CHAPTER 5 Conducting Clinical Research 75

»

» ^{Imputation: Imputation is where you replace a missing value with a value}

you impute, or create yourself. When analysts impute in a clinical trial, they

typically take the mean or median of all the available values for that variable

and fill that in for the missing variable. In reality, you have to keep the

original variable, and then save a separate, imputed variable so that you

can document the type of imputation applied. There are a lot of downsides

to imputation. If you are imputing a small number of values, it’s not worth it

because it adds bias. You may as well just exclude those cases. But if you

impute a large number of values, you are basically making up the data

yourself, adding more bias.»

» ^{Last Observation Carried Forward (LOCF): LOCF is a special case of}

imputation. Sometimes during follow-up, one of a series of sequential

measurements on a particular participant is missing. For example, imagine

that there were supposed to be four weekly glucose values measured, and

you were missing a measurement only on week three. In that case, you could

use the most recent previous value in the series, which is the week two

measurement, to impute the week three measurement. This technique is

called Last Observation Carried Forward (LOCF) and is one of the most widely

used strategies. Although imputation adds bias, LOCF adds bias in the

conservative direction, making it more difficult to demonstrate efficacy. This

approach is popular with regulators, who want to put the burden of proof on

the drug and study sponsor.

Handling multiplicity

Every time you perform a statistical significance test, you run a chance of being

fooled by random fluctuations into thinking that some real effect is present in

your data when, in fact, none exists (review Chapter 3 for a refresher on statistical

testing). If you declare the results of the test are statistically significant, and in

reality they are not, you are committing Type I error. When you say that you require

p < 0.05 to declare statistical significance, you’re testing at the 0.05 (or 5 percent)

alpha (α) level. This is another way of saying that you want to limit your Type I

error rate to 5 percent. But that 5 percent error rate applies to each and every sta-

tistical test you run. The more analyses you perform on a data set, the more your

overall α level increases. If you perform two tests at α = 0.05, your chance of at

least one of them coming out falsely significant is about 10 percent. If you run 40

tests, the overall α level jumps to 87 percent! This is referred to as the problem of

multiplicity, or as Type I error inflation.

Chapter 11 covers dealing with multiplicity when making multiple comparisons.

One approach discussed in Chapter 11 is performing post-hoc tests following an

ANOVA for comparing several groups. Post-hoc tests incorporate a built-in

adjustment to keep the overall α at only 5 percent across all comparisons. This can