Application of structured statistical analyses to identify a biomarker predictive of enhanced tralokinumab efficacy in phase III clinical trials for severe, uncontrolled asthma

Table 2 Assessment of interaction between biomarkers and treatment effect in the STRATOS 1 all-comers population (full analysis set)^a

DPP-4 Dipeptidyl peptidase, FeNO Fractional exhaled nitric oxide, IgE Immunoglobulin E, Q2W Every 2 weeks, Q4W Every 4 weeks, Tralo Tralokinumab
^aLikelihood ratio tests have low power and are only able to identify linear relationships
^†p-values were calculated using negative binomial models including treatment group, geographical region, age, number of exacerbations in the previous year, biomarker and treatment*biomarker as covariates. The log of each participant’s corresponding follow-up time was used as an offset variable in the model to adjust for participants having different exposure times during which asthma exacerbations occurred. p-values for each individual biomarker represents the Wald statistic for a biomarker*treatment interaction term using separate models. The test of all five biomarkers was based on a likelihood ratio test comparing a model including the baseline covariates above, biomarker and treatment interaction terms for all five biomarkers with a model including terms for the baseline covariates and biomarkers but not any treatment interaction terms

ISSN: 1471-2466