Research Design Flashcards

1
Q

If the factor (cause) is present, the effect (disease) will always occur.

A

Sufficient cause:

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2
Q

The factor (cause) must be present for the effect (disease) to occur; however, a necessary cause may be present without the disease occurring.

A

Necessary cause:

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3
Q

: If the factor is present, the probability that the effect
will occur is increased.

A

Risk factor

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4
Q

The factor exerts its effect in the absence of intermediary factors (intervening variables).

A

Directly causal association

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5
Q

The factor exerts its effect through intermediary factors.

A

Indirectly causal association

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6
Q

The relationship between two variables is statistically significant, but no causal relationship exists because the temporal relationship is incorrect (the presumed cause comes after, rather than before, the effect of interest) or because another factor is responsible for the presumed cause and the presumed effect.

A

Noncausal association

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7
Q

○ Process of answering question that can be answered by appropriate collected data

A

Research

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8
Q

○ Rules that govern the process of collecting and arranging data for analysis

A

Research Design

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9
Q

○ Creating educated ‘guesses’ about a phenomenon

A

Hypothesis generation

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10
Q

○ Making predictions from hypothesis and examining data to determine if predictions are correct.

A

Hypothesis testing

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11
Q

To describe the pattern of health problems accurately or enable a fair, unbiased comparison to be made between a group WITH and a group WITHOUT a risk factor, disease, or a preventive or therapeutic intervention

A

Research Design

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12
Q

○ If cause is present, disease will occur ○ Example: genetic abnormalities lead to some fatal diseases (HLA-B27 gene for Ankylosing Spondylitis)

A

Sufficient Cause

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13
Q

○ Cause must be present for disease to occur, although it does not always result in the disease ○ Example: MTB as prerequisite for tuberculosis (though some people can be asymptomatic carriers)

A

Necessary Cause

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14
Q

○ Exposure, behavior, or attribute that, if present and active, increases the probability of a disease to occur

A

Risk Factor

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15
Q

■ First and most basic requirement for a casual relationship to exist between outcome of interest and presumed case

A

Association

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16
Q

■ Difference must be large enough to be “unlikely” if the exposure really has no effect (event is not random)

A

Statistically Significant

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17
Q

○ Occurs when factor under consideration exerts effect without intermediary factors (e.g.blow to the head)

A

Direct Causal Association

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18
Q

○ When one factor influences or more other factors through intermediary variables (e.g.poverty)

A

Indirect Causal Association

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19
Q

○ If presumed cause occurs after the effect

A

Noncausal Association

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20
Q

○ Each of two variables may reciprocally influence the other.

A

Bidirectional Causation

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21
Q

: A differential error that produces findings consistently distorted in one direction as a result of nonrandom factors.

A

Bias

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22
Q

A nondifferential error that produces findings that are too high and too low in approximately equal frequency because of random factors.

A

Random error

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23
Q

: The confusion of two supposedly causal variables, so that part or all of the purported effect of one variable is actually caused by the other.

A

Confounding

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24
Q

: The interaction of two or more presumably causal variables, so that the total effect is greater than the sum of the individual effects.

A

Synergism

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25
A phenomenon in which a third variable alters the direction or strength of association between two other variables.
Effect modification (interaction)
26
- first step in clinical trial involving assembling participants to be studies
Assembly bias
27
participants are allowed to select the study group to join
Selection bias
28
investigators assign participants to study groups in a non-random way
Allocation bias
29
- failure to detect a case or risk factor of disease (false-negative reactions)
Detection bias
30
wrong choice of measurement methods (false increase/decrease)
Measurement bias
31
- members of one group are more likely to remember than the other group
Recall bias
32
○ Investigator simply observes study participants ○ Assignment of treatment group vs control remains outside of investigator’s control
Observational Studies
33
Observational Studies can be classified to
Descriptive and Analytic
34
- no hypothesis specified, use existing date
Descriptive
35
- specified hypothesis, use of new data
Analytic
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○ Investigator has control over participants or variables
Experimental Studies
37
○ Involves an investigation of clinical issues by using anthropologic techniques:
. Qualitative Studies
38
Qualitative studies: anthropologic techniques:
■ Ethnographic observation ■ Open-ended semi structured interviews ■ Focus group discussions (FGDs) ■ Key information interviews (KIIs)
39
Gaol: Provide rich, narrative information that tells story
Qualitative study
40
○ Survey of a population at a single point in time ○ Interviews at home, through telephone, mailed surveys, emails, or web-based questionnaires
Cross-Sectional Surveys
41
Advantages: • Fairly quick and easy to perform • Determines knowledge, attitudes, and health practices
Cross-Sectional Surveys
42
Disadvantages: • Difficulty in determining cause and effect Biased toward longer-lasting and mild disease cases
Cross-Sectional Surveys
43
CROSS-SECTIONAL SURVEY BIASES
Neynam bias (AKA late-look bias) Health participant bias
44
Sever and rapidly fatal diseases are less likely to be found when doing a survey Length bias in screening programs, which tend to find (and select for) less aggressive illnesses
Neynam bias (AKA late-look bias)
45
Not good in testing effectiveness of interventions like vaccination programs where people concerned about their health would less likely expose themselves to diseases, and not as direct result of intervention
Health participant bias
46
- collected soon after symptoms appear in the patient
Acute sera (1st sample)
47
- collected 10 to 28 days later when disease subsides.
Convalescent sera (2nd sample)
48
High IgG, No IgM =
infection occurred in distant past
49
High IgM, Low IgM =
current or very recent infection
50
High IgM, High IgG =
fairly recent infection
51
Relate frequency with characteristics and outcome of interest in the same geographic area
Cross-Sectional Ecological Studies
52
Unit of analysis: populations (not individuals) Useful for suggesting hypothesis (associations) Cannot be used to draw causal conclusions
Cross-Sectional Ecological Studies
53
■ Arriving at general conclusions based only on analyses of group data
Ecological Fallacy
54
○ Measures trend in disease rates over many years in a defined population ○ Use ongoing surveillance or frequent repeated cross-sectional survey data ○ Epidemiologists can determine the impact of these changes on disease rates.
Longitudinal Ecological Studies
55
○ Clearly identified group of people to be studied
Cohort Studies
56
Assembling / choosing people specifically or taking a random sample of a given population Characteristic: groups are typically defined on the basis of exposure and are followed for outcomes.
Cohort Studies
57
The Present Assemble cohorts in the present and collect data on present risk factors (present exposures) The Future Collect data at a time in the future on outcomes that arise
Prospective (longitudinal) cohort study
58
The Past When exposures occur defines cohorts The Present Assemble cohorts in the present based on past risk factors (past exposures), and collect data on present outcomes
Retrospective cohort study
59
The Past When exposures occur that may be associated with outcomes The Present Assemble cases and controls in the present based on presentoutcomes, and collect data on past risk factors (past exposures)
Case-control study
60
The Present Associations between presentexposures and present outcomes (both occurring at a single point in time)
Cross-sectional study
61
○ Investigator selects the case group and the control group on the basis of a defined outcome
Case-Control Studies
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Compares the groups in terms of their frequency of past exposure to possible risk factors Can estimate relative risk (odds ratio) Useful when study needs to be performed quickly and inexpensively or when disease is rare (prevalence<1%) ; Major drawback: potential recall bias
Case-Control Studies
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○ Cohort of participants is first defined ○ Baseline characteristics of participants are obtained by interview, physical examination, and pertinent laboratory or imaging studies
Nested Case-Control Studies
64
Participants who develop the condition = cases Those who don’t develop the condition = control Data from two groups are compared using appropriate analytical methods (patient-time risk) Another variant: case-cohort study
Nested Case-Control Studies
65
○ Patients are enrolled in a study and randomly assigned to one of the following 2 groups: ■ intervention or treatment group ■ control group (given placebo or standard treatment)
Randomized Controlled Clinical Trials (RCCT/RCT)
66
Considered “gold standard” for studying interventions due to minimal bias in patient information obtained
Randomized Controlled Clinical Trials (RCCT/RCT)
67
– only participants are unaware
Single-blind study
68
– participants and investigators are unaware
Double-blind study
69
– participants, investigators, analysts are all unaware (most optimal)
Triple-blind study
70
- is an inert substance or treatment which is designed to have no therapeutic value.
Placebo
71
○ Intervention is usually preventive rather than therapeutic and conducted in the community ○ Participants randomly allocated to receive preventive measure or to receive placebo ○ Followed overtime to determine the rate of disease in each group
Randomized Controlled Field Trials
72
○ Sometimes referred to as “data fishing” ○ Danger of finding data that does not exist
Data dredging
73
○ Committee responsible for reviewing all proposed research and ensuring that it is ethical
Institutional Review Board (IRB)