Research Design Flashcards
If the factor (cause) is present, the effect (disease) will always occur.
Sufficient cause:
The factor (cause) must be present for the effect (disease) to occur; however, a necessary cause may be present without the disease occurring.
Necessary cause:
: If the factor is present, the probability that the effect
will occur is increased.
Risk factor
The factor exerts its effect in the absence of intermediary factors (intervening variables).
Directly causal association
The factor exerts its effect through intermediary factors.
Indirectly causal association
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.
Noncausal association
○ Process of answering question that can be answered by appropriate collected data
Research
○ Rules that govern the process of collecting and arranging data for analysis
Research Design
○ Creating educated ‘guesses’ about a phenomenon
Hypothesis generation
○ Making predictions from hypothesis and examining data to determine if predictions are correct.
Hypothesis testing
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
Research Design
○ If cause is present, disease will occur ○ Example: genetic abnormalities lead to some fatal diseases (HLA-B27 gene for Ankylosing Spondylitis)
Sufficient Cause
○ 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)
Necessary Cause
○ Exposure, behavior, or attribute that, if present and active, increases the probability of a disease to occur
Risk Factor
■ First and most basic requirement for a casual relationship to exist between outcome of interest and presumed case
Association
■ Difference must be large enough to be “unlikely” if the exposure really has no effect (event is not random)
Statistically Significant
○ Occurs when factor under consideration exerts effect without intermediary factors (e.g.blow to the head)
Direct Causal Association
○ When one factor influences or more other factors through intermediary variables (e.g.poverty)
Indirect Causal Association
○ If presumed cause occurs after the effect
Noncausal Association
○ Each of two variables may reciprocally influence the other.
Bidirectional Causation
: A differential error that produces findings consistently distorted in one direction as a result of nonrandom factors.
Bias
A nondifferential error that produces findings that are too high and too low in approximately equal frequency because of random factors.
Random error
: 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.
Confounding
: The interaction of two or more presumably causal variables, so that the total effect is greater than the sum of the individual effects.
Synergism
A phenomenon in which a third variable alters the direction or strength of association between two other variables.
Effect modification (interaction)
- first step in clinical trial involving assembling participants to be studies
Assembly bias
participants are allowed to select the study group to join
Selection bias
investigators assign participants to study groups in a non-random way
Allocation bias
- failure to detect a case or risk factor of disease (false-negative reactions)
Detection bias
