Abstract Loss of power and clear description of treatment differences are key issues in designing and analyzing a clinical trial where non-proportional hazard is a possibility. A log-rank test may be inefficient and interpretation of the hazard ratio estimated using Cox regression is potentially problematic. In this case, the current ICH E9 (R1) addendum would suggest designing a trial with a clinically

Abstract Positive predicted value and negative predicted value are used by clinicians to evaluate how likely a disease stage is present given the test results. In contrast, positive and negative likelihood ratios are used in practice to assess the potential utility of a specific diagnostic test and the likelihood of a patient having the condition. This article introduces the concepts and the derivation

Abstract Under suitable assumptions we prove that there does not exist a perfect exact multiple test procedure that would apply simultaneously to any positive correlation coefficient even with a known distribution of test statistics. This nonexistence theorem holds for all simple tests under normal distribution, and holds for all tests under Ferguson’s distribution. Given the nonexistence of a perfect

Abstract The success of drug development of targeted therapy often hinges on an appropriate selection of the sensitive patient population, mostly based on patients’ biomarker levels. At the planning stage of a phase II study, although a potential biomarker may have been identified, a threshold value for defining sensitive patient population is often unavailable for adopting many existing biomarker-guided

ABSTRACT Longitudinal studies of rapid disease progression often rely on noisy biomarkers; the underlying longitudinal process naturally varies between subjects and within an individual subject over time; the process can have substantial memory in the form of within-subject correlation. Cystic fibrosis lung disease progression is measured by changes in a lung function marker (FEV1), such as a prolonged

Abstract In a recent paper, Larsen and Josiassen (2020) present a new estimand for the analysis of clinical trials with non-compliance. The treatment effect in patients who would comply if treated has the advantage of being less remote from the observed data than other principal stratum estimands (since we know who belongs to this stratum in the active treatment group). Nevertheless, we feel that the

Researchers designing a clinical trial to demonstrate superiority or non-inferiority of a new treatment to an established control face an important choice: to conduct a randomized controlled trial (RCT), or to take advantage of historical data on the control treatment and conduct a single-arm historically controlled trial (HCT). The primary advantage of the RCT is that it minimizes bias between the

Abstract The research landscape in the era of personalized medicine is shifting its focus to smaller trials due to increasing reliance on predictive genomic biomarkers to guide more targeted therapies. This is especially true for phase II single arm trials conducted in rare cancer settings that are challenging in terms of selecting, recruiting and treating sufficient numbers of patients in a reasonable

The COVID-19 pandemic presents unprecedented challenges for drug developers seeking to evaluate the safety and efficacy of potential treatments for COVID-19. Clinical researchers must work quickly and adapt to emerging data. Building upon the FDA guidance document and Duke-Margolis’ critical path to rapid development and access to safe and effective COVID-19 therapeutics, this paper focuses on statistical

Abstract Proof-of-concept trials enable sponsors decide whether to continue or discontinue a compound’s development based on preliminary evidence of safety and efficacy. Many accounts exist on using quantitative approaches for this decision. Still, these accounts are devoid of quantitative decisions under unequal covariances and post-treatment variances for continuous endpoints. Filling this void is

Abstract Identification of underlying subpopulations to account for unobserved heterogeneity in the population is a challenging statistical problem, mainly because no explicit information about the latent classes is available. Although latent class analysis via finite mixture models is often used successfully to probabilistically identify subpopulations in applications, it often fails with data for

SUMMARY Multi-regional clinical trials have a hierarchical data structure because several regions form a patient population and individual patients are nested within their own regions. Data are obtained from two different levels: regions and patients. In order to incorporate such a hierarchical structure, hierarchical linear models were proposed for the response variables following a normal distribution

Abstract The spread of COVID-19 has created tremendous challenges to ongoing clinical studies essential to finding effective treatments and cures for a myriad of diseases, with some studies having suspended enrollment altogether. This perspective paper focuses on the loss of power in clinical studies disrupted by the pandemic. It introduces an innovative use of the recently developed propensity score-integrated

Abstract For the sequential parallel comparison design with normally distributed outcomes and re-randomization at the start of phase 2, we propose fitting separate repeated measures models for the two phases. We show analytically the asymptotic independence between the treatment effect estimators in the two phases. We recommend pre-specification of the weights for combining the treatment effects in

Shrinkage estimation for dose-response modeling in phase II trials with multiple schedules. Statistics in Biopharmaceutical Research. Accepted 5 November 2020.

Abstract In this comment on Eric Gibson's comprehensive paper on "The role of p-values in judging the strength of evidence and realistic replication expectations" we extend the discussion on the use of p-values to judge the strength of evidence to complex clinical trials. We adress adjusted p-values for group sequential and adaptive trials, trials with multi treatment arms, endpoints and subgroups

(2020). Multivariate Shared-Parameter Mixed-Effects Location Scale Model for Analysis of Intensive Longitudinal Data. Statistics in Biopharmaceutical Research. Ahead of Print.

(2020). Bootstrap Cross-Validation Improves Model Selection in Pharmacometrics. Statistics in Biopharmaceutical Research. Ahead of Print.

(2020). Is the p-Value a Suitable Basis for the Construction of Measures of Evidence? Comment on “The Role of p-Values in Judging the Strength of Evidence and Realistic Replication Expectations”. Statistics in Biopharmaceutical Research. Ahead of Print.

(2020). Comment on “The Role of p-Values in Judging the Strength of Evidence and Realistic Replication Expectations”. Statistics in Biopharmaceutical Research. Ahead of Print.

Abstract The current outbreak of respiratory disease is caused by a novel coronavirus. The virus has been named SARS-CoV-2 and the disease it causes has been named Coronavirus Disease 2019 (COVID-19). On January 31, 2020, the Department of Health and Human Services (HHS) issued a declaration of a public health emergency related to COVID-19, effective January 27, 2020, and mobilized the Operating Divisions

Abstract We focus our comments on the five steps Gibson (2020) proposes for the best practice for the use of p-values. Using the examples he presents we elaborate on ways to appropriately adjust for the effect of multiplicity and selective inference using hierarchical testing and conditional confidence intervals and estimators.

Abstract In longitudinal clinical trials, missing data is inevitable despite every effort made to retain patients in the trial. Missing data causes difficulty in the estimation and interpretation of the treatment effect. When the primary objective is to assess the treatment effect in a realistic setting, it is necessary to take into consideration the impact of noncompliance to the treatment regimen

Abstract The use of multi-regional clinical trials (MRCTs) in clinical developments increased to accelerate patients’ access to new drugs globally. MRCTs inherently assume that the treatment effect is consistent across regions; therefore, we generally estimate the overall treatment effect using a weighted mean of regional estimates with the proportions of enrolled patients as a weight. However, recent

Abstract Biosimilar products are a relative newcomer in the pharmaceutical industry. It was only in 2006 that the first biosimilar product, Omnitrope® (somatropin), a human growth hormone produced by Sandoz was approved by the European Medicine Agency (EMA), followed later the same year by the US Food & Drug Administration (FDA) [1], and in 2009 by Pharmaceuticals and Medical Devices Agency (PMDA)

(2020). Statistical Challenges in the Conduct and Management of Ongoing Clinical Trials During the COVID-19 Pandemic. Statistics in Biopharmaceutical Research: Vol. 12, No. 4, pp. 397-398.

ABSTRACT Many clinical trials of treatments for patients hospitalised for COVID-19 use an ordinal scale recommended by the World Heath Organisation. The scale represents intensity of medical intervention, with higher scores for interventions more burdensome for the patient, and highest score for death. There is uncertainty about use of this ordinal scale in testing hypotheses. With the objective of

Abstract–In this article, we provide guidance on how safety analyses and reporting of clinical trial safety data may need to be modified, given potential impact from the COVID-19 pandemic. Impact could include missed visits, alternative methods for assessments (such as virtual visits), alternative locations for assessments (such as local labs), and study drug interruptions. Starting from the safety

(2020). Rejoinder to Letter to the Editor “The Hazards of Period Specific and Weighted Hazard Ratios”. Statistics in Biopharmaceutical Research: Vol. 12, No. 4, pp. 520-521.

Abstract Cell-based biological assays (also called bioassays) are an essential analytical tool used for quality assessment of biologic drug substances and drug products. In particular, they are used to determine the potency compared against a standard preparation under assumptions of similarity. In the design of bioassays, orthogonality between preparations and nuisance factors is desired. Block designs

Abstract We consider a simplified exploratory master protocol in oncology that intends to investigate multiple test drugs in the same tumor indication (i.e., an umbrella trial) or investigate the same test drug in multiple tumor indications (i.e., a basket trial). The primary objective of an exploratory umbrella trial is to test whether any of the drugs is effective, whereas individual drug cohorts

Abstract Joint modeling of longitudinal and survival data is an active area of statistical research that has received much attention recently. Although it is a common practice to analyze complex longitudinal data using nonlinear mixed-effects (NLME) or nonparametric mixed-effects (NPME) models in literature, the following issues may standout: (i) In the practice, the profile of each subject’s longitudinal

Abstract In randomized clinical trials to compare overdispersed count data between treatment groups, blinded sample size re-estimation (BSSR) is an effective approach to ensure power control even under possible misspecifications of the variance function on overdispersion specified at the design stage. Based on interim clinical trial data, the existing BSSR methods try to find a more appropriate value

Abstract Cancer biomarker discoveries typically involve utilizing patient specimens. In practice, there is often strong desire to preserve high quality biospecimens for studies that are most likely to yield useful information. Previously, we proposed a two-stage adaptive design for binary endpoints which terminates the biomarker study in a futility interim if the model performance is unsatisfactory

Abstract A confirmatory randomized controlled trial can be severely underpowered if the variability and/or effect size estimated from a small-scale pilot study is not taken into account in the sample size calculation. This paper reviews and summarizes four existing methods and proposes five new approaches, namely a tolerance probability (TP) based approach and four expected power (EP) based approaches

A Bayesian Posterior Probability Is the Real Replication Probability. Statistics in Biopharmaceutical Research. Accepted 16 September 2020.

ABSTRACT Because of structural complexity, a “biosimilar” will not be exactly the same as its reference biologic treatment, but is required to be equivalent in all relevant attributes, including efficacy. Therapeutic equivalence is often assessed at a single time point and trajectory up to that time point ignored. This paper describes a measure to assess therapeutic equivalence in rheumatoid arthritis

Clinical patient management is dynamic. It is not based on a single decision but on a sequence of decisions, with adjustments of therapy overtime, where adjustment are personalized to individual patients. However strategies allowing for such adjustments are infrequently studied. In the treatment of serious bacterial infections, there are two therapeutic decision points: empiric therapy when the patient

The two one-sided tests (TOST) procedure is widely applied for assessing bioequivalence between two different drugs in clinical studies. The same notion can be applied to construct TOST method for establishing dispersion equivalence. In addition to the TOST extension, a uniformly most power test (UMPT) for equivalence in variability has been described in the literature. This article presents analytic

Biomarker is the foundation of precision medicine. The identification of prognostic and predictive biomarkers is an important scientific component in advancing the drug discovery and development pipeline. Many machine learning methods have been developed to identify important prognostic biomarkers. However, most existing algorithms are not applicable for identifying predictive biomarkers because individual

Efficient Multiple Imputation for Sensitivity Analysis of Recurrent Events Data with Informative Censoring. Statistics in Biopharmaceutical Research. Accepted 26 August 2020.

In this paper, we illustrate the method of designing a group-sequential randomized clinical trial based on the difference in restricted mean survival time (RMST). The procedure is based on theoretical formulations of Murray and Tsiatis (1999). We also present a numerical example in designing a cardiology surgical trial. Various practical considerations are discussed. R codes are provided in the Supplementary

After an overview of FDA’s draft guidances to industry on adaptive designs and enrichment strategies for clinical trials, we describe recent advances in adaptive confirmatory trial designs and statistical methods for their analysis. We then focus on biomarker-guided personalized therapies in the era of precision medicine, and precision-guided drug development and master protocols, and conclude with

Patients’ heterogeneity poses a fundamental problem in the rapidly developing field of precision medicine. Based on a prespecified cutoff, biomarker-based designs provide a flexible approach to selecting a subset of biomarker-positive patients who are most likely to benefit from the new therapeutics. However, a natural question is how to determine the biomarker cutoff that distinguishes biomarker-positive

Joint models with random effects are proposed for the analysis of mixed overdispersed binomial and normal longitudinal data. A new parametric distribution, called the Log Lindley-Binomial distribution, is also introduced for analyzing overdispersed binomial data. The new distribution can be considered as an alternative to the classical Beta-Binomial distribution. Random effects are used to take into

The promising zone approach in adaptive trial design has gained popularity since its inception due to the use of the conventional test statistic at the final analysis stage rather than a weighted version that seemingly penalizes the second stage data when the adaptive trial strategy activates a sample size increase. However, this perceived advantage suffers loss of efficiency. This paper is to show

Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule — making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries

Large molecule pharmaceutical products frequently require refrigerated storage at 2-8 °C in order to maintain their efficacy and safety over the duration of their shelf life, but there are benefits if a product can be stored at room temperature, such as 25 °C, for the last few weeks of its shelf life, prior to use. With this type of “hybrid storage”, common methods for estimating shelf life may not

Abstract The Cape-Covid trial is an embedded clinical trial within the ongoing Cape-Cod trial. The Covid-19 pandemic appeared while we were conducting a randomized trial assessing the effectiveness of corticosteroids in severe community-acquired pneumonia. We took advantage of this ongoing trial to embed a sub-trial assessing hydrocortisone in SARS-CoV-2 infected patients. In this manuscript, we wish

Abstract Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the

The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if “herd immunity” will eventually reduce the risk or if a successful vaccine can be developed

Abstract The COVID-19 outbreak is impacting clinical trials in many ways, such as patient recruitment, data collection and data analysis. To proceed in this difficult time, the adoption of new technologies and new approaches for conducting clinical trials needs to be accelerated. Simultaneously, regulatory agencies such as the US FDA and EMA have issued guidance to help the pharmaceutical industry

Abstract–The COVID-19 pandemic has a global impact on the conduct of clinical trials of medical products. This article discusses implications of the COVID-19 pandemic on clinical research methodology aspects and provides points to consider to assess and mitigate the risk of seriously compromising the integrity and interpretability of clinical trials. The information in this article will support discussions

ABSTRACT In this paper, we provide guidance on how safety analyses and reporting of clinical trial safety data may need to be modified, given potential impact from the COVID-19 pandemic. Impact could include missed visits, alternative methods for assessments (such as virtual visits), alternative locations for assessments (such as local labs), and study drug interruptions. Starting from the safety analyses

Abstract–The COVID-19 pandemic has impacted ongoing clinical trials. We consider particular impacts on noninferiority clinical trials, which aim to show that an investigational treatment is not markedly worse than an existing active control with known benefit. Because interpretation of noninferiority trials requires cross-trial validation involving untestable assumptions, it is vital that they be run

(2020). Introduction to the 2020 Special Issue on Vaccines and Biologics. Statistics in Biopharmaceutical Research: Vol. 12, 2020 Special Issue on Vaccines and Biologics, pp. 253-253.

Abstract–In the pharmaceutical industry, all analytical methods must be shown to deliver unbiased and precise results. In an assay qualification or validation study, the trueness, accuracy, and intermediate precision are usually assessed by comparing the measured concentrations to their nominal levels. Trueness is assessed by using Confidence Intervals (CIs) of mean measured concentration, accuracy

Abstract–The aim of this work is to present an adaptive two-stage seamless design for a Phase II/III clinical trial in chronic obstructive pulmonary disease (COPD). This approach implies sample size re-estimation based on the primary outcome efficacy variable, namely the annual acute exacerbation rate. Patient recruitment in COPD trials can be slow; the proposed statistical approach may allow for a

Meta-analysis can be especially useful for evaluation of vaccine safety, since individual studies often have limited power to detect an increase in safety risk. To gain a perspective on the current state of utilization of the technique of meta-analysis for evaluation of vaccine safety and to assess the methodological characteristics of these meta-analyses, we conducted a systematic review of published

As a national public health surveillance resource, Vaccine Adverse Event Reporting System (VAERS) is a key component in ensuring the safety of vaccines. Numerous methods have been used to conduct safety studies with the VAERS database. These efforts focus on the downstream statistical analysis of the vaccine and adverse event associations. In this article, we primarily focus on processing the raw data