**Table Of Contents**

**Odds Ratio**

- Calculated in Case Control
- (A/C)/(B/D) or AD/BC
- Control studies to compare exposure of participants with the disease (cases) to those without the disease (controls)
- Remember: Odds of
**exposure**in the cases divided by Odds of**exposure**in the controls (because you are looking back in time). So it’s not like in RR where you are looking forward at**risk of disease/outcome**(**risk of disease/outcome**in exposed vs.**risk of disease/outcome**in non-exposed)

**Relative Risk**

- Calculated in Cohort
- A/(A+B) / C/(C+D)
- Remember: In RR you are looking forward at risk of a disease/outcome in the
**exposed vs unexposed groups** **risk of disease/outcome**in exposed vs.**risk of disease/outcome**in non-exposed- RR comparing groups
- If the RR of an outcome in group A as compared to group B is x then the RR in group B as compared to group A is 1/x

**Calculate Sensitivity**

- TP/(TP+FN)
- probability that pt with a given disease will have positive result, so a negative result helps rule it out
- High sensitivity means low FN

**Calculate Specificity**

- TN/(TN+FP)
- probability that the pt does not have a given disease and has negative result, so a positive result helps rule it in
- High specificity means low FP

**Positive Predictive Value**

- TP/(TP+FP)
- probability that a positive test correctly identifies a pt with the disease
- Since higher specificity decreases FP, then PPV increases as well
- PPV depends on Prevalence, and increases as prevalence increases

**Negative Predictive Value (NPV)**

- TN/(TN+FN)
- probability that a negative test correctly identifies a pt without the disease
- Since higher sensitivity decreases FN, then NPV increases as well

**Likelihood Ratio**

- the probability of a given test result occurring in a patient with the disorder compared with the probability of the same result occurring in the patient without the disorder
- – Independent of prevalence like sens/spec
- – used to grade clinical significance of various results when >2 different test results are possible
- Positive LR= sensitivity/(1-specificity)
- Negative LR= (1-sensitivity)/specificity)

**Number Needed to Treat**

- used to measure efficacy of a therapy and the risk of adverse events
- NNT= 1/ARR

**Number Needed to Harm**

- Number needed to harm=1/ARI
- number of pts that need to be exposed to risk factor over certain period of time before harmful event occurs to 1 patient

**Risk Reduction**

- RR<1 means decreased risk in numerator group
- RR=1 means no difference in risk between groups
- RR>1 means increased risk in numerator group
- RR is a measure of the strength of association between exposure and disease

**Relative Risk Reduction (RRR)**

- RRR= (risk in unexposed-risk in exposed)/risk in unexposed
- RRR=1-RR where RR=risk in exposed/risk in unexposed

**Absolute risk reduction calculation (ARR)**

- subtract risk of tx group from risk of placebo group

**Attributable risk reduction (ARR)**

- (risk in exposed-risk in unexposed)/risk in exposed
- OR
- (RR-1)/RR
- Measure of excess risk

**Type I Error (false positive)**

- Occurs when a study rejects a null hypothesis when it is true
- Usually reflects the significance of a test
- A higher rate of TI errors (denoted by alpha) decreases type II errors

**Type II Error (false negative)**

- Occurs when study fails to reject null hypothesis when it is false
- Related to the power of a study
- TII error=1-power or Power=1-type II error
- (ranges from 0-1)

**Confounding Bias**

- occurs when the exposure-disease relationship is obscured by the effect of an extraneous factor that is associated with both.
- Randomization helps to remove the effects of both known and unknown confounders

**Lead time bias**

- the overestimation of survival due to early diagnosis

**Observer Bias**

- when observers misclassify data due to individual differences in interpretation or preconceived expectations regarding treatment

**Measures of Central Tendency**

- In Right/Negatively skewed mean<median<mode
- In Left/Positively skewed mode<median<mean
- In strongly skewed distributions, median is a better measure of central tendency than mean

**Power of a study**

- Is the ability to detect the difference between two groups. Increasing sample size increases power and narrows confidence interval

**Biostatistics Formula**

Name | Formula |
---|---|

2 x 2 table | Disease vs Exposure Reality ——> I A I B I Test Result –> I C I D I |

Sensitivity | A/(A+C) or TP/(TP+FN) |

Specificity | D/(D+B) or TN/(TN+FP) |

PPV | A/(A+B) or TP/ (TP+FP) |

NPV | D/(D+C) or TN/ (TN+FN) |

Relative Risk | A/(A+B) / C/(C+D) Use in Cohort |

Odds Ratio | (A/C)/(B/D) or AD/BC Use in Case Control |

Absolute Risk Reduction (ARR) | Event Rate in Control – Event Rate in Exposed CER – EER |

Absolute Risk Increase (ARI) | Event Rate in Exposed – Event Rate in Control EER – CER |

Relative Risk Reduction | ARR/Event Rate in Control group CER-EER/CER or also: 1-RR |

NNT | 1/ARR |

NNH | 1/ARI |

Positive Likelihood Ratio | sensitivity/specificity LR+ = sensitivity / (1-specificity) |

Negative Likelihood Ratio | sensitivity/specificity LR- = (1-sensitivity)/specificity |

Standardized Mortality Ratio (SMR) | Observed Number of Deaths/Expected Number of Deaths |

Standardized Incidence Ratio (SIR) | Observed Number of Cases/Expected Number of Cases |

**High Yield Notes:**

- (+) Likelihood ratio = sensitivity / 1-‐specificity = likelihood of having the disease given a positive result. This is different from PPV in that PPV is prevalence dependent.

- (-‐) Likelihood ratio = 1-‐sensitivity/specificity = likelihood of not having the disease after a test result comes back negative. NPV, in contrast, is prevalence dependent.

- PPV increases with increased specificity. NPV increases with increased sensitivity. Therefore a test with the highest PPV will have the highest specificity. A test with the highest NPV will have the highest sensitivity.

- Higher prevalence increases PPV and decreases NPV. Lower prevalence decreases PPV and increases NPV.

- Nominal data is dichotomous and only has two categories (e.g., male vs female).

- Ordinal data has ranking but no numerical value (e.g., freshman, sophomore, junior, senior).

- The median is a better indicator of central tendency (vs mean) in data with a highly skewed distribution.
- Hazards Ratio:
- Measure of how much effect something actually had.
- Value of 1.00 means there is no difference between the two groups.
- A ratio < 1 indicates a protective effect, and > 1 indicates a detrimental effect.
- If the confidence interval of the hazard ratio includes 1.00 (null value), then the effect wasn’t statistically significant.
- If the interval doesn’t include the value, the difference was significant.