The identification and estimation of heterogeneous treatment effects in biomedical clinical trials are challenging, because trials are typically planned to assess the treatment effect in the overall trial population. Nevertheless, the identification of how the treatment effect may vary across subgroups is of major importance for drug development. In this work, we review some existing simulation work and perform a simulation study to evaluate recent methods for identifying and estimating the heterogeneous treatments effects using various metrics and scenarios relevant for drug development. Our focus is not only on a comparison of the methods in general, but on how well these methods perform in simulation scenarios that reflect real clinical trials. We provide the R package benchtm that can be used to simulate synthetic biomarker distributions based on real clinical trial data and to create interpretable scenarios to benchmark methods for identification and estimation of treatment effect heterogeneity.

The assessment of consumer behavior in brand-choice models may be greatly influenced by accurately modeling and evaluating a brand-loyalty parameter. The most well-known approaches for estimating brand loyalty employ a household’s past purchase data and account for habit formation with a single smoothing parameter that indicates the weight assigned to households’ current decisions versus their distant shopping history. In this study, we present a method for estimating time-varying smoothing parameters for heterogeneous households. We estimate smoothing parameters for American households from 2014 to 2017 using Nielsen Homescan data for the US beer retail market. Using this more flexible method, we discover that the smoothing parameter varies significantly among households and time frames (for the same households). We next include a brand-loyalty index based on this approach into a brand-choice model of the American beer retail market, demonstrating that this new method improves estimation results.

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Data Monitoring Committees (DMCs) have the important task to protect the safety of current and future patients during the conduct of a clinical study. Unfortunately, their work is often made difficult by voluminous DMC reports that are poorly structured and difficult to digest. In this article, we suggest improved solutions. Starting from a principled approach and building upon previous proposals, we offer concrete and easily understood displays, including related computer code. While leveraging modern tools, the most important is that these displays support the DMC’s workflow in answering the relevant questions of interest. We hope that the adoption of these proposals can ease the task of DMCs, and importantly, lead to better decision-making for the benefit of patients.

This paper presents a new methodology, called AFSSEN, to simultaneously select significant predictors and produce smooth estimates in a high-dimensional functionon-scalar linear model with a sub-Gaussian errors. Outcomes are assumed to lie in a general real separable Hilbert space, H, while parameters lie in a subspace known as a Cameron Martin space, K, which are closely related to Reproducing Kernel Hilbert Spaces, so that parameter estimates inherit particular properties, such as smoothness or periodicity, without enforcing such properties on the data. We propose a regularization method in the style of an adaptive Elastic Net penalty that involves mixing two types of functional norms, providing a fine tune control of both the smoothing and variable selection in the estimated model. Asymptotic theory is provided in the form of a functional oracle property, and the paper concludes with a simulation study demonstrating the advantage of using AFSSEN over existing methods in terms of prediction error and variable selection.