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Table 7 HIGH clinical trial: Detection of treatmen-by-subset interaction when K=3

From: Enrichment Bayesian design for randomized clinical trials using categorical biomarkers and a binary outcome

  \(\boldsymbol {\widehat {\theta }}\)global CI95% \(\boldsymbol {\widehat {\theta _{A}}}\)CI95% \(\boldsymbol {\widehat {\theta _{B}}}\)CI95% \(\boldsymbol {\widehat {\theta _{C}}}\)CI95% Decision nA nB nC Proportion interaction effect*
Age Partition Age≤58 58<Age≤68 Age>68       
1er interim analysis   1.062[0.484-1.972] 0.804[0.444-1.280] 1.558[0.806-2.969] Go with entire population 61 74 59 0.21& 0.46
2nd interim analysis   0.913[0.518-1.493] 0.979[0.612-1.450] 1.437[0.920-2.217] Go with entire population 127 135 126 0.10& 0.12
3th interim analysis   0.755[0.475-1.108] 0.975[0.670-1.381] 1.086[0.782-1.485] Go with entire population 197 202 186 0.01& 0.00
Final analysis 0.993[0.823-1.18] 0.850[0.570-1.209] 0.986[0.719-1.334] 1.141[0.838-1.499] Go with entire population 255 268 253 0.00& 0.00
  1. The reported intervals are 95% credibility intervals, defined as [quantile(2.5%), quantile(97.5%)] of the posterior distribution.
  2. *In case of Gail & Simon, it refers to the posterior probabilities Pquali and Pquanti, respectively, as described in equation (5) & (6).