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Emma > Epi Bio Review Slides > Flashcards

Epi Bio Review Slides Flashcards

1
Q

Prevalence

A

Total # diseased / # total population
- measures disease burden

Also
Prevalence=incidence * duration

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2
Q

Incidence

A

new cases / population at risk during a given time period

Measures rate of appearance of new cases

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3
Q

Cumulative Incidence

A

New cases in a specified time period / Total # at risk at the start of the time period

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4
Q

Incidence Density

A

New cases in a specified time period / # Units of person-time

MOST PRECISE

Because accounts for variable follow-up, including lost-to-follow-up

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5
Q

Observational vs. Intervention studies

A

Observational- investigator just observes and collects data

Interventional- investigator applies some new treatment or intervention and examines the result on a health outcome

^ both can be either descriptive or analytic

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6
Q

Descriptive vs. Analytic Studies

A

Observational or interventional studies can be

Descriptive- measure and report data without addressing a hypothesis

Analytic- address specific hypothesis

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7
Q

Observational Descriptive studies

A

Case report
Case series (*no comparison group)
Cross-sectional
Correlation studies

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8
Q

Cross-sectional studies advantages and disadvantages

A

studies that examine the exposure and outcome at the same point in time (exp. exercise and arthritis)

Advantage – relatively quick and easy
Disadvantage – temporal relationship between exposure & outcome sometimes hard to establish

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9
Q

Observational Analytic studies

A

Cohort studies

Case-control studies

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10
Q

Intervention Descriptive studies

A 
Case report
Case series (exposure is chosen by investigator, no comparison group)
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11
Q

Intervention Analytic studies

A

RCT

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12
Q

Case series studies

A

No comparison group. Tracking pts with known exposure & examining records for exposure and outcome.

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13
Q

Cohort study

A

OBSERVATIONAL STUDY
-researcher does not determine exposure status or intervene in any way. Subjects are chosen based on their exposure status.

  • multiple outcomes can be assessed
  • researchers determine incidence rate of outcome, so can use RR or OR to determine association between exposure and outcome.
  • Good for rare exposures, bad for rare outcomes.

Exposure is not randomized –> difficult to match subjects in exposed and unexposed groups, makes cohort studies subject to confounding.

Fewer ethical issues vs. RCT

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14
Q

Prospective Cohort Study

A

subjects assembled based on exposure status at the beginning of the study, then followed up in real time until the study ends at a pre-specified time (ex. ten years).

  • Exposure definition should be clear and precise
  • Assessment of outcome should be blind to exposure
  • Good at establishing true risk, but expensive and need lg #s
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15
Q

Retrospective Cohort Study

A

subjects with historical records of both exposure and outcome are gathered; the follow-up has already occurred in the past.

  • Assessment of exposure should be blind to outcome
  • Assessment of outcome should be blind to exposure
  • MORE BIAS PRONE
  • can be relatively quick/inexpensive
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16
Q

Case-control study

A

OBSERVATIONAL STUDY
-All case-control studies start by assembling cases of the disease and controls without the disease. (subjects selected based on their outcome status, cannot be randomized –> more subject to confounding)

  • Case-control status should be assessed blind to exposure status. After cases and controls have been identified, exposure should be assessed blind to outcome.
  • Multiple exposures can be assessed
  • Good for rare outcomes; bad for rare exposures
  • Fewer ethical issues vs. RCT
  • Relatively quick/inexpensive
  • Incidence cannot be calculated (bc started with cases, people who already have disease of interest), so cant calculate RR.
  • ONLY odds ratio can be measured
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17
Q

Which types of cases are usually preferred for case-control studies?

A

Incident cases: newly diagnosed, usually preferred

vs prevalent cases: existing at a point in time

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18
Q

Disadvantages of cohort studies

A
  • Bad for rare outcomes (selected by exposure status)
  • Time consuming (if prospective)
  • Several sources of bias (e.g confounding, bc not randomized)
  • Loss-to-follow-up
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19
Q

Disadvantages of case-control studies

A
  • Bad for rare exposures
  • Difficult to choose valid controls
  • Many sources of bias (recall & selection)
  • Cannot (usually) measure incidence
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20
Q

Randomization

A

Randomization: Allocation to a treatment group based on chance

Helps remove bias

Removes investigator & volunteer treatment preference (e.g. investigator cannot put sickest patients in exciting new treatment arm)

Balances the trial arms = similar risk profiles (esp. large studies)

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21
Q

Intention-to-Treat Analyses

A

participants are analyzed in the group in which they were originally randomized, regardless of whether or not they adhered to their treatment

(exp: If a person allocated to the experimental treatment group doesn’t take the experimental drug, they will still be analyzed as a part of the experimental treatment group)

22
Q

RCT

A

“Gold standard” –> internal validity (absence of bias) is greater than in other study designs

  1. Enroll participants according to strict inclusion and exclusion criteria
  2. Allocate participants to treatment groups by randomization
    - Allocation should be blinded to both the participant and the researcher
  3. Follow participants for a relevant period of time
  4. Ascertain outcome of participants using objective procedures
  5. Analyze results (intention to treat analysis)
23
Q

How does placebo reduce potential bias?

A

By assessing the impact of the treatment over the placebo effect, the investigator can determine the impact of a
new treatment

  • Removes investigator influence by blinding
  • Removes volunteer influence by blinding.
24
Q

Odds Ratio

A
  • assesses the association between an exposure and an outcome by comparing the odds of the outcome occurring in the exposed group vs. the unexposed group

ad/bc

25
Q

3 criteria for confounding

A

Confounding occurs when there is an imbalance in risk factors across study arms

  1. Risk factor for outcome
  2. Associated with the exposure under study (unevenly distributed among exposure groups)
  3. Cannot be a mediator in causal pathway between exposure and outcome
26
Q

Avoiding confounding

A
  1. Randomization: helps balance risk factors across study arms (RCT)
  2. Restriction (restrict to all women if worried about gender)
  3. Matching (Exp. If worried about confounding by smoking - match one exposed (smoker) to one unexposed who smokes)
  4. Stratification of results (Adjusting for confounders) - calculate association (RR or OR) within confounding groups
  5. Multivariate analysis (adjustment for multiple cofounders at once) - fancy form of stratification; key words are “adjustment” or “control”
27
Q

Selection bias

A

Measure of association between the exposure and outcome is not representative of the true association in the target population (depression –> stress and aging)
*inflated risk ratio

To avoid selection bias, study subjects should be assembled in a manner that dissociates the exposure from the outcome; volunteers should not be aware of the study hypothesis

primarily a problem for retrospective studies as the outcome has already occurred

Randomized trials cannot have selection bias (joined the study before they know their exposure status, and because the outcome has not yet occurred)

28
Q

Random misclassification

A
  • Always towards the null (RR=1)
  • happens in same groups (exp. subjective measures like in yoga trial - exposure affected) (exp. difficult disease to diagnose accurately - outcome affected), affects both exposure or outcome groups equally
  • to avoid –> use well-defined and precise measures
29
Q

Non-random misclassification

A
  • Can bias toward OR away from null
  • effects only ONE of the groups (moms with FAS babies lie about alcohol use –> outcome group affected) (Investigators over-diagnose lung cancer (outcome) in smokers –> exposure group affected)
  • to avoid–> use blinding of investigators or volunteers
30
Q

Loss-to-Follow-up Bias

A
  • Exp. in RCT, if sickest people leave placebo group to get other tx, results show healthier placebo group –> underestimated difference between experimental tx and placebo
  • if the loss to follow-up is not related to the exposure or outcome, it occurs at random, and does NOT cause bias.
  • To avoid –> stay in touch with volunteers
31
Q

Bradford Hill’s viewpoints

A

-NOT needed for RCTs

  1. Strong association
  2. Consistency across studies
  3. Specific result (one cause–>one outcome)
  4. Temporality (exposure precedes outcome?)
  5. Biological gradient (direct relationship between the risk factor and people’s status on the outcome variable)
  6. Biological plausibility
  7. Coherence (between epidemiological and laboratory findings)
  8. Experimental Evidence
  9. Analogy (exp. induced smoking in lab rats –> lung cancer
32
Q

Data types

A
  1. Nominal: unordered (hair color), binary if only 1 of 2 values (HIV –> pos/neg)
  2. Ordinal: breast cancer staging (0-IV)
  3. Discrete data: restricted to specific values, exp. number live births, number joints with arthritis
  4. Continuous: not restricted to any specific value (height, weight, LDL CHL) *use histogram or box plots
33
Q

Positive (right) skew

A

median < mean

*more common

34
Q

Negative (left) skew

A

Mean < median

35
Q

Interquartile Range

A

Compute the difference between the 75th and 25th percentiles.

36
Q

The 68-95-99 % rule for normal distributions

A 
In a normal distribution:
- 68% of the values lie within 1
standard deviation of the mean
- 95% lie within 2 standard
deviations of the mean
- 99% lie within 3 standard
deviations of the mean
  • The mean, median, and mode are equal
  • The curve is bilaterally symmetric
37
Q

How to interpret z-scores

A

z-score: number of ‘Standard deviations’ that
‘the value’ is above or below the ‘Mean’

z = (Value – Mean) / Standard Deviation

If a person’s z-score is 0, then you know their data value is average. If it is 1, then you know their data value is slightly higher than average, but not impressively so. If their z-score is 6, then you know their data value is WAY out in the upper tail of the distribution. This could be a good thing (if we are measuring high jump ability, for example) or a bad thing (if we are measuring LDL cholesterol).

z-score of 0 –> at the median (50th percentile)

38
Q

Interpretation of the

95% Confidence Interval

A

Range of values that we expect includes the true population parameter we are estimating (whereas a sample mean is our best estimate of the population parameter)

We are 95% confident that the lower and upper bounds of the interval will contain the true value we are trying to estimate.

exp. “95% certain that the true population mean glucose is between 105.7 and 107.9 mg/dl.”

Bc in repeated samples, 95% of the confidence intervals will contain the unknown population parameter we are trying to estimate.

-studies w/ larger sample size –> smaller CI

39
Q

Confidence interval calculation

A

• If a CI includes the null value, do not reject HO
- Example: RR = 1.23 CI = 0.95, 1.43, do not reject Ho
• If a CI does not include the null value, reject Ho

40
Q

SE vs. SD

A

standard error (SE) is the standard deviation of many sample means

standard deviation (SD) –> single sample

41
Q

Things that make confidence interval wider (less precise in capturing the true population value)

A

Increasing the level of confidence, say from 95% to 99%.

More variability among the observations (i.e., larger standard error)

A smaller sample size

42
Q

Assessing p-value

A

If P-value < .05 Reject Null Hypothesis
If P-value < .05 DO NOT Reject Null Hypothesis

“Given the null hypothesis is true, the probability of obtaining a result at least as extreme as the one observed.”

The p value does NOT tell us:
How likely the Ho is true
How likely it is that Ha is true 
The clinical significance of the result
Anything about the role of bias or confounding in the study
43
Q

Two sample t-test

A

Used to compare the means of two groups (the outcome is a continuous variable and the exposure is binary)
• Example: Comparing the mean blood pressures between men and women

Abstract: “compared two means” and outcome is continuous –> two-sample t-test

44
Q

Chi-square test

A

Appropriate when both exposure and outcome are nominal (often binary)
Used to compare two or more proportions (percentages)

Allows you to calculate a p-value associated with RRs and ORs
• Compare observed values to expected values (expected values if Ho were true)
- If observed values are similar to expected values (small χ2), Ho is plausible
- If observed values are different than expected values (large χ2), Ho is rejected

45
Q

Type I error rate

A

Type I error rate: reject HO, when HO is actually true

- α = 0.05 means that there is a 5% chance of a type I error

46
Q

Type II error rate (β)

A

do not reject HO, when HO should

be rejected in favor of HA

47
Q

Power (1-β)

A

probability that a statistical test will result in

a correct rejection of HO

48
Q

Pearson Correlation Coefficient (r)

A

quantifies the strength (magnitude) and direction of linear relationships between two continuous variables

Has no units
- Ranges from -1 to +1
- 0 = no linear relationship
-1 = perfect inverse linear relationship
\+1 = perfect positive linear relationship

Problems: some variables may be related to each other, but not linearly; may distort data if extreme data points exist

49
Q

Linear Regression

A

y= β0+ β1 X+ ε
• Used to determine how the outcome variable (Y) changes as we change the level of exposure (X)

• Allows us to quantify the association (slope of the line, β1)
• Outcome must be continuous (exp. bp); exposure may be dichotomous,
ordinal, discrete, or continuous
• The regression line minimizes the space between the data
points and the best fit line
• Slope (β1) is the change in Y for a one-unit change in X
• Intercept (β0) is the value of Y when X is zero

50
Q

Logistic Regression

A

used when the outcome is binary (yes/no, exp. mortality, MI); produces ADJUSTED odds ratios; can be used with one or multiple exposures

51
Q

Cox Proportional Hazards Regression

A

outcome is also binary (mortality) but INCLUDES TIME ELEMENT

useful for adjusting survival analyses for extraneous variables (i.e. confounders and effect modifiers); yields hazard ratios (like relative risks)

52
Q

Multivariable Regression/Multiple Linear Regression

A

assesses multiple exposures and potential confounding, used to identify effect modification
(same as linear regression, but with at least two exposure variables)

Emma (18 decks)

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