EBM Flashcards

1
Q

what is the odds of a person having an outcome?

A

number of individuals with the outcome divided by the number of individuals without the outcome

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

what are odds ratios used for?

A

(relative odds) are used to compare whether the likelihood of a certain event occurring is the same for two groups

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

how do you work out OR?

A

odds of the outcome in one group divided by the odds of the outcome in another group

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

what do different OR values mean?

A
  • If the OR = 1 there is no difference between the two groups
  • With an OR < 1, there is a greater likelihood of events in the control group – and the lower the odds ratio, the more likely that the treatment reduces the risk of events
  • An OR < 1 means that the exposure is associated with a reduced likelihood of the outcome. An OR > 1 implies that the outcome is associated with exposure, and increases as the exposure increases.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

what does chi-square test measure?

A

the fit of the observed values to ‘expected’ values

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

what kind of data is chi-squared used with?

A

categorical variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

what are the two types of chi-square test?

A

tests of goodness of fit and tests of independence

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

chi-square test of goodness of fit

  • what does it establish
  • what used for
A
  • Establishes whether or not an observed frequency distribution differs from a theoretical distribution
  • A simple application is to test the hypothesis that, in the general population, values would simply occur with equal frequency
  • But you might also want to test whether a sample from a population would resemble the population
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

what is the chi-square test of independence?

  • what does it assess
  • what does value of less than 0.05 tell you
  • what do ‘crude’ odd ratios do?
A
  • Assesses whether paired observation on two variables, expressed in a contingency table, are independent of each other
  • So, you might perform a Chi-square test (of independence), testing whether the proportion of people with hypertension is significantly different in a group of people who have had a stroke, compared with a control group who haven’t. The null hypothesis in this example is that the proportions in the two groups are no different from each other.
  • A chi-square probability (p value) of less than 0.05 is commonly interpreted as justification for rejecting the null hypothesis
  • This suggests that there is an association or relationship between the variables, but the test doesn’t tell us what the structure or nature of that relationship is.
  • ‘crude’ odds ratios – don’t take into account the other variables that may be having an effect on the outcome
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

what is a confounding variable?

A
  • A confounding variable, or confounder, has an effect on the outcome and is also correlated to the exposure e.g. people who smoke also tend to drink more
  • Common confounders include age, socioeconomic status and gender
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

what controls for any potential confounders at one time?

A

Multiple (or multivariate) logistic regression controls for any potential confounders at one time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

what is regression? what are the two types? what each used for?

A
  • Statistical procedure which attempts to predict the values of a given variable (dependent, outcome) based on the values of one or more other variables (independent, predictors, or covariates)
  • The result of a regression is usually an equation which summarises the relationship between the dependent and independent variable
  • Linear regression is used to predict the values of a continuous outcome variable (such as height, weight, systolic blood pressure), based on the values of one or more independent predictor variables
  • Logistic regression is intended for the modelling of dichotomous categorical outcomes
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

logistic regression

  • what used to do
  • it combines what to estimate what
  • what are the two types of logistic regression?
A
  • Used to analyse relationships between a binary/dichotomous dependent variable and numerical or categorical independent variables
  • It combines the independent variables to estimate the probability that a particular event will occur
  • In general, it calculates the odds of someone getting a disease based on a set of covariates
  • simple and multiple
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

simple (of bivariate) logistic regression

  • used for what
  • give example
A
  • Used to explore associations between one (dichotomous) outcome and one (continuous, ordinal, or categorical) exposure variable
  • How does smoking affect the likelihood of having pancreatitis?
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

multiple (or multivariate) logistic regression

  • what used to explore
  • what is purpose
  • give example
A
  • Used to explore associations between one (dichotomous) outcome and two or more exposure variables (which may be continuous, ordinal or categorical)
  • Purpose is to let you isolate the relationship between the exposure variable from the effects of one or more other variables (covariates or confounders)
  • How does smoking affect the likelihood of having pancreatitis, after accounting for (unconfounded by) alcohol consumption, BMI etc.
  • = adjustment = accounting for covariates or confounders
  • Essentially, for each variable, multivariate logistic regression gives you an odds ratio showing the effect of a variable on the outcome, after controlling for the effects of the covariates
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

what can comparing the results of simple and multiple logistic regression help with?

A

to answer the question ‘how much did the covariates in the model alter the relationship between exposure and outcome? (i.e. how much confounding was there)?

17
Q

where on graph are false positives and false negatives?

A

make up the area of overlap where the test can’t distinguish normal from disease

18
Q

how can the relative number of false positives and false negatives be changed?

A

by shifting the position of the cutoff point (so, as one goes down, the other will go up)
- But the numbers of people correctly identified as having the disease or not having the disease will also change

19
Q

what are sensitivity and specificity?

A
  • Measures for assessing the performance of diagnostic and screening tests
  • Sensitivity is a measure of the probability of correctly diagnosing a condition, whilst specificity is a measure of the probability of correctly identifying a non-diseased person (only concerned with those that have the disease – even if not diagnosed)
  • Sensitivity is the proportion of people with the disease correctly identified by the test – the probability that a test results will be positive when the disease is present (referred to as the true positive rate)
  • Specificity is the proportion of people without the disease correctly identified by the test – the probability that a test result will be negative when the disease is not present (referred to as the true negative rate) (only concerned with those that don’t have the disease)
20
Q

what is the positive predictive value? how calculate?

A

probability that the disease is present when the test is positive (only concerned with positive test results – those to the right of the cutoff point)

  • PPV = probability that the disease is present when the test is positive
  • True positives / (true positives + false positives)
21
Q

what is the negative predictive value? how calculate?

A

probability that the disease is not present when the test is negative (only concerned with negative test results – those to the left of the cutoff point)

  • Probability that the disease is not present when the test is negative
  • True negatives / (false negatives + true negatives)
22
Q

what is the equation for sensitivity?

A

true positives / (true positives + false negatives)

Or true positives / those with the disease

23
Q

what is the false negatives rate?

A

false negatives / (true positives + false negatives)

24
Q

what is the equation for specificity?

A

true negatives / (false positives + true negatives)

Or true negatives / those without the disease

25
Q

what is the false positives rate?

A

false positives / (false positives + true negatives)
or
1 - specificity

26
Q

what are ROC curves? what is perfect test? what is worthless test?

A
  • These values of sensitivity and specificity can be presented graphically
  • This type of graph = Receiver Operating Characteristics curve (ROC curve)
  • Plot of the true positive rate (i.e. sensitivity) against the false positive rate (i.e. 1 - specificity) for the different possible criteria (or cutoff points) of a diagnostic test
  • Shows trade-off between sensitivity and specificity; any increase in sensitivity will be accompanied by a decrease in specificity
  • The area under the curve is a measure of test accuracy
  • Accuracy of test depends on how well the test separates the group being tested into those with and without the disease in question
  • An area of 1 represents a perfect test – where sensitivity and specificity are both 100%
  • An area of 0.5 represents a worthless test – where sensitivity and specificity are both 50% - found with a diagonal line, whereas the close the curve follows the left-hand border and then the top border, the more accurate the test