1. Power, Sample Size and Confounding

2. Kofinas et al, “Adjunctive Social Media for More Effective Contraceptive Counseling: A Randomized Controlled Trial”

3. CONSORT http://www.consort-statement.org/consort-statement/overview0/
CONsolidated Standards Of Reporting Trials
CONSORT statement
Minimum set of recommendations for reporting clinical trials
Includes checklist
Flow Diagram

7. Registration of Clinical Trials
Done through
Why do it?
Legal requirement for certain types of trials
Requirement for publication
Records results in standardized format
Informs patients and clinicians
Reduces publication bias and sets context for studies in the literature
Assists sponsors with efficient allocation of resources

8. Power and Sample Size
Power of a statistical test is the probability of correctly rejecting the null hypothesis when the null is false
Ability to detect an effect when there really is one
The higher the power, the lower is the chance of a Type II error (false negative)
Increase power of a study, by increasing sample size
Should we continue enrolling patients once an adequate sample size is reached?

9. Other Considerations
Can the results of this study be generalized to your patients?
Study participants recruited from well-educated population
What else can be done to improve patient recall following counseling?
Consider health literacy level of your patients
Ask patients which media, formats they prefer

10. Aiken et al, “Factors Influencing the Likelihood of Instrumental Delivery Success”

11. Confounding
A confounder is a factor associated with both your independent variable of interest and the outcome of interest
Physician variability may be associated with both maternal characteristics and whether the instrumental delivery was successful
Your results may be biased if you don’t adjust for confounders

12. Adjusting for Confounders
In the study design
Matching – match cases to controls for the factor that is the suspected confounder
Matched unsuccessful with successful deliveries occurring within same shift
In the analysis of the data
Stratification – run separate analyses
Separate analyses by obstetrician type
Statistical adjustment – use multivariate analyses, dummy variables

13. Presentation of Results
Table 1 – data are reported as numbers rather than proportions
The larger the group, the larger the numbers
Statistical tests pertain to the proportions
For example rotation required:
Comparing 317/3552=8.9% with 48/246=19.5%
Report proportions for dichotomous (yes/no) and categorical variables

14. Interpretation of Results (Tables 2-5)
Dependent variable is dichotomous (successful or unsuccessful)
Independent variables are NOT all dichotomous
Caution in interpretation of the Odds Ratios
Clinical relevance not easy to determine
Table 2 – Time fully dilated OR 1.01 ; 95% CI ( )
Time fully dilated is in minutes not (yes/no)