Preparing the Petition | UW Human Subjects
Review | Sample Petition
Data Management | Data
Analysis | The Final Report
Preparing an III Petition
for an Empirical Study
The III petition should
be brief, generally 1-2 typed pages, but should provide enough information
to give the committee a good idea of what you plan to do. The intent
of the committee is to spare you the grief and heartache that come from
discovering at the end of your third year that your III project is
too ambitious, is giving you uninterpretable data, or is in some other
A good petition (and a good study) begins with a simple,
clear purpose. This purpose should be reflected in each of the components
of the study described below. The purpose will dictate which subjects
to choose, what study design to use, what variables to measure, and what
analyses to perform.
The III committee is diverse in terms of medical and technical
background. Therefore, write your petition for a general audience.
Avoid using jargon, and define any technical terms you can't avoid.
If the information can be better presented in non-narrative form (graph,
bulleted list, flow diagram, etc.), by all means do so.
Below are some guidelines for what to include. Because each
study is different, not all of the items below will be pertinent to every
Background Provide a brief introduction to the problem
you are investigating. This might include:
- Why is the problem important?
- What is already known about the problem and what remains unknown?
- What distinguishes your study from others tackling the same problem?
A hypothesis is a testable
assertion about the relationship between variables in your study.
The study hypothesis is different from the null hypothesis which is
a statistical construct only.
- What are the inclusion and exclusion criteria for subject selection
(experimental and control subjects)?
- How will subjects be identified/recruited?
- How many subjects will be used?
Study design. This doesn't have to be a lexical nightmare (i.e., a retrospective
placebo-controlled double-blind cohort study). Just be sure to indicate
what the basic structure of the study is. Again, this should flow directly
from your purpose.
- Are 2 (or more groups) of subjects being compared?
- Are you looking to see whether 2 (or more) variables in a single
group of subjects are correlated?
- Are you comparing pre-intervention to post-intervention
- Are you trying to predict an outcome variable from 1 (or
more) other variables?
Generally, variables fall into one of 3 categories:
- Independent variables. These are the variables that are putative
causal factors. In many cases these play out in the study as the basis
for subject selection. For example, in a study looking at the role
of body weight on survival after MI, the study could be conducted
by comparing a group of obese patients to a group of normal weight
- Dependent variables. These are the outcomes that are thought
to be caused or influenced by the independent variable. In a study
of the effect of a dietary regimen in diabetes, body weight could
be a dependent variable.
- Control (confounding) variables. These are variables which,
if not controlled, could exert influence on the dependent variable
and so cloak or accentuate the apparent role of the independent variable.
For example, imagine a study investigating the effect of hormone replacement
therapy on breast cancer risk. Factors such as age at first pregnancy,
family history, and body weight all contribute to breast cancer risk.
Unless these factors are controlled in some way, the effect of hormone
replacement therapy cannot accurately be assessed. In a prospective
study, random assignment to treatment groups can effectively control
confounding variables. In a retrospective study, other control means
must be used.
Notice that body weight played a different role in each of the studies
described above. Because of this, it is crucial that you describe
the variables you will use in terms of the role they play in your study.
This can be done simply and elegantly via a bulleted list of each
type of variable.
- How will data collection take place?
- If the study is a spin-off of a larger study being conducted by
your sponsor, which aspects of the study you are responsible for?
- How will you use the measures you collect to test your hypothesis?
The statistical procedures you choose will depend on:
- Your purpose
- Your study design
- The scale of measurement of the variables
- For help, try Selecting
The majority of III petitions require some tweaking before they are
- The study was not adequately described. You must present sufficient
detail about what you want to do and how you will do it.
- There is no hypothesis. There is a difference between general
data gathering and data gathering targeted toward answering a specific
question or testing a specific hypothesis. In some cases, a descriptive
or hypothesis-generating study can be appropriate, but only when there
is not sufficient prior knowledge to formulate a hypothesis.
- There is not sufficient power. If you are looking for a subtle
effect or need to control more that 1 or 2 confounding variables by
means of multivariate statistics, you will need lots of subjects.
- The purpose is not clear/design doesn't conform to stated purpose.
The subjects sampled, the outcome variables, the study design and the
analysis plan should all reflect the study's purpose. Are you collecting
data that's not directly relevant to your research question? Is the
study design described as a comparative study but the statistical analysis
only mentions correlations? Is it clear just what you will do with all
the data you collect?
- There is a logical flaw in the study. A common example is when
important confounding variables are not controlled in the study or when
cause and effect relationships will be inferred from data that is only
- The data to be collected is unlikely to be valid. Survey questions
of a sensitive nature, for example, or questions where the respondent
is asked to admit to poor professional or personal conduct are unlikely
to yield valid results.
- The data you intend to use isn't really accessible. Find out
what is and is not recorded in patient charts. Physicians' intentions
or reasons for taking action (or not taking action), for example, cannot
be found in charts.
- Student's role was not clear. The student should make an intellectual
contribution to the study, not serve as the sponsor's technician.
- Project is too ambitious/complex. Ask yourself whether your
study is feasible. Can you really get 250 subjects to respond to your
survey? Are you looking at multiple outcomes or asking multiple research
questions? Are there multiple phases to your project? I have never seen
a petition go back for revision because the study was too simple.
Click here for Sample petition
Preparing the Petition | UW
Human Subjects Review | Sample Petition
Data Management | Data
Analysis | The Final Report