# 5 Biostats

rename
ambirnba's
version from
2015-10-31 15:33

## Section 1

Question | Answer |
---|---|

Effect size - concept | how big an effect did the intervention produce? |

Effect size - equation | risk of a given outcome (e.g. death) w/ intervention - risk of outcome w/o intervention |

Linear regression | indicates how a dependent/outcome variable (y) changes in relation to changes of an independent/predictor variable (x), e.g. y = mx + b |

Multiple linear regression | indicates how dependent variable (y) changes in relation to changes in >1 independent variable (x1, x2, etc.) |

r | correlation coefficient; tells you how well your linear regression line fits the data Ranges between -1 and 1; close to |1| → very good correlation |

r^2 | coefficient of determination; tells you how much of the variation in y is accounted for by x |

Selection bias | subjects don’t accurately reflect population. e.g. b/c volunteers have their own agenda; very sick decline participation; no outreach to underrepresented groups |

Recall bias | people’s memories of past exposures influenced by various factors e.g. difficult to remember diet; people tend to search for explanations for unpleasant experiences, such as illnesses, & may over-emphasize importance of certain factors as a result |

Response bias: | people lie.. e.g. obese subjects tend to under-report actual weight |

Misclassification bias: | exposure or outcome may be misclassified. e.g. due to unclear records, mix-ups of people w/ similar names, improper calibration |

Publication bias: | negative/equivocal results tend not to be published |

Bias from confounding: | when 3rd variable associated w/ both exposure & outcome variables (adjust for confounder → becomes covariate) |

Performance bias: | when skill has an impact on exposure variable (e.g. surgical technique performed), must account for that in analysis (e.g. by randomizing which surgeon operates on which pt) |

Hawthorne effect: | process of being observed changes people’s behavior |

Digit preference: | people prefer to round #s to end in 0 or 5; may skew outcome data |

Attrition bias: | people may drop out of study at different rates (may be skewed towards best or worst outcomes) |

what kind of study reduces risk of bias? | Randomized control trial |

## Section 2

Question | Answer |
---|---|

Lead time bias: | perceived increase in survival time of a disease due to screening methods that enable earlier diagnosis, without actually altering course of disease or improving survival |

Length time bias: | slower-growing cancers have greater probability of being found by screening, and are more likely to be curable, thus making screening appear to be more effective than it actually is; screening programs tend to miss the most aggressive cancers |

External validity | how generalizable is this study? How well do the conditions of the study approximate the real-world situation we care about? |

Internal validity | how sure are we that the change in the dependent variable is due to the change in the independent variable? How well was the study designed (incl. eliminating confounders), and how well was it run? |

scale: nominal | data in categories |

scale: ordinal | ranking |

scale: interval | consistent difference between each |

scale: ratio | interval that drops to zero |

if _________, you can reject the null hypothesis and accept the alternate hypothesis | p<0.05 |

Type I error: | rejecting a true null hypothesis May be due to errors in study design (e.g. more healthy people receive the treatment and more sick people receive the placebo, so the treatment looks effective even though it’s not) |

α is your risk of making a type __error - it is the probability that the results____. usually it is set at _____ | 1; will show something that is not really present, that the results were just a matter of chance; 5% |

Power = | 1-β |

β = the probability that _____; by convention, β is generally set at ______. β is the probability of making a type _____ error | the sample size is too small to detect a difference; 0.20. ; II |

You can increase power by increasing | sample size |

type II error is _____. you run a risk of making this error if.... | missing something that is actually there.... if your sample size is too small |

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