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Basic Biostatistics

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Updated note on basic biostatistics.

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Basic Biostatistics

  1. 1. Basic Biostatistics Prof. Dr. Jamalludin Ab Rahman MD MPH Department of Community Medicine
  2. 2. Learning outcomes At the end of this workshop, you should be able to 1. Describe about population & sample, causation, level of measurement, distribution of data 2. Summarise categorical & numerical data 3. Use appropriate statistical test for bi-variable analyses using SPSS 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 2
  3. 3. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 3 We observe, we believe. What we observe might not be the truth
  4. 4. Population vs. Sample 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 4
  5. 5. Parameter vs. Statistics Parameter characteristic of the whole population Statistics characteristic of a sample, presumably measurable. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 5
  6. 6. Statistics estimate parameters  Representative  Sampling error  Different samples yield different estimates  Statistics = Parameter if sampling done properly  How to prove? 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 6
  7. 7. N = 35 Red = 18/35 = 51.4% N = 6 Red = 3/6 = 50% N = 6 Red = 2/6 = 33.3% N = 6 Red = 4/6 = 66.7% Average % Red = 50% + 33.3% + 66.7% 3 = 50% 50%  51.4% Parameter Statistics Statistics Parameter 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 7
  8. 8. Variable & its role  A value and whose associated value may be changed DependentIndependent 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 8
  9. 9. Causation  Relation of events (cause and effect)  But correlation (between two events) does not (always) imply causation  Rooster's crow does not cause the sun to rise  Switch does not cause the bulb to light 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 9
  10. 10. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 10
  11. 11. Hill’s criteria 1. Strength of association 2. Consistency 3. Specificity 4. Temporality 5. Biological gradient 6. Plausibility 7. Coherence 8. Experiment 9. Analogy 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 11 Hill AB. The environment and disease: Association or causation? Proceed Roy Soc Medicine – London. 1965;58:295–300.
  12. 12. Time & causation Time Exposure Exposure Exposure Outcome 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 12
  13. 13. Time & causation (example) Time Age Exposure to silica Smoking Lung Cancer 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 13
  14. 14. Causal web  Web of causation  Conceptual framework  Path analysis/web  Relationship between variables  Cause and effect 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 14
  15. 15. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 15
  16. 16. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 Slide 16 Outcome Exposure Exposure Exposure Exposure Exposure Effect modifier or Moderator Confounder Mediator
  17. 17. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 17
  18. 18. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 18
  19. 19. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 19http://www.apa.org/science/about/psa/2008/06/ahn.aspx
  20. 20. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 20http://www.apa.org/science/about/psa/2008/06/ahn.aspx
  21. 21. 28December 2017 BasicBiostatistics(C)Jamalludin AbRahman2015 21 Type of data (Level of measurement) Categorical Nominal Ordinal Numerical Discrete Continuous e.g. Gender, Race e.g. Cancer staging, Severity of CXR for PTB e.g. Parity, Gravida e.g. Hb, RBS, cholesterol.
  22. 22. Distribution (shape) of data  Applicable to numerical value  Discrete or Continuous  Discrete ~ Binomial, Poisson, Negative Binomial, Hypergeometry, Multinomial etc.  Continuous ~ Normal, t, chi-square, F etc. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 22
  23. 23. Central limit theorem “Given a distribution with a mean μ and variance σ², the sampling distribution of the mean approaches a normal distribution with a mean (μ) and a variance σ²/N as N, the sample size, increases” (David M. Lane) 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 23
  24. 24. Normal Distribution 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 24 𝑓 𝑥; 𝜇, 𝜎2 = 1 𝜎 2𝜋 𝑒− 1 2( 𝑥−𝜇 𝜎 )2 ) Why Normal? - Because many biological & psychological variables are distributed normally
  25. 25. Characteristics  Bell shaped curve  Symmetrical  Unimodal 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 25
  26. 26. Test of Normality  Anderson–Darling Test  Corrected Kolmogorov–Smirnov Test (Lilliefors Test)  Cramér–von-Mises Criterion  D'agostino's K-squared Test  Jarque–Bera Test  Pearson's Chi-square Test  Shapiro–Francia  Shapiro–Wilk Test 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 26
  27. 27. Use Normality test with caution  Small samples almost always pass a normality test. Normality tests have little power to tell whether or not a small sample of data comes from a Gaussian distribution.  With large samples, minor deviations from normality may be flagged as statistically significant, even though small deviations from a normal distribution won’t affect the results of a t test or ANOVA. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 27
  28. 28. Why run statistical test? 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 28 1. Measure magnitude of event 2. Determine presence of difference (or similarity) 3. Determine degree of difference 4. Determine the direction of changes (trend) 5. Predict changes (outcomes)
  29. 29. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 29 A B C Is there any difference between A & B? How big is the difference between A & B? Which one is taller? A or B? Is C different from A & B? Is there any pattern now? If there will be D, can you predict how tall is D?
  30. 30. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 30 Statistical analysis AnalyticalDescriptive e.g. Describe socio- demographic characteristics - Age, Sex, Race etc. e.g. Prevalence of hypertension. e.g. Compare demographic characteristics between two population – Compare age between male & female e.g. Distribution of gender by hypertension status e.g. How demographic characteristics (more than one factors) explain hypertension Univariable Bivariable Multivariable IV DV IV DV IV DV IV
  31. 31. Descriptive statistics 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 31
  32. 32. Descriptive Statistics  Explain one variable at one time  Method based on type of measure Categorical Frequency (Percentage) Numerical Central measures (e.g. mean, median) & Dispersion (e.g. variance, standard deviation, range, min-max, interquartile range) 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 32
  33. 33. Data Categorical Frequency (count) & Percentage Numerical Normal Mean (SD) Not Normal Median (Range/IQR) How to describe a data 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 33
  34. 34. Analytical statistics 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 34
  35. 35. Comparing difference A B C Which of the following shows true difference between two populations? 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 35
  36. 36. 3 methods to compare values 1. P-value 2. Confidence interval 3. Effect size 28December2017BasicBiostatistics 36
  37. 37. P value  P-value is ‘likely’ or ‘unlikely’ that Ho is true  Taking 0.05 as the cut-off point (a), if P ≤ 0.05, it is then ‘unlikely’ Ho is true, therefore reject Ho 28December2017BasicBiostatistics 37
  38. 38. Hypothesis testing Truth Ho True Ho False Test Do not reject Ho Correct Type II error () Reject Ho Type I error (a) Correct P-value is the probability to make Type I error (based on frequentist inference) 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 38
  39. 39. One-tailed vs. two-tailed  Is there a difference between Hb 14 g% vs. Hb 12 g% in male & female respectively? Ho: HbM – HbF = 0 H1: HbM = HbF H2: HbM > HbF H3: HbM < HbM  Note: Should be determined a priori 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 39
  40. 40. One-tailed vs. two-tailed Two-sided Right-sidedLeft-sided 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 40
  41. 41. P & Sample Size 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 41
  42. 42. The truth about P value  Measures effectiveness (even by US FDA)  < P means statistically significant, NOT clinical significant  But, be careful when interpreting P value  P is affected BOTH by effectiveness AND sample size  P can be < 0.05 even though the effectiveness is marginal when sample size is huge  Compare Ps between studies only appropriate if the sampling & sample size is the same 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 42
  43. 43. P < 0.05  Why 5%?  Cut-off point proposed by Sir Ronald A. Fisher 1925 to reject or not to reject a hypothesis  If P < 0.05 = Probability to make Type I error is less than 5%  If P > 0.05, > 5% of the difference occurred by chance & not due to the TRUE difference 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 43
  44. 44. Hypothesis Testing using bivariable analysis  Try to prove that Exposure causes the Disease e.g. Smoking causing Lung Cancer  Ho: No difference of risk to get Lung Cancer between smoker and non-smoker 44 28December2017BasicBiostatistics(C)JamalludinAbRahman2015
  45. 45. Lung Cancer No Lung Cancer Smoking 20 (18.2%) 90 (81.8%) Not Smoking 5 (4.5%) 105 (95.5%) The occurrence of lung cancer is significantly higher (18.2%) among smokers compared to non-smokers (4.5%) (2 (df=1)= 10.15, P =0.001, OR = 4.7 (CI95% 1.7 – 13.0)) BasicBiostatistics(C)JamalludinAbRahman2015 45 28December2017
  46. 46. Confidence Interval  Range of plausible values  Narrow interval  high precision Wide interval  poor precision  How narrow is narrow? And how wide is wide? Base on your clinical judgment BasicBiostatistics(C)JamalludinAbRahman2015 47 28December2017
  47. 47. Interpret single CI  Compare with the null value i.e. can be 0 for % or 1 for risk  Compare with practical significance or the clinical significance/indifference 48 Null Null A B Source: http://www.childrens-mercy.org/stats/journal/confidence.asp Null Null C D 28December2017BasicBiostatistics(C)JamalludinAbRahman2015
  48. 48. Comparing multiple CIs 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 49 A B C
  49. 49. Effect size  The measure of effect irrespective of sample size  Cohen (1988) classify effect size into Low (<0.3) Medium (0.3-0.7) Large (> 0.7)  Manual calculation or web based calculation 50 28December2017BasicBiostatistics(C)JamalludinAbRahman2015
  50. 50. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 51
  51. 51. Statistical Test  Bivariable (univariate) ~ One dependent & one independent  Multivariate ~ Multiple dependent & multiple independent variable 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 52
  52. 52. What test to use? Variable 1 Variable 2 Test Categorical Categorical Chi-square Categorical (2 pop) Numerical (Normal) Independent sample t-test Categorical (2 pop) Numerical (Not Normal) Mann-Whitney U test Categorical (> 2 pop) Numerical (Normal) One-way ANOVA Categorical (> 2 pop) Numerical (Not Normal) Kruskal-Wallis test Numerical (Normal) Numerical (Normal) Pearson Correlation Coefficient Test Numerical (Normal/ Not Normal) Numerical (Not Normal) Spearman Correlation Coefficient Test Numerical (Normal) Numerical (Normal) – Paired Paired t-test Numerical (Not Normal) Numerical (Not Normal) – Paired Wilcoxon Signed Rank Test 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 53
  53. 53. Bivariable Analyses  Compare means Independent sample t-test (Unpaired t-test) ~ Two unrelated means Paired t-test ~ Two related means One-way ANOVA ~ More than 2 means  2 Test ~ Between categorical variables  Non-parametric tests (Kruskall-Wallis, Man-Whitney U tests) ~ If data is not normally distributed 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 54
  54. 54. Writing plan for statistical analysis #1 28 December 2017Basic Biostatistics (C) Jamalludin Ab Rahman 2015 55 Data were analyzed using the complex sample function of SPSS (version 13.0). Sampling errors were estimated using the primary sampling units and strata provided in the data set. Sampling weights were used to adjust for nonresponse bias and the oversampling of blacks, Mexican Americans, and the elderly in NHANES. The prevalence of hypertension, as well as the awareness, treatment, and control rates, were age adjusted by direct standardization to the US 2000 standard population.10 To analyze differences over time, the 2003–2004 data were compared with the 1999–2000 data. Estimates with a coefficient of variation >0.3 were considered unreliable. A 2-tailed P value <0.05 was considered statistically significant. (Ong et al. 2009)
  55. 55. Writing plan for statistical analysis #2 28 December 2017Basic Biostatistics (C) Jamalludin Ab Rahman 2015 56 To assess the effect of the selection process on the characteristics of the cases, we compared cases included in the final analysis to the rest of the cases. Since controls included in the present analysis were different from the rest of the diabetes free participants by design, no similar comparisons were performed for that group. To compare baseline characteristics of cases and controls appropriate univariate statistics were used. Similar binary logistic and multiple linear regression models were built with incident diabetes or HbA1c as respective outcomes and additive block entry of adiponectin and potential confounders. For linear regression CRP and triglycerides were log transformed. Since HbA1c could be modified by drug treatment, we ran a sensitivity analysis excluding all participants on antidiabetic medication. A p-value of <0.05 was considered significant. Analyses were performed with SPSS 14.0 for Windows.
  56. 56. Reporting analysis (example) 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 57
  57. 57. 58 BasicBiostatistics(C)JamalludinAbRahman201528December2017 Reporting analysis (example)
  58. 58. Reporting analysis (example) 28 December 2017Basic Biostatistics (C) Jamalludin Ab Rahman 2015 59
  59. 59. Summary 1. Identify & define variables 2. Type – independent vs. dependent 3. Level of measurements – nominal, ordinal or continuous 4. Check distribution – Normal vs. Not Normal 5. Decide what to do - descriptive vs. analytical 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 60
  60. 60. Practical  Download data from http://iiumedic.net/biostatistics/v4/academics/ med4106/med4106-biostatistics- practical/healthstatus-practical-sav/  or https://goo.gl/GM7BG1 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 61
  61. 61. Chapter 2 Introduction to SPSS IBM SPSS Statistics v21 for Windows JamalludinAbRahmanMDMPH DepartmentofCommunityMedicine KulliyyahofMedicine
  62. 62. IBM SPSS Statistics IBM Corporation Software Group Route 100 Somers, NY 10589 Produced in the United States of America May 2012 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 63
  63. 63. SPSS Layout 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 64
  64. 64. Layout 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 65 Main menu Toolbar Variables
  65. 65. Data editor 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 66 Rows = variables Rows = each data Define & describe your variables here Enter your data here
  66. 66. Viewer 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 67 The output of analyses will be displayed here. Output is separated from data
  67. 67. Syntax 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 68 We can compile all the steps of the analyses here. Extend the programming function in SPSS. Ability to perform complex steps e.g. “looping”
  68. 68. Creating Dataset 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 69
  69. 69. Before even you start SPSS! 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 70  You must identify & define relevant variables  Define means 1. Name – preferably short single name, begins with alphabet, no special character, no space 2. Type of data – e.g. Numeric, Date, String 3. Width & Decimal Places (if numeric) 4. Label – description for the Name (will be displayed in Viewer) 5. Values – labels for value e.g. 1=Male, 2=Female 6. Missing – define missing value e.g. 999 for N/A
  70. 70. Define your variables 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 71
  71. 71. Variable Types 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 72
  72. 72. Variable Type 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 73 Decide the suitable variable type For numeric, determine Width & Decimal. Decimal < Width For String, no option for Decimal Place New option for Numerics with leading zeros
  73. 73. Value Labels 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 74
  74. 74. Missing Value 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 75 This question is Not Applicable to male e.g. Assign 999 to represent N/A value & this won’t be included in any analysis
  75. 75. Chapter 3 Descriptive Statistics IBM SPSS Statistics v21 for Windows JamalludinAbRahmanMDMPH DepartmentofCommunityMedicine KulliyyahofMedicine
  76. 76. Exercise data 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 77  Hypothetical  Study to describe factors related to HbA1c & Homocystein (HCY)  N=301  13 variables
  77. 77. Retrieve file information 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 78
  78. 78. healthstatus001 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 79
  79. 79. Task 1 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 80 1. Describe socio-demographic characteristics of the respondent (age, gender & race) 2. Describe the explanatory variables 1. Exercise 2. smoking status 3. BMI status & 4. BP status 3. Describe HbA1c (taking cut-off for Poor HbA1c ≥ 6.5%) & HCY
  80. 80. Describe numerical data 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 81
  81. 81. Explore 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 82
  82. 82. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 83
  83. 83. Results 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 84 Check for Normality. Is Age data distributed Normally?
  84. 84. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 85 Is this Normal distribution?
  85. 85. Describe age 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 86 Normal  The subjects distributed between 23-67 years old with the average of 34 (SD=8) years. If not Normal  The subjects distributed between 23-67 years old with the median of 33 (IQR=11) years
  86. 86. Describe categorical data 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 87
  87. 87. Frequency 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 88
  88. 88. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 89
  89. 89. Results 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 90
  90. 90. TRANSFORM 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 91
  91. 91. Compute 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 92
  92. 92. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 93 weight / ((height / 100) ** 2)
  93. 93. Visual binning 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 94
  94. 94. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 95 Normal < 23 Overweight 23 to < 27.5 Obese >= 27.5
  95. 95. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 96
  96. 96. Chapter 4 Bivariable analyses IBM SPSS Statistics v21 for Windows JamalludinAbRahmanMDMPH DepartmentofCommunityMedicine KulliyyahofMedicine
  97. 97. To check association of two variables? 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 98 HbA1cAge
  98. 98. The steps 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 99 1. Determine which is dependant & which is independent 2. Determine level of measurements 3. Determine Normality of the numerical measurement 4. Determine the suitable statistical test
  99. 99. What are the tests? 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 100 Variable 1 Variable 2 Test Categorical Categorical Chi-square Categorical (2 pop) Numerical (Normal) Independent sample t-test Categorical (2 pop) Numerical (Not Normal) Mann-Whitney U test Categorical (> 2 pop) Numerical (Normal) One-way ANOVA Categorical (> 2 pop) Numerical (Not Normal) Kruskal-Wallis test Numerical (Normal) Numerical (Normal) Pearson Correlation Coefficient Test Numerical (Normal/ Not Normal) Numerical (Not Normal) Spearman Correlation Coefficient Test Numerical (Normal) Numerical (Normal) – Paired Paired t-test Numerical (Not Normal) Numerical (Not Normal) – Paired Friedman test
  100. 100. Tasks 2 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 101 1. Determine association between socio-demographic characteristics & all the risk factors with HbA1c 2. Determine association between socio-demographic characteristics & all the risk factors with HCY Note: It would be good if you could construct dummy table for the answers even before the analyses started
  101. 101. HCY normal range 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 102
  102. 102. Independent sample t-Test Comparing Two Means 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 103
  103. 103. Age vs. BP 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 104
  104. 104. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 105
  105. 105. Results 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 106 Levene’s test check equality between variances. Ho: There is no difference of variances. So if P is significant, we reject Ho, and therefore equal variances not assumed. The original t-test (Student’s t-test) assumes equal variances for equal sample sizes. However if the variances are equal, it is robust for different sizes. Welch's correction
  106. 106. Table – Distribution of age by blood pressure status 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 107 N Mean SD Statistics df P Normal BP 156 33.9 7.9 t=0.431 299 0.667 High BP 145 34.4 8.9
  107. 107. Chi-squared Test Difference of Two Proportions 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 108
  108. 108. Gender vs. BP 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 109
  109. 109. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 110
  110. 110. Results 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 111 Describe this table first. What is your impression? 49% women vs. 47% men with high BP Some books may suggest the use of Continuity Correction at ALL time, but recent simulations showed that CC (or Yate’s correction) is OVERCONSERVATIVE. Hence, use Pearson 2 when < 20% of cells have expected count < 5 When ≥ 20% of cells have EC < 5, use Fisher’s Exact Test This is given because we code the variables using numbers. Can be used to measure P-trend
  111. 111. One-way ANOVA Comparing More than Two Means 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 112
  112. 112. Race vs. HbA1c 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 113
  113. 113. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 114
  114. 114. Results 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 115 Describe these results first. What is your impression? HbA1c between races? 6.4 (SD 2.1) vs. 6.7 (SD 2.2) vs. 6.5 (SD 2.2) The F test shows that there is no single significant difference between any two groups
  115. 115. Results – BMI status vs. HbA1c 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 116 The F test shows that at least there is ONE pair with significant different. Either N vs. OW, N vs. OB or OW vs. OB We need to run Post-hoc test to determine which of the PAIR is significant. To decide which Post-hoc test to choose, we have to test for equality of variances i.e. Homogeneity of variances (Levene’s test)
  116. 116. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 117
  117. 117. Results – Post hoc 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 118 The significant difference is only for Normal vs. Obese (P=0.002)
  118. 118. Report 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 119 There is a significant association between BMI Status and HbA1c (F(2,298)=13.129, P<0.001). Post-hoc test showed that Obese subjects have significantly higher HbA1c compared to Normal and Overweight subjects (P=0.001 and P < 0.001 respectively).
  119. 119. Mann-whitney U Non Parametric Tests 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 120
  120. 120. Gender vs. HCY 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 121
  121. 121. Results 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 122 This ranks table is not to be cited in the research paper. Instead, describe their MEDIAN
  122. 122. KRUSKALL WALLIS Non-Parametric TESTS 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 123
  123. 123. Race vs. HCY 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 124
  124. 124. Results 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 125
  125. 125. Correlation Test RELATIONSHIP OF TWO NUMERICAL DATA 28December2017 Basic Biostatistics (C) Jamalludin Ab Rahman 2015 126
  126. 126. Age vs. HbA1c 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 127
  127. 127. Results 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 128
  128. 128. Age vs. HCY 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 129
  129. 129. 28December2017BasicBiostatistics(C)JamalludinAbRahman2015 130

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