ABSTRACT: The development of precision medicine requires precise analytical approaches, including the consideration of biological sex. In 2016, the National Institutes of Health (NIH) implemented a policy requiring the consideration of sex as a biological variable (SABV) in all funded studies. Despite the policy being in place for years, sex-based data are often not being analyzed using rigorous approaches. Our preliminary analysis of SABV-compliant studies showed that when authors report sex-specific effects, they tested statistically for such effects only 29% of the time. Instead, sex-specific findings typically rest on assertion alone. Our past findings indicate clearly that the implementation of SABV by researchers has not been as rigorous as it could be and that the process of peer review of SABV-compliant research needs to improve. Findings based on improper analytical approaches can lead to the wasting of resources, erroneous decisions about treatments, and missed opportunities to provide effective health care. The intended goals of SABV, namely to enhance reproducibility and to facilitate sex-based precision medicine, are not being met. In this project we will determine the impact of inappropriate analytical approaches on the accurate reporting of sex-specific effects, identify factors that inform decisions about analytical approaches to sex-based data, and disseminate tools for designing and evaluating studies that consider SABV. We will use a mixed method approach that includes analyses of published journal articles, interviews with scientists, and development of best practices. In Aim 1, we will conduct a large-scale analysis of the biomedical literature to show how inappropriate statistical approaches to sex-based data are impacting the rigorous implementation of SABV. In Aim 2, we will conduct semi-structured interviews with authors of some of those publications to identify the factors that facilitate best practices. Together, the first two aims will inform the development of a workflow for appropriate analysis of sex-based data. In Aim 3, we will make our optimized workflow publicly available and disseminate it widely. A large part of this effort will involve journal editors and other likely reviewers to apply the tools directly in the context of publishing, thus increasing rigor in the reporting of sex distinctions and, in turn, the effectiveness of SABV policies.