1. STATISTICS MADE EASY
Nachiket Shankar
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4. WHAT WE ARE GOING TO COVER
Variables – types and importance
Descriptive statistics – creation of statistical models
Inferential statistics – generalizability of models
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5. INTRODUCTION
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6. WHAT IS RESEARCH ? Finding answers to questions in a systematic manner
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7. PURPOSE OF RESEARCH
Uncertainty
Certainty
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8. RESEARCH
STEPS
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10. Inferential Statistics
Universe : Ask a Question
P < 0.05
P > 0.05
Error limit fixed by you
Type 1 ( False positive) : 5
Type 2 ( False negative) : 20
[ Power : type 2 = 80 % ]
Test of significance
Sample
Study Design
Result /
Summary measure
Descriptive Statistics
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11. VARIABLES
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12. NUMERICAL VARIABLES
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13. NOMINAL VARIABLES
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14. ORDINAL VARIABLES
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15. IDENTIFY THE TYPE OF VARIABLE
Systolic blood pressure of 35 pregnant women
Stage of carcinoma of 50 cervical carcinoma patients
Number of children of 100 couples in a neighbourhood in Bengaluru
HIV status in 200 pregnant women attending am antenatal clinic
APGAR score of 20 neonates
Provision of television in OPD waiting area
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16. TYPES OF VARIABLES Independent (predictor) Dependent (outcome)
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17. IDENTIFY THE DEPENDENT AND INDEPENDENT VARIABLE
Effect of anaemia on pregnancy outcomes
Calculation of gestational age from fundal height
Association between maternal phenytoin intake and congenital malformations
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18. WHAT IS RESEARCH ? Finding relationships between variables
Statistics helps us find these relationships
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19. STATISTICAL ANALYSIS
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20. TYPES OF ANALYSIS Univariate Bivariate Multivariate 11/02/2017
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21. KEY CONSIDERATIONS Identify variables – number and type
Identify groups – number and independence
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22. FUNDAMENTAL DESIGN OF BIOSTATISTICS
POPULATION
Measures of model fit:
Tests of significance
(Inferential statistics)
Error limits
fixed by you
SAMPLE
STATISTICAL MODEL
Descriptive
statistics

23. WHAT IS A STATISTICAL MODEL?
A simplified view of the complex reality
Different kinds of variables produce different models
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24. COMPONENTS
Central value
Error
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25. COMMON TYPES OF MODELS
Mean and standard deviation – numerical variables
Median and interquartile range – ordinal variables
Proportions – nominal variables
Correlation coefficient
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26. Mean ± SD
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27. Median and interquartile range
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28. Proportions
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29. LINEAR CORRELATION
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30. COMMON TYPES OF MODELS More than 1 group
Difference in means – numerical variables
Odds ratio – nominal variables
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31. ODDS RATIO
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32. 95% CONFIDENCE INTERVAL
Study A
Study B
Study C
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33. FUNDAMENTAL DESIGN OF BIOSTATISTICS
POPULATION
Measures of model fit:
Tests of significance
(Inferential statistics)
Error limits
fixed by you
SAMPLE
STATISTICAL MODEL
Descriptive
statistics

34. INFERENTIAL STATISTICS
POPULATION
P ≤ 0.05
P > 0.05
SAMPLE
STATISTICAL MODEL

35. TESTS OF SIGNIFICANCE
Parametric
Non parametric
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36. ONE GROUP, ONE PROPORTION
Anaemia
Pregnant women in slums
Descriptive statistics – proportion, 95% CI
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37. ONE GROUP, ONE QUANTITATIVE PARAMETER
Hb%
Pregnant women in slums
Descriptive statistics – mean and SD, 95% CI
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38. TWO GROUPS, ONE QUANTITATIVE PARAMETER
Hb%
Pregnant women in slums
Pregnant women in rural areas
Descriptive statistics – difference in means, 95% CI
Inferential statistics – independent sample T test
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39. THREE GROUPS, ONE QUANTITATIVE PARAMETER
Hb%
Pregnant women in middle class urban areas
Pregnant women in slums
Pregnant women in rural areas
Descriptive statistics – difference in means, 95% CI
Inferential statistics – one-way ANOVA
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40. TWO GROUPS, ONE PROPORTION
Anaemia
Pregnant women in slums
Pregnant women in rural areas
Descriptive statistics – odds ratio, 95% CI
Inferential statistics – Chi-square test
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41. TWO QUANTITATIVE PARAMETERS ASSOCIATION (ONE GROUP)
Hb%
Distance from hospital
Descriptive statistics – correlation coefficient, 95% CI
Inferential statistics – ANOVA
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42. ONE QUANTITATIVE PARAMETER (BEFORE AND AFTER) - (ONE GROUP)
Hb% (before)
Hb% (after)
Oral iron
Descriptive statistics – Difference in means, 95% CI
Inferential statistics – Paired T test
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43. ONE QUANTITATIVE PARAMETER (MULTIPLE TIME POINTS) - (ONE GROUP)
Hb% - T1
Hb% - T2
Hb% - T3
Oral iron
Oral iron
Descriptive statistics – Difference in means, 95% CI
Inferential statistics – Repeated measures ANOVA
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44. STATISTICAL vs CLINICAL SIGNIFICANCE
Statistical significance is a necessary precondition for consideration of clinical importance
But says nothing about the actual magnitude of the effect
Effect size - how important an effect is
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45. TEST VALIDATION
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46. SENSITIVITY
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47. SPECIFICITY
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50. LIKELIHOOD RATIO Positive LR (LR+) – Prob. true (+) / Prob. false (+)
(for diagnosis)
Negative LR (LR-) - Prob. false (-) / Prob. true (-)
(for screening)
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51. INTERPRETATION OF LR
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52. MULTIVARIATE ANALYSIS
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53. SCENARIO 1
65 anaemic pregnant women received ferric carboxymaltose up to 15 mg/kg between 24 and 40 weeks of pregnancy
Treatment effectiveness was assessed by repeat haemoglobin (Hb) measurements and patient report of well-being in the postpartum period.
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54. SCENARIO 2
To determine whether capillary blood PCV (cPCV) differed from venous blood PCV (vPCV) of normal pregnant women
PCV was estimated using pairs of venous and capillary blood samples from 200 consecutive pregnant women
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55. SCENARIO 3
Patients were randomized to daily oral ferrous sulphate 250 mg with or without a single intravenous iron polymaltose infusion
Hb% was estimated
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56. SCENARIO 4
This study compared the validity of the haemoglobin colour scale (HCS) and clinical signs in diagnosing anaemia against Sahli's haemoglobinometer method as the gold standard, and assessed the reliability of HCS.
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57. SCENARIO 5
Comparison of three types of intervention to enhance placental redistribution in term newborns
Outcome - Hb%
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58. SCENARIO 6
Malaria and anaemia in pregnancy: a cross-sectional study of pregnant women in rural communities of South-eastern Nigeria
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59. SUMMARY Statistics is used for two purposes:
to create a statistical model (descriptive statistics)
to test whether the results can be generalized to the population using tests of significance (inferential statistics)
Statistical model – simplified view of complex reality
Different variables – different models
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60. SUMMARY
Tests of significance – p value which tells us the generalizability of the model
p value < 0.05 considered statistically significant
Statistical and clinical significance are not synonymous
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61. THANK YOU
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