First Aid Behavioral Science Epidemiology/Biostatistics Equations
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 Formula for prevalence?
 Prevalence = (total cases in population at a given time)/(total population)
 Formula for Incidence?

Incidence =
(NEW cases in population over a given time period)/(total population at risk during that time);
*Note: when calculating incidence, don't forget that people peviously positive for a disease are no longer considered at risk.  Prevalence is approximately equal to (formula)?
 Prevalence is approx. to incidence * disease duration
 When is prevalence > incidence?
 chronic diseases (e.g., diabetes)
 When is prevalence = incidence?
 acute diseases (e.g., common cold)
 Sensitivity is the number of [⬦] divided by the number of all people with the disease.
 Sensitivity is the number of TRUE POSITIVES divided by the number of all people with the disease.
 Sensitivity is the probability of a [⬦] given that a person has the disease.
 Sensitivity is the probability of a POSITIVE TEST given that a person has the disease.
 Specificity is the number of [⬦] divided by the number of all people without the disease.
 Specificity is the number of TRUE NEGATIVES divided by the number of all people without the disease.
 Specificity is the probability of a [⬦] given that a person is free of the disease.
 Specificity is the probability of a NEGATIVE TEST given that a person is free of the disease.
 The false [⬦] rate is equal to 1sensitivity.
 The false NEGATIVE rate is equal to 1sensitivity.
 The false [⬦] rate is equal to 1specificity.
 The false POSITIVE rate is equal to 1specificity.
 Formula for PPV?
 PPV = a/(a+b)
 Formula for NPV?
 NPV = d/(c+d)
 Formula for sensitivity?
 sensitivity = a/(a+c)
 Formula for specificity?
 specificity = d/(b+d)
 Number of true positives divided by the number of people who tested positive for the disease?
 Positive Predictive Value (PPV)
 The probability of having a condition given a positive test?
 Positive Predictive Value (PPV)
 The number of true negatives divided by the number of people who tested negative for the disease?
 Negative Predictive Value (NPV)
 The probability of not having the condition given a negative test?
 Negative Predictive Value (NPV)
 Unlike sensitivity and specificity, predictive values are dependent on the [⬦] of the disease.
 Unlike sensitivity and specificity, predictive values are dependent on the PREVALENCE of the disease.
 Odds Ratio (OR)?
 Odds of having disease in exposed group divided by odds of having disease in unexposed group.
 For Odds Ratio, odds are calculated [⬦] as the number with disease divided by the number without disease.
 For Odds Ratio, odds are calculated WITHIN A GROUP as the number with disease divided by the number without disease.
 In what situation does Odds Ratio (OR) approximate Relative Risk?
 if prevalence of disease is not too high.
 Odds Ratio is used for [⬦] studies.
 Odds Ratio is used for CASECONTROL studies.
 Formula for Odds Ratio?
 OR = (a*d)/(b*c)
 Formula for Relative Risk?
 RR = a/(a+b) divided by c/(c+d)
 Formula for Attributable Risk?
 AR = a/(a+b) minus c/(c+d)
 Relative Risk (RR)?
 Disease risk in exposed group divided by disease risk in unexposed group.
 Risk is calculated [⬦] as the number with disease divided by the total number of people in the group.
 Risk is calculated WITHIN A GROUP as the number with disease divided by the total number of people in the group.
 Relative Risk (RR) is used for [⬦] studies.
 Relative Risk (RR) is used for COHORT studies.
 To commit a Type I error (alpha) is to state what?
 There IS an effect or difference when none exists (to mistakenly accept the experimental hypothesis and reject the null hypothesis).
 p is judged against [⬦], a preset level of significance (usually < 0.05).
 p is judged against alpha, a preset level of significance (usually < 0.05).
 p = ?
 p = probability of making a type I error.
 If p < 0.05, then there is less than a 5% chance that [⬦].
 If p < 0.05, then there is less than a 5% chance that THE DATA WILL SHOW SOMETHING THAT IS NOT REALLY THERE.
 Layman's way of describing alpha?
 alpha = you "saw" a difference that did NOT existfor example, convicting an innocent man.
 In a four quadrant box, power lies in what region?
 Power is at the intersection of column H1 (reality) and row H1 (study results)
 In a four quadrant box, alpha lies in what region?
 Alpha is at the intersection of column H0 (reality) and row H1 (study results)
 In a four quadrant box, beta lies in what region?
 Beta is at the intersection of column H1 (reality) and row H0 (study results)
 To commit a Type II error (beta) is to state what?
 There is NOT an effect or difference when one exists (to fail to reject the null hypothesis, when, infact H0 is false).
 Beta is the probability of making a type [⬦] error.
 Beta is the probability of making a type II error.
 Layman's way of describing beta?
 Beta = you did not "see" a difference that does existfor example, setting a guilty man free.
 Qualitative definition of Power?
 Power is the probability of rejecting the null hypothesis when it is, in fact, false.
 Power depends upon what (3 items)?

1. Total number of end points experienced by population.
2. Difference in COMPLIANCE b/w treatment groups (differences in the mean values b/w groups).
3. Size of expected effect.  If you [⬦] sample size, you increase Power.
 If you INCREASE sample size, you increase Power. There is Power in numbers.
 Formula for SEM?
 SEM = SD/(square root of sample size)
 SEM [⬦] SD?
 SEM < SD?
 SEM [⬦] as sample size increases?
 SEM DECREASES as sample size increases?
 For a Normal (Gaussian) distributional curve, SD of 1 = x%?
 SD 1 = 68%
 For a Normal (Gaussian) distributional curve, SD of 2 = x%?
 SD 2 = 95%
 For a Normal (Gaussian) distributional curve, SD of 3 = x%?
 SD 3 = 99.7%
 CI = range from [⬦] to [⬦]?
 CI = range from [mean  Z(SEM)] to [mean + Z(SEM)]
 The 95% CI corresponds to what p value?
 p = 0.05
 For the 95% CI, Z = [⬦].
 For the 95% CI, Z = 1.96.
 If the 95% CI for a [⬦] between 2 variables includes 0, then there is no significant difference and H0 is NOT rejected.
 If the 95% CI for a MEAN DIFFERENCE between 2 variables includes 0, then there is no significant difference and H0 is NOT rejected.
 If the 95% CI for [⬦] or [⬦] includes 1, then H0 is NOT rejected.
 If the 95% CI for ODDS RATIO or RELATIVE RISK includes 1, then H0 is NOT rejected.
 Chi squared checks what?
 difference b/w 2 or more percentages or proportions of categorical outcomes (NOT mean values).
 Chi squared =
 compare percentages (%) or proportions
 r squared =
 Coefficient of determination
 Mnemonic for reportable diseases IN ALL STATES?

"B.A. S.S.S.M.M.A.R.T. Chicken or you're Gone:"
Hep B
Hep A
Salmonella
Shigella
Syphilis
Measles
Mumps
AIDS
Rubella
TB
Chickenpox
Gonorrhea  Which disease can vary by state for reporting?
 HIV
 Medicare Part A =
 hospital
 Medicare Part B =
 doctor bills