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TRANSCRIPT
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Copyright 2009, The Johns Hopkins University and John McGready. All rights reserved. Use of these materials permitted only in accordance with license rights granted. Materials provided “AS IS”; no representations or warranties provided. User assumes all responsibility for use, and all liability related thereto, and must independently review all materials for accuracy and efficacy. May contain materials owned by others. User is responsible for obtaining permissions for use from third parties as needed.
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Section B
Logistic Regression and Case Control Studies
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3 Continued
Logistic Regression
We can always translate coefficient estimates into (adjusted) odds ratios
Odds ratios can be estimated regardless of what type of study we have
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4 Continued
Logistic Regression
Logistic regression is very useful in case-control studies for controlling confounding, estimating, adjusted relationships
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5
Logistic Regression
In cohort studies, we can translate our logistic regression results into estimates of prevalence (recall curves from sepsis example) and we can calculate adjusted RR
We can only calculate adjusted OR for case control studies
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6 Continued
Example
Example Risk factors for esophageal cancer Choose 200 cases and 778 controls Ask about exposures—tobacco and alcohol
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7 Continued
Example
We over-sampled cases
Thus we cannot estimate the probability of getting cancer
So we can’t draw graphs like in the sepsis example from the last lecture that display the probability of being a case
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8
Example
We can still do a logistic regression where case/control is the outcome variable and tobacco and alcohol are the x’s and obtain odds ratios
We can calculate adjusted odds ratios
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9 Continued
Esophageal Cancer Example
Portion of results table related to tobacco
* Also adjusted for age (linear term), which was signi"cant (p < .001)
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10 Continued
Esophageal Cancer Example
Portion of results table related to tobacco
* Also adjusted for age (linear term), which was signi"cant (p < .001)
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11 Continued
Esophageal Cancer Example
After accounting for tobacco and age, the odds ratio associated with high alcohol consumption (> 120+ grams/day) is 36 relative to low consumption (0–39 gm/day)
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12
Esophageal Cancer Example
High alcohol consumption is associated with an almost 36 fold increase in risk of esophageal cancer even after accounting for the effects of tobacco and age
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13 Continued
Adjusting for Confounders in Case-Control Studies
Include the confounder as a variable in the logistic regression
For example, age was included in the esophageal cancer example
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14 Continued
Adjusting for Confounders in Case-Control Studies
Match cases with controls For example, each case that is identi!ed could be
matched to a control who is of the same age In this way, the cases and the controls will have
comparable ages
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15
Adjusting for Confounders in Case-Control Studies
Match cases with controls Of course, if you do that you cannot determine if
age is a risk factor because you forced the cases and controls to have similar ages
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16 Continued
Conditional Logistic Regression
A special kind of logistic regression is used for the analysis of matched case-control studies
This is called conditional logistic regression
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17
Conditional Logistic Regression
Conditional logistic regression does an analysis very similar to the usual logistic regression except it keeps track of which case was matched with which control
The computer output and presentation of conditional logistic regression looks very similar to usual logistic regression
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18 Continued
Example
Case-Control Study of Exposure to Medication and the Risk of Injurious Falls Requiring Hospitalization among Nursing Home Residents
Mustard, et al. (1997), AJE, 145: 738–745
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19 Continued
Example
N = 2,972 1,486 cases with injurious falls 1,486 matched controls
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20
Example
Controls drawn from population of nursing home residents, matched on
Sex Year of residence Age Level of dependency Duration of residence in home Chronic disorder
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21 Continued
Results
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22
Results
Continued
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23
Results
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24 Continued
Conclusion
“Matched case-control analyses identi"ed medication classes in which a prescription dispersed in the previous 30 days was associated with an elevated risk of injurious falls: Anti-psychotic drugs (odds ratio = 1.31–95% CI 1.06–1.61) and sedatives/hypnotics (odds ratio = 1.35–95% CI 1.09–1.68).”
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25
Conclusion
“An unexpected protective effect was associated with the use of inotropic agents (OR = .69, 95% CI .54–.89).”
“These results support the hypotheses that psychotropic medications are an independent risk factor for injurious falls in nursing home settings.”