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Tobias Mielke

Publications and source records attributed to Tobias Mielke.

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Statistical Methodology Groups in the Pharmaceutical Industry

Research and Development is the largest budget position in the pharmaceutical industry, with clinical trials being a critical, yet costly and time-consuming component to inform decisions. Beyond drug efficacy, the probability of success and efficiency of research and development are highly dependent on the approaches used for designing, analyzing, and interpreting clinical trials. Deep understanding of statistical methodology and quantitative approaches is therefore essential. Consequently, dedicated methodology groups have emerged in mid-size and large pharmaceutical companies and CROs. Their remit is to lead the conception and implementation of innovative quantitative methodologies in order to improve drug development, often by addressing complexities or offering more efficient designs. To achieve this, they collaborate internally and externally (e.g., with academics, regulators) to identify common challenges and tear down silos in order to invest in methods with the highest impact on efficiency and value to the portfolio. Given the immense financial stakes of drug development -- where delays carry massive implications -- these groups represent a critical strategic investment. However, to realize this business impact, statistical innovations must be rigorously validated and seamlessly integrated. This manuscript explores the setup, remit, and value of dedicated methodology groups, alongside the critical organizational considerations and success factors required to maximize their impact on the speed, efficiency, and probability of success.

stat.OT

Clinical trials with interim analyses: Standardizing Terminology to increase clarity

Interim analyses for group-sequential decision making are prevalent in clinical trials. Methodology is well established and has been routinely implemented over the last decades. Still, confusions and uncertainties on aspects of how to operationalize and interpret interim analyses exist for many stakeholders. In this paper, a team of statisticians from the pharmaceutical industry, academia, and regulatory agencies provide a multi-stakeholder perspective on the key concepts behind interim analyses, with the aim to introduce standard terminology to mitigate misunderstandings and facilitate clearer discussions.

stat.AP

Optimal allocation strategies in platform trials

Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change over time. Determining optimal allocation rates to allocate patients to the treatment and control arms in platform trials is challenging because the change in treatment arms implies that also the optimal allocation rates will change when treatments enter or leave the platform. In addition, the optimal allocation depends on the analysis strategy used. In this paper, we derive optimal treatment allocation rates for platform trials with shared controls, assuming that a stratified estimation and testing procedure based on a regression model, is used to adjust for time trends. We consider both, analysis using concurrent controls only as well as analysis methods based on also non-concurrent controls and assume that the total sample size is fixed. The objective function to be minimized is the maximum of the variances of the effect estimators. We show that the optimal solution depends on the entry time of the arms in the trial and, in general, does not correspond to the square root of $k$ allocation rule used in the classical multi-arm trials. We illustrate the optimal allocation and evaluate the power and type 1 error rate compared to trials using one-to-one and square root of $k$ allocations by means of a case study.

stat.ME