Optimality of testing procedures for survival data in the nonproportional hazards setting

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration.

KMDATA: a curated database of reconstructed individual patient-level data from 153 oncology clinical trials

We created a database of reconstructed patient-level data from published clinical trials that includes multiple time-to-event outcomes such as overall survival and progression-free survival. Outcomes were extracted from Kaplan–Meier (KM) curves reported in 153 oncology Phase III clinical trial publications identified through a PubMed search of clinical trials in breast, lung, prostate and colorectal cancer, published between 2014 and 2016. For each trial that met our search criteria, we curated study-level information and digitized all reported KM curves with the software Digitizelt. We then used the digitized KM survival curves to estimate (possibly censored) patient-level time-to-event outcomes. Collections of time-to-event datasets from completed trials can be used to support the choice of appropriate trial designs for future clinical studies. Patient-level data allow investigators to tailor clinical trial designs to diseases and classes of treatments. Patient-level data also allow investigators to estimate the operating characteristics (e.g. power and type I error rate) of candidate statistical designs and methods.

Database URL: https://10.6084/m9.figshare.14642247.v1

Radiological assessment schedule for 1p/19q-codeleted gliomas during the surveillance period using parametric modeling

Background

There have been no evidence-based guidelines on the optimal schedule for the radiological assessment of 1p/19q-codeleted glioma. This study aimed to recommend an appropriate radiological evaluation schedule for 1p/19q-codeleted glioma during the surveillance period through parametric modeling of the progression-free survival (PFS) curve.

Methods

A total of 234 patients with 1p/19q-codeleted glioma (137 grade II and 97 grade III) who completed regular treatment were retrospectively reviewed. The patients were stratified into each layered progression risk group by recursive partitioning analysis. A piecewise exponential model was used to standardize the PFS curves. The cutoff value of the progression rate among the remaining progression-free patients was set to 10% at each scan.

Results

Progression risk stratification resulted in 3 groups. The optimal magnetic resonance imaging (MRI) interval for patients without a residual tumor was every 91.2 weeks until 720 weeks after the end of regular treatment following the latent period for 15 weeks. For patients with a residual tumor after the completion of adjuvant radiotherapy followed by chemotherapy, the optimal MRI interval was every 37.5 weeks until week 90 and every 132.8 weeks until week 361, while it was every 33.6 weeks until week 210 and every 14.4 weeks until week 495 for patients with a residual tumor after surgery only or surgery followed by radiotherapy only.

Conclusions

The optimal radiological follow-up schedule for each progression risk stratification of 1p/19q-codeleted glioma can be established from the parametric modeling of PFS.

Comparing the Efficacy of Cancer Therapies between Subgroups in Basket Trials

The need to test anticancer drugs in multiple indications has been addressed by basket trials, which are Phase I or II clinical trials involving multiple tumor subtypes and a single master protocol. Basket trials typically involve few patients per type, making it challenging to rigorously compare responses across types. We describe the use of permutation testing to test for differences among subgroups using empirical null distributions and the Benjamini-Hochberg procedure to control for false discovery. We apply the approach retrospectively to tumor-volume changes and progression-free survival in published basket trials for neratinib, larotrectinib, pembrolizumab, and imatinib and uncover examples of therapeutic benefit missed by conventional binomial testing. For example, we identify an overlooked opportunity for use of neratinib in lung cancers carrying ERBB2 Exon 20 mutations. Permutation testing can be used to design basket trials but is more conservatively introduced alongside established approaches to enrollment such as Simon’s two-stage design.

Basket clinical trials simultaneously test a single drug in multiple tumor subtypes, but statistical challenges limit the comparison of responses across subtypes. We describe a rigorous approach to permutation testing using empirical null distributions that can identify previously overlooked opportunities for use of targeted therapy in genetically defined cancer subtypes.