  In the era of precision medicine, more and more clinical trials are now
driven or guided by biomarkers, which are patient characteristics objectively
measured and evaluated as indicators of normal biological processes, pathogenic
processes, or pharmacologic responses to therapeutic interventions. With the
overarching objective to optimize and personalize disease management,
biomarker-guided clinical trials increase the efficiency by appropriately
utilizing prognostic or predictive biomarkers in the design. However, the
efficiency gain is often not quantitatively compared to the traditional
all-comers design, in which a faster enrollment rate is expected (e.g. due to
no restriction to biomarker positive patients) potentially leading to a shorter
duration. To accurately predict biomarker-guided trial duration, we propose a
general framework using mixture distributions accounting for heterogeneous
population. Extensive simulations are performed to evaluate the impact of
heterogeneous population and the dynamics of biomarker characteristics and
disease on the study duration. Several influential parameters including median
survival time, enrollment rate, biomarker prevalence and effect size are
identitied. Re-assessments of two publicly available trials are conducted to
empirically validate the prediction accuracy and to demonstrate the practical
utility. The R package \emph{detest} is developed to implement the proposed
method and is publicly available on CRAN.
