General Flashcards

1
Q

Formula for incidence rate:

A

No. of new cases of disease/total person-time at risk
Presented as something like “cases per 100 people” - just multiply result by 100

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2
Q

Formula for relative risk:

A

(No. of cases per exposure group/total no. of individuals in that exposure group)/(no. of cases in non-exposed group/total no. of individuals in non-exposed group)

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3
Q

Formula for odds ratio:

A

(No. of cases in exposed group/no. of non-cases in that group)/(no. of cases in non-exposed group/no. of non-cases in that group)

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4
Q

Risk difference formula:

A

(No. of cases in exposure group/total no. of individuals in that group) - (no. of cases in non-exposure group/total no. of individuals in that group)

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5
Q

What is the standard deviation?

A

The square root of the variance

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6
Q

Describe the sensitivity of a diagnostic test

A
  1. Pr(B|A)
  2. The probability that a person with the disease has a positive test result
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7
Q

Describe the specificity of a diagnostic test

A
  1. PR(not B|not A)
  2. The probability that a person without the disease has a negative test result
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8
Q

Describe a false positive fraction in a diagnostic test

A

1 - specificity OR Pr(B|Not A)

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9
Q

Describe a positive predictive value in a diagnostic test

A
  1. Pr(A|B)
  2. The proportion of patients with positive test results who have the disease
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10
Q

Describe a negative predictive value in a diagnostic test

A
  1. Pr(not A|not B)
  2. The proportion of patients with negative test results who don’t have the disease
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11
Q

When π is not known…:

A

It is estimated using p=X/n, the proportion of successes in the sample data

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12
Q

The mean of a binomial distribution (nπ) is estimated by:

A

np

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13
Q

What is the multiplier for 95% CI?

A

1.96

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14
Q

What is the multiplier for 99% CI?

A

2.58

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15
Q

Type I error:

A

Rejecting H0 when H0 is actually true (false positive)

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16
Q

Type II error:

A

Retaining H0 when HA is actually true (false negative)

17
Q

Power:

A

Power is the ability of a study to correctly detect a true effect (reject the null hypothesis when it is wrong)