Top 10 Reasons Why You Should Understand Biostatistics
Biostatistics is the branch of statistics in charge of interpreting the scientific data that is generated in the health sciences, including the public health sphere. The purpose of biostatistics is to detangle data into valid inferences to solve problems in public health. Why is it important for medical, clinical trial and clinical research professionals, and researchers to be literate in biostatistics? Here are the top reasons:
To Understand and Assess Medical Literature
To Reap the Professional Benefits
To Effectively Collaborate with Statisticians
To Discover the Patterns Obscured by the Variability of Responses in Living Systems
To Establish that a Test Therapeutic Product is Safe and Effective
To Uphold the Integrity of Clinical Trial
It Is a Decision-Making Tool
To Freely Communicate Statistical and Epidemiological Information to Patients and Colleagues
To Avoid Mis-Interpretation of Statistical Methods
To Properly Use the Statistical Tools
Measures of statistics such as variability and central tendency are often used in the medical literature. Errors do occur in data that is published in medical research, sometimes even in the well-respected textbooks. Possessing the necessary knowledge will help you critically evaluate and apply original research data.
Understanding the most frequently used and the crucial descriptive and inferential biostatistical methods will help appreciate how the application of the theories of measurement, statistical inference, and decision trees contribute to improved clinical decisions and eventually to improved patient care and outcomes.
Having an adequate understanding of the vocabulary and fundamental concepts of biostatics will help in a fruitful collaboration with biostatisticians. A strong statistical practice is important in many medical research projects. Also, medical thinking is crucial to the formulation and application of statistical strategies.
Statistics provides the tools to make an appropriate choice by judging the "significance" of the observed differences or changes.
In a pivotal clinical trial, to demonstrate that a therapeutic product is safe and effective, a sample of the population is treated. Then a statistical inference is used to determine the safety and efficacy of the product
Understanding the key role in the drug development process right from trial design to protocol development will help in protecting the integrity of the clinical trial.
Statistics is a useful decision-making tool in the clinical research arena. When working in a field where a p-value can determine the next steps on the development of a drug or procedure, decision-makers must understand the theory and application of statistics.
Given that many are vulnerable to the suggestions of published literature and other questionable sources, it is imperative to keep abreast of medical knowledge and communicate with confidence.
A Harvard report on clinical research suggests that researchers often misinterpret statistical methods due to poor knowledge of statistical concepts. A clinical research professional will do well to understand statistical concepts such as confidence Intervals, Multiplicity, Subgroup Analysis, Parametric vs. Non-parametric statistical methods, Sample Size Calculation, Types of endpoints, Statistical Reporting, Missing Data, Adaptive Trial Design, and Bayesian Model.
Much statistical software is now available to professionals. However, these software were developed for statisticians and can often be daunting to non-statisticians. How do you know if you are pressing the right key, let alone performing the best test?
Attend the course 'Biostatistics for the Non-statistician' for a thorough review of the basics for those who need a refresher on statistical theory and types of statistical analyses. It gives you the information and skills necessary to understand statistical concepts and findings as it applies to clinical research, and to confidently convey the information to others. This seminar provides information that can be used immediately by personnel involved in analysis of clinical trial data. Emphasis will be placed on the actual statistical (a) concepts, (b) application, and (c) interpretation, and not on mathematical formulas or actual data analysis. A basic understanding of statistics is desired, but not necessary.
