1. Journal Club Notes

2. Learning Objectives Explain what is meant by confounding
Identify and apply strategies to address confounding
Explain regression analysis
Use logistic regression analysis when appropriate

3. Confounding
Confounder – variable that is associated with both the risk factor/exposure (independent variable) and the outcome/disease (dependent variable)
Not all variables that predict the outcome are confounders
Failure to control for confounders leads to inaccurate estimates of the association between the risk factor and the outcome

4. Identifying Confounders
From the existing literature
What have other studies identified as confounders?
Run bivariate analysis between
Potential confounder and risk factor/exposure of interest
Potential confounder and the outcome/disease
If you have a statistically significant association in both cases, then you have a confounder

5. Example #1 Smoking, Drug Use and Low birthweight
Is drug use a confounder? YES
Drug use associated with smoking
Drug use associated with low birthweight
Failure to control for drug use will overestimate the association between smoking and low birthweight

6. Example #2 Smoking, multiple gestation and low birthweight
Is multiple gestation a confounder? NO
Multiple gestation associated with low birthweight
Multiple gestation NOT associated with smoking
Failure to control for multiple gestation should not affect the magnitude of the association between smoking and low birthweight

7. Strategies to Deal with Counfounding
Two most common strategies
Stratification
Run separate analysis
Separate analyses for drug users and nondrug users
Multivariate analyses
Statistical method to adjust for confounders
Regression analysis

8. Which Strategy to Use
How large is your sample?
If you have a small sample, stratifying data may result in too small of a sample and inadequate power
You may not be able to detect a significant association even if one exists
Do you have statistical software capable of multivariate analyses?
Excel can’t do this
SPSS and SAS commonly used

9. Multivariable Logistic Regression
Commonly used in epidemiology and health services research
Statistical method used study the effects of multiple risk factors/exposures on outcomes/disease simultaneously
Used when the outcome is binary (has two values – e.g., yes/no)

10. Kawakita et al. Duration of Oxytocin and Rupture of the Membrances before Diagnosing a Failed Induction of Labor

11. Choice of Study Design Why retrospective cohort design?
Randomized control trial not appropriate – can’t randomize length of latent phase
Prospective cohort – very expensive for large sample
Retrospective cohort – data already being collected in a systematic manner; allows for large sample size; much more feasible than prospective cohort
Case-control – would have to start with the outcomes and look backwards; lower level of evidence than cohort
Cross sectional – lose the temporality (cause and effect)

12. Statistical Analyses
Chi-square test – used to compare two or more proportions
Fisher exact test – used to compare proportions when cell size is less than 6
Wilcoxon rank sum test – used to compare means of continuous variables
Nonparametric test
Used when variables are not normally distributed
If normally distributed can use T-test (if comparing two means) or ANOVA (if comparing more than two means)

13. Use of Logistic Regression
Outcomes are binary (Dependent variables)
Chorioamnionitis (yes/no)
Endometritis (yes/no)
Postpartum hemorrhage (yes/no)
NICU admission (yes/no)
Mechanical ventilation (yes/no)
Neonatal sepsis (yes/no)

14. Use of Logistic Regression
For each maternal and newborn outcome, studied the association between that outcome and remaining/exiting the latent phase (key independent variable)
Adjusted for gestational age at delivery, race-ethnicity, BMI on admission, and hospital type
Are these potential confounders?