TAILIEUCHUNG - Lecture Basics of Meta-analysis

This lecture includes these contents: Basics of meta analysis, the effect measures, the meta analysis, absolute risk reduction, risk on treatment,. Invite you to consult this lecture. | Basics of Meta-analysis Steff Lewis, Rob Scholten Cochrane Statistical Methods Group (Thanks to the many people who have worked on earlier versions of this presentation) Introduction Session plan Introduction Effect measures – what they mean Exercise 1 Meta-analysis Exercise 2 Heterogeneity Exercise 3 Summary Before we start this workshop will be discuss binary outcomes only . dead or alive, pain free or in pain, smoking or not smoking each participant is in one of two possible, mutually exclusive, states There are other workshops for continuous data, etc Where to start You need a pre-defined question “Does aspirin increase the chance of survival to 6 months after an acute stroke?” “Does inhaling steam decrease the chance of a sinus infection in people who have a cold?” Where to start Collect data from all the trials and enter into Revman For each trial you need: The total number of patients in each treatment group. The number of patients who had the relevant outcome in each treatment group Effect measures – what they mean Which effect measure? In Revman you can choose: Relative Risk (RR) = Risk Ratio, Odds Ratio (OR) Risk Difference (RD) = Absolute Risk Reduction (ARR), Risk 24 people skiing down a slope, and 6 fall risk of a fall = 6 falls/24 who could have fallen = 6/24 = ¼ = = 25% risk = number of events of interest total number of observations Odds 24 people skiing down a slope, and 6 fall odds of a fall = 6 falls/18 did not fall = 6/18 = 1/3 = (not usually as %) odds = number of events of interest number without the event Expressing it in words Risk the chances of falling were one in four, or 25% Odds the chances of falling were one third of the chances of not falling one person fell for every three that didn’t fall the chances of falling were 3 to 1 against Do risks and odds differ much? Control arm of trial by Blum 130 people still dyspeptic out of 164 chance of still being dyspeptic risk = 130/164 = ;

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