_sg

Integer. Subgroup sample size.

p_event

Numeric. Event probability (0–1), averaged across arms.

Value

Numeric. Effective information.

Details

For the model \(\text{logit}(p_i) = \beta_0 + \beta V_i\) with balanced arms (\(n_1 = n_0 = n_H/2\)): $$\text{Var}(\hat\beta) = \frac{4}{n_H \bar{p}(1-\bar{p})}$$

Examples

d_eff_binary(n_sg = 100, p_event = 0.30)
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