Week 7 Flashcards
(32 cards)
What is Test accuracy
Of all the people int he sample, this is the proportion of people who were correctly identified as either a) having or b) not having the condition
What is prevalence
Of all the people in the sample, this is the proportion who have the condition
What is sensitivity
Of all the people in the sample, this is the proportion who test positive
What is specificity
Of all the people in the sample who do not have the condition, this is the proportion who test negative
Screening tests
- It is not east to find a test that has a high sensitivity and high specificity
Choice has to be made:
Either:- High sensitivity and lower specificity (many true positives, little false negatives)
- Or
High specificity and lower sensitivity (many true negatives, little false positives)
Evaluative tests
- Used to measure a variable of interest and to monitor the response to treatment
- Tests are often called outcome measures when used in this way
- Studies of intervention efficacy report outcome measures
- These same measures are used in practice to measure and monitor individual client or patient responses to intervention
- Although best research evidence indicates the amount of benefit on average a person can expect from the treatment there is no average patient
- Practitioners can therefore use outcome measures to quantify the benefits of treatment to an individual person or group
Predictive tests
- Measure a variable or variables in the present and use that to predict something in the future e.g. risk of developing a particular health condition or probability that a particular event will take place in the future
- Helps determine the cause of a persons illness or problem
- Results are used to determine the likelihood that a person has or does not have a particular condition
- Diagnostic tests are used in differential diagnosis - the process of sorting out which of a number of possible conditions a person may have
- Tests with high specificity increase the probability or likelihood that the person has a particular condition
- Tests with high sensitivity decrease the probability that a person has a condition
Screening tests
- Use to look for an uncommon but serious health condition
- Often people who do not have an identified health problem
- e.g. pap smear for cervical cancer allows early detection of a serious health problem
- In screening it is important to have a few false negatives ( a person with the condition testing negative for that condition)
- Designed to have maximum test sensitivity so people who test negative can be confidently said to not have the condition of interest
People with positive result undergo additional diagnostic tests to determine probability of the condition
Key concepts of testing
- list the key differences between diagnostic and screening tests.
- explain the characteristics of a well-designed Level II diagnostic study.
- explain the following characteristics of a test and calculate the corresponding values:
- sensitivity and specificity;
- positive and negative predictive value;
- accuracy;
- prevalence;
- likelihood ratios.
- list the possible harms of testing.
How can you tell if a study on diagnostic test accuracy is believable? Hierarchy?
- The hierarchy shows that the level 1 evidence is a systematic review of level 2 studies
- Level 2 studies are defined as ‘a study of test accuracy with an independent blinded comparison with a valid reference standard among consecutive persons with a defined clinical presentation
Table 2 outlines why these and other features of studies of diagnostic test accuracy are important in avoiding bias and how each type of bias impacts on the validity of the study’s estimates of test accuracy
Index test
- In diagnostic studies of test accuracy a new test called the index test is compared against a reference standard test (also called gold standard test)
- The gold standard test is generally the best available for determining the truth about the presence or absence of the condition
- Alternatives are often developed if the gold standard is high risk, invasive, or expensive
- Gold standard tests themselves are not always 100 accurate
- Sometimes there is no true gold standard against which the index can be compared, in which case the researchers need to justify the reference criterion
A study should never include the index test as part of the gold standard criterion
Contingency tables and test results
- A paper used the analogy of a number of people on trial for murder to explain the interplay between test results and the unknown truth about the presence of absence of a condition
- Ten men on trial: 3 committed murder 7 are innocent
- A jury hears the cases and: Finds 6 men guilty, 2 of the convicted are true murders, 4 wrongly in prison, 1 guilty man walks free
Sometimes the Jury’s verdict is wrong and in these cases, the guilty go unpunished and the innocent are incarcerated
When administering a diagnostic test
- When administering a diagnostic test, it would be ideal if a positive test always meant the person had the health condition and a negative test always meant they did not
- However testes are rarely 100% accurate - some positive tests are false positives and some negative results are false negatives
- This means a positive test does not necessarily mean that the person has the condition and a negative test does not necessarily mean that they do not
- The process of diagnosis is a bit like a trial - the practitioner collects a range of evidence, weighs it up and makes a decision on the balance of probability that a particular health condition is most likely to be present
The person then treated for that condition
What is positive predictive value
Of all the people in the sample with a positive test result this is the proportion who have the condition
Negative predictive value
Of all the people in the sample with a negative test result this is the proportion who do not have the condition
Interpreting and answering your research question
- Main question is: What is the probability that a person with a positive or negative test result actually has the condition for which they are being tested?
- The positive predictive value (PPV) and Negative Predictive Value (NPV) tell us the probability that a person in the study (or in a situation where the prevalence is identical in that study) who tests positive or negative has or does not have the condition
However because the prevalence of a condition can vary enormously the PPV and NPV often cannot be used to determine the probability of the person having the condition
Rule of thumb for Likelihood ratios
1 = no change to the probability (pre-test or post-test) that the person has the condition
+LR> 10= a very large shift in probability (the person is more likely to have the condition)
+LR> 5 = A substantial shift in probability
-LR< 0.1 = a very large shift in probability (e.g. the person is substantially less likely to have the condition)
Likelihoods ratio
- LR overcomes this problem
- The LR is the probability of a test result, in a person with the condition divided by the probability of the rest result in a person without the condition
A likelihood ratio can be calculated for a positive and negative test result - If the prevalence is used as the pre-test probability the PPV and NPV will give you the post-test probability
In clinical practice, you will more often be using your own diagnostic reasoning with regard to the pre-test probability for an individual patient
In simple terms, a likelihood ratio (LR) assesses how much a test result changes the likelihood of a patient having a disease compared to not having it. It compares the probability of a positive or negative test result in a person with a disease to the probability of the same result in a person without the disease
Methods that can be used to calculate post-test probabilities
- Likelihood ratio nomogram
- Probability to odds conversion
- Posterior probability of disease calculator
Rough estimates of magnitude of change
Summary of steps in using the nomogram to calculate post-test probabilities
- Mark your pre-test probability on the vertical line on the left of the nomogram
- Mark the LR of your selected test on the middle vertical line (for both a positive test and a negative test)
To determine the post-test probability, if the test is positive and if It is negative, draw a line through each set of two points
- Mark the LR of your selected test on the middle vertical line (for both a positive test and a negative test)
To test or not to test
- If a clinician is already very sure that a person has a condition they should commence treatment for the condition
- Testing is indicated when the pre-test probability is between 20-80%.
- The exception is when the test is a screening test
Screening tests
- Used to screen for conditions
- The pre-test probability is often low
- A good screening tests has high sensitivity (low rate of false negatives)
- A negative result in a test with high sensitivity means that there person almost certainly does not have the condition
- = SnOUT: Sensitivity, negative result, rules the condition out
- = SpIN: Specificity, positive result rules the condition in
The trade-off of achieving high sensitivity in a screening test is that there will often be a high rate of false positives - everyone with the condition tests positive, but so do a large number of people without the condition
Population and clinic based screening tests
Population based screening
- Expensive so must be based on an evaluation of the evidence of the benefits, harms and costs of testing
- e.g. women for cervical cancer and prostate for men
Clinic-based screening tests
Some tests performed in particular clinical circumstance to rule out a condition that if left undetected can have serious consequences
SnOUT and SpIN
- SnOUT: High sensitivity means a negative result rules out
SpIN: High specificity means a positive result rule IN