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Paeds > Statistics > Flashcards

Statistics Flashcards

1
Q

How do you calculate the positive likelihood ratio?

A

Positive likelihood ratio = sensitivity / (1 - specificity)

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2
Q

Describe what percentage of people like within 1, 2, and 3 standard deviations from the mean

A
  • 1 standard deviation account for 68% of the values
  • 2 standard deviations account for 95% of the values
  • 3 standard deviations account for 99.7% of the values
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3
Q

Describe the positive predictive value and how to calculate it

A
  • If the test is positive, what is the chance the patient has the disease?
  • PPV = true +ve/true+ve and false +ves
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4
Q

Describe the negative predictive value and how to calculate it

A
  • If the test is negative, what is the chance the patient doesn’t have the disease?
  • NPV = true -ve/true-ve and false -ves
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5
Q

Describe sensitivity and how to calculate it

A
  • True positive rate
  • Sensitivity = true +ve/true +ve and false -ves
  • A negative result in a test with a high sensitivity is useful for ruling out a disease
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6
Q

Describe specificity and how to calculate it

A
  • True negative rate
  • Specificity = true -ve/true -ve and false +ves
  • A positive result in a test with a high specificity is useful for ruling in a disease
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7
Q

What is the value of a likelihood ratio?

A
  • Assess the value of performing a diagnostic test
  • Uses sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition exists
  • If you have a pre-test probability and a likelihood ratio, you can work out the post-test probability (+ve LR increases this, -ve LR decreases this)
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8
Q

Describe the positive likelihood ratio and how to calculate it

A
  • Likelihood ratio of a positive test is the probability of a true positive to a false positive
  • +ve LR = sensitivity/100% - specificity
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9
Q

Describe the negative likelihood ratio and how to calculate it

A
  • Likelihood ratio of a negative test is the probability of a false negative to a true negative
  • -ve LR = 100% - specificity/ sensitivity
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10
Q

What is the difference between incidence and prevalence?

A
  • Incidence is a rate e.g. cases per 5 years

- Prevalence is a cross section in time e.g. point prevalence studies

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11
Q

What is the most important property of a screening test?

A

Sensitivity

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12
Q

What are the criteria for a screening test (Wilson’s criteria)?

A
  1. The condition sought should be an important health problem.
  2. There should be an accepted treatment for patients with recognized disease.
  3. Facilities for diagnosis and treatment should be available.
  4. There should be a recognizable latent or early symptomatic stage.
  5. There should be a suitable test or examination.
  6. The test should be acceptable to the population.
  7. The natural history of the condition, including development from latent to declared disease, should be adequately understood.
  8. There should be an agreed policy on whom to treat as patients.
  9. The cost of case-finding (including diagnosis and treatment of patients diagnosed) should be economically balanced in relation to possible expenditure on medical care as a whole.
  10. Case-finding should be a continuing process and not a “once and for all” project.
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13
Q

What is the relative risk and how do you calculate it?

A
  • The probability of an event occurring in the exposed group vs. the non exposed group (event occurring/ total events)
  • RR = a/ (a+b)
    ————
    c/ (c+d)
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14
Q

What is the odds ratio and how do you calculate it?

A
  • The event occurring: event not occurring
  • e.g. OR of a baby girl is 1:1, probability (RR) is 50%
  • Used when population risk is unknown in case-control studies. With low prevalence, OR~RR
  • OR = ad/cb
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15
Q

What is the absolute risk and how do you calculate it?

A
  • The real difference in absolute terms between the two exposure or treatment groups
  • Same as the risk difference
  • e.g. if death occurs in 20% of placebo and 10% of drug, then RRR = 50%, but ARR = 10%
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16
Q

What is the number needed to treat and how do you calculate it?

A
  • The number of people needed to be treated to avoid one adverse outcome
  • NNT= 1/ (incidence of unexposed – incidence of exposed)
  • NNT = 100%/ARR = 1/ (probability in exposed - probability in non-exposed)
17
Q

What is the number needed to harm and how do you calculate it?

A
  • NNH = 100%/ARI (absolute risk increase)
18
Q

What is a type 1 error?

A

= alpha error = false positive = p value

  • Rejecting a null hypothesis when it is actually true
  • Usually 0.05
19
Q

What is a type 2 error?

A

= beta error = false negative

- failing to reject the null hypothesis when it should have been rejected

20
Q

What increases the risk of a type 2 error (rejecting a good treatment)?

A

= beta error = false negative
= failing to reject the null hypothesis when it should have been rejected
= risk when the study is underpowered

21
Q

What is the power of a study and what is it determined by?

A
  • The power is the probability that the test will reject a false null hypothesis
  • Power = 1 - beta error (false negatives) = sensitivity
  • Usually 0.8 or 0.9
  • Determined by: population size, size of effect, variance within populations
22
Q

Describe the phases of drug design

A

Stage 0 - first in human studies, looks at pharmacokinetics and pharmacodynamics in humans, single sub-therapeutic dose given
Stage 1 - safety, healthy volunteers, determines safe dosing ranges, identify adverse events
Stage 2 - safety and efficacy (placebo), dosing requirements. Some combine stage 1 + 2
Stage 3 - confirmation of safety and efficacy, compare to active treatment (assess gold standard), RCT, requires approval from FDA
Stage 4 - continual pharmacovigilance (post marketing surveillance), new uses/populations, children, larger populations but less controlled, longer follow-up, interactions with other meds

23
Q

What are types of observational studies?

A
  • Cohort - divide on exposure yes or no, prospective or retrospective
  • Case-control - divide on disease yes or no
  • Cross-sectional e.g. community survey
  • Longitudinal - follow over time
  • Ecological - 1+ variable measured at a group level
24
Q

What is the difference between internal and external validity?

A
  • Internal - study well designed and minimises possibility of systematic bias (randomisation, blinding, ITT, published protocol, allocation concealment)
  • External - applicable to a different unit/environment (multi-center, wide inclusion, limited exclusion criteria)
25
Q

What is random bias?

A
  • Results due to sampling variability or measurement precision
  • Can be minimised but not voided
26
Q

What is systematic bias and what are examples?

A
  • Reproducible inaccuracies that produce consistently false patterns of differences between observed and true values
  • Can be avoided by a good design
  • Selection bias, performance bias, detection bias, recall bias, attrition bias, reporting bias
27
Q

What is attrition bias?

A

Differences in withdrawal rates (incomplete outcome data)

28
Q

What is response bias?

A

The participant consciously or unconsciously gives a response that they think the interviewer wants to hear.

29
Q

What is optimistic bias?

A

Optimistic bias refers to overestimating the probability of positive events and underestimating the probability of negative events happening to themselves or their own children in the future, while they may be more objective in estimating outcomes for other children

30
Q

What is confirmation bias?

A

During the interpretation of data, the researcher looks for evidence to support their pre-existing beliefs.

31
Q

What is recall bias?

A

The outcomes of the treatment (good or bad) influence participants’ recollections of events prior to or during the treatment process.

32
Q

What is the best measure to describe the frequency of occurrence in an epidemic?

A
  • Prevalence is the number of new and existing cases of a disease in a population at a specific point in time
  • The best measure to determine an epidemic is prevalence, because an epidemic refers to an increase in the number of cases of disease above what is normally expected in a specified population and region at a specific time.
33
Q

What is a student’s t-test?

A
  • Compares two samples of data

- Tests probability that two normally-distributed samples come from a population with the same mean value

34
Q

What is a chi square test?

A

Chi-square test evaluates if there is a significant difference between expected frequencies and observed frequencies in one or more sets of categorical data

35
Q

How do you calculate precision?

A

true positives/ (true positives + false positives)

= positive predictive value

36
Q

What is a Fisher’s exact test?

A

Used to determine if there are non-random associations between two categorical variables

Paeds (25 decks)

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