1. Overview Biostatistics Applications of Statistics in Research
Bandit Thinkhamrop, Ph.D.(Statistics)
Department of Biostatistics and Demography
Faculty of Public Health
Khon Kaen University

2. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

3. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

4. Begin at the conclusion

5. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

6. Identify the primary research question
Where to find the research question?
Title of the study
The objective(s)
The conclusion(s)
If more than one, find the primary aim.
Try to make the question “quantifiable”

7. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

8. Identify the primary study outcome
It is the “primary” dependence variable
It is the main finding that was used as the basis for the conclusion of the study
It is the target of the statistical inference
It is the basis for sample size calculation
It resided in the :
Title
Research question
Objective
Sample size calculation
Main finding in the RESULTS section of the report
Conclusion

9. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

10. Type of the study outcome: Key for selecting appropriate statistical methods
Dependent variable or response variable
Focus on primary study outcome if there are more
Type of the study outcome
Continuous
Categorical (dichotomous, polytomous, ordinal)
Numerical (Poisson) count
Even-free duration

11. Continuous outcome Primary target of estimation: Mean (SD)
Median (Min:Max)
Correlation coefficient: r and ICC
Modeling:
Linear regression
The model coefficient = Mean difference
Quantile regression
The model coefficient = Median difference
Example:
Outcome = Weight, BP, score of ?, level of ?, etc.
RQ: Factors affecting birth weight

12. Categorical outcome Primary target of estimation : Modeling: Example:
Proportion or Risk
Modeling:
Logistic regression
The model coefficient = Odds ratio (OR)
Example:
Outcome = Disease (y/n), Dead(y/n), cured(y/n), etc.
RQ: Factors affecting low birth weight

13. Numerical (Poisson) count outcome
Primary target of estimation :
Incidence rate (e.g., rate per person time)
Modeling:
Poisson regression
The model coefficient = Incidence rate ratio (IRR)
Example:
Outcome = Total number of falls
Total time at risk of falling
RQ: Factors affecting elderly fall

14. Event-free duration outcome
Primary target of estimation :
Median survival time
Modeling:
Cox regression
The model coefficient = Hazard ratio (HR)
Example:
Outcome = Overall survival, disease-free survival, progression-free survival, etc.
RQ: Factors affecting survival

15. The outcome determine statistics
Continuous
Mean
Median
Categorical
Proportion
(Prevalence Or
Risk)
Count
Rate per “space”
Survival
Median survival
Risk of events at T(t)
Poisson Reg.
Linear Reg.
Logistic Reg.
Cox Reg.

16. Statistics quantify errors for judgments
Parameter estimation
[95%CI]
Hypothesis testing
[P-value]

17. Statistics quantify errors for judgments
Parameter estimation
[95%CI]
Hypothesis testing
[P-value] 7

18. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

19. Types of Research Qualitative Quantitative Observational Experimental
Phenomenology
Grounded Theory
Ethnography
Description
Observational
Experimental
Clinical trial
Field trial
Community intervention trial
Parallel or Cross-over or factorial
Fixed length or group sequential
With or without baseline
Descriptive
Analytical
Quasi-experimental
Randomized-controlled
Cross-sectional descriptive
Prevalence survey
Poll
Cross-sectional
Case-control
Cohort
Prevalence case-control
Nested case-control
Case-cohort case-control
Prospective cohort
Retrospective cohort
Ambi-spective cohort
Systematic review
Meta-analysis

20. Caution about biases Selection bias Information bias Confounding bias
Research Design
Prevent them
Minimize them

21. Caution about biases If data available: Selection bias (SB)
Information bias (IB)
Confounding bias (CB)
If data available:
SB & IB can be assessed
CB can be adjusted using multivariable analysis

22. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

23. Generate a mock data set
General format of the data layout id y x1 x2 X3 1 2 3 4 5 … n
Variables
Data

24. Generate a mock data set
Continuous outcome example id y x1 x2 X3 1 2 21 22 12 19 3 4 20 89 5 14 18 … n 6 45
Mean (SD)

25. Generate a mock data set
Continuous outcome example id y x1 x2 X3 1 21 22 2 12 19 3 4 20 89 5 18 … n 45
n, percentage

26. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

27. Common types of the statistical goals
Single measurements (no comparison)
Difference (compared by subtraction)
Ratio (compared by division)
Prediction (diagnostic test or predictive model)
Correlation (examine a joint distribution)
Agreement (examine concordance or similarity between pairs of observations)

28. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

31. Dependency of the study outcome required special statistical methods to handle it
Example of dependency or correlated data:
Before-after or Pre-post design
Measuring paired organs i.e., ears, eyes, arms, etc.
Longitudinal data, repeated measurement
Clustered data, many observation unit within a cluster
Choices of approaches:
Ignore it => use ordinary analysis as independency - not save
Simplify it => use summary measure then analyze the data as it is independent – not efficient
Handle it => Mixed model, multilevel modeling, GEE - recommended

32. Dependency of the study outcome required special statistical methods to handle it
Continuous
Categorical
Count
Survival
Mean
Median
Proportion
(Prevalence Or
Risk)
Rate per “space”
Median survival
Risk of events at T(t)
Linear Reg.
Logistic Reg.
Poisson Reg.
Cox Reg.
Mixed model, multilevel model, GEE

33. Back to the conclusion Continuous Categorical Count Survival
Appropriate statistical methods
Mean
Median
Proportion
(Prevalence or Risk)
Rate
per “space”
Median survival
Risk of events at T(t)
Answer the research question
based on lower or upper limit of the CI
Magnitude of effect
95% CI
P-value

34. Always report the magnitude of effect and its confidence interval
Absolute effects:
Mean, Mean difference
Proportion or prevalence, Rate or risk, Rate or Risk difference
Median survival time
Relative effects:
Relative risk, Rate ratio, Hazard ratio
Odds ratio
Other magnitude of effects:
Correlation coefficient (r), Intra-class correlation (ICC)
Kappa
Diagnostic performance
Etc.

35. Touch the variability (uncertainty) to understand statistical inferenceid A
(x- )
(x- ) 2 1 2 -2 4 3 -4 16 5 14 10
100
Sum () 20
128
Mean( )
32.0 SD
5.66
Median X X
= 20
= 20 = 4
Variance = SD2 X
Standard deviation = SD

36. Touch the variability (uncertainty) to understand statistical inference
Measure of
central tendency id A
(x- )
(x- ) 2 1 2 -2 4 3 -4 16 5 14 10
100
Sum () 20
128
Mean( )
32.0 SD
5.66
Median X X
Measure of variation X

37. Degree of freedom Standard deviation (SD) =
The average distant between each data item to their mean
Degree of freedom

38. Same mean BUT different variationid A 1 2 3 4 5 14
Sum () 20
Mean SD
5.66
Median id B 1 2 3 4 5 8
Sum () 20
Mean SD
2.91
Median id C 1 4 2 3 5
Sum () 20
Mean SD
0.71
Median
Heterogeneous data
Skew distribution
Heterogeneous data
Symmetry distribution
Homogeneous data
Symmetry distribution

39. Facts about Variation
Because of variability, repeated samples will NOT obtain the same statistic such as mean or proportion:
Statistics varies from study to study because of the role of chance
Hard to believe that the statistic is the parameter
Thus we need statistical inference to estimate the parameter based on the statistics obtained from a study
Data varied widely = heterogeneous data
Heterogeneous data requires large sample size to achieve a conclusive finding

40. The Histogram id A 1 2 3 4 5 14 id B 1 4 2 3 5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 2 3 4 5 6 7 8 9 10 11 12 13 14

41. The Frequency Curve id A 1 2 3 4 5 14 id B 1 4 2 3 5 1 2 3 4 5 6 7 8 94 5 14 id B 1 4 2 3 5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 2 3 4 5 6 7 8 9 10 11 12 13 14

42. Area Under The Frequency Curveid A 1 2 3 4 5 14 id B 1 4 2 3 5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 2 3 4 5 6 7 8 9 10 11 12 13 14

43. Central Limit Theorem  Right Skew X1 Symmetry X2 Left Skew X3
Normally distributed X1 XX Xn 

44. Central Limit Theorem  Distribution of the raw data Distribution ofX1 X2 X3
Distribution of
the sampling mean X1 XX Xn 

45. (Theoretical) Normal Distribution
Central Limit Theorem
Distribution of
the raw data X1 XX Xn 
Distribution of
the sampling mean
(Theoretical) Normal Distribution
Large sample

46. Standardized for whatever n, Mean = 0, Standard deviation = 1
Central Limit Theorem
Many X, , SD X
Standard deviation of the sampling mean
Standard error (SE) X1 XX Xn 
Many , , SE X
Standardized for whatever n, Mean = 0, Standard deviation = 1
Large sample
SE = SD n 
Estimated by

47. (Theoretical) Normal Distribution

48. 99.73% of AUC
Mean ± 3SD

49. 95.45% of AUC
Mean ± 2SD

50. 68.26% of AUC
Mean ± 1SD

51. Sample n = 25 X = 52 SD = 5 Population Parameter estimation [95%CI]
Hypothesis testing
[P-value]

52. Z = 2.58
Z = 1.96
Z = 1.64
5 = 1 5

53. Sample n = 25 X = 52 SD = 5 SE = 1 Population Parameter estimation
Z = 2.58
Z = 1.96
Z = 1.64
Population
Parameter estimation
[95%CI] : (1) to (1)
50.04 to 53.96
We are 95% confidence that the population mean would lie between and 53.96

54. Sample n = 25 X = 52 SD = 5 SE = 1 Population Hypothesis testing
HA :   55
Z = 55 – 52 1 3

55. Hypothesis testing H0 :  = 55 HA :   55 52 55 -3SE +3SE
If the true mean in the population is 55, chance to obtain a sample mean of 52 or more extreme is
Z = 55 – 52 1 3
P-value = =

56. Report and interpret p-value appropriately
Example of over reliance on p-value:
Real results: n=5900; ORDrug A vs Drug B = 1.02 (P<0.001)
Inappropriate: Quote p-value as < 0.05 or put * or **** (star) to indicate significant results
Wrong: Drug A is highly significantly better than Drug B (P<0.001)
What if 95%CI: to 1.300?
This is no clinical meaningful at all….!

57. Report and interpret p-value appropriately
Example of over reliance on p-value:
Real results: n=30; ORDrug A vs Drug B = 9.2 (P=0.715)
Inappropriate: Quote p-value as > 0.05
Wrong: There is no statistical significant difference of the treatment effect (P<0.05). Thus Drug A is as effective as Drug B
What if 95%CI: 0.99 to 28.97?
This is study indicated a low power, NOT suggested an equivalence…!
Correct: There was no sufficient information to concluded that => inconclusive findings

58. P-value is the magnitude of chance NOT magnitude of effect
P-value < 0.05 = Significant findings
Small chance of being wrong in rejecting the null hypothesis
If in fact there is no [effect], it is unlikely to get the [effect] = [magnitude of effect] or more extreme
Significance DOES NOT MEAN importance
Any extra-large studies can give a very small P-value even if the [magnitude of effect] is very small

59. P-value is the magnitude of chance NOT magnitude of effect
P-value > 0.05 = Non-significant findings
High chance of being wrong in rejecting the null hypothesis
If in fact there is no [effect], the [effect] = [magnitude of effect] or more extreme can be occurred chance.
Non-significance DOES NOT MEAN no difference, equal, or no association
Any small studies can give a very large P-value even if the [magnitude of effect] is very large

60. P-value vs. 95%CI (1) An example of a study with dichotomous outcome
A study compared cure rate between Drug A and Drug B
Setting:
Drug A = Alternative treatment
Drug B = Conventional treatment
Results:
Drug A: n1 = 50, Pa = 80%
Drug B: n2 = 50, Pb = 50%
Pa-Pb = 30% (95%CI: 26% to 34%; P=0.001)

61. P-value vs. 95%CI (2) Pa > Pb Pb > Pa
Pa-Pb = 30% (95%CI: 26% to 34%; P< 0.05)

62. P-value vs. 95%CI (3)
Adapted from: Armitage, P. and Berry, G. Statistical methods in medical research. 3rd edition. Blackwell Scientific Publications, Oxford page 99

63. Tips #6 (b) P-value vs. 95%CI (4)
Adapted from: Armitage, P. and Berry, G. Statistical methods in medical research. 3rd edition. Blackwell Scientific Publications, Oxford page 99
There were statistically significant different between the two groups.

64. Tips #6 (b) P-value vs. 95%CI (5)
Adapted from: Armitage, P. and Berry, G. Statistical methods in medical research. 3rd edition. Blackwell Scientific Publications, Oxford page 99
There were no statistically significant different between the two groups.

65. P-value vs. 95%CI (4) Save tips:
Always report 95%CI with p-value, NOT report solely p-value
Always interpret based on the lower or upper limit of the confidence interval, p-value can be an optional
Never interpret p-value > 0.05 as an indication of no difference or no association, only the CI can provide this message.

66. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

67. The outcome determine statistics
Continuous
Categorical
Count
Survival
Mean
Median
Proportion
(Prevalence Or
Risk)
Rate per “space”
Median survival
Risk of events at T(t)
Linear Reg.
Logistic Reg.
Poisson Reg.
Cox Reg.

68. Dependency of the study outcome required special statistical methods to handle it
Continuous
Categorical
Count
Survival
Mean
Median
Proportion
(Prevalence Or
Risk)
Rate per “space”
Median survival
Risk of events at T(t)
Linear Reg.
Logistic Reg.
Poisson Reg.
Cox Reg.
Mixed model, multilevel model, GEE

69. Back to the conclusion Continuous Categorical Count Survival
Appropriate statistical methods
Mean
Median
Proportion
(Prevalence or Risk)
Rate
per “space”
Median survival
Risk of events at T(t)
Magnitude of effect
95% CI
P-value
Answer the research question
based on lower or upper limit of the CI

70. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

71. Perform the data analysis using a software
Use the data being generated as if it would be after completion of the research
Analyze according to the analysis plan
Try to understand the computer output and to find if the research question has been answered:
What is the magnitude of effect and its 95% confidence interval?
Was the results due to the role of chance?

72. Steps of Statistical Applications (Practical guides for beginners)
Begin at the conclusion
Identify the primary research question
Identify the primary study outcome
Identify type of the study outcome
Identify type of the study design
Generate a mock data set
Identify type of the main statistical goal
List choices of the statistical methods
Select the most appropriate statistical method
Perform the data analysis using a software
Report and interpret the results from the outputs

73. Writing Results Sections
Outline Sections:
Study algorithm
Characteristics of the study sample
Results of an exploratory analysis to support ways to answer the RQ
Results to answer the RQ
Results of an exploratory analysis to know more about the answer of the RQ
Follow formats required by the research sponsor or the target journal
Best done with SAP – Statistical Analysis Plan
Narrated tables or figures with key messages and avoid repetitions
Do not include explanations in Results section

74. Tips for Writing Results Section
Report results with purpose
Refer to the corresponding table or figures early at the beginning of the descriptions
Report sufficient data to allow evaluation of the calculation while avoid redundancy
Document steps of data analysis from which the results were transcribed
Provide statistical inference for the main findings that are the basis for the conclusions
Always report the confidence intervals, p-value can be an optional – not the main target 9

75. Q & A
Thank you