1. LIES, MORE LIES AND STATISTICS
AUTHORSHIP WORKSHOP: TRANSLATING YOUR THESIS INTO A PUBLICATION
7th December 2016
Dr James Kigera
University of Nairobi

2. Data Flow Data management Study data Analysis base Clinical data
Trial report
Study data
base
Clinical data
Source
document
CRF
Publication
Analysis
Data base
closure
Checked &
cleaned

3. Useful description
Some people are young and some people are a bit older, and other people are even older and very few people are extremely old, and I forgot .. Some people are extremely young when they finish medical school
???

4. VARIABLES DISCRETE CONTINOUS BINARY/DICHOTOMOUS CONSTANT
SCALES OF MEASUREMENT
NOMINAL
ORDINAL
RATIO
INTERVAL

6. DESCRIPTIVE CENTRAL TENDANCY MODE MEDIAN MEAN VARIATION RANGE
QUARTILES
VARIANCE
STANDARD DEVIATION
SHAPE
SKEWNESS
KURTOSIS

7. Epidemiological measures
Measures of frequency
Prevalence (point prevalence, period prevalence)
Incidence (risk / cumulative incidence, odds, incidence rate)
Quantifying the size of the problem, descriptive
Measures of association
Absolute measures (attributable risk, population attributable risk)
Quantifying public health impact
Relative measures (risk ratio, rate ratio, odds ratio)
Quantifying strength of association between exposure and outcome, use for evaluation of causality

8. INFERENTIAL STATITICS
CONFIDENCE INTERVALS
HYPOTHESIS TESTING
RELATIONSHIP BETWEEN GROUPS
T TEST
ANOVA
CHI SQUARE
FISCHERS EXACT
SIGN TEST
RANK SUM TEST
SIGNED RANK TEST
MANN-WHITENEY
KRUSKAL-WALLIS

9. Choice of test
Fisher’s exact test if exp value < 5

10. It must be alpha! Beautiful knight alpha? Ugly knight beta?
He saw a relationship where there was none –alpha error
He failed to see a relation where there was one – beta error
It must be alpha!

12. P Hypothesis testing The P- value, what is it?
The probability of obtaining a value of the test statistic that is at least as extreme as the one that was actually observed, given that the null hypothesis is true
[Cave : in essence, the p-value is an attribute of the test statistic, NOT of the effect measure. This is often wrongly explained in reference text books , including Fletcher & Fletcher, the LSHTM intro book and the Gao Smith & Smith what Petrie and Hennekens
Who can tell me what it is? For example, the OR for the association between crowding and TB acquisition is 3.5, the associated X2 is 4.1 and the p-value is What does this p-value of 0.10 mean?
That if in reality there is no association between crowding and TB, the probability of obtaining a X2 of 4.1 or higher is Or: if we would repeat the study an innumerable amount of times, in 1 out of 10 times we will incorrectly conclude that there is a difference in TB acquisition between the crowding groups while in reality there isn’t any difference.
Notice that the size of the effect measure is not at all considered with hypothesis testing. This is a major objection against the use of p-values alone. 12 12

13. Applied Clinical Research Course, M2, Kigali, 7-11 July 2008
Hypothesis testing
Caution with p-values!
Black /white , yes / no result
Always state the probability , NEVER write P <0.05 or P >0.05
Lends a false air of authority or objectivity
If results are “not significant “ they may not be published
Confusion of statistical significance with clinical significance
Hypothesis testing is appealing because it seems to simplify the hard work of interpreting data . However, there are a number of poblems involved with hypothesis testing: [see slide]
Applied Clinical Research Course, M2, Kigali, 7-11 July 2008 13 13

14. COMPARATIVE STATISTICS
RELATIONSHIP BETWEEN VARIABLES
VISUAL
DIRECTION
SHAPE
CORRELATION COEFICIENT

15. Case study Location: KNH Study type: Intervention study
Randomized clinical trial
Subjects: consecutive CHOLELITHIASIS PATIENTS aged
>=18
Outcome: PAIN AND SCAR APPEARANCE ASSESSED BY BLINDED NURSE

16. Two Interventions Laparascopic cholecystectomy and scar massage
Randomization
Patients underwent Open Cholecystectomy
Patients underwent Laparascopic Cholecystectomy
Patients underwent Open Cholecystectomy and Scar Massage

17. Study 3 N = 412 patients randomized Results:
223 patients had cholelithiasis
Pain and Scar Composite Scores at 6 months follow-up
Open group (n=75)  7200 Median
(IQR: 1200,0000 to 21120)
Laparascopic Group (n =75) Median
(IQR: to 43812)
Laparascopy and Massage group (n= 73)  Median
(IQR to 53350)

18. Study 3: Question 1 How many groups do we have?
Is data normally distributed? No
How many groups do we have?
3 groups.
Is the data paired or unpaired (independent)?
unpaired groups – independent from each other
What type of data is this?
Numerical/ continuous
Is the sample size large (>20)?
Yes (n = 223)

19. Choice of test
Fisher’s exact test if exp value < 5

20. SUMMARY DATA ANALYSIS Collect Useful Data Process Appropriately
Understand your Variables
Use the correct Test
Statistical vs Clinical Significance