Testing for similarity of multivariate mixed outcomes using generalized joint regression models with application to efficacy-toxicity responses.

A common problem in clinical trials is to test whether the effect of an explanatory variable on a response of interest is similar between two groups, for example, patient or treatment groups. In this regard, similarity is defined as equivalence up to a pre-specified threshold that denotes an acceptable deviation between the two groups. This issue is typically tackled by assessing if the explanatory variable's effect on the response is similar.

Covariate adjustment in randomized controlled trials: General concepts and practical considerations.

There has been a growing interest in covariate adjustment in the analysis of randomized controlled trials in past years. For instance, the US Food and Drug Administration recently issued guidance that emphasizes the importance of distinguishing between conditional and marginal treatment effects. Although these effects may sometimes coincide in the context of linear models, this is not typically the case in other settings, and this distinction is often overlooked in clinical trial practice.

Distribution-free hyperrectangular tolerance regions for setting multivariate reference regions in laboratory medicine.

Reference regions are important in laboratory medicine to interpret the test results of patients, and usually given by tolerance regions. Tolerance regions of dimensions are highly desirable when the test results contains outcome measures. Nonparametric hyperrectangular tolerance regions are attractive in real problems due to their robustness with respect to the underlying distribution of the measurements and ease of intepretation, and methods to construct them have been recently provided by Young and Mathew [Stat Methods Med Res.

Decentralized Clinical Trials: Scientific Considerations Through the Lens of the Estimand Framework.

While industry and regulators' interest in decentralized clinical trials (DCTs) is long-standing, the Covid-19 pandemic accelerated and broadened the adoption and experience with these trials. The key idea in decentralization is bringing the clinical trial design, typically on-site, closer to the patient's experience (on-site or off-site). Thus, potential benefits of DCTs include reducing the burden of participation in trials, broadening access to a more diverse population, or using innovative endpoints collected off-site.

Confidence sets for a level set in linear regression.

Regression modeling is the workhorse of statistics and there is a vast literature on estimation of the regression function. It has been realized in recent years that in regression analysis the ultimate aim may be the estimation of a level set of the regression function, ie, the set of covariate values for which the regression function exceeds a predefined level, instead of the estimation of the regression function itself. The published work on estimation of the level set has thus far focused mainly on nonparametric regression, especially on point estimation.

Optimal test procedures for multiple hypotheses controlling the familywise expected loss.

We consider the problem of testing multiple null hypotheses, where a decision to reject or retain must be made for each one and embedding incorrect decisions into a real-life context may inflict different losses. We argue that traditional methods controlling the Type I error rate may be too restrictive in this situation and that the standard familywise error rate may not be appropriate. Using a decision-theoretic approach, we define suitable loss functions for a given decision rule, where incorrect decisions can be treated unequally by assigning different loss values.

Generating the Right Evidence at the Right Time: Principles of a New Class of Flexible Augmented Clinical Trial Designs.

To support informed decision making, clear descriptions of the beneficial and harmful effects of a treatment are needed by various stakeholders. The current paradigm is to generate evidence sequentially through different experiments. However, data generated later, perhaps through observational studies, can be difficult to compare with earlier randomized trial data, resulting in confusion in understanding and interpretation of treatment effects.

Missing data imputation using utility-based regression and sampling approaches.

Data are often missing not at random (MNAR) in scientific experiments. We treat the MNAR problem as an imbalanced learning task. Standard predictive error measures of regression (e.

Rationale for the update algorithm of the graphical approach to sequentially rejective multiple test procedures.

The graphical approach by Bretz et al. is a convenient tool to construct, visualize and perform multiple test procedures that are tailored to structured families of hypotheses while controlling the familywise error rate. A critical step is to update the transition weights following a pre-specified algorithm.

Combined statistical decision limits based on two GH-2000 scores for the detection of growth hormone misuse.

The growth hormone-2000 biomarker method, based on the measurements of insulin-like growth factor-I and the amino-terminal pro-peptide of type III collagen, has been developed as a powerful technique for the detection of growth hormone misuse by athletes. Insulin-like growth factor-I and amino-terminal pro-peptide of type III collagen are combined in gender-specific formulas to create the growth hormone-2000 score, which is used to determine whether growth hormone has been administered. To comply with World Anti-Doping Agency regulations, each analyte must be measured by two methods.

[What Is an Estimand?-The Most Important Clinical Question in Oncology Trials].

Patients can experience different disease journeys and clinical trials that investigate the benefit of oncology treatments need to account for this diversity. When defining the treatment effect of interest in a trial, researchers thus have to account for events occurring after treatment initiation, such as the start of a new therapy, before observing the variable of interest. We review the estimand framework recently introduced by the International Council for Harmonisation(ICH, 2019)to structure discussions on the relationship between patient journeys and the treatment effect of interest in oncology trials.

[Multiple Endpoints in Oncology Clinical Trials].

Like most complex(or multifactorial)diseases, cancer results not from a single factor, but rather from the interaction of multiple genes and environmental factors. Thus patients can experience different signs and symptoms that reflect more than one consequence of suffering the disease. When evaluating the effects of new treatments in cancer clinical trials, the multidimensional assessment using multiple outcomes to measure improvements in the patients' signs and symptoms associated with treatments would be preferred.

Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling.

In clinical practice, the composition of missing data may be complex, for example, a mixture of missing at random (MAR) and missing not at random (MNAR) assumptions. Many methods under the assumption of MAR are available. Under the assumption of MNAR, likelihood-based methods require specification of the joint distribution of the data, and the missingness mechanism has been introduced as sensitivity analysis.

Estimands and Complex Innovative Designs.

Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs.

Connecting Instrumental Variable methods for causal inference to the Estimand Framework.

Causal inference methods are gaining increasing prominence in pharmaceutical drug development in light of the recently published addendum on estimands and sensitivity analysis in clinical trials to the E9 guideline of the International Council for Harmonisation. The E9 addendum emphasises the need to account for post-randomization or 'intercurrent' events that can potentially influence the interpretation of a treatment effect estimate at a trial's conclusion. Instrumental Variables (IV) methods have been used extensively in economics, epidemiology, and academic clinical studies for 'causal inference,' but less so in the pharmaceutical industry setting until now.

Machine learning for clinical trials in the era of COVID-19.

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).

Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue?

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 challenges faced by the pandemic and sketch out possible solutions including adaptive designs.

Simultaneous confidence tubes for comparing several multivariate linear regression models.

Much of the research on multiple comparison and simultaneous inference in the past 60 years or so has been for the comparisons of several population means. Spurrier seems to have been the first to investigate multiple comparisons of several simple linear regression lines using simultaneous confidence bands. In this paper, we extend the work of Liu et al.