Conventional Analysis Of Diagnostic Test Data Flashcards
What’s been conventionally the standard way of describing the accuracy of diagnostic test? When is that performed?
two-by-two table.
When the test results are recorded as dichotomous outcomes (positive/negative results).
Gold standard? How is that measured?
This standard may be another test but more expensive diagnostic method or invasive method but more accurate or combination of tests may be available in clinical follow up, surgical verification, autopsy, biopsy or by a panel of experts
From the frequency of test results among patients with and without disease based on gold standard, one can derive?
probability of a positive test result for patients with disease and probability of negative test results for patients without disease
What are the two other useful indices in clinical practice?
The positive predicted values (PPV) and the negative predicted values (NPV)
PPV?
Probability of disease for positive test results
NVP?
Probability of being healthy for negative test results
Although these two measures (NPV JA PPV) are useful for clnical decision they are?
They are influenced by the prior prevalence of disease in population. PPV is elevated with a higher prevalence of disease while the NPV decreases with a higher prevalence
The PPV and NPV can also be calculated from?
Bayes’ theorem using the estimates of sensitivity and specificity and the prior probability of disease (or prevalence of diseased in population) before the test is applied.
The PPV and NPV are calculated through? IN Bayes’ theorem?
the posterior probability of the diseased after the test results are known.
When does one calculate the PPV and NPV?
If one knows the sensitivity, specificity and pre-test probability of the diseased in population = prevalence .
When disease prevalence is high?
The PPV increases and the NPV decreases
The likelihood ratio?
Todennäköisyys suhde.
The likelihood ratio is defined as the ratio of two density functions of test results conditional in diseased and non-diseased subjects.
Todennäköisyyssuhde määritellään testitulosten kahden tiheysfunktion suhteena, jotka ovat ehdollisia sairailla ja ei-sairailla koehenkilöillä.
The positive LR of test?
Is nothing more than the ratio = suhde of sensitivity to 1-specificity
The LR+ is ranged from 0 to infinity
What is the worst case about LR+?
When does that happen?
When LR+=0
When sensitivity becomes close to 0
The largest value of LR+ oocurs when?
Specificity tends to be close to 1 and the sensitivity also be close to 1.
Thus the higher value of LR+ has a greater information value for diagnostic test.
On the other hand the negative likelihood ratio?
LR- is the ratio of probability of negative test in diseased to non diseased.
The lower (eli close to 0) LR- has?
A greater information values of a negative test. The larger value of LR- has lower information values.
he Bayesian analysis combines the data?
likelihood ratio of test and prior odds of disease in order to obtain the posterior odds of disease among positive and negative test results
Posterior odds of disease = ?
likelihood ratio × prior odds of disease
TPF?
True Positive Fraction (Sensitivity)= TP/ (TP+FN)= a/(a+c)
FNF?
False Negative Fraction (1-Sensitivity)= FN/ (TP+FN)= c/(a+c)
TNF?
True Negative Fraction (Specificity)= TN/ (TN+FP)= d/(b+d)
FPF
False Positive Fraction (1-specificity)= FP/ (TN+FP)= b/(b+d)
PPV
Positive Predicted Value=TP/(TP+FP)=a/(a+b)
NPV
Negative Predicted Value=TN/(TN+FN)=d/(c+d)
What does the conventional analyses consider?
the sensitivity and the specificity of a diagnostic test as the primary indices of accuracy since these indices are considered to be independent of the prior probability of disease.
Why is using a single sensitivity and a single specificity as measures of accuracy problematic?
these measures depend on a diagnostic criterion (i.e. cut-off) for positivity which is often chosen arbitrarily. For example, one observer may choose a lenient decision criterion and the other may choose a stringent decision criterion for positivity.
Eli nämä mittaukset riippuvat diagnostisesta kriteereistä eli raja-arvoista positiiviselle, mikä taas voidaan valita sattumanvaraisesti. Joku voi valita lievästi/helposti päätöksen ja toinen taas tiukan päätöksen kriteereistä positiivisuudelle.
What does ROC analysis do?
ROC analysis circumvents this arbitrariness.
Why total information content of diagnostic test can be summarized either it’s LR+ or LR- ?
higher value of LR+ has a greater information value for diagnostic test. The lower (i.e close to 0) LR- has a greater information values of a negative test
