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24 Cards in this Set
- Front
- Back
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Clarifications from last week
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Descriptive: Case-series
Cross-sectional can be descriptive if presume no comparison group Initial study/hypothesis-generating Rare exposures: Cohort best Rare disease: Case-control best Confounding = Confounding Bias Experimental/controlled study: Not many reasons to do non-random control Randomization decreases bias and confounding |
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Sensitivity and specificity are TEST characteristics and don’t rely on
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the prevalence in contrast to NPV and PPV.
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Sensitivity rules out
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SNOUT. So high sensitivity good for screening test since it will rule out well. Ex: ELISA Ab
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High specificity helps rule in a disorder like
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like Western Blot for HIV.
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A test with low sensitivity
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will miss cases (persons with the disease) and produce a large proportion of false negative results
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A test with low specificity
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will result in a healthy persons being told that they have the condition. Large proportion of false positives
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Prior probability
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frequency of a disease in a particular sub population
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Prevalence
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is the number of people having a disease in a given population
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prevalence is not the same as prior probability, for instance
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while the prevalence of AIDS in the general population may be 1% the prior probability in the subpopulation of intravenous drug users may be considerable higher
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predictive value allows to answer
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given a positive test result, what is the probability that is correct..that the positive result is a true positive.
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the predictive value of a negative test answers
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given a negative test result what is the probability that is it correct. The lower the prevalence the higher the predictive value of a negative test
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The lower the prevalence
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the lower the predictive value of a positive test
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Criteria for Useful Screening
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Disease should be:
Common Serious Treatable Slow to become symptomatic Treatment in asymptomatic phase has to be superior to treatment after symptoms develop |
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Screening test should:
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Be inexpensive
Be acceptable to physicians Be acceptable to patients Have high predictive values in the population to be screened Based on sensitivity/specificity AND prevalence |
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Combinations of sensitivity and specificity superior to the use of any single test by strategic uses of multiple tests
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Serial testing: Use >1 test in sequence, stopping at the 1st negative test. Diagnosis requires all tests be positive
Parallel testing: Use >1 test simultaneously, diagnosing if any test is positive |
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Serial Testing Clinical utility
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used when treatment is hazardous (chemotherapy, surgery) and also as cost-saving measure
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Serial Testing
increases decreases |
Increases specificity of any one test
Decreases sensitivity of any one test Used to rule-in disease |
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Parallel Testing Clinical utility
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used to rule out serious but treatable conditions
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Parallel Testing
increases decreases |
Increases sensitivity of any one test
Decreases specificity of any one test Used to rule out disease CPK, CK-MB, and Troponin for ruling out myocardial infarction |
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Screening Program Biases: Lead-Time Bias
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The systematic error of apparent increased survival from detecting disease in an early stage
Can make early diagnosis appear to increase survival time, even when it has had no effect, or even a damaging effect Example: Huntington’s Disease |
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Proportion of slowly progressing disease picked up by screening will be greater than
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than the proportion picked up by standard clinical practice, since rapidly progressing disease will tend to be symptomatic more quickly
Therefore, patients diagnosed by screening will, as a group, progress more slowly than those diagnosed by conventional means Example: Prostate cancer |
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Screening Program Biases: Volunteer bias
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Volunteers more health conscious assuming greater responsibility and more likely to comply with therapy
Improved survival among those screened does not have to do with early detection by screening Rather, but due to same reason that led them to volunteer for screening in the first place |
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Likelihood ratio positive (LR+) Gradient test results: “Test of a test
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Probability of a +test result in a patient with disease compared to the probability of a +test in a patient without disease
Formula = sensitivity/(1-specificity) Start with estimating a prior probability, determine LR from a table, and then use nomogram to get post-test probability |
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Likelihood ratio positive (LR+) Gradient test results: “Test of a test used to
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Used to predict what the prior probability of a disease should be for a test to be useful
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