{'fraction_non_alphanumeric': 0.023387668320340185, 'fraction_numerical': 0.014883061658398299, 'mean_word_length': 5.595794392523365, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Characterizing Expected Benefits of Biomarkers in Treatment Selection

This working paper is hosted by The Berkeley Electronic Press (bepress) and may not be commercially reproduced without the permission of the copyright holder. 1 Introduction In many clinical settings for disease prevention and control, there is significant heterogeneity in patient response to an intervention. Biomarkers associated with this heterogeneity, such as demographic or genetic characteristics, can be used to help patients and clinicians select interventions such that a therapy is only delivered to a patient who is likely to benefit from it. Examples of such treatment selection markers include: the K-RAS gene mutations for selecting antiepidermal growth factor receptor therapy for colorectal cancer (Karapetis and others, 2008; Allegra and others, 2009); the FGFR2 and MRP30 genes for selecting hormone therapy in breast cancer prevention of postmenopausal women (Prentice and others, 2009; Huang and others, 2011); and the Oncotype-Dx, a 21-gene score for selecting adjuvant chemotherapy for the treatment of estrogen-receptor positive breast cancer (Paik and others, Statistical measures of the value of a marker-based treatment selection rule are essential in the development of treatment selection markers. The test of a marker by treatment interaction is a common strategy for identifying treatment selection markers, however, recently there has been a growing emphasis on developing measures of treatment selection ability which are directly linked to clinical outcomes (Janes and others, 2011). Much of this work focuses on the effect of treatment on the targeted disease outcome of interest. For example, the population benefit of using a marker-based treatment-selection strategy, defined as the reduction in population disease rate through treatment-selection (Huang and others, 2012); the difference in average treatment effect between marker-positive group and the overall population (Foster and others, 2011); the distribution of the disease risk difference conditional on a marker or marker model between comparative interventions treatment difference in a subgroup of subjects whose estimated treatment difference were greater than some threshold (Zhao and others, 2013) all quantify, in some fashion, the impact of the marker on the rate of disease. In addition to its effect on the targeted disease of interest, an intervention may impact the population through other costs such as side effect burden or monetary cost. Thus, another important consideration in assessing a treatment selection rule is the proportion of subjects selected for treatment (Janes and others, 2013). If two treatment selection strategies are equally effective in controlling population disease rate, then the strategy that recommends fewer …