Evidence Based Medicine Flashcards
Cross-Sectional Study
- Frequency of disease and frequency of risk-related factors are assessed in the present.
- Asks, “What is happening?”
- Measures disease prevalence.
- Can show risk factor association with disease, but does not establish causality.
Case-Control Study
- Compares a group of people with disease to a group without disease.
- Looks to see if odds of prior exposure or risk factor differs by disease state.
- Asks, “What happened?”
- Measures odds ratio (OR).
- Patients with COPD had higher odds of a smoking history than those without COPD.
Cohort Study
- Compares a group with a given exposure or risk factor to a group without such exposure.
- Looks to see if exposure or risk factor is associated with later development of disease.
- Can be prospective (asks, “Who will develop disease?”) or retrospective (asks, “Who developed the disease [exposed vs nonexposed]?”)
- Measures relative risk (RR).
- Smokers had a higher risk of developing COPD than nonsmokers.
Twin Concordance Study
- Compares the frequency with which both monozygotic twins vs both dizygotic twins develop the same disease
- Measures heritability and influence of environmental factors (“nature vs nurture”)
Adoption Study
- Compares siblings raised by biological vs adoptive parents
- Measures heritability and influence of environmental factors
Clinical Trial
- Study involves humans
- Strongest if randomized controlled trial
- Best if double or triple blind
- Four Phases
Phases of a Clinical Trial
1 - Small number of healthy volunteers or patients with disease of interest. Is it Safe?
2 - Moderate number of patients with disease of interest. Does it Work?
3 - Large number of patients randomly assigned either to the treatment under investigation or to the best available treatment (or placebo). Is it an Improvement over old treatment?
4 - Postmarketing surveillance of patients after treatment is approved. Can it stay on the Market?
Sensitivity (True Positive)
=Proportion of all people with disease who test positive, or the probability that when the disease is present, the test is positive.
= TP / (TP + FN)
= 1 – FN rate
-SN-N-OUT= highly SeNsitive test, when Negative, rules OUTdisease
Specificity (True negative rate)
=Proportion of all people without disease who test negative, or the probability that when the disease is absent, the test is negative.
= TN / (TN +FP)
= 1 – FP rate
-SP-P-IN= highly SPecific test, when Positive, rules INdisease
Positive Predictive Value (PPV)
=Probability that a person who has a positive test result actually has the disease
=PPV = TP / (TP + FP)
Negative Predictive Value (NPV)
=Probability that a person with a negative test result actually does not have the disease
=NPV = TN / (TN + FN)
-NPV varies inversely with prevalence or pretest probability
Likelihood Ratio
- Likelihood that a given test result would be expected in a patient with the target disorder compared to the likelihood that the same result would be expected in a patient without the target disorder.
- LR+> 10 and/or LR–< 0.1 indicate a very useful diagnostic test.
- LRs can be multiplied with pretest odds of disease to estimate posttest odds
- LR+ = sensitivity/1-specificity = TP rate/FP rate
- LR-= 1-sensitivity/specificity = FN rate/TN rate
Odds Ratio
-Typically used in case-control studies. OR depicts the odds of a certain exposure given an event (eg, disease; a/c) vs the odds of exposure in the absence of that event (eg, no disease; b/d)
OR = (a/c)/(b/d) = ad/bc
Relative Risk
-Typically used in cohort studies. Risk of developing disease in the exposed group divided by risk in the unexposed group (eg, if 5/10 people exposed to radiation get cancer, and 1/10 people not exposed to radiation get cancer, the relative risk is 5, indicating a 5 times greater risk of cancer in the exposed than unexposed). For rare diseases (low prevalence), OR approximates RR
-RR = 1 no association between exposure and disease.
-RR > 1 exposure associated with disease occurrence.
-RR < 1 exposure associated with disease occurrence
RR = (a/(a+b))/(c/(c+d))
Attributable Risk
-The difference in risk between exposed and unexposed groups (eg, if risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then the attributable risk is 20%)
AR = (a/(a+b)) - (c/(c+d))
Relative Risk Reduction
-The proportion of risk reduction attributable to the intervention as compared to a control (eg, if 2% of patients who receive a flu shot develop the flu, while 8% of unvaccinated patients develop the flu, then RR = 2/8 = 0.25, and RRR = 0.75)
RRR = 1-RR
Absolute Risk Reduction
-The difference in risk (not the proportion) attributable to the intervention as compared to a control (eg, if 8% of people who receive a placebo vaccine develop the flu vs 2% of people who receive a flu vaccine, then ARR = 8% −2% = 6% = .06
ARR = (c/(c+d)) - (a/(a+b))
Number Needed to Treat
- Number of patients who need to be treated for 1 patient to benefit. Lower number = better treatment
- NNT = 1/ARR
Number Needed to Harm
- Number of patients who need to be exposed to a risk factor for 1 patient to be harmed. Higher number = safer exposure
- NNH = 1/AR
Incidence vs Prevalence
- Incidence = # new cases/# people at risk
- Prevalence = # existing cases /Total # of people in population
Precision vs Accuracy
- Precision (reliability) = The consistency and reproducibility of a test. The absence of random variation in a test. Low SD and high power.
- Accuracy (validity) = The trueness of test measurements. The absence of systematic error or bias in a test.
Selection Bias
- Nonrandom sampling or treatment allocation of subjects such that study population is not representative of target population. Most commonly a sampling bias
- Strategies to reduce: randomization and ensure the choice of the right comparison/reference group
Recall Bias
- Awareness of disorder alters recall by subjects; common in retrospective studies.
- Strategies to reduce: decrease time from exposure to follow-up
Measurement Bias
- Information is gathered in a systemically distorted manner
- Strategies to reduce: Use objective, standardized, and previously tested methods of data collection that are planned ahead of time. Use placebo group
Procedure Bias
- Subjects in different groups are not treated the same
- Ex: treatment group spends more time in hospital
- Strategies to reduce: blinding
Observer-expectancy Bias
- Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment (aka, Pygmalion effect).
- Ex: observer expecting treatment group to show sings of recovery
- Strategies to reduce: blinding
Confounding Bias
- When a factor is related to both the exposure and outcome, but not on the causal pathway, it distorts or confuses effect of exposure on outcome
- Strategies to reduce: Multiple/repeated studies. Crossover studies (subjects act as their own controls). Matching (patients with similar characteristics in both treatment and control group
Lead-time Bias
- Early detection is confused with survival
- Strategies to reduce: Measure “back-end” survival (adjust survival according to the severity of disease at the time of diagnosis)
Length-time Bias
- Screening test detects diseases with long latency period, while those with shorter latency period become symptomatic earlier.
- Strategies to reduce: A randomized controlled trial assigning subjects to the screening program or to no screening
