A new diagnostic test detects 60 out of 100 schizophrenia patients correctly. It does not wrongly diagnose anyone in a sample of 100 controls.
How accurate is this test in detecting schizophrenia?
B.
Using the table above:
Accuracy = all true observations/total population studied = (100 + 60)/200 = 160/200 = 80%
Reference:
What are the chances that the text will turn negative in your next patient with schizophrenia?
D. This question asks the candidate to calculate the probability of a negative test in someone with the disorder – false-negative rate (FNR).
This is given by FNR = false negative/total diseased = 40/100 = 40%
FNR is same as (1 – sensitivity); similarly false-positive rate (FPR) is same as (1 – specificity).
Which of the following properties of a screening test increases with increasing disease prevalence in the population?
E. Sensitivity, specificity, and accuracy are measures that reflect the characteristics of the test instrument. These measures do not vary with changes in the disease prevalence. Positive predictive value increases while negative predictive value decreases with rising population prevalence of the disease studied. The prevalence can be interpreted as the probability before the test is carried out that the subject has the disease, known as the prior probability of disease. The positive and negative predictive values are the revised estimates of the same probability for those subjects who are positive and negative on the test, and are known as posterior probabilities. Thus the difference between the prior and posterior probabilities is one way of assessing the usefulness of the test.
Two observers are rating MRI scans for the presence or absence of white matter hyperintensities. On a particular day from the records, they are observed to have an agreement of 78%. If they could be expected to agree 50% of the time, even if the process of detecting hyperintensities is by pure chance, then the value of kappa statistics is given by:
C. Agreement between different observers can be measured using the kappa (κ) statistic for categorical measures such as the one highlighted in this question (presence or absence of MRI hyperintensities). Kappa is a measure of the level of agreement in excess of that which would be expected by chance. It is calculated as the observed agreement in excess of chance, expressed as a proportion of the maximum possible agreement in excess of chance. In other words kappa = the difference between observed and expected agreement/(1 – expected agreement). In this example, the observed agreement is 78%. The expected agreement is 50%. Hence kappa = (0.78 – 0.50)/(1 – 0.50) = 0.28/0.50 = 56%.
The number of days that a series of five patients had to wait before starting cognitive behavioural therapy (CBT) at a psychotherapy unit is as follows: 12, 12, 14, 16, and 21.
The median waiting time to get CBT is:
C. The median is calculated by placing observations in a rank order (either ascending or descending) and picking up the most central value. If the number of observations is even (multiples of two), then the median is taken as the arithmetic mean of the two middle values.
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