EBM Flashcards
Relative Risk (RR)
Risk or incidence of the outcome in the treatment group / risk or incidence of the outcome in the control group :
RR tx grp / RR control grp
How many time more likely it is that the event will occur in the tx group relative to the control group.
RR = 1 : no diferrence bw 2 grps = tx no effect
RR < 1 : tx decreases the risk of the outcome
RR > 1 : tx increased the risk of the outcome
Absolute Risk Reduction (ARR) or absolute risk difference
Risk or incidence of outcome in control group - risk or incidence of the outcome in the treatment group
ARR = Risk control grp - Risk tx grp
Tells us the absolute difference in the rates of events between two groups and gives an indication of the baseline risk and treatment effect.
A ARR of 0 = no difference btw the two groups thus the tx had no effect.
ex : 0.15-0.10 = 0.05 or 5% (5% reduction in the death rate)
Background questions
Background : General knowledge about condition, test, treatment ;
1- a question root (who, what, where, when, how, why) + verb
2 - A disorder, test, treatment or other aspect of health care
So : how does health failure cause pleural effusion ? or
what cause swine flu ?
Foreground questions
Ask for specific knowledge to inform clinical decisions or actions. (PICO)
P : Patient, population, predicament, or problem.
I : Intervention, exposure, test, or other agent.
C : Comparison intervention, exposure, test, and so on, if relevent
O : Outcomes of clinical importance, including time, when relevant.
So : In adults with heart failure and reduced SF, would adding the implantation of an electronic re synchronization device to standard therapy reduce morbidity or mortality enough over 3 to 5 years to be worth the potential addtionnal harmful efffects and costs?
Relative Risk Reduction (RRR)
RRR = absolute risk reduction (ARR) / risk of the outcome in the control group.
An alternative way to calculate the RRR is to subtract the RR from 1 (eg. RRR = 1 - RR)
The relative risk reduction is the complement of the RR and is probably the most commonly reported measure of treatment effects.
It tells us the reduction in the rate of the outcome in the treatment group relative to that in the control group.
BUT !! always look NNT and NNH
Number Needed to Treat (NNT)
NNT = 1 / ARR
inverse of the ARR
The number needed to treat represents the number of patients we need to treat with the experimental therapy in order to prevent 1 bad outcome and incorporates the duration of treatment.
Clinical significance can be determined to some extent by looking at the NNTs, but also by weighing the NNTs against any harms or adverse effects (NNHs) of therapy.
We would need to treat 20 people for 2 years in order to prevent 1 death.
If NNT is 3.3 = NNT = 4
Number Needed to Harm (NNH)
The number needed to harm represents the number of patients we need to treat with the experimental therapy in order to create 1 bad outcome and incorporates the duration of treatment.
Clinical significance can be determined to some extent by looking at the NNTs, but also by weighing the NNTs against any harms or adverse effects (NNHs) of therapy.
If we treat 20 people there will be 5 patients with adverse effects
If NNH = 2,3 = 3
Odds ratio (rapport de cote) lors études cas-témoins grp avec maladie et grp sans maladie)
Odds of a disease = number of patients with the disease / number of patients without the disease
Odds ratio = odds of the disease in exposed patients / odds of the disease in unexposed patients
< 1 : likely to protect
1 : no difference
> 1 : likely to cause
Si OR très variable (1.84 et 74) : facteur de risque mais bcp de variation)
likelyhood ratio LR (tableau)
plus OR proche de 1 : facteur causal et plus proche de 0.1 : facteur de protection
Confidence Intervals (CI)
if 95% means 95 % chances that the true answer is in this interval. (if interval is narrow = more reliable ; if interval wide = less reliable)
< 1 : likely to protect
1 : no difference OR if 1 is in the interval = not significant
> 1 : likely to cause
p valu < 0.05 = significatif= plus le p-value est petit plus le résultat n’est pas le fruit du hasard
Sensitivity (Sn)
a/(a+c)
Sensitivity = the proportion of people with the condition who have a positive test result.
The sensitivity tells us how well the test identifies people with the condition. A highly sensitive test will not miss many people and lower % of false negative rate.
10 people (4%) with dementia were falsely identified as not having it. This means the test is fairly good at identifying people with the condition.
Specificity (Sp)
d/(b+d)
Specificity (Sp) = the proportion of people without the condition who have a negative test result.
The specificity tells us how well the test identifies people without the condition. A highly specific test will not falsely identify many people as having the condition and therefor lower % of false positive result.
150 people (20%) without dementia were falsely identified as having it. This means the test is only moderately good at identifying people without the condition.
Positive Predictive Value (PPV)
a/ (a+b)
Positive Predictive Value (PPV) = the proportion of people with a positive test who have the condition.
This measure tells us how well the test performs in this population.
It is dependent on the accuracy of the test (primarily specificity) and the prevalence of the condition.
Of the 390 people who had a positive test result, 62% will actually have dementia.
Negative Predictive Value (NPV)
d/(c+d)
Negative Predictive Value (NPV) = the proportion of people with a negative test who do not have the condition.
This measure tells us how well the test performs in this population. It is dependent on the accuracy of the test and the prevalence of the condition.
Of the 610 people with a -ve test , 98% will not have dementia.
Likelihood Ratios (LR)
Spécialistes vont ulitiser LR pour débuter un tx sans nécessairement faire examen dx invasif (ex: faire seulement le test sanguin mais pas gastro)
> 10 = strong increase (+45%)
5-10 = moderate increase ( 5 = +30%)
2-5 = small increase ( 2= +15%)
1 - 2 = minimal increase
1 = no change
0,5 - 1 = minimal decrease ( 0.5 = - 15%)
0.2 - 0.5 = small decrease (0.2 = - 30%)
< 0.2 = strong decrease (0.2 = - 30% et 0.1 = - 45%)
Prevalence (P)
% of people with the disease at a particular point in the time.
P = number of people with disease / number total of people
ex: 12 students on 24 got the flu so the prevalence is 50%
