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Flashcards in Biostats Deck (48):
1

Specificity

Diagnostic test; probability a test shows non-disease when no disease is present. d/(b+d)

2

Sensitivity

screening test; probaility that test detects disease when disease is present. a/(a+c)

3

Positive predictive value

Proportion of positive test results that are true positives (given a positive test result the chance the person actually has disease). Increases with increased prevalence. a/(b+a)

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Negative predictive value

proportion of negative test results that are true negatives (probability that person actually is disease free given a negative result). Decreases with decreased prevalence. d/(d+c)

5

case-control study

comparing a group with a disease to a group without a disease to identify a risk factor. It measures an Odds Ratio = (a/b)/(c/d)

6

Cohort

Study of an identified risk factor and wether it is associated with a disease. Compares group with exposure of risk factor to a group without exposure. Determines relative Risk (RR) = [a/(a+b)]/[c/(c+d)]; you can see that if the prevalence of the disease is not high the OD is a good estimate of the RR

7

Cross-sectional study

looks at people to asses prevalence of disease and related risk factors at a particular point in time. It determinse Disease prevalence and can show risk factor but NOT causality.

8

Twin concordance

Compares frequency with which both monozygotic or both dizygotic twins develop a disease. Measures heritability

9

Adoption Study

Compares siblings raised by biologic vs. adoptive parents. Measures heritability and influence of environmental factors.

10

Phase I clinical trial

Tests healthy volunteers to assess safety, toxicity, and kinetics

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Phase II clinical trials

Small number of patients with disease are tested for efficacy, dosing, and adverse effects

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Phase III clinical trials

Large number of patients randomly assigned to treatment or placebo. Measures new treatment agains current standard of care.

13

Crossover Study

Subjects are randomly allocated to a sequence of two or more treatments given consecutively (ie initially a placebo or initially treatment then placebo in different orders and comparing the two groups.) Allows for subjects to be own control.

14

Prevalence

total cases in population at a given time divided by the total population. It approximately equals (incidence)*(time), incidence = prevalence for acute and > for chronic diseases

15

Incidence

new cases in population over given time divided by total population AT RISK (people who already have disease are not at risk)

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Odds Ratio

Odds of having disease in exposed goup over odds of having disease in unexposed. (a/b)/(c/d)

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Relative Risk

Probability of getting a disease in the exposed group divided by probability in unexposed group. [a/(a+b)]/[c/(c+d)]

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Attributable risk

Difference in risk between exposed and unexposed groups. a/(a+b) - c/(c+d)

19

Absolute risk reduction

Reduction in risk associated with a treatment compared to standard treatment or placebo. c/(c+d) - a/(a+b)

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Numer needed to treat

1/absolute risk reduction. (number of patients need to treat to get beneficial effect)

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Number needed to harm

1/attributable risk

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Case fatality rate

# fatal cases / total population w disease

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Precision

RELIABILITY. Reproducibility of a test. Absence of random variation

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Accuracy

VALIDITY. Trueness of test measurement

25

Random error

reduces PRECISION

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Systematic error

Reduces accuracy

27

Selection bias

nonrandom assignment to study group (ie Berksons bias)

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Recall bias

knowledge of presence of disorder alters recall by subjects

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Sampling bias

subjects are not representative to general population

30

Late-Look bias

Information gathered at inappropriate time like using a survey to study a fatal disease (patients who are dead can't answer)

31

Procedure bias

subjects in different groups are not treated the same- ie. More care and attention is given to treatment group which leads to more compliace and better overall health compared to control

32

Confounding bias

occurs with two closely related factors. The effect of one factor distorts or confuses the effect of the other. This can be controlled for with Crossover studies while each subject acts as own control

33

lead-time bias

early detection confused with increased survival. Seen with improved screening

34

Pygmalion effect

occurs when a researcher's belief in efficacy changes outcome

35

Hawthorne effect

occurs when group behaves different owing to the knowledge of being studied

36

Observer bias

occurs when investigators decision is affected by prior knowledge of exposure status

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Effect modification

occurs when the effect of a main exposure on an outcome is modified by another variable

38

Positive skew statistical distribution

mode

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negative skew distribution

mode>median>mean

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Type I error (alpha)

investigator falsely rejects the null hypothesis by stating there is an effect or difference when none exists

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Type II error (beta)

Stating there is NOT an effect when in fact one exists (failure to reject null hypothesis).

42

Power

probability of rejecting null hypothesis when it is in fact false (ie finding a difference if one truly exists). Power = 1 - beta. Power increases with sample size

43

Meta-analysis

pools data to increase statistical power. Limited by quality of individual studies or bias in study selection

44

Confidence interval

estimated range of reproducibility. Values in which specified probability of the means of repeated samples would be expected to fall. CI = mean + Z(SEM); SEM = st'd Dev/(sqrt(n)). For 95% CI, Z = 1.96. For 99% CI, Z = 2.58. If the 95% CI for a mean difference between 2 variables includes 0 then there is no significant difference and null hypothesis is accepted. If 95% odds ratio or relative risk includes 1 then null is not rejected. If CI between 2 groups overlaps then there groups are not significantly different.

45

t-test

difference between two means

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ANOVA

Analysis of Variance. Checks difference between means of 3 or more groups

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Chi-squared

compares percentages or proportions between two ro more categorical outcomes (not mean values)

48

correlation coefficient

always between -1 and 1. the coser to 1 the stronger the correlation between 2 variables