1. FRAMING RESEARCH QUESTIONS The PICO Strategy

2. PICO P: Population of interest I: Intervention C: Control O: Outcome

3. PICO P: Population of interest Patient characteristics or the problem to be addressed I: Intervention Exposure to be considered–treatments/ tests C: ControlControl 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.

4. 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.

5. What type of question is being asked? Therapy/ prevention Diagnosis Etiology Prognosis

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

11. 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

12. 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.