A maturity model for the scientific review of clinical trial designs and their informativeness.

Background: Informativeness, in the context of clinical trials, defines whether a study's results definitively answer its research questions with meaningful next steps. Many clinical trials end uninformatively. Clinical trial protocols are required to go through reviews in regulatory and ethical domains: areas that focus on specifics outside of trial design, biostatistics, and research methods.

Participatory development of a target policy profile to support soil-transmitted helminth elimination.

Introduction: Soil-transmitted helminths (STH) are parasitic worms that infect nearly a quarter of the world's population, particularly those living in communities without access to adequate water, sanitation, and housing. Emerging evidence suggests that it may be possible to interrupt transmission of STH by deworming individuals of all ages via community-wide MDA (cMDA), as opposed to only treating children and other focal populations. Transitioning from a policy of STH control to STH elimination in targeted areas would require a fundamental shift in STH policy and programming.

A template for the authoring of statistical analysis plans.

A number of principal investigators may have limited access to biostatisticians, a lack of biostatistical training, or no requirement to complete a timely statistical analysis plan (SAP). SAPs completed early will identify design or implementation weak points, improve protocols, remove the temptation for p-hacking, and enable proper peer review by stakeholders considering funding the trial. An SAP completed at the same time as the study protocol might be the only comprehensive method for at once optimizing sample size, identifying bias, and applying rigor to study design.

Clinical trials in the pandemic age: What is fit for purpose?

It is critical to ensure that COVID-19 studies provide clear and timely answers to the scientific questions that will guide us to scalable solutions for all global regions. Significant challenges in operationalizing trials include public policies for managing the pandemic, public health and clinical capacity, travel and migration, and availability of tests and infrastructure. These factors lead to spikes and troughs in patient count by location, disrupting the ability to predict when or if a trial will reach recruitment goals.

Big Data's Role in Precision Public Health.

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems.