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

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

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!

Vintzileos and Ananth AJOG 2010;202:344.e1-6 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

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

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

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

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

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

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…!

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 http://www.dagitty.net/

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)

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

Results section Errors to avoid First Avoid Wrong Interpretation/Conclusions

Introduction Materials & Methods Comment Results Human Brain Analysis Information Conclusion

Vintzileos and Ananth AJOG 2010;202:344.e1-6 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

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

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

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

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

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

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)

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.

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

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 2002-2013) 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

If you can’t convince them, confuse them Harry S. Truman (http://www.brainyquote.com/quotes/authors/h/harry_s_truman.html) Clarification Don’t follow Harry Truman’s advice in your manuscripts