Three steps toward dose optimization for oncology dose finding.

Background: Traditional dose selection for oncology registration trials typically employs a one- or two-step single maximum tolerated dose (MTD) approach. However, this approach may not be appropriate for molecularly targeted therapy, which tends to have toxicity profiles that are markedly different than cytotoxic agents. The US Food and Drug Administration launched Project Optimus to reform dose optimization in oncology drug development and has recently released a related guidance for industry.

A gated group sequential design for seamless Phase II/III trial with subpopulation selection.

Background: Due to the high cost and high failure rate of Phase III trials where a classical group sequential design (GSD) is usually used, seamless Phase II/III designs are more and more popular to improve trial efficiency. A potential attraction of Phase II/III design is to allow a randomized proof-of-concept stage prior to committing to the full cost of a Phase III trial. Population selection during the trial allows a trial to adapt and focus investment where it is most likely to provide patient benefit.

A hybrid design for dose-finding oncology clinical trials.

Identifying the maximum tolerated dose (MTD) and recommending a Phase II dose for an investigational treatment is crucial in cancer drug development. A suboptimal dose often leads to a failed late-stage trial, while an overly toxic dose causes harm to patients. There is a very rich literature on trial designs for dose-finding oncology clinical trials.

Mixture survival trees for cancer risk classification.

In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification.

Inferring latent heterogeneity using many feature variables supervised by survival outcome.

In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high-risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution.

Using artificial intelligence tools in answering important clinical questions: The KEYNOTE-183 multiple myeloma experience.

The phase III, randomized, active-controlled, multicenter, open-label KEYNOTE-183 study (NCT02576977) evaluating pomalidomide and low dose dexamethasone (standard-of-care [SOC]) with or without pembrolizumab in patients with refractory or relapsed and refractory multiple myeloma (rrMM) was placed on full clinical hold by the US FDA on July 03, 2017 due to an imbalance in the number of deaths between arms. Clinically-led subgroup analyses are typically used to shed light on clinical findings. However, this approach is not always successful.

Dynamic RMST curves for survival analysis in clinical trials.

Background: The data from immuno-oncology (IO) therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the proportional hazards (PH) assumption is often violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation.

Analysis of time-to-event data using a flexible mixture model under a constraint of proportional hazards.

Cox proportional hazards (PH) model evaluates the effects of interested covariates under PH assumption without specified the baseline hazard. In clinical trial applications, however, the explicitly estimated hazard or cumulative survival function for each treatment group helps to assess and interpret the meaning of treatment difference. In this paper, we propose to use a flexible mixture model under the PH constraint to fit the underline survival functions.

Approximate confidence limit for the reference scaled bioequivalence with a parallel design.

The reference scaled bioequivalence has been proposed with many successful applications for the highly variable products. The statistical properties for the reference scaled bioequivalence have been studied for the commonly used crossover design. However, a crossover design may not be feasible in a real application such as the biosimilar study, instead a parallel design is a more timely and cost-effective choice.

A flexible parametric survival model for fitting time to event data in clinical trials.

Time-to-event data are common in clinical trials to evaluate survival benefit of a new drug, biological product, or device. The commonly used parametric models including exponential, Weibull, Gompertz, log-logistic, log-normal, are simply not flexible enough to capture complex survival curves observed in clinical and medical research studies. On the other hand, the nonparametric Kaplan Meier (KM) method is very flexible and successful on catching the various shapes in the survival curves but lacks ability in predicting the future events such as the time for certain number of events and the number of events at certain time and predicting the risk of events (eg, death) over time beyond the span of the available data from clinical trials.

Sample size allocation in multiregional equivalence studies.

With the increasing globalization of drug development, the multiregional clinical trial (MRCT) has gained extensive use. The data from MRCTs could be accepted by regulatory authorities across regions and countries as the primary sources of evidence to support global marketing drug approval simultaneously. The MRCT can speed up patient enrollment and drug approval, and it makes the effective therapies available to patients all over the world simultaneously.

Quantifying an agreement study.

In medical and other related sciences, clinical or experimental measurements usually serve as a basis for diagnostic, prognostic, therapeutic, and performance evaluations. Examples can be assessing the reliability of multiple raters (or measurement methods), assessing the suitability for tumor evaluation of using a local laboratory or a central laboratory in a randomized clinical trial (RCT), validating surrogate endpoints in a study, determining that the important outcome measurements are interchangeable among the evaluators in an RCT. Any elegant study design cannot overcome the damage by unreliable measurement.

Comparability of critical quality attributes for establishing biosimilarity.

To develop a biosimilar product, it is essential to demonstrate the biosimilarity between the proposed biosimilar product and the reference product first in terms of quality in a stepwise approach that can then help inform the extent of safety and efficacy data that will be required to establish biosimilarity. These comparability studies should have direct side-by-side comparisons of the test and the reference products. In this paper, we develop a statistical method for unpaired head-to-head quality attribute comparisons.

Assessing similarity in bioanalytical methods.

Unlabelled: Due to the comparative nature of a bioassay, the relative potency is usually used to describe the potency of a sample. Only when the two samples are similar can a valid and meaningful estimate of relative potency be obtained. Thus, assessing similarity is a crucial part in developing a bioanalytical method.

Sample size calculation for an agreement study.

It is often necessary to compare two measurement methods in medicine and other experimental sciences. This problem covers a broad range of data. Many authors have explored ways of assessing the agreement of two sets of measurements.

A new approach for outliers in a bioavailability/bioequivalence study.

It is well known that outliers can have a significant effect on the conclusion of a bioavailability/bioequivalence study. Existing approaches for outlier detection are ANOVA type based on the assumptions on log-AUC, and they are disconnected from the pharmacokinetics (PK) literature. However, the observations from a bioavailability/bioequivalence study are the correlated concentrations, not the AUCs.

Assessing the reproducibility of an analytical method.

The reproducibility of a validated analytical method may require reassessment because of various reasons, such as the transfer between laboratories or companies, changes in the instruments or software platforms (or both), or changes in critical reagents, among others. This paper is a demonstration of an assay bridging study in evaluating reproducibility. The approach is simple but very informative and offers many advantages over existing approaches.

Agreement for curved data.

An agreement problem usually involves assessing the concordance of two sets of measurements, and the problem covers a broad range of data. In practice, the observations are often curves instead of the traditional points. In this article, the agreement problem is studied for curved data.

A linear mixed-effects calibration in qualifying experiments.

In many applications, controls are used to monitor the process or experiment and to assess whether the process is in control or the experiment is valid. In this case, the traditional fixed-effects calibration is usually not adequate, but a mixed-effects model is appropriate. In this article, a linear mixed-effects calibration model is considered to qualify an experiment.

Comparing the concentration curves directly in a pharmacokinetics, bioavailability/bioequivalence study.

In a traditional pharmacokinetics (PK), bioavailability (BA) /bioequivalence (BE) study, the same number of time points and sampling times are used for each subject. Often, an indirect inference is then made on some PK parameters such as area under the plasma concentration curve (AUC), maximum plasma concentration (C(max)), time to maximum plasma concentration (T(max)) or half-life. However, since these PK parameters are summarized from repeated measurements, a lot of information can be lost.