IPPCR 2018: Measurement Part 3: Sensitivity to Change

>> David Luckenbaugh: Hello, my name is David Luckenbaugh. I'm a statistician at the National Institutes of Health in the Office of Equity, Diversity, and Inclusion in the Office of the Director. I want to thank you for listening today and we're going to talk about sensitivity to change. So, our main objective is looking at sensitivity

to change within clinical research. Some of you may know sensitivity to change as responsiveness. These are the same concepts that you may know from one -- just depends on the field, whether people talk about it as sensitivity to change or responsiveness. So, I'm going to go back. I'm going to talk about --

a little bit about depression, tremor, and heart disease. In talking about sensitivity to change, one of the things is we have many ways of measuring the same constructs. The problem is that we have to decide which one is our favorite one. In heart disease, you might measure cholesterol, or you might measure C-reactive protein,

but the context of the study might determine what, in fact, you want to do. If your interest is looking at brain functioning, probably looking at cholesterol or C-reactive protein is not going to be the best measurement that you have.

So, sensitivity to change is the ability to detect -- the ability to detect improvement or worsening. So, one way of measuring this is by looking at effect size. Let's say, for example, that we have two groups that we want to measure. One group is going to be on drug A, one group is going to be on drug B, which is a placebo.

What we can do is we can look at the mean of those two groups, divide it by the pooled standard deviation, and we come up with Cohen's d. This measurement is also known as the standardized mean difference. The standard interpretation for this is that an effect size or a Cohen's d of point two is considered a small effect.