In the Name of God

1. In the Name of God Biostatistics in Obstetrics Mitra Ahmad Soltani 2008 Med-ed-online.org

2. References: • Ahmad Soltani M. Regression Analysis of Labor Duration. The Internet Journal of Gynecology and Obstetrics. Texas: Vol 5, No 2. 2006 • Clements JM. Synergy Medical Education Alliance Research Design Core Curriculum. Module2&3.2008 • Kramer M et al. Prepregnancy Weight and the Risk of Adverse Pregnancy Outcomes. New England Journal of Medicine.1998 Vol:338, N3:147-152 • Lyon D. Use of Vital Statistics in Obstetrics. emedicine. Dec 2007 • Pritchard JA, MacDonald PC, Gant NF. Obstetrics in broad perspective. In: Williams Obstetrics. 22nd ed. New York, NY: McGraw-Hill; 2005

3. Birth rate number of births 1000 population • It includes men in the population.

4. Fertility Rate number of live births 1000 women aged 15-44 years • While a woman with 2 second-trimester miscarriages would be considered fertile, her deliveries would not be included in the fertility rate.

5. Reproductive Mortality rate contraceptive use plus direct maternal deaths 100000 women • This is  perhaps the most sensitive measure of a population's ability to provide safety for women.

6. Maternal Mortality Rate number of direct or indirect maternal deaths 100,000 live births • A condition in which both mother and fetus are lost would both increase the numerator (maternal death) and decrease the denominator (live birth).

7. Infant Mortality Rate infants who die prior to their first birthday 1000 live Births • IMR is often one of the sentinel indicators used to evaluate a population's overall health and access to health care.

8. Neonatal Mortality Rate losses between 0-28 d of life (inclusive) 1000 live births • This rate is often divided into early (first 7 d) and late (8-28 d) rates, as etiologies within these 2 categories vary somewhat.

9. Fetal Death rate (stillbirth rate) number of stillbirths 1000 infants (total Births) • Infants means “live and still” born.

10. Perinatal Mortality Rate Fetal deaths+neonatal deaths 1000 total Births

11. Still birth • Delivery after 20 weeks' EGA (and more than 500 g birthweight) in which the infant displays no sign of life (gasping, muscular activity, cardiac activity) is considered a stillbirth.

12. Live Birth • Delivery after 20 weeks' EGA in which any activity is noted is classified as a live birth. This is a difficult definition, as the lower limit of reasonable viability currently remains around 23 weeks' EGA. Thus, a spontaneous delivery at 21 weeks' EGA with reflex motion but no ability to survive with or without intervention would nonetheless be considered a live birth.

13. Abortion • The most common definition of an abortion is any loss of a fetus that is less than 20 weeks' completed gestational age (since last menstrual period) or that weighs less than 500 grams.

14. Preterm Infants • Preterm infant is another arbitrary definition because a subtle gradient of maturity exists. Most states define premature as a delivery before 37 completed weeks' gestational age, although the vast majority of babies born after 35 weeks‘ GA have uncomplicated perinatal courses.

15. Postterm Infants • The generally accepted definition of a postterm pregnancy is one that progresses beyond 42 weeks' completed gestational age based on last menstrual period (LMP). In practice, many clinicians use a lower cutoff such as 41 weeks‘ GA when LMP is certain.

16. Testing for statistical significance of the difference for nominal data • Small unmatched sample: Fisher’s exact test • Small matched sample: Sign test • Large unmatched sample: Chi-square, with Yates correction • Large matched sample:McNemar’s test

17. Testing for statistical significance of the difference for ordinal data • One comparison(2 groups),unmatched sample: Mann-Whitney U • One comparison(2 groups)Matched sample:Wilcoxon matched pairs • More than 2 groups unmatched sample: Kruskal Wallis one-way ANOVA • More than 2 groups matched sample: Friedman 2-way ANOVA

18. Testing for statistical significance of the difference for continuous data • One comparison(2 groups)unmatched sample: t-test • One comparison(2 groups)matched sample: matched t-test • More than two groups unmatched sample:F test for analysis of variance followed by pairwise comparisons • More than two groups matched sample: F test for analysis with blocking or analysis of covariance

19. Measure the size of difference • Nominal/ordinal data: differences in proportions or percentages in each category • Continuous data: Differences in mean values between the groups+ SD for each group

20. Tests to Determine Association Between Groups Measure the degree of Association • Nominal data: odds Ratio/Relative Risk • Ordinal Data(nonlinear):Spearman’s rho/Kendall’s tau • Continuous Data: Pearson’s Correlation Coefficient ( r )

21. Tests to Determine Association Between Groups testing for statistical significance of association • Nominal Data: Statistical Significance of odd’s Ratio • Ordinal data(nonlinear): Statistical Significance of rho or tau • Continuous Data(linear):Statistical significance of Pearson’s r

22. Tests to Determine Association Between Groups- Extent Association Explains Variation Between Groups • Nominal data: Attributable Risk • Ordinal data(nonlinear):Spearman’s rho or Kendall’s tau • Continuous data(linear):Pearson’s coefficient of determination

23. For describing one group • Mean, SD for measurement of Parametric Distributions • Median, interquartile range for rank,score or measurement of non-parametric distributions • Proportion for Binominal (2 possible outcomes)

24. Compare one group to a hypothetical value • One sample t-test for measurement of Parametric Distributions • Wilcoxon test for rank,score or measurement of non-parametric distributions • Chi-square for Binominal (2 possible outcomes)

25. Compare two unpaired groups • Unpaired t-test for measurement of Parametric Distributions • Mann-Whitney test for rank, score or measurement of non-parametric distributions • Fischer test(or chi-square for large samples) for Binominal (2 possible outcomes)

26. Compare two paired groups • Paired t-test for measurement of Parametric Distributions • Wilcoxon test for rank, score or measurement of non-parametric distributions • McNemar’s test for Binominal (2 possible outcomes)

27. Compare three or more unmatched groups • One-way ANOVA for measurement of Parametric Distributions • Kruskal Wallis test for rank, score or measurement of non-parametric distributions • Chi-square for Binominal (2 possible outcomes)

28. Compare three or more matched groups • Repeated measures ANOVA for measurement of Parametric Distributions • Friedman test for rank, score or measurement of non-parametric distributions • Cochrane Q for Binominal (2 possible outcomes)

29. Quantify association between two variables • Pearson Correlation for measurement of Parametric Distributions • Spearman Correlation for rank, score or measurement of non-parametric distributions • Contingency coefficients for Binominal (2 possible outcomes)

30. Predict value from another measured variable • Simple linear regression or non-linear regression for measurement of Parametric Distributions • Non-parametric regression for rank, score or measurement of non-parametric distributions • Simple logistic regression for Binominal (2 possible outcomes)

31. Predict value from several measured or binominal variables • Multiple linear or nonlinear regression for measurement of Parametric Distributions • Multiple logistic regression for Binominal (2 possible outcomes)

32. summary

33. P=parametric/N=nonparametric/B=binominalM=matched/ U=unmatched/G=group/~=versus/ H=Hypothetical value

34. Statistics Related to Diagnostic Tests • Sensitivity = True Positives/(True Positives + False Negatives) • Specificity = True Negatives/(False Positives + True Negatives) • Positive Predictive Value = True Positive/(True Positive + False Positive) • Negative Predictive Value = True Negative/(True Negative+False Negative)

35. Likelihood Ratio = compares the likelihood of a result in a patients with the disease to the likelihood of a result in patients without disease. • Positive LR = (a/a+c)/(b/b+d) • Negative LR = (c/a+c)/(d/b+d)

37. How much do LRs change disease likelihood? • – LRs>10 or <0.1 cause large changes in likelihood • – LRs 5-10 or 0.1-0.2 cause moderate changes • – LRs 2-5 or 0.2-0.5 cause small changes • – LRs between <2 and 0.5 cause little or no changes

39. Statistics to Interpret Importance &Precision of Therapeutic Results • Control Event Rate (CER) = c/(c+d) • Experimental Event Rate (EER) = a/(a+b) • Relative Risk (RR) = EER/CER = (a/a+b)/(c/c+d) • Relative Risk Reduction (RRR) = CER-EER/CER • Absolute Risk Reduction (ARR) = CER-EER • Number Needed to Treat (NNT) = 1/ARR

41. Relative Risk and Odds ration There is strong association of RR or OR>1 There is strong association if RR>3 or OR>4

43. Sample size If dependent variable is nominal or ordinal: • n= p (1-p) /d² If dependent variable is continuous: • n=s²/d²

44. Regression analysis of labor duration (A sample research)

45. IntroductionDetermining labor duration has been the focus of different researches . The main aim is to lower the rate of cesarean section and undue hospitalization. Friedman’s, Hendrick’s , and Philpott’s Partographs and Nesheim’s regression equation are the results of such efforts. The advantage of an equation over a partograph is its predictive value in determining obstructed labor in advance and on an individualized basis.

46. WHO partograph

48. Linear Regression Estimates the coefficients of the linear equation, involving one or more independent variables, that best predict the value of the dependent variable. The two-variable model Y = A + B X

49. Materials and methods

50. 230 Laboring women were interviewed and examined according to a checklist from April – August 2004 .