First Aid Behavioral Science Epidemiology/Biostatistics Equations
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- Question
- Answer
- 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 1-sensitivity.
- The false NEGATIVE rate is equal to 1-sensitivity.
- The false [⬦] rate is equal to 1-specificity.
- The false POSITIVE rate is equal to 1-specificity.
- 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 CASE-CONTROL 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 exist--for 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 exist--for 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