1. Statistical Methods Section
Cande V. Ananth, PhD, MPH
Columbia University, NY

2. Outline The expectations in AJOG Correct use of analytic methods
Causality versus Confounding
Missing data

3. Expectations in AJOG Clean and transparent statistical analysis
Pushing the boundaries: Innovation
By design
By analytic methods
Addressing potential biases
No longer an option… a necessity!

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Methods of Analysis
Comparison
Continuous response
2 groups, different subjects
Unpaired t-test/ANOVA
≥3 groups, different subjects
ANOVA
Before-after design
Paired t-test
≥3 groups of same subjects
Repeated measures (ANOVA)
Association between 2 vars
Linear regression
Vintzileos and Ananth AJOG 2010;202:344.e1-6

5. Vintzileos and Ananth AJOG 2010;202:344.e1-6
Methods of Analysis
Comparison
Categorical response
2 groups of different subjects
Χ2, Fisher’s exact tests
≥3 groups of different subjects
Χ2 test
≥3 groups of same subjects
Cochran’s Q
Before-after design
McNemar’s Χ2
Association between 2 vars
Pearson’s correlation
Vintzileos and Ananth AJOG 2010;202:344.e1-6

6. Vintzileos and Ananth AJOG 2010;202:344.e1-6
Methods of Analysis
Comparison
Ordinal response
2 groups of different subjects
Mann-Whitney test
≥3 groups of different subjects
Kruskal-Wallis test
Before-after design
Wilcoxon signed-rank test
≥3 groups of same subjects
Friedman test
Association between 2 vars
Spearman’s corr
Vintzileos and Ananth AJOG 2010;202:344.e1-6

7. Vintzileos and Ananth AJOG 2010;202:344.e1-6
Methods of Analysis
Dependent variable
Models
Continuous
Linear regression
Binary
Logistic/log-linear regression
Count
Poisson regression
Survival time
Cox proportional hazards regression
Polynomial regression
Powers of independent variables
Vintzileos and Ananth AJOG 2010;202:344.e1-6

8. Confounding: A Frequent Threat
Am I doing the right adjustments?
Failure to adjust Confounding bias
Over-adjustment Also biased!
Over-adjustment − A frequent issue in several manuscripts
Inappropriate adjustment for variables classified as confounders

9. Preeclampsia-Stillbirth
RR=1.22 (95% CI 1.18, 1.27)

10. We Still Make a Big Deal of Preeclampsia Something Doesn’t Add Up…!
Conclusions… Thus Far
Preeclampsia is
“Protective” for mortality at preterm gestations
Not associated with increased mortality risk at term
Are we done?
We Still Make a Big Deal of Preeclampsia
Something Doesn’t Add Up…!

11. DAG’s Directed Acyclic Graphs
A streamlined set of epidemiologic principles for assessing pathways amongst the exposure, outcome and intermediary variables
Understanding causal relationships
Confounders that qualify for adjustment

12. Unmeasured confounders
Causal Pathway: DAG
Observed confounders
Stillbirth
Preeclampsia GA
Unmeasured confounders
For details, attend Perinatal Epidemiology Session (Wed, 3:00 to 5:00 pm)

13. Missing Data Missing data is very prevalent
More so in observational studies than RCT’s
Deleting missing observations can bias associations
Assigning missing observations to a new level can also lead to bias
Multiple imputation methodology

14. Results section Errors to avoid
First
Avoid Wrong Interpretation/Conclusions

15. Introduction Materials & Methods Comment Results
Human Brain Analysis
Information
Conclusion

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Results
Transparency is essential
Give results for all outcome measures (primary and secondary) that are described under “Materials & Methods”
Do not give results of outcome measures which are not mentioned under “Materials & Methods”
Avoid redundancy
Vintzileos and Ananth AJOG 2010;202:344.e1-6

17. Vintzileos and Ananth AJOG 2010;202:344.e1-6
Results
Do not use “mean” Apgar scores, gravidity, parity
Do not calculate sensitivity, specificity, PPV and NPV if there is intervention as a result of a positive test that can alter the outcome
Case-control studies do not allow for determination of: PPV, NPV, prevalence or incidence of a disease
Vintzileos and Ananth AJOG 2010;202:344.e1-6

18. Vintzileos and Ananth AJOG 2010;202:344.e1-6
Results (text)
Analysis and interpretation of the descriptive and outcome variables (follow the order that figures and tables appear)
Just describe the findings (do not explain the clinical significance)
Highlight the important findings (these findings may or may not be statistically significant)
Do not use percentages without the raw numbers
Do not repeat all the information which is included in figures or tables
Vintzileos and Ananth AJOG 2010;202:344.e1-6

19. Vintzileos and Ananth AJOG 2010;202:344.e1-6
Results (Tables)
Purpose: make easier for the reader to interpret and understand the results
There should be zero ambiguity what the table shows
Each table should stand by its own
Tables do not lie
Make sure that the numbers are consistent with text
Use confidence intervals liberally
Although you have the opportunity to report all your raw data, do not overcrowd (no more than 1 page), make sure that it is pleasant to read
Vintzileos and Ananth AJOG 2010;202:344.e1-6

20. Common Errors With Tables
Confusing labels
Inconsistencies in the numbers (within same table or other tables)
Use of percentages without the raw numbers
Use of raw numbers without percentages
Overuse of uncommon abbreviations
Long tables (>1 page)
Lack of explanatory information as footnote
Vintzileos and Ananth AJOG 2010;202:344.e1-6

21. Are The Comparison Groups Comparable?
Do the comparison groups have similar baseline risks?
Did the comparison groups have similar medical management?
Medical managements depends on the circumstances of care
geographic setting
health care setting
type of HCP
time period
likelihood of confounding interventions
impact of consensus statements
Vintzileos et al AJOG 2014;210:112-6

22. To be used in all comparative To be used in historical control studies
February 2014
To be used in all comparative
studies
(total score 0-8)
To be used in historical control studies
(total score 0-12)

23. The Effect of the Comparability Score: Examples From The Literature
(1)
(2)
1) McPherson JA,et al. Maternal seizure disorder and risk of adverse pregnancy outcomes Am J Obstet Gynecol 2013;208:378.e1-5.
Baud D et al. Expectant management compared with elective delivery at 37 weeks for gastroschisis.
Obstet Gynecol 2013;121:990-8.

24. General Useful Tips
Assume that the reader has average knowledge about the subject
Do not send the reader (or reviewer) to previous publications in order to understand the methodology
Respect the editorial space
Do not repeat the same information
Do not use long sentences
Avoid contradictions

25. N=635 reviewed manuscripts (years 2002-2013)
REVIEWERS’ INFLUENCE
The Relationship Between a Reviewer’s Recommendation and Editorial Decision of Manuscripts Submitted for Publication in Obstetrics (Vintzileos et al Am J Obstet Gynecol 2014;211:703.e1-5)
N=635 reviewed manuscripts (years )
5 Reviewers’Recommenations
Reject
306 (48%)
MJ-RV
187 (30%)
MN-RV or Accept
142 (22%)
Reject
285 (93%)
Accept
21 (7%)
Reject
113 (60%)
Accept
74 (40%)
Reject
47 (33%)
Accept
95 (67%)
Final disposition was independent of seniority, years of review,
years of practice, qualifying degrees or quality of review

26. If you can’t convince them, confuse them Harry S. Truman(
Clarification
Don’t follow Harry Truman’s advice in your manuscripts