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Boards 23' > Biostats/Epi > Flashcards

Biostats/Epi Flashcards

1
Q

Three ways to characterize the center of normal distribution

A

Mean: average of all numbers
Median: middle number of data set when all lined up in order
Mode: most commonly found number

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2
Q

Skewness

A

positive or negative based on location of tail

if tail is pointing toward lower/negative #s then it is negative; if pointing toward larger/positive #s then is positive

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2
Q

least likely to be affected by outlier in central tendency

A

mode
adding one outlier changes mean and median; it will only change the mode if it changes most common number and one outlier is unlikely to change the most common number

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3
Q

Central Tendency key points

A

if distribution is equal: mean=mode=median

mode is ALWAYS at the peak

In skewed data: mean is always furthest away from the mode toward the tail

Mode is the least likely to be affected by outliers

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4
Q

Z score

A

describes a single data point; how far a data point is from the mean

z score of 0 is the mean
z score of +1 is 1SD above mean
z score of -1 is 1SD below mean

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5
Q

Standard of the mean

A

how far is the dataset mean from the true population mean

SEM = SD/number of population squared

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6
Q

Confidence intervals

A

range of 95% of repeated measurements would be expected to fall; 95% chance true population falls within this range

CI95% = mean +/- 1.96*(SEM)

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7
Q

Null hypothesis

A

H0: there is no difference

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8
Q

type 1 (alpha) error

A

there is no difference in reality but our study finds a difference

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9
Q

type 2 (beta) error

A

there is a difference in reality but our study misses it

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10
Q

Power

A

chance of detecting difference
power = 1 - beta

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11
Q

P-value

A

represents chance that the null hypothesis is correct; used to accept or reject the null hypothesis

if p<0.05 we usually reject the null hypothesis; difference in means is “statistically significant”

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12
Q

The 3 ways the power of a study increased

A

Increased sample size (the one thing you can control)
large difference means
less scatter of data

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13
Q

Power calculation

A

1 - Beta (type II error); if want to increase then need to increase the number of subjects for a high power; common power goal is 80%

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14
Q

new drugs that improve survival on incidence and prevalence

A

incidence is unchanged (not preventing new people from getting the disease)
prevalence changes (people are living longer with the disease)

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15
Q

vaccines on incidence and prevalence

A

both incidence and prevalence will fall

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16
Q

test that is good at ruling OUT disease

A

high sensitivity (TP/TP+FN)

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17
Q

test that is good at ruling IN disease

A

high specificity (TN/TN+FP)

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18
Q

a test is negative in 80% of people who do not have the disease is telling you what?

A

the true negative of the test; specificity

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19
Q

a test is positive in 50% of the people who do have the disease is telling you what?

A

the true positive of the test; sensitivity

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20
Q

Positive predicative value (PPV)

A

of all the people that test positive on a test, what percentage of those are true positives?

PPV=TP/TP+FP

21
Q

Negative predicative value (NPV)

A

of all the people that test negative on a test, what percentage of those are true negatives?

NPV=TN/TN+FN

22
Q

Accuracy

A

quantified by the area under the ROC curve (AUC); the more accurate the test is, the closer the AUC value is to 1.0

23
Q

Selection Bias

A

errors in selection or retention of study groups

subtypes: sampling bias, attrition bias, berkson’s bias, nonresponse bias, prevalence bias

24
Q

Sampling Bias

A

a subtype of selection bias; pts do not representative of actual practice

ex: avg age of HF trials pt 65 yrs vs avg age of actual HF pts is 80+ yrs

25
Q

Attrition Bias

A

a subtype of selection bias that occurs in prospective study due to lost to follow-up unequally between the groups

26
Q

Berkson’s Bias

A

a subtype of selection bias that involves hospitalized pts; they may have more severe symptoms and better access to care which may alter the results of the study

27
Q

Nonresponse Bias

A

a subtype of selection bias that occurs with survey and questionnaire studies; the non-responders are not included and the pts that do respond may represent a selected group

28
Q

Prevalence Bias

A

aka Neyman Bias is a subtype of selection bias that occurs in studies trying to associate exposure with disease; exposure occurs long before the disease assessment and pts exposed who die quickly are not included

prevalence of disease based on select group survivors

29
Q

Length-time Bias

A

pts with severe disease do not get studied because they die; or anything to do with a benign disease that’s causing ppl to live longer

ex: analysis of HIV+ pt show it is asymptomatic; may overestimate because severe cases were pts died may have been missed

30
Q

Lead-time Bias

A

screening test identifies disease earlier so survival appears longer when it is not

31
Q

Measurement Bias

A

sloppy research technique\protocol not followed

ex: BP measurement incorrectly in one arm; avoided by standardized data collection

32
Q

Recall Bias

A

form of measurement bias; inaccurate recall of past events by study subjects; common in survey studies

33
Q

Observer Bias

A

form of measurement bias where investigators know exposure status of pt; avoid by blinding

ex: pathologist reviewing specimens knowing the pt has cancer

34
Q

Procedure bias

A

one group receives procedure (surgery) and the other does not; more care and attention given to the procedure pts; avoided by blinding and by using placebo (sham surgery performed)

35
Q

Confounding bias

A

unmeasured factor confounds study results; avoided in stratified analysis

control for confounders by randomization or matching in a case-control study

36
Q

paired t-test

A

compares the mean of 2 related groups; test requires that a quantitative dependent variable (outcome) be evaluated in 2 related groups (ex: matched/paired) groups

comparing data against oneself (pre/post surgery or tx)

37
Q

chi-square test

A

evaluates the association between 2 categorical variables as in a study evaluating the association between sex (ex: male v female) and MI (presence v absence)

38
Q

Crossover study

A

subjects are randomly allocated to a sequence of 2 or more treatments given consecutively; a WASHOUT (no tx) period is often added between intervals to limit the confounding effects of prior tx

39
Q

Accumulation effect

A

the concept of accumulation effect can be applied to disease pathogenesis and exposure to risk modifiers; cumulative exposure to a risk factor or risk reducer must sometimes occur for prolonged periods before a clinically significant effect is detected

40
Q

Ecological study

A

unit of analysis in ecological studies is populations rather than individuals

41
Q

Prevalence

A

equals the incidence rate multiplies by the average disease duration; changing diseases prevalence in a steady-state population w constant incidence rate means that there is an additional factor affecting the duration that prolongs disease duration (improved quality of care) due to pts surviving longer

42
Q

Phase I of clinical trial

A

address whether new treatments are effective and safe for their intended use in a target population; conducted in small number of *HEALTHY subjects

43
Q

Negative correlation coefficient

A
44
Q

Reducing the significance level of alpha (ex: 0.05 to 0.01)

A

allows researchers to report any significant findings with greater confidence

45
Q

Number needed to harm (NNH)

A

NNH = 1/Absolute risk increase

46
Q

Cumulative incidence

A

number of new cases of a disease over a specific period divided by the total population at risk at the beginning of the study; make sure to subtract the people who already have the disease

47
Q

Risk

A

probability of developing a disease over a certain period of time divide the number of affected subjects by the total number of subjects in the corresponding exposure group

48
Q

Relative risk reduction (RRR)

A

1-RR

49
Q

Attack rate

A

ratio of number of ppl who contract an illness divided by the number of people who are at risk of contracting that illness

50
Q

Misclassification bias

A

incorrect categorization of subjects regarding their exposure, outcome status or both; in case-control studies, recall bias usually leads to misclassification of the exposure status

51
Q

allele frequency

A

the frequency of an allele is equal to the # of that specific allele divided by the total # of alleles in the population

Boards 23' (29 decks)

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