What is a type II error?

A type II error (β) is the probability not rejecting the null hypothesis when it is false --> false negative error

What is power?

The probability of rejecting the null hypothesis when it is false.

Power= 1 - β

You can increase power (by decreasing β) by increasing the sample size, expected effect size, or precision of measurement.

What is a type I error?

A type I error (α) is the probability of incorrectly rejecting a null hypothesis --> false positive error α is often set to < 0.05

Formula for calculating confidence interval?

Mean ± Z(SEM)

For 95% CI, Z = 1.96

For 99% CI, Z = 2.58

SEM = SD/√n

How to appropriately use statistical tests?

Compare means of 2 groups --> t-test

Compare means of 2+ groups --> ANOVA

Compare percentages or proportions of categorical variables in 2+ groups --> Chi-square

Describe accurary vs. precision

Accuracy = Validity = trueness of a test measure (absence of systematic error or bias)

Precision = reliability = consistency and reproducibility of a test (absence of random variation)

Describe sensitivity

Sensitivity = true positive rate = a/(a+c)

SNOUT --> a highly sensitive test with a negative result rules out disease (low false negative rate)

A high sensitivity test is good for screening in populations with low prevalence

Describe specificity

Specificity = true negative rate = d/(d+b)

SPIN --> a highly specific test with a positive result rules in disease (low false positive rate)

Used for confirmation after a positive screening test or in the case of severe disease

Describe positive predictive value

Positive predictive value = probability that a person actually has the disease given a positive test result = a/(a+b)

As prevalence ↑, PPV ↑

Describe negative predictive value

Negative predictive value = probability that a person is disease-free given a negative test result = d/(c+d)

As prevalence ↑, NPV ↓

Describe the distribution of a normal curve?

Describe how sensitivity and specificity change based on a test's cut-off point?

Sensitivity increases as the cut-off decreases/moves to the left.

Specificity increases as the cut-off increases/moves to the right

How do you calculate likelihood ratios?

Positive likelihood ratio = Sensitivity/(1-Specificity) = likelihood of having the disease given a positive result

Negative likelihood ratio = (1-Sensitivity)/Specificity = likelihood of having the disease given a negative result

How do you calculate attributable risk?

AR = Incidence in exposed - Incidence in unexposed

Attributable risk percent (ARP) = (RR-1)/RR

What is the rare disease assumption?

OR approximates RR when prevalence is low (<1%)

How do you calculate absolute risk reduction and number needed to treat?

ARR = Rate in untreated - Rate in treated

NNT = 1/ARR

How do you calculate relative risk reduction?

RRR = 1 - RR

How do you calculate number needed to treat and number needed to harm?

NNT = 1/(Rate in untreated - Rate in treated) = 1/Absolute risk reduction

NNH = 1/(Adverse event rate in treatment group - Adverse event rate in control group)

How are incidence and prevalence related?

Average duration of disease ≈ Prevalence/Incidence

As duration ↑, prevalence ↑