74 PART 2 Examining Tools and Processes

Collecting and validating data

Data in a clinical trial are typically collected digitally and manually. Examples of

data collected digitally include a participant filling out an online survey or a blood

pressure monitor collecting data from a participant. Data are collected manually

when they are written down on paper first, then undergo data entry to become

digital. Either way, some paper forms may be included, and many digital forms

are created for data entry in a clinical trial. These forms are for data entry of data

collected from various parts of the study, but in clinical trial lingo, they are all

referred to as case report forms, or CRFs.»

» ^{In the case of digitally collected data, the central analytic team will run}

routines for validating the data. They will communicate with study staff if they

find errors and work them out.»

» ^{In the case of manually collected data, data entry into a digital format will be}

required. Study staff typically are expected to log into an online database with

CRFs and do data entry from data collected on paper.»

» ^{The sponsor of the study will provide detailed data entry instructions and}

training to ensure high-quality data collection and validation of the data

collected in the study.

Analyzing Your Data

In the following sections, we describe some general situations that come up in all

clinical research, regardless of what kind of analysis you use.

Dealing with missing data

Most clinical trials have incomplete data for one or more variables, which can be

a real headache when analyzing your data. The statistical aspects of missing data

are quite complicated, so you should consult a statistician if you have more than

just occasional, isolated missing values. Here we describe some commonly used

approaches for coping with missing data:»

» ^{Exclusion: Exclude a case from an analysis if any of the required variables for}

that analysis is missing. This seems simple, but the downside to this approach

is it can reduce the number of analyzable cases, sometimes quite severely.

And if the result is missing for a reason that’s related to treatment efficacy,

excluding the case can bias your results.