Quiz 3 Content Flashcards
Define epidemiology.
Study of the distribution of disease in relation to person, place and time, and measures of risk associated with exposures to disease
The study of how often diseases occur in different groups of people and why
Define incidence.
Count of new cases over a period of time in a population
E.g. Number of new cases of diagnosed Type 2 Diabetes in BC in 2009
Define prevalence.
Total number of cases of the disease in the population at a given time
E.g. Total number of BC residents with cancer in 2009
Discern between mortality and morbidity.
Mortality - death rates
Morbidity – incidence of ill health/disease
Describe epidemiological research.
Exposure → potential cause of an outcome; may be referred to as ‘independent variable’
Outcome → may be influenced by the exposure; also known as ‘dependent variable’ or ‘endpoint’
Compare cross-sectional, cohort, and case-control studies regarding the timing of outcome and exposure.
Cross-sectional: O + E
Cohort: E (follow over time) O
Case-control: O (look back for) E
What are the four steps of case-control study design? [4]
1. Investigator selects cases and controls
2. Data about exposure to possible etiologic factors collected in both groups
3. The frequency/amount of exposure among the two groups is compared
4. An odds ratio may be calculated to compare frequency of exposure among cases compared to controls
How are cases and controls identified for a case-control study?
CASES:
Inclusion criterion: Have the outcome of interest
There should be a defined method for case ascertainment
CONTROLS:
Inclusion criteria: Do NOT have outcome of interest
Must be selected to deal with potential confounding:
1. May choose controls with another condition to control for interaction with health care system
2. May match to cases for age, sex, ethnicity, education, income, area of residence, …
Where are cases and controls selected from for a case-control study?
From the general population:
E.g. registries, households, telephone sampling
Can be costly and time consuming
Prone to low response rate
From clinic/hospital:
Often easier to identify
Typically higher response rate
What are advantages of case-control studies? [4]
Useful for studying:
- Rare conditions
- Conditions with a very long lag between exposure and outcome
- Usually requires fewer subjects than cohort or cross-sectional studies
- Generally quicker and cheaper than cohort studies
What are limitations of case-control studies? [6]
1. Difficult to establish temporality
Did exposure precede disease OR did diet/lifestyle change because of disease?
2. How far back?
Must determine appropriate time period in the past in terms of biological plausibility
E.g. For folic acid and neural tube defect – appropriate to ask about intake from 1 year ago BUT for ω-3 fatty acids and heart disease, how far back to ask about? – 1 year, 5 years, 10?
3. Reliance on Memory
Difficult to recall past practices accurately
4. RECALL BIAS
Cases more motivated than controls to search their memory to make sense of why they have the condition
Thus, cases may be more likely to report past exposure
5. Misclassification
Misclassification of cases
Misclassification of exposure status
6. Cannot calculate disease incidence or prevalence
Proportions of cases in the study sample ≠ proportions with the outcome in the population
What is non-differential misclassification?
Exposure status of cases and controls equally likely to be misclassified
E.g. FFQ was not valid, therefore estimation of exposure was inaccurate in both cases and controls
May affect ability to see an association when one exists
What is differential misclassification?
Cases and controls differ in probability of misclassification
Method of assessing exposure leads cases to be more likely than controls to be misclassified
Eg. If cases and controls are assessed by different research assistants and one of the research assistants overestimates nutrient intake compared to the other
Bias may contribute to a false association
What is the difference between an odds ratio and risk?
Risk (Probability) = likelihood of event of event A total number of events
Odds = likelihood of event A likelihood of alternate event
Example: Risk vs. Odds of rolling a 6 on a die
Risk = 1/6
Odds = 1/5
If the risk is ½ (50%), what are the odds?
1:1
If the odds are 3:1, then the risk is…?
¾ (75%)
If the risk is 1/10 (10%), then the odds are…?
1:9
If the odds are 1:99, then the risk is…?
1/100 (1%)
What is an odds ratio?
= Odds of exposure among cases Odds of exposure among controls
Exposure refers to independent variable, could be nutrient intake, supplement intake etc.
How is an odds ratio interpreted?
OR > 1 → exposure more likely in cases than controls (There is a positive association between the exposure and outcome)
OR < 1 → exposure is less likely in cases than controls (There is a negative association between the exposure and outcome OR there is a protective effect of the exposure)
OR = 1 → no difference in odds of exposure between cases and controls
When is an odds ratio considered statistically significant?
An OR is considered statistically significant when the confidence interval (CI) excludes 1.0
The Confidence Interval is the range of values within which the true population value likely exists
Eg. 95% CI
95% chance that the true value for the population lies within the CI
Eg. 99% CI
99% chance that the true value for the population lies within the CI
For α = 0.05 (Type I error), a 95% (1 – α) CI is used
Eg. OR 3.01 (95% CI 1.86-4.85)
Sample odds ratio = 3.01
95% chance that the true population value lies somewhere between 1.86 and 4.85
Generally, the larger the sample size, the smaller (and more precise) the CI
Generally, the larger the sample size, the larger (and less precise) the confidence interval.
True or false?
False.
The larger the sample size, the smaller (and more precise) the confidence interval.
What three questions should you ask yourself when you interpret an odds ratio?
Is the OR >1 or <1?
Does the CI include 1?
Is the CI very wide or very small?
What is a multivariate (‘adjusted’) odds ratio?
Cases and controls may differ on variables other than the exposure variable that could act as confounders (e.g., age, BMI, income, etc)
To attempt to control for possible confounding, the other variables can be accounted for in analysis → This gives an adjusted or multivariate odds ratio
Non-adjusted odds ratio = crude odds ratio
What is considered a ‘strong’ odds ratio?
>4 or <0.25 = strong
2-4 or 0.25-0.5 = moderate
1.5-2 or 0.5-0.67 = possible but weak
<1.50 or >0.7 = possible but very weak
Describe cohort studies. [4]
Exposure is assessed at baseline
Participants followed over time
Characteristics of those who develop the outcome are compared to characteristics of those who do not
Disease incidence can be determined
What is relative risk?
Likelihood that outcome occurs in exposed group compared to unexposed group
Ratio of the risk among exposed to the risk among unexposed
Null hypothesis → relative risk = 1 (no relationship)
E.g., Subjects who took Vitamin C supplements were [RR] as likely to die from heart disease as subjects who did not take Vit C supplements
When is relative risk statistically significant?
RR is statistically significant when the confidence interval does NOT include 1
What is risk difference?
Risk Difference = (Risk of outcome in exposed group) – (Risk of outcome in unexposed group)
Null hypothesis: RD = 0
What is experimental research?
Researcher controls/manipulates the exposure of interest
Describe when post-test only control group design would be useful and name potential limitations.
Appropriate when pretest is not convenient or possible
Generally okay when groups are randomized (assume similar on most characteristics)
Potential limitations:
Failure of randomization (groups differ)
Inability to assess whether results vary depending on pretest level
Describe the effects of pre-testing.
Taking a pretest could “sensitize” controls to the issue being investigated
May affect knowledge and attitudes
May affect final score, even in the control group
E.g. Study to assess effect of education program on nutrition knowledge and attitudes. By giving pre-test, both controls and treatment group are exposed to the topic – controls may then seek info on nutrition on their own
Describe solomon four-group design.
See image.
Describe cross-over design.
Each subject receives both the treatment and control
Washout period: period between treatments that allows for return to baseline values
Compare each person’s responses to treatment versus control
Each subject serves as their own control
NOTE: Non-crossover study = parallel groups study
What is power in sample size calculations?
Power to detect a difference among groups (if one exists)
Probability of rejecting the null hypothesis if the null hypothesis is false
Probability of NOT making a type II error
Explain the influence of effect size on sample size calculations.
With smaller differences between groups, need larger sample size to detect the differences
Explain the influence of variability on sample size calculations.
With greater variability, need more data points to represent the group
