Clinical biostatistics is the backbone of interpreting drug studies and delivering evidence-based care in pharmacy practice. Pharmacists and healthcare providers are constantly exposed to clinical trial data—from journal articles and guideline updates to drug monographs and formulary reviews. Yet for many, the statistical components remain a source of confusion or hesitation. Without a firm grasp of key values and statistical methods, even experienced clinicians may question whether a finding is truly meaningful or clinically relevant. This uncertainty can weaken our therapeutic recommendations, hinder collaboration with providers, and impact the quality of patient care.

This intermediate-level course is designed to close that gap. By focusing on the most commonly encountered statistical values in clinical drug literature, it offers a practical, pharmacy-centered approach to interpreting clinical data with clarity and confidence. 

Participants will revisit core biostatistical principles—not as abstract academic concepts, but as essential tools for evaluating treatment outcomes, drug efficacy, and safety signals in the real world.

The course begins with a discussion of p-values, one of the most frequently cited and most frequently misunderstood metrics in clinical research. We’ll unpack what a p-value means, how it relates to statistical significance, and why a "significant" result doesn’t necessarily equate to clinical value. From there, we’ll explore confidence intervals (CIs), which provide more than just a range; they offer a snapshot of precision and help clinicians assess whether the observed effect is likely to be meaningful in practice.

Building on those fundamentals, the course will dive into risk-based measures such as relative risk (RR), absolute risk reduction (ARR), and number needed to treat (NNT). These values are critical for translating statistical results into patient-centered decision-making. Understanding how a treatment reduces risk - and how that benefit scales across populations is vital when discussing new therapies, treatment alternatives, or comparative effectiveness with both providers and patients.

We’ll also introduce odds ratios (OR) and hazard ratios (HR): two measures commonly found in drug studies involving chronic disease, hospital readmission, or long-term treatment outcomes. These ratios can seem complex at first glance, but once understood, they offer powerful insights into treatment effects over time and between study arms. The course will walk through real-world examples that show how to interpret these values correctly and avoid common pitfalls.

We’ll also briefly touch on study power and sample size calculations, two often-overlooked elements that influence the reliability of study conclusions. Participants will learn how to identify whether a study is sufficiently powered and why underpowered studies can fail to detect real clinical differences, even when they exist.

Throughout the session, clinical case examples will be used to connect the statistical principles back to everyday pharmacy practice—whether you’re reviewing a clinical trial for a new formulary agent, evaluating comparative effectiveness of treatment options, or responding to a prescriber’s inquiry with data-backed confidence.

Ultimately, this course aims to reduce the fear, uncertainty, and doubt that can come with interpreting biostatistics. By the end of the session, participants will walk away with a clearer understanding of the most clinically relevant statistical tools and the ability to apply them meaningfully in their own practice settings.

Areas Covered    

Who Should Attend

Healthcare professionals, including pharmacists, clinical researchers, physicians, nurse practitioners, and public health professionals.

Why Should You Attend

In pharmacy practice, evidence-based decision-making is the standard, but interpreting the evidence isn't always straightforward. For many pharmacists and healthcare providers, clinical biostatistics can feel like a barrier instead of a bridge to better care. Numbers, graphs, and statistical jargon often trigger a sense of fear, uncertainty, or doubt (FUD), especially when our education in biostatistics feels distant or abstract from real-world application.

Clinical data drives nearly every aspect of modern pharmacy—from formulary decisions and guideline updates to patient counseling and collaborative practice. If we don’t feel confident interpreting the statistics behind the science, we may hesitate to challenge questionable claims, support therapeutic decisions, or explain outcomes to patients and prescribers. This uncertainty can compromise our role as medication experts and limit our impact on clinical care.

The truth is, most of us weren’t taught to read a forest plot or dissect a Kaplan-Meier curve in the context of a busy practice. But as new therapies emerge and clinical trials become more complex, the ability to cut through statistical noise is no longer optional—it's essential.

This CE webinar, “Decoding Drug Data: Understanding Clinical Biostatistics in Pharmacy Practice,” is designed to eliminate the fear and replace it with clarity. We’ll revisit core concepts like p-values, confidence intervals, relative risk, and statistical power—but through a practical, pharmacy-focused lens. You’ll learn how to recognize meaningful results, identify red flags in study design, and apply data to real patient scenarios with confidence.

By transforming confusion into competence, this session empowers pharmacists and healthcare providers to make informed, statistically sound clinical decisions—without the fear, uncertainty, or doubt.

Topic Background

In modern pharmacy practice, the ability to interpret clinical data is essential for making informed, evidence-based decisions. Clinical biostatistics provides the foundation for understanding how drug studies are designed, analyzed, and evaluated. From assessing p-values and confidence intervals to understanding risk ratios and number needed to treat, pharmacists must be equipped to critically appraise statistical outcomes that influence therapeutic recommendations. As medication experts, pharmacists are increasingly called upon to interpret clinical trial data, synthesize outcomes, and translate findings into real-world practice. Whether participating in formulary decisions, answering provider questions, or counseling patients, an understanding of biostatistics enhances our role in patient care, safety, and outcomes research.