Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

CPH 319 > Exam 2 > Flashcards

Exam 2 Flashcards

1
Q

Experimental vs. observational

A 
Experimental= population based with participants randomly chosen and randomly assigned to either exposure or non-exposure
Observational= population based with participants not randomly put in exposed or unexposed (they chose to or not to participate in the risk factor.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Definition of a cohort

A

Any designated group of persons who are followed over a period of time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Cohort study

A
  • Determine exposed/non-exposed groups first, then follow them to determine risk.
  • start with people free of disease
  • usually applied to rare exposures
  • most rigorous observational study
  • also known as prospective, longitudinal, incidence, or follow-up studies.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Strengths of cohort

A
  • good for rare exposures
  • can evaluate multiple effects of exposure
  • clear temporal relationship between exposure and disease
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Weaknesses of cohort

A
  • loss to follow up
  • requires a large sample
  • costly
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Framingham study

A
  • large, longitudinal cohort study in 1948
  • investigated risk factors for CVD (first use of “risk factor”
  • 1948- first cohort
  • 1971- second generation cohort(orig. participants’ children and spouses)
  • 1994- first Omni cohort
  • 2002- third generation cohort
  • 2003- second Omni cohort
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Framingham study risk factors

A
  • high blood pressure and cholesterol
  • diabetes
  • obesity and physical inactivity
  • smoking
  • blood triglyceride and HDL cholesterol levels
  • age
  • sex
  • psychological issues
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Open or dynamic cohort

A

Members come and go and eligibility changes over time

Defined by changeable characteristics like smoking or residence in certain area

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Fixed cohort

A

Membership defined at outset and no new members are gained later. Loss to follow up may occur
Defined by an irrevocable event like serving in the military or surviving natural disaster.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Defining cohorts

A
  • if population is selected before exposures are known they may investigate more than one exposure or outcome and it is a single-sample or population based study
  • if exposed groups are selected at the start based on their exposure then they are likely focusing on one exposure (common for occupational studies)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Types of exposed populations

A

Occupational- like miners, nurses, etc.
clinical- groups undergoing a particular medical treatment
Lifestyle factors or conditions
Veterans of specific wars
Geographically defined areas
Time period defined by exposure experience

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Determining exposure status

A

Exposed- should be disease free but susceptible to disease, representative of exposed population, defined carefully
Unexposed- should be disease free but susceptible to disease, representative of the general population, and from the same underlying population as exposed group

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

How exposure data is collected for cohorts

A

-pre existing records (less expensive but less detail)
- questionnaires, interviews (good info but possible recall bias)
Direct testing (physical exams, environmental sampling, etc. )
-biological sample banks

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

How outcome data is collected for cohorts

A
  • active participation- medical exams, lab tests, questionnaires
  • no active participation of cohort members- death certificates, disease registries, medical records
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Internal comparison

A

Exposed vs. unexposed members of the same cohort

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

External comparison

A

Exposed members of one cohort vs. unexposed cohort from another study

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

General population comparison

A

Members of cohort vs. general population
Common in occupational studies
Healthy worker effect- those that are employed are often healthier than unemployed.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

2x2 table

A

Disease(a). No disease(b)

Exposed (c)

Not exposed (d)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Risk difference

A

[a/(a+b)] - [c/(c+d)] also known as attributable risk

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Relative risk

A

[a/(a+b)]/[c/(c+d)]

First part= risk among exposed
Second part= risk among non- exposed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Interpreting relative risk

A

<1 = risk of disease for exposed is less than the risk of disease for non exposed (protective)

=1 means that the risk of disease is equal among exposed and non exposed (null value)

> 1 = risk of disease for exposed is greater than risk of disease for non exposed.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

95% CI interpretation

A

If it includes 1.0, it is not a statistically different association
If it doesn’t include 1.0, it is a statistically significant association

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Case control study

A

A type of observational analytic study in which subjects are selected on the basis of whether they do (cases) or do not (controls) have a particular disease under study
Also known as retrospective because previous exposure is assessed after identifying cases and controls.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

Strengths of case control studies

A
  • good for rare diseases/outcomes

- can evaluate multiple risk factors for the same disease/ outcome

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

Weaknesses of case control studies

A
  • vulnerable to recall bias
  • may have poor exposure information
  • difficult to infer temporal relationship
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

Finding cases for case controls

A
  • use incident cases instead of prevalence cases
  • patients ( clinics, hospitals)
  • disease registries (cancer, birth defects, trauma)
  • surveys
  • death certificates
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

Finding controls for case control studies

A
  • Patients at clinics/hospitals- illnesses among controls must be unrelated to exposure, have same referral pattern to facility
  • Population- tax lists, voter registration, drivers license rosters, telephone directories, etc.
  • friends, spouses, relatives- likely to share socioeconomic status, race, etc, but cases reluctant to nominate, may share same characteristics.
  • deceased controls.
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

How exposure data is generated for case controls

A

Same as for cohorts

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

Probability

A

The fraction of times you expect to see the event in many trials (range between 0-100%) because they are proportions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

Odds

A

The probability that an event will occur divided by the probability that it will not occur (any positive number because they are ratios)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

Odds ratio

A

The ratio of odds of exposure among cases to odds of exposure among controls

AD/ BC

Provides an estimate of relative risk assuming that:

  • Cases and controls are representative of diseased and general populations, respectively
  • disease is rare(<10% incidence among unexposed).
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

Interpreting the odds ratio

A

<1 means that the odds of exposure for cases are less than the odds of exposure for controls
=1 means that the odds of exposure for cases are equal among cases and controls (null value)
>1 means that the odds of exposure for cases are greater than odds of exposure for controls

OR is sometimes called the point estimate (will always be included in 95% CI)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

Matched case control studies

A
  • purpose is to maximize similarity between cases and controls with respect to factors other than the exposures of interest
  • helps to avoid or decrease confounding
  • Most common to match on age, gender, race
  • frequency matching- does not match individual cases and controls, only matches distribution of characteristics among cases and controls
  • Individual matching- each case is matched to a control with similar characteristics.

Disadvantages: sometimes difficult to find controls who fulfill criteria

  • cannot “unmatch”
  • cannot study the matching factors
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
34
Q

Concordant pairs (case control studies)

A

Both case and control are exposed
Or
Neither case nor control is exposed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
35
Q

Discordant pairs (case control studies)

A
  • exposed case, unexposed control

- unexposed case, exposed control

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
36
Q

Hierarchy of evidence

A 
Systemic review
Experimental
Cohort
Case control
Cross sectional
Case report/case series
Ecological (population)
37
Q

Cross sectional study

A

A study that examines the relationship between diseases and exposures as they exist at one particular point in time

  • like a snapshot of disease and exposure
  • exposure, outcome determined simultaneously
  • also known as prevalence studies because they measure prevalence instead of incidence
38
Q

Cross sectional possible groups

A
  • exposed, have disease
  • exposed, do not have disease
  • not exposed, have disease
  • not exposed, do not have disease
39
Q

Strengths of cross sectional studies

A
  • can often be completed with existing dats, so relatively quick and inexpensive
  • can adjust for effects of other variables (confounding)
  • exposure usually current , easier for subjects to recall
40
Q

Weaknesses of cross sectional studies

A
  • cannot establish temporal relationship between exposure and disease (which came first?)
  • prevalent cases are not necessarily representative of incident cases (those who survive longer more likely to be in study, might be healthier)
  • selection bias- some people might be more likely to participate based on their exposure or disease status.
41
Q

Prevalence ratio

A

An estimate of relative risk

[a/(a+b)]/[c/(c+d)]

42
Q

Prevalence difference

A

[a/(a+b)]-[c/(c+d)]

The difference in prevalence between the exposed and unexposed

43
Q

Sources of data for cross sectional studies

A
  • large national surveys (national health interview study NHIS, national health and nutrition examination survey NHANES, national hospital discharge survey… replaced by national hospital care survey, national nursing home survey, behavioral risk factor surveillance system BRFSS)
  • existing studies
44
Q

Case report

A
  • simplest type of individual- level design
  • more often associated with medical vs. epidemiological studies
  • description of single individuals disease (personal characteristics, symptoms, diagnosis, treatment, outcome)
  • may refrrence other reports, highlighting similarities and differences
45
Q

Case series

A

A survey of a group of individuals with a particular disease

-like a case report, is only descriptive

46
Q

Strengths of case reports and case series

A
  • Important for describing new diseases and conditions and for adverse effects of drugs or other therapies
  • can address several aspects of an individual’s medical history in detail
  • good for studying mechanisms of disease
  • also good for hypothesis generation
47
Q

Weaknesses of case reports and case series

A
  • essentially anecdotal
  • unremarkable cases unlikely to be reported (publication bias)
  • highly susceptible to selection bias
48
Q

Ecology

A

The study of the relationships among living organisms and their environment
-human ecology= study of human groups as influenced by environmental factors including social, behavioral, etc.

49
Q

Ecological study

A

A study in which the units of analysis are populations or groups, rather than individuals

  • geography based analytic units - investigate their association between exposure level and disease are among populations of countries, states, counties, census tracts, etc.
  • temporal trend or time series studies- investigate changes in the exposure level and disease rate in one or more populations over time
50
Q

Ecological analysis

A

Analysis based on aggregated or grouped data

51
Q

Sources of data for ecological studies

A

-vital statistics
- disease registries
- hospital admissions
- department of motor vehicles
- U.S census
-utility companies
- environmental data (weather reports)
-government policies and laws
- large government surveys
ALL DATA SHOULD BE POPULATION BASED

52
Q

Strengths of ecological studies

A
  • good for hypothesis generation (which questions should we be asking?)
  • especially good for rare diseases since analytic units are populations
  • good for evaluation of population- wide changes or differences, perhaps related to policies, laws
  • inexpensive and quick since they rely on existing data
53
Q

Weaknesses

A

Ecological fallacy- inappropriate conclusions regarding g relationships at the individual level, based on observations at the group level

  • strong associations between two factors in group level data are not always good evidence of a casual link at the individual level.
  • very difficult to control for confounding
  • joint distribution of exposure and disease unknown at individual level.
54
Q

Confounding

A

A situation in which a measure of the effect of an exposure on risk is distorted because of the association of exposure with other factors

55
Q

Descriptive statistics

A

Summarize a sample selected from a population
Questions about a sample:
-what is the average value in the sample
- what is the most common value in the sample
- what is the range of values in the sample
- how common is an event or characteristic in the sample

56
Q

Inferential statistics

A

Make inferences about population parameters based on sample statistics

  • inference- a conclusion reached on the basis of evidence and reasoning
  • this boils down to asking questions about a population based on a sample
57
Q

Normal distribution

A

Used as a model of continuous data (ex blood pressure, height, weight, etc.)

  • symmetric about the mean (mean=median=mode)
  • described by mean and standard deviation
58
Q

Mode

A

Most common value

59
Q

Two broad types of statistical inference

A

Estimation- what is the average value in the population, based on the sample

Hypothesis testing-is the average value in the population above a certain value? Is it different than the corresponding values in another population?

For both of these it is assumed that the sample drawn from the population is random (representative of the population)

60
Q

Point estimate

A

The best single value estimate of the parameter
With confidence interval it is :

Point estimate plus or minus the margin of error

61
Q

Hypothesis testing

A

Research hypothesis is generated about an unknown population parameter
- null hypothesis
- res3rch/ alternative hypothesis
Sample data are analyzed and determined to support or refute the research hypothesis

62
Q

5 steps of hypothesis testing

A

5 steps:
1. Determine the null and research hypothesis
Null hypothesis= no change
Research hypothesis= what investigator believes child be true
2. Select test statistic
3. Set up decision rule (p-value)
4. Compute test statistic
5. Draw conclusion and summarize findings

63
Q

Continuous data

A

Data represent measurable quantities, not restricted to specified values. Equal differences between scale values do have equal quantitative meaning (age, temp, blood pressure)

64
Q

Categorical data

A
  • unordered categories (race, state, county, occupation, etc. )
  • two categories (disease/no disease, exposed/unexposed) is BINARY, DICHOTOMOUS
65
Q

Ordinal

A

Order is important

Ex) symptom sevarity(1=fatal, 2=severe, 3=moderate, 4=minor) stages of cancer

66
Q

Two sample t-test (student’s t-test)

A

-compares mean values of some quantity between two UNRELATED groups (categories of people) to determine if they are likely the same

Assumptions:

  • independent observations- no measurements on the same subject
  • normally distributed data for each group
  • equal variances for each group- this is usually safe to ignore if sample sizes are equal and relatively large
67
Q

P-value

A

The probability that the observed result would occur assuming the null hypothesis is true

<0.05 is SIGNIFICANT —-> reject null hypothesis because there is a difference between the means of the two groups

> 0.05 is NOT SIGNIFICANT —-> accept null hypothesis because there is no difference between the means of the two groups

68
Q

Matched pair t-test

A

Used for repeat observations on THE SAME PEOPLE or for observations that are otherwise paired

69
Q

Analysis of variance (ANOVA)

A
  • statistical technique that examines the variation in values within and between groups to determine if they are the same
  • equivalent to t-text with only two groups
  • most commonly used when groups ARE 3+

Null hypothesis: all groups are drawn from the same population with the same mean value

Alternative hypothesis: at least one group comes from a population with a different mean value

70
Q

Assumptions for ANOVA and students t-test

A
  • independent observations
  • normal distribution
  • variances of populations are approximately equal
71
Q

Correlation/ simple regression

A

Used for comparing two continuous variables

X= independent (or predictor variable, exposure) 
Y= dependent  (or outcome variable, disease)

Can also be used to compare rates at the ecological level
Ex) country level smoking rates and county level Athena rates

72
Q

Correlation

A

Nature and strength of linear association between variables

73
Q

(Linear) regression

A

Equation that best describes the relationship between variables

74
Q

Correlation coefficient

A
  • always between -1 and 1
  • sign indicates nature of the relationship (negative= inverse, positive=direct)
  • magnitude indicates strength of association
    0. 8 or more (or 0.8 or less) is generally considered highly correlated
75
Q

Coefficient of determination

A

Expresses the proportion if variance in one variable (Y variable, outcome variable) that is explained by another variable (X variable, exposure variable)

76
Q

Chi squared test

A

Used to examine the relationship between two categorical variables
- for categorical data, proportions or frequencies rather than means
Null hypothesis: no association- observed frequencies =expected frequencies

Alternative hypothesis: association- observed frequencies not equal to expected frequencies

  • non parametric test- makes no assumptions about normal distribution of data because it is not continuous
  • cell sizes should be greater than 5
77
Q

Causation

A

The act of causing something

78
Q

Causality

A

The relationship between the cause and effect (refers to a truly meaningful relationship)

79
Q

Correlation or association

A

A statistical dependence between two or more events, characteristics or other variables

  • correlation or association are merely observations of coincidences that may or may not be related
80
Q

Apparent association

A

“Eyeballing” data, maybe no statistical association

81
Q

Statistical association

A

Correlation, but not necessarily causation

82
Q

Causal association

A
  • also statistical association
  • direct association (straight from factor to disease)
  • indirect association (steps between the factor and disease)
83
Q

Sequence of studies

A

Clinical observations —> available data —> case control studies —> cohort studies —>randomized trials

84
Q

Koch’s postulates

A
  • the organism is always found with the disease
  • the organism is not found with any other disease
  • the organism, when isolated from one who has the disease and cultured through several generations, produces the disease in a new host

Work better for infectious diseases than chronic diseases because chronic diseases have multiple causes, so causation is more complex

85
Q

Hill’s criteria for causality

A

~Strength- the stronger the relationship between the independent and dependent variable, the less likely it is due to chance.
~consistency- multiple observations of association by different people under different circumstances increase belief that the association is real.
~specificity- showing an outcome is predicted by one primary factor ads credibility to causal claim. ~temporality- the cause does precede the effect.
~biological gradient (dose-response)- there should be a direct relationship between the degree of risk and probability of outcome (greater exposure=greater disease).
~plausibility- a rational, theoretical basis for association between the risk factor and the disease.
~coherence- the association does not conflict with other knowledge about the risk and disease.
~experiment- research based on experiments makes the causal inference more believable.
~analogy- using a commonly accepted association in one area to another similar area.

86
Q

Cause of disease

A

Not a single component, but a minimal set of conditions or events that produces an outcome
(Pie is whole cause and slice is component cause)

Could be a number of sufficient causes for one disease ( multiple pies)

87
Q

Sufficient cause

A

A complete causal mechanism that inevitably produces a disease

If one of the individual component factors is missing, disease does not occur

88
Q

Necessary cause

A

A required component cause for a disease to occur
Present in ALL sufficient causes

Ex Ebola doesn’t occur without Ebola virus

CPH 319 (1 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions