Browsing by Subject "treatment selection"

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    A Clinician's Perspective on Biomarkers.
    (Focus (American Psychiatric Publishing), 2018-04-27) Rush, A John; Ibrahim, Hicham M
    Psychiatrists and mental health professionals regularly perform various clinical tasks (e.g., detection, differential diagnosis, prognostication, treatment selection and implementation). How well they perform each of these tasks has a direct impact on patient outcomes. Measurement-based care has brought greater precision to these tasks and has improved outcomes. This article provides an overview of the types of biomeasures and biomarkers, the clinical uses of biomarkers, and the challenges in their development and clinical use. Although still in their infancy, biomarkers hold the promise of bringing even greater precision and even better outcomes in mental health. Biomeasures that could become biomarkers include genetic, proteomic, metabolomic, and immunologic measures, as well as physiological, functional, and brain structural measures. Mechanistic markers reflect and are based on the specific pathobiological processes that are involved in the development of a clinically defined condition. Some clinically relevant biomarkers may rely on this mechanistic understanding while others may not. Clinical biomarkers serve three broadly defined goals. Diagnostic markers define what is wrong. Prognostic markers define what will happen in the natural course of the condition, although they may also predict the course of illness during treatment. Theranostic markers address issues pertinent to treatment by defining whether, when, whom, and how to treat. Other biomarkers may be used to monitor the overall effect of treatment regardless of the therapeutic effects or to monitor the specific therapeutic effects of the intervention on the disorder itself. Biomarkers can also be used to estimate susceptibility/risk of developing the condition or the biological consequences of having had the disorder.
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    On enrichment strategies for biomarker stratified clinical trials
    (Journal of Biopharmaceutical Statistics, 2017-09-07) Wang, X; Zhou, J; Wang, T; George, SL
    In the era of precision medicine, drugs are increasingly developed to target subgroups of patients with certain biomarkers. In large all-comer trials using a biomarker strati ed design (BSD), the cost of treating and following patients for clinical outcomes may be prohibitive. With a fixed number of randomized patients, the efficiency of testing certain treatments parameters, including the treatment effect among biomarker positive patients and the interaction between treatment and biomarker, can be improved by increasing the proportion of biomarker positives on study, especially when the prevalence rate of biomarker positives is low in the underlying patient population. When the cost of assessing the true biomarker is prohibitive, one can further improve the study efficiency by oversampling biomarker positives with a cheaper auxiliary variable or a surrogate biomarker that correlates with the true biomarker. To improve efficiency and reduce cost, we can adopt an enrichment strategy for both scenarios by concentrating on testing and treating patient subgroups that contain more information about specifi c treatment parameters of primary interest to the investigators. In the first scenario, an enriched biomarker strati ed design (EBSD) enriches the cohort of randomized patients by directly oversampling the relevant patients with the true biomarker, while in the second scenario, an auxiliary-variable-enriched biomarker strati ed design (AEBSD) enriches the randomized cohort based on an inexpensive auxiliary variable, thereby avoiding testing the true biomarker on all screened patients and reducing treatment waiting time. For both designs, we discuss how to choose the optimal enrichment proportion when testing a single hypothesis or two hypotheses simultaneously. At a requisite power, we compare the two new designs with the BSD design in term of the number of randomized patients and the cost of trial under scenarios mimicking real biomarker strati ed trials. The new designs are illustrated with hypothetical examples for designing biomarker-driven cancer trials.