P04 A simulation study to assess how diagnostic test accuracy affect clinical utility study outcome for MRD tests

Minimal Residual Disease (MRD) is strongly related to cancer recurrence. Early detection of MRD can potentially be useful in providing treatment (e.g., adjuvant chemotherapy) to patients before other forms of clinical evidence (e.g., imaging) become available. The clinical utility of MRD tests as a predictive biomarker has been evaluated in recent or ongoing prospective clinical trials, in which postoperative cancer patients with a positive MRD test result are randomized into an adjuvant treatment arm and a control arm. Clinical outcomes such as disease-free survival are compared between treatment arms. Efficacy of adjuvant treatment in such trials is primarily driven by three classes of parameters: sensitivity and specificity of the MRD assay, effectiveness of treatment and study cohort characteristics such as sample size and recurrence rate.

To evaluate the impact of these parameters to the clinical trial outcome, we performed a simulation study across the parameter space defined by total cohort size (100 - 1000), recurrence rate (30% - 70%), sensitivity (60% - 95%), specificity (80% - 100%), and treatment efficacy hazard ratio (1.5 - 10). For each combination of parameters, 50 simulated clinical trials were created. In each simulated trial, the primary endpoint was the statistical significance of the hazard ratio between the treated and untreated arms. Unsurprisingly the results demonstrate that the most important factor is the efficacy of treatment on the MRD population followed by the number of patients with detected MRD. Results show that high sensitivity values can offset lower sample sizes and MRD rates, as expected, although high sensitivity is not always sufficient for consistent success. High specificity is an important factor as well, especially when the baseline hazard ratio is close to one. The enrollment of MRD-negative patients by mistake can muddle the results dramatically, as MRD-negative patients are less likely to have negative events compared to patients with MRD regardless of treatment.

We believe this simulation analysis can help clinical study planners estimate MRD-guided clinical trial success probability and determine sample size based on MRD test accuracy and study cohort characteristics. The analysis may also help assay developers determine product requirements. This simulation framework can potentially be applied to general biomarker-driven clinical trials.

Diagnostic (e.g., ctDNA, Biomarker, Precision Medicine, Imaging)

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