Basic Statistics in
Clinical Research
Slides created from article by Augustine Onyeaghala (MSc, PhD, PGDQA,
PGDCR, MSQA, FMLSCN) and available on Global Health Trials
(www.globalhealthtrials.org)
Dr. Augustine Onyeaghala
Biomedical Scientist/ Clinical
Research Consultant
15/7/2015,
Ibadan, Nigeria
www.theglobalhealthnetwork.org
What is statistics?
the collection,
arrangement, analysis,
interpretation and
reporting of data
So…..
Statistics is just something that a statistician does,
in a trial, and it happens just after we’ve finished
collecting the data, right?
Wrong!
Knowledge of statistics is important right
through the project:
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Sample size
Blinding
Randomisation procedure
Inclusion/exclusion criteria
Outcomes
Type of statistical test used
Interpretation of data
Type of control group
Data management
Missing data
Confounding factors
Data safety monitoring
Problems seen in trials, which could result
in defective statistical analysis:
Poor
study
design
Inadequate
recruitment
Missing
data
Adherence
to
inclusion/ex
clusion
Confounding
factors
Bias
Wrong
application
of statistics
Case study: Remedy X
Hypothesis
•
•
Hypothesis: Local Remedy X has an effect on plasma glucose levels
Null hypothesis: Local Remedy X has no effect on plasma glucose
levels
Null
Hypothesis
Alternative
Trial designs:
• The investigational drug (new drug) is betterhypothesis
than or superior to the
standard, control drug or placebo (superiority trial design)
• New drugs perform as good as the standard treatment (equivalence
trial design )
• New drug is less effective than the standard treatment (inferiority
trial design)
P values
• P = probability value shows us whether the
difference observed is just due to chance, or if
it’s statistically significant.
• If P>0.05, accept the null hypothesis (i.e. there
IS NOT a statistically significant difference)
• If P<0.05, reject the null hypothesis (i.e. there
IS a statistically significant difference)
BUT……
• Results are always probabilistic – you have
never proved either hypothesis, simply
indicated that the probability that the null in
true is lower than your critical value so that
you can reject the null, and accept the
alternative as the most probable explanation.
Bias
• This is an error associated with the study
design, conduct, analysis and publication that
exaggerates or underestimates the
effectiveness of the investigational product.
Randomisation and blinding
Randomisation and blinding are important ways
of minimising types of bias.
Variables:
Confounding Variables
These are factors that are not normally
measured during the study, but may be
accountable for the effects observed in research.
Distribution of data
Bell curve = normal distribution
Non-parametric
• Questionnaire answers are a good example of
data that is not normally distributed
Why does distribution
matter?
Expectation of your data distribution impacts
the statistical tests you could use to analyse the
data.
Parametric tests include:
t-test
What if my data is not
normally distributed?
• Some data might not be normally distributed:
Takeaway message:
Researchers should decide and choose at
the planning stage the type of statistical
technique which should be applied to
enable them arrive at a good conclusion.
Types of Error
the incorrect rejection of a null hypothesis which
is actually true (a "false positive")
the failure to reject a false null hypothesis (a
"false negative").
Study Design is important for
statistics!
•
Design: visit Equator-Network.org for free, useful advice on study
planning and analysis
•
Patient selection (appropriate sample size, sampling, inclusion/exclusion
criteria etc)
•
Define the measurable outcome and look out for it.
•
State the null hypothesis clearly, and avoid type1 and type 2 errors.
•
Ensure randomization and blinding.
•
Use the right and adequate control group.
•
Avoid confounding factors
•
Apply the right statistical calculations
Some free eLearning courses in
statistical analysis:
• Online Statistics Education: an Interactive Multimedia Course
(onlinestatbook.com)
• Biostatistics Lecture Series from John Hopkins (ocw.jhsph.edu)
• Essentials of Probability and Statistical Inference from John
Hopkins (ocw.jhsph.edu)
• Introduction to Biostatistics from John Hopkins
(ocw.jhsph.edu)
• Basic Biostatistics Concepts and Tools (Robert Stempel College
of public health and social work)
Acknowledgements:
Original article written by Augustine Onyeaghala (MSc,
PhD, PGDQA, PGDCR, MSQA, FMLSCN) and available on
Global Health Trials (www.globalhealthtrials.org)
With thanks for slide preparation to Brigid Davidson
Funding
The Bill and Melinda Gates Foundation