Simple Statistics for Clinical Trials Part I: Understanding Data Types

CPT Labs

CPT Labs

This is the first of a four-part series on simple statistics for clinical trials. Without delving in to too much technical detail, this series of posts is intended to offer some background in how statistical planning can influence study effectiveness and value.

Types of data

Let’s begin with the most basic underpinning for any discussion of statistics and clinical study planning. When planning a clinical study, it’s important to know what kinds of data will be helpful, as well as how it will be collected and used. There are many types of data  and some data fits into more than one data category. However, most data can be identified by type in a simple organization like this:

Quantitative data is data that can be measured or counted and then expressed numerically. It answers questions like how many, how much, how often or how big. Quantitative data can be used in mathematical calculations. There are two basic types of quantitative data.

  • Continuous data is quantitative data that has an infinite number of possible values within a range, such as measurements like temperature, speed or height.
  • Discrete data is quantitative data that can be counted individually, such as the number of cars in a garage or the number of pitches thrown in a baseball game.

Qualitative data is data that is expressed in words and sometimes in numbers that can’t be used in mathematical calculations. Qualitative data is also known as categorical data because it can be organized into categories instead of being measured numerically. There are two basic types of qualitative, or categorical, data.

  • Nominal data is qualitative data that describes characteristics by name, such as hair color, marital status or ethnicity.
  • Ordinal data is qualitative data that describes an element’s position in a given sequence, such as grade in school, satisfaction on a scale of 1 to 5 or ranking in a tournament.

Again, qualitative data is sometimes expressed in numbers, but these numbers aren’t useful in mathematical calculations. (For example, three second-graders don’t equal a sixth-grader!) However, qualitative data can be very useful in statistical analysis. We’ll cover some methods for using both quantitative and qualitative data to interpret clinical trial results in Part IV of this series.

Special concerns for qualitative data

Qualitative data isn’t always subject to objective evaluation and often is derived from opinion. For the purposes of clinical trials, qualitative data may be collected in study intake documents or in subject surveys, diaries and questionnaires.

One technique used for questionnaire development is the Likert Scale, which asks subjects to respond to statements by selecting answers from a scale including values like “Strongly Agree,” “Agree,” and so on. We cannot control subjects’ candor on these scales. There also is a risk of subjects falling under the influence of a logical fallacy known as the central tendency, which causes people to tend toward avoiding strong positive or negative expressions.

However, there are study planning and data analysis techniques to help control for these would-be weaknesses and strengthen the results of studies that must rely on qualitative data. These will be addressed in Part IV as well.

Stay tuned for more on statistics

Now that you’ve read about data types, we invite you to continue exploring this series for more information to help you develop stronger, more credible clinical studies.

Careful attention to the principles of statistics supports every successful clinical trial and should always be a key component of early planning. When considering your next study, be sure to work with an established, trustworthy professional clinical testing team that actively involves a qualified clinical statistician in the planning process.

You’ll be rewarded with a more efficient study that provides more compelling data than if statistical analysis had been merely a final step in the study process.