Evidence Based Medicine Terms

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		Outcome
		Event	No Event
Exposure	Treated	a	b	a+b = n1
	Control	c	d	c+d = n0
		a+c	b+d

Experimental Event Rate (EER) =

Event rate in treated group = a/n1 or a/(a+b)

Control Event Rate (CER) =

Event rate in control group = c/n0 or c/(c+d)

Absolute Risk = Risk of having a disease. If the incidence of a disease is 1 in 1000, then the absolute risk is 1 in 1000 or 0.1%.

Relative Risk = Event rate in treatment group divided by the event rate in the control group. Also known as risk ratio. RR is used in randomized trials and cohort studies.

Relative Risk=	a/n1	=	a / (a+b)
	-----------------------		-----------------------
	c/n0		c / (c+d)

Odds ratio is used in case control trials:

		Outcome
		Event	No Event
Exposure	Case	a	b
	Control	c	d

Odds ratio =	a/c
	-----------------------
	b/d

Odds of a case patient being exposed divided by odds of a control patient being exposed.

See Odds Ratio vs. Relative Risk

When the outcome of interest is rare in the population studied then the Odds Ratio approximates the Relative Risk.

When the experimental treatment reduces the risk for a undesirable outcome/event

Relative Risk Reduction = |EER-CER|/CER

In clinical studies it is important to look at both the absolute risk and the relative risk. For example, say the disease A occurs in 1 in 100,000 people but taking drug X reduces the incidence to 1 in 10,000,000. The absolute risk of disease is 0.001%. The relative risk is 0.00001/0.001 = 0.1 and the relative risk reduction is 1- 0.1 = .9 or 90% while the absolute risk reduction is 0.00001-0.001=-0.00099 or 0.099%. Probably not something you will really care about unless the disease is rapidly fatal and the drug has absolutely no side effects. Guess which figure the drug advertisement is going to state? In contrast, disease B has a mortality rate of 50% and drug Y reduces mortality from 50% to 40%. The absolute risk of death with disease B is .5 or 50% and the relative risk is .4/.5 = 0.8 or 80%. The relative risk reduction is 1-0.8 = 0.2 or 20% while the absolute risk reduction is 0.4-0.5= .1 or 10%. In this case the relative risk reduction is 20% (much below the RRR for drug X in disease A) while the absolute risk reduction is much higher, 10%. So even though the drug is not very effective, you would still prescribe drug Y in disease B to reduce mortality by 10% unless a more effective drug was available.

Absolute Risk Reduction = |EER-CER| = c/n0 - a/n1 = (ARR) is the difference in the event rate between treatment group and control groups.

Number Needed to Treat = 1/absolute risk reduction. NNT is the number of patients who need to be treated to prevent one bad outcome. If a drug reduced the risk of a bad outcome (e.g. stroke, MI, death) from 60 % to 40% then the ARR is the amount your therapy reduced the risk of the bad outcome.

ARR = |0.4 (EER or the event rate in the experimental/treatment group) - 0.6 (CER or event rate in the control group)|

ARR = |0.4 - 0.6 | = 0.2 (20 per cent)

So,

NNT = 1/ARR = 1/0.2 = 5

You would need to treat 5 people with the drug to prevent one bad outcome.

When the experimental treatment increases the risk of a undesirable outcome/event

Relative Risk Increase (RRI): = |EER-CER|/CER = An increase rate of bad outcomes in the experimental group compared to the control group.

Absolute Risk Increase (ARI): = |EER-CER|. When the treatment harms more patients than the control treatment.

Number Needed to Harm (NNH): = 1/ARI = The number of patients who would need to be treated to cause one bad outcome.

When the experimental treatment increases the chance of a desirable outcome/event

Relative Benefit Increase (RBI) = |EER-CER|/CER = an increase in the rates of good events when comparing experimental and control groups.

Absolute Benefit Increase (ABI) = |EER-CER|.

Number Needed to Treat (NNT) = 1/ABI

Proportion: A ratio in which the denominator also contains the numerator

Probability: The likelihood that a particular event will occur or the proportion of people in whom a particular characteristic is present.

Decision Analysis: The application of explicit, quantitative methods to analyze decisions under conditions of uncertainty.

See
NNTs