Background

The statistical plan of a phase II trial should balance minimizing the premature termination of potentially beneficial therapies (i.e. false negatives) and the further, costly testing of ineffective drugs (i.e. false positives). We sought to examine the methodology, reporting, and bias in the interpretation of outcomes of phase II oncology trials in recent years.

Materials and methods

In a retrospective cross-sectional analysis, we reviewed all full-length articles published on PubMed from 1 January 2021 to 20 June 2022. We searched for data regarding the sample size calculation (number, α value, power, and expected effect size), the primary and secondary outcomes and results, and the authors' conclusion of the study.

Results

About 5.4% of studies (n = 10) used a statistical power that was inferior to 80%, and 16.7% (n = 34) did not indicate the level of power for the sample size calculation. Approximately 16.7% (n = 31) of studies used a one-sided α level of ≤0.025; 17.7% (n = 33) of studies used a predefined threshold (no comparator effect size or difference between groups) to determine the sample size for efficacy. The percentage of studies with a positive authors' conclusion but not meeting the primary endpoint, or the endpoint was equivocal, was 27.4% (n = 51).

Conclusion

Many randomized phase II studies in oncology failed to report essential data for determining sample size calculations, many did not actually use a comparator to determine efficacy even though the studies were randomized, and many had positive conclusions even though the results were indeterminate or the primary endpoint was not met.

Background

When a new drug comes to the market, the incentive for the sponsoring company is to maximize the treatment duration in order for the patient to reap the full therapeutic benefit of the product and achieve a positive trial result. We sought to enumerate instances when an already-approved oncology drug was used as a comparator for a newer drug seeking approval and compare the duration of treatment when it is used in the intervention arm to when it is used as a comparator.

Patients and methods

In a cross-sectional analysis, we searched drug approval announcements for advanced, metastatic, or unresectable cancers between 2009 and 2020. We included studies reporting on an approved drug and studies reporting on when the same drug was used as a comparator for other drugs seeking Food and Drug Administration (FDA) approval. We examined median progression-free survival and duration of treatment for when the drug was initially approved and for when the drug was used as a comparator for other drugs that were seeking approval.

Results

Of the 23 instances when an approved drug was later used as a comparator against a newer drug seeking FDA approval, we found 11 instances (47.8%) where the drug, when used as a comparator arm, had a shorter duration of treatment than when it was used in the intervention arm. The median duration of treatment in the study initially testing the drug was 6.0 months (range: 2.2-12.7 months), whereas the median duration of treatment when the same drug was used as a comparator was 4.9 months (range: 1.7-12.0 months).

Conclusions

These results suggest that there is bias in how long a patient receives a given therapy, and this bias favors the newer therapy. Clinical trialists should seek to utilize methodology that reduces bias so that the relative efficacy of newer drugs can be objectively assessed.

The intrinsic properties of nanomaterials offer promise for technological revolutions in many fields, including transportation, soft robotics, and energy. Unfortunately, the exploitation of such properties in polymer nanocomposites is extremely challenging due to the lack of viable dispersion routes when the filler content is high. We usually face a dichotomy between the degree of nanofiller loading and the degree of dispersion (and, thus, performance) because dispersion quality decreases with loading. Here, we demonstrate a potentially scalable pressing-and-folding method (P & F), inspired by the art of croissant-making, to efficiently disperse ultrahigh loadings of nanofillers in polymer matrices. A desired nanofiller dispersion can be achieved simply by selecting a sufficient number of P & F cycles. Because of the fine microstructural control enabled by P & F, mechanical reinforcements close to the theoretical maximum and independent of nanofiller loading (up to 74 vol %) were obtained. We propose a universal model for the P & F dispersion process that is parametrized on an experimentally quantifiable " D factor". The model represents a general guideline for the optimization of nanocomposites with enhanced functionalities including sensing, heat management, and energy storage.