This is the first of a four-part series on simple statistics for clinical trials. Without 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, then expressed numerically. It answers questions like how many, how much, how often, or how big, and it 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, for a few examples.
- 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 can be thought of as data that is non-numeric and descriptive. For example, information that is obtained from things like interviews, journals, audio recordings, and text. Qualitative data can sometimes be expressed by way of numerical categories. Categorical data can be thought of as the classification of subjects or objects by a particular characteristic which has a finite number of possibilities (categories). There are two basic types of categorical data.
- Nominal data is data that describes mutually exclusive, non-numeric variables though categorization into groups such as hair color, marital status, or ethnicity. A key feature is there is no ranking or order to the data, and there are only a certain number of categories a subject can fall under.
- Ordinal data is data that describes an element’s position in a given sequence, such as grade in school (A, B, C, or D), satisfaction on a scale of 1 to 5, or ranking in a tournament. A key difference between nominal and ordinal data is that there is a ranking in ordinal data, while nominal data are categorized in name only; there is no hierarchy or ranking.
Again, qualitative data can be obtained and then expressed in numbers, which can then be utilized in some mathematical calculations, although nuance can be lost, and there is some subjectivity. Although indirectly, 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, although reverse scoring, changing the questionnaire order, and cross-checking responses can help determine subject consistency. There also is a risk of subjects falling under the influence of something known as the neutral response bias, 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.
Most of us will never become experts in statistics. Still, 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.
