framing research questions the pico strategy. pico p: population of interest i: intervention c:...
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FRAMING RESEARCH QUESTIONSThe PICO Strategy
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PICO
• P: Population of interest• I: Intervention• C: Control• O: Outcome
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PICO• P: Population of interest• Patient characteristics or the problem to be addressed
• I: Intervention• Exposure to be considered–treatments/ tests
• C: Control Control or comparison intervention treatment/placebo/standard of care
• O: Outcome• Outcome of interest: what you are trying to measure, improve
or affect; may be disease-oriented or patient –oriented.
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PICO- Controls• The “C”, Controls, is the the only optional component in
the PICO question.• Can look at an intervention without exploring an
alternative.• May not be an alternative.
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What type of question is being asked?
• Therapy/ prevention• Diagnosis• Etiology• Prognosis
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What type of question is being asked?
• Therapy/ prevention• Questions of treatment in order to achieve an outcome
• Diagnosis• Questions of identification of a disorder in a patient with
specific symtoms.
• Etiology/ Harm• Questions of negative impact from an intervention or exposure.
• Prognosis• Questions of progression of a disease or the likelihood of a
disease occurring.
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How large was the treatment effect?• Most RCTs look at a dichotomous outcome: (“yes” or “no”,
did death occur or not, did a patient suffer an event or not?).
• Can express impact of treatment as Relative Risk:
• The risk of events among patients on the new treatment, relative to that risk among patients in the control group.
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Relative Risk• If RR=1 risk in treatment group (exposed) equals risk in
non-treatment group (non-exposed).
• If RR>1 risk in treatment group (exposed) is greater than in non- treatment group (non-exposed); positive association, possibly causal.
• If RR<1 risk in treatment group (exposed less than risk in non-exposed); negative association, possibly protective.
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P values• A probability statement. Statistical inference.
• Null hypothesis (Ho) generally presumes two groups, exposures, or treatments are not different.
• The experiment generally sets out to prove that there is a difference in the intervention and control group (or to compare them). H1.
• If the null hypothesis is true, what is the probability of the observed statistic (result) or a more extreme result occurring?
• P values answer this: Small p values provides good evidence against the null hypothesis, or says that a statistically significant difference exists.
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The significance of a test:• When P>0.10, the observed difference is not significant.
• When 0.05< P< 0.10, the observed difference is said to be marginally significant.
• When 0.01<P<0.05, the observed difference is said to be significant.
• When P<0.01, the observed difference is said to be highly significant.
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Confidence Intervals (CI):• Another form of statistical inference: estimation.
• Point estimate provides a single estimate of a parameter.
• Interval estimation provides a range of values (confidence interval) that seeks to capture that parameter.
• This interval extends a margin of error (“wiggle room”) above and below the point estimate
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What does a 95% CI mean?• The confidence level of a confidence interval refers to
the success rate of the method in capturing the parameter it seeks.
• 95% CI is the level of confidence: it says that we are 95% confident that the true value of the parameter we are looking at is within our confidence interval.