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Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
Biostatistics in Obstetrics - Slide 1
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  • 1. Biostatistics in Obstetrics Mitra Ahmad Soltani 2008 In the Name of God
  • 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=binominal M=matched/ U=unmatched/G=group/~=versus/ H=Hypothetical value P N B Describing Mean &SD Median&range Proportion Compare 1G~H One sample T-test Wicoxon Chi-Square Compare 2GU Unpaired T-test Mann-Whitney Fisher’s Compare 2GM Paired T-test wilcoxon McNemar’s Compare 3GU 1 way ANOVA Kruskal Wallis Chi-square Compare 3GM Repeated ANOVA Friedman Cochrane Q association Pearson Spearman Contingency coef. predict Linear regression Non-para. Regr. Logistic regres.
  • 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)
  • 36. True + =(a) False + =(b) + sum False – =(C) True – =(d) sicks sum
  • 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
  • 38. True + = (a) False + =(b) + sum False – =(C) True – =(d) sicks sum
  • 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
  • 40. True + =(a) False + =(b) + sum False – =(C) True – =(d) sicks sum
  • 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
  • 42. use formula Relative risk RCT & cohorts (a/a+b)/(c/c+d) Odds Ratio Case-control (a/c)/(b/d) or ad/bc
  • 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. Introduction Determining 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
  • 47. 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 
  • 48. Materials and methods
  • 49. 230 Laboring women were interviewed and examined according to a checklist from April – August 2004 .
  • 50. The inclusion criteria were: 1-Singleton pregnancy 2- Vertex presentation 3- Gestational age 36-42 weeks 4- no medical or obstetric disease 5- Bishop score of 10-12 6- normal FHR 7- spontaneous initiation of labor 8- non elective cesarean section 9- no diagnosis of CPD
  • 51. 1-mother’s height 2- maternal age 3- prepregnancy weight 4- maternal BMI 5- drugs used except oxytocin 6- 9interventions ( amniotomy – c/s/vacuum/ enema or any other way of bowels preparation) 10- 12- Duration , intensity and frequency of labor pain (in the initial stages before oxcytocin administration) The independent variables were:
  • 52. 13-abnormal events like cord prolapse or fetal heart abnormality or occiput posterior delivery 14- occupation 15-31- lifestyle in terms of alcohol consumption, smoking, exercise, meals, Consumption of grain , vegetables, fruit, dairy and type of dairy, meat ad meat products, fat and dressings , water, snacks , and score as the sum of items The independent variables were:
  • 53. Framingham Health Assessment Questionnaire is presented (though in small font size) as a reference for a print. It can help provide an account of life style risk for health. Four items were changed based on Iranian pregnant women characteristics: Alcohol consumption smoking Exercise and type of dairy products
  • 54. 13.0 Consumption of alcohol How often do you consume alcohol? _____1) Never drink _____2) 2 days or less per week _____3) 3 days per week _____4) 4 or more days per week 14.0 Number of alcoholic beverages On the days you drink, on the average how many drinks do you have? _____1) Never drink _____2) 1 to 2 drinks _____3) 3 to 4 drinks _____4) 5 or more drinks 15.0 Caffeine How often do you consume caffeine in your diet including coffee, tea, cola or chocolate? _____1) Never _____2) Occasionally but not every day _____3) 1 to 3 servings daily _____4) 3 to 5 servings daily _____5) More than 5 servings daily 16.0 Smoking status Indicate which of the following best represents your current status NOTE: Check all that apply. _____1) Have never smoked _____2) Quit smoking less than 5 years ago _____3) Quit smoking more than 5 years ago _____4) Smoke pipe or cigar _____5) Smoke less than 1 pack of cigarettes per day _____6) Smoke more than 1 pack of cigarettes per day LIFESTYLE ITEMS
  • 55. Exercise Program 18.0 Exercise Frequency On the average, how many days per week do you exercise? _____1) 3 or more days per week _____2) Less than 3 days per week _____3) No regular exercise program 19.0 Proper stretching Do you perform stretching prior to exercise? _____1) Always _____2) Sometimes _____3) Never _____4) Currently not exercising 20.0 Warm-up and cool down Do you warm-up and cool-down after exercising? _____1) Always _____2) Sometimes _____3) Never _____4) Currently not exercising Section E Nutrition Habits 21.0 Daily Meals On the average how many meals do you consume per day? _____1) 3 meals with "healthy" snacks _____2) 3 meals _____3) 2 meals or less _____4) No regular eating pattern 22.0 Consumption of grain/bread products On the average, indicate the type and amount of grain products you normally consume per day. NOTE: A serving is 1 sl. bread, 1/3 cup beans / peas, 1/3 cup oatmeal, rice or other grain products. _____1) Whole grains at least 6 to 11 servings per day _____2) Whole grains 6 servings or fewer servings per day _____3) Refined grains such as white bread/rolls/processed flour at least 6 to 11 servings per day _____4) Refined grains such as white bread/rolls/processed flour 6 or less servings per day _____5) Rarely consume grain products
  • 56. 23.0 Consumption of vegetables On the average, how many servings of vegetables do you consume per day? Note: A serving is approximately 1 cup of raw or 1/2 cup of cooked. _____1) At least 3 to 5 servings per day _____2) Less than 3 servings per day _____3) Rarely consume vegetables 24.0 Consumption of fruits On the average, how many servings of fruit do you consume per day? Note: A serving is approximately 1 piece of fruit. _____1) At least 2 to 4 servings per day _____2) Less than 2 servings _____3) Hardly ever consume fruit 25.0 Daily consumption of dairy products On the average, how many servings of dairy products do you consume per day? Note: A serving is approximately 1 cup of milk or 1 oz. of cheese. _____1) At least 2 servings per day _____2) Less than 2 servings _____3) Hardly ever consume dairy products 26.0 Type of Dairy products Indicate the type of dairy products you consume. _____1) Nonfat selections only _____2) Both low fat and nonfat about the same _____3) Low fat only _____4) Usually high fat selections _____5) Do not consume dairy products 27.0 Daily consumption of meats and meat products Indicate the type of meat you normally consume. _____1) Do not consume meat or meat products _____2) Consume less than 6 oz. of poultry or fish per day _____3) Consume more than 6 oz. of poultry or fish per day _____4) Consume less than 6 oz. of red meat per day _____5) Consume more than 6 oz. of red meat per day 28.0 Consumption of fats, dressings and spreads Indicate the type and number of servings of fat, dressings and spreads you consume each day. High fat examples: Butter, lard, and margarine Low fat examples: Non-fat or Low-fat salad dressing-mayonnaise-cheese _____1) Use low fat selections sparingly (less than 3 per day) _____2) Use low fat selections frequently (3 or more per day) _____3) Use both low fat and high fat about the same sparingly (3 or less) _____4) Use high fat selections sparingly (less than 3 per day) _____5) Use high fat selections (more than 3 per day)
  • 57. On the average, how many glasses of water do you consume per day? Note: A serving is one 8-oz. glass of water only; do not include coffee, soda or other beverages. _____1) At least 8 glasses per day _____2) About 4 to 8 glasses per day _____3) Less than 4 glasses per day _____4) Seldom consume water 30.0 Convenience and snack food consumption On the average how many times per day do you eat convenience foods or forms of fast food? _____1) Never _____2) Less than 1 time per day _____3) More than 1 time per day
  • 58. The independent variables were: 32- Gravida ** 33- Para** 34-Last delivery** 35-education, 36- residency district 37- maternal BG and Rh
  • 59. 38- newborn weight ( not known before delivery) 39- newborn sex ( not known before delivery) 40- time of delivery:( 8 am – 8 pm is considered as day time) ( not known before delivery) 41- parity( based on crosstabs testing) 42-last delivery ( based on crosstabs testing) 43- gravida ( based on crosstabs testing) The confounding variables were:
  • 60. The dependent variables were: 44 - labor duration active phase 45 - labor duration 46 - rate 4 cm- delivery
  • 61. Rate • Rate of cervical dilation means cm dilation per hour. Compared to other dependent variable , RATE was more reliable for analysis.
  • 62. Assumptions 1- First do no harm is the basic assumption of any medical intervention! 2- Obstructed labor should be defined by individual characteristics of women.
  • 63. Codes for nominal variables They are arranged according to less high risk to more high risk states in terms of labor duration (based on review on related literature ) • Disease: • no=1/yes=2 • Hospital district : • Affluent districts=1 • Non affluent districts=2 • Time of delivery: • night=1/day=2 • Sex of the newborn: • girl=1/boy=2
  • 64. Codes for nominal variables They are arranged according to less high risk to more high risk states in terms of labor duration (based on review on related literature ) • Interventions: • done=1/not done=2 • Pain intensity: • good=1/not good=2 • Blood group: • A=1/Non A=2 • RH: • pos=1/neg=2
  • 65. Codes for nominal variables They are arranged according to low risk to high risk states in terms of labor duration (based on review on related literature ) • Occupation: • Non sedentary=1/Sedentary=2 • Education:Education: • educated=1/ illiterate=2
  • 66. Results
  • 67. Gravida and rate Symmetric Measures .138 .081 1.744 .083c .183 .081 2.328 .021c 159 Pearson's RInterval by Interval Spearman CorrelationOrdinal by Ordinal N of Valid Cases Value Asymp. Std. Errora Approx. Tb Approx. Sig. Not assuming the null hypothesis.a. Using the asymptotic standard error assuming the null hypothesis.b. Based on normal approximation.c.
  • 68. Young Child and rate Symmetric Measures .278 .155 2.241 .029c .163 .127 1.281 .205c 62 Pearson's RInterval by Interval Spearman CorrelationOrdinal by Ordinal N of Valid Cases Value Asymp. Std. Errora Approx. Tb Approx. Sig. Not assuming the null hypothesis.a. Using the asymptotic standard error assuming the null hypothesis.b. Based on normal approximation.c.
  • 69. Para and rate Symmetric Measures .129 .080 1.629 .105c .196 .082 2.495 .014c 158 Pearson's RInterval by Interval SpearmanCorrelationOrdinal by Ordinal NofValidCases Value Asymp. Std. Errora Approx. Tb Approx. Sig. Not assuming thenull hypothesis.a. Usingtheasymptotic standarderrorassumingthenull hypothesis.b. Basedonnormal approximation.c.
  • 70. • To reduce the effect of Confounding Variables (number of pregnancies (G), number of previous deliveries (P), and the years passed since last delivery( YC)), the stepwise regression computation was done for all independent variables and dependent variable (rate) only for those cases with previous deliveries not equal to zero (p#0).
  • 71. correlation coefficients of rate and rate predictors
  • 72. Independent : BMI Dependent : rate MTH:LIN Rsquare =043 Df=146 F=6.60 Sig f =0.011 B0 =.5744 B1 = .0965
  • 73. Which means: Rate=0.57 +0.09 BMI or
  • 74. Conclusion • In this study, women of lower BMI had a longer labor course. • According to Kramer, in a developedcountry lean women are likely to have adequate nutritionalstores to meet the basic requirements of pregnancy. So a low BMI is accompanied by a lower pregnancy risk than a higher BMI. This must not be generalized to developing countries, particularlythose in which maternal undernutrition is highly prevalent.
  • 75. THE END

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