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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).