1–2α CI and TOST at α 0.05 [General Sta­tis­tics]

posted by Helmut Homepage – Vienna, Austria, 2017-03-28 01:47 (2576 d 14:12 ago) – Posting: # 17189
Views: 8,964

Hi GM,

❝ If the 90%CI is one side of hundred, i.e. either (85.00-98.00) or (102.00-119.00) then there should be significant treatment effect. Is this statement correct...?


Absolutely.

❝ And why we are generally checking the treatment effect at 5% level of significance...?


The inclusion of a 100(1–2α) confidence interval within the common acceptance range [L, U] of 80–125% is operationally equivalent to Two One-Sided t-Tests (TOST). One t-test is for ≤80% and the other one for ≥125%, both at a level of α 0.05. Null and alternative hypotheses:

Inclusion of the 100(1–2α) CI:

[image]

TOST at α 0.05:

[image]


In BE the Null is inequivalence.

Extreme example:

library(PowerTOST)
lower <- 0.80
upper <- 1.25
pe    <- sqrt(lower*upper)
n     <- 24
alpha <- 0.05
CV    <- CVfromCI(lower=lower, upper=upper, n=n, alpha=alpha, design="2x2")
p     <- pvalues.TOST(pe=pe, CV=CV, n=n, design="2x2")
cat(sprintf("%g%% %s %.2f–%.2f%%", 100*(1-2*alpha),
    "CI:", 100*lower, 100*upper), "\n"); print(p)

# 90% CI: 80.00–125.00%
#  p.left p.right
#    0.05    0.05


Although the treatment-effect (and its p-value) is part of the standard output of most software packages, it is not relevant for the BE-decision. Statistical significant  clinically relevant…
I don’t know why you are checking it – I don’t.

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