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oncoPoS

Overview

oncoPoS performs Probability of Success (PoS) calculations for a phase 3 oncology study using a Bayesian Hierarchical model. The prior in this model is based on the data observed in an earlier study, design features for the phase 3 study that is being assessed and the industry benchmark for success. The benchmark PoS is specified via a Beta distribution using its mean and variance, which can be derived from historical success rates or predictive models (e.g., random forest). The framework supports both single-arm and two-arm early-phase designs and allows for indication-specific ORR–PFS regression when surrogate endpoints are used.

Indication-specific ORR-PFS regression

When early endpoint objective response rate (ORR) is used to predict phase 3 outcome progression-free survival (PFS), the relationship varies by cancer type. To account for this heterogeneity, oncoPoS groups cancer indications into five categories, each associated with a distinct set of ORR–PFS regression parameters derived from prior Bayesian hierarchical modeling.

Indication Groups

  • Group 1: Hematologic malignancies
    Includes classical Hodgkin lymphoma (CHL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), multiple myeloma (MM), non-Hodgkin lymphoma (NHL), and peripheral T-cell lymphoma (PTCL).

  • Group 2: Gynecologic cancers
    Includes cervical, endometrial, and ovarian cancers.

  • Group 3: Thoracic malignancies
    Includes non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC), and mesothelioma.

  • Group 4: Urologic and gastrointestinal solid tumors
    Includes bladder cancer, gastric cancer, and renal cell carcinoma (RCC).

  • Group 5: Breast cancer
    Includes breast cancer.

If no indication is specified by the user, oncoPoS defaults to using the average ORR–PFS relationship across all indication groups.

Installation

You can install development version of oncoPoS from GitHub with:

if (!requireNamespace("remotes")) {
  install.packages("remotes")
}
remotes::install_github("MSDLLCPapers/oncoPoS", ref = "develop")

Example

Below is a simple example which assumes the following information is available for the an oncology phase 3 trial PoS calculation:

  • Disease area:

    • This trial is planned for breast cancer, which falls under Indication Group 5 in the model’s ORR–PFS regression framework.

  • Design features of a phase 3 trial:

    • Progression-free survival (PFS) is the primary endpoint with the target hazard ratio (HR) of 0.7;
    • Two analyses using group sequential approach are planned;
    • The number of target events at each analysis is 370 and 468;
    • The approximate HR bound at each analysis is 0.7790 and 0.8204;
    • The randomization ratio is 2:1.

  • The above phase 3 trial is planned following promising results in an earlier phase 2 study, which reported PFS HR (95% CI) of 0.53 (0.31, 0.91).

  • In addition to PFS, objective response rate (ORR) data is available from a single-arm phase 2 trial, with 40 responders out of 100 patients and historical control response rate assumed to lie between 0.05 and 0.20.

  • The benchmark probability of success (omega) is modeled using a Beta prior with mean 0.3 and variance 0.03, reflecting prior belief about the historical success rate in similar studies.

All the above information is synthesized in oncoPoS::gen_pos() using Bayesian Hierarchical model to generate a PoS estimate:

gen_pos(
   target_hr = 0.70,
   J = 2,
   nevents3 = c(370, 468),
   hr_bound = c(0.7790, 0.8204),
   thres = 0.01,
   ratio = 2,
   omega_mean = 0.3,
   omega_var = 0.03,
   est_obs_pfs = 0.53,
   low_obs_pfs = 0.31,
   upp_obs_pfs = 0.91,
   use_pfs = TRUE,
   indication = 5,
   n_trt2 = 100,
   n_resp_trt2 = 40,
   low_soc_rr = 0.05,
   upp_soc_rr = 0.2,
   use_orr = TRUE,
   single_arm = TRUE,
   seed = 222
 )