Clinical Trial Data Analysis Using R

Abstracts not available for BookReview

Principal Stratum Strategy: Potential Role in Drug Development

A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post-randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called {\it intercurrent events} in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post-hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions

Assessing the Impact of COVID-19 on the Objective and Analysis of Oncology Clinical Trials -- Application of the Estimand Framework

COVID-19 outbreak has rapidly evolved into a global pandemic. The impact of COVID-19 on patient journeys in oncology represents a new risk to interpretation of trial results and its broad applicability for future clinical practice. We identify key intercurrent events that may occur due to COVID-19 in oncology clinical trials with a focus on time-to-event endpoints and discuss considerations pertaining to the other estimand attributes introduced in the ICH E9 addendum. We propose strategies to handle COVID-19 related intercurrent events, depending on their relationship with malignancy and treatment and the interpretability of data after them. We argue that the clinical trial objective from a world without COVID-19 pandemic remains valid. The estimand framework provides a common language to discuss the impact of COVID-19 in a structured and transparent manner. This demonstrates that the applicability of the framework may even go beyond what it was initially intended for.Comment: Paper written on behalf of the industry working group on estimands in oncology (www.oncoestimand.org). Accepted for publication in a special issue of Statistics in Biopharmaceutical Researc

Genetic Structures of Copy Number Variants Revealed by Genotyping Single Sperm

Copy number variants (CNVs) occupy a significant portion of the human genome and may have important roles in meiotic recombination, human genome evolution and gene expression. Many genetic diseases may be underlain by CNVs. However, because of the presence of their multiple copies, variability in copy numbers and the diploidy of the human genome, detailed genetic structure of CNVs cannot be readily studied by available techniques.Single sperm samples were used as the primary subjects for the study so that CNV haplotypes in the sperm donors could be studied individually. Forty-eight CNVs characterized in a previous study were analyzed using a microarray-based high-throughput genotyping method after multiplex amplification. Seventeen single nucleotide polymorphisms (SNPs) were also included as controls. Two single-base variants, either allelic or paralogous, could be discriminated for all markers. Microarray data were used to resolve SNP alleles and CNV haplotypes, to quantitatively assess the numbers and compositions of the paralogous segments in each CNV haplotype.This is the first study of the genetic structure of CNVs on a large scale. Resulting information may help understand evolution of the human genome, gain insight into many genetic processes, and discriminate between CNVs and SNPs. The highly sensitive high-throughput experimental system with haploid sperm samples as subjects may be used to facilitate detailed large-scale CNV analysis

Adaptive Informational Design of Confirmatory Phase III Trials With an Uncertain Biomarker Effect to Improve the Probability of Success

<p>Oncology drug developers sometimes decide to initiate Phase III randomized confirmatory trials at risk after significant preliminary anti-tumor activities are observed in small Phase I/II single arm studies. There are two clear challenges. First, these investigational drugs may have a greater benefit in a biomarker enriched population. But the limited data from Phase I/II can hardly provide the much-needed information for selecting a biomarker cutpoint or prioritizing a biomarker hypothesis for Phase III testing. Second, the data seldom provide any insight on how the treatment benefit evolves over time. Risk-mitigation strategies such as conventional adaptive-designs that rely on interim analyses for modifying the study design are less reliable because the treatment effect observed at an interim analysis may not be the same as in the final analysis. The use of an intermediate endpoint for interim decision makes it even more unreliable because the predictive value of an intermediate endpoint is often unknown for drugs with a new mechanism of action. In this article, we present an alternative design strategy to mitigate the risks. The idea is to add an analysis of the primary endpoint at the end of the Phase III trial in a subgroup of patients representing the overall study population. We call it informational analysis and the corresponding design informational design to emphasize its difference from the conventional event-time or calendar-time-driven interim analysis. From a high-level perspective, the subgroup analysis is equivalent to a Phase II trial conducted under the same study design at the same time in the same population at the same sites as the Phase III trial. It provides a more reliable resource of information for inference than a separate Phase II trial or a conventional interim analysis. The strategy is applied to address a wide range of statistical issues encountered in expedited development of personalized medicines, including alpha splitting between a biomarker subpopulation and the overall population and de-selection of nonperforming biomarker subpopulations. Applications to hypothetical Phase III trials are illustrated. Although the strategy is motivated by oncology studies, it may be applied to drug development in other therapeutic areas with similar concerns. Supplementary materials for this article are available online.</p