A presentation covering basic statistics for the FRCS (Urol) Exam

1. Nikhil Vasdev, David Thomas
Department of Urology
Freeman Hospital
Newcastle upon Tyne

3.  Help in understanding clinical evidence that influences our day to day
practice
 Essential to have a thorough understanding to function as a successful
urologist
 Important to validate literature
 Essential for the FRCS (Urol) exam

4.  As Urologist we must be aware of a number of different ‘biases’ present
in current literature which include
 Media
 Pharmaceutical Industry
 Colleagues

5.  Terminology
1. Prevalence – Total number of cases in a population at a given time
2. Incidence – The number of new cases in a population per unit time
3. Prevalence = Incidence X disease duration
4. Prevalence > Incidence = Applicable for chronic disease
5. Prevalence = Incidence – for acute disease (e.g. common cold)

6.  Sensitivity
 Number of true positives divided by number of all people with the
disease
 “Sensitivity = Positive in disease”
 Specificity
 Number of true negatives divided by number of all people without the
disease
 “Specificity = Negative in health”

7.  Positive Predictive Value (PPV)
 Number of true positives divided by number of people who tested positive for a disease
 The probability of having a condition, given a positive test
 Negative Predictive Value (NPV)
 Number of true negatives divided by number of people who tested negative for the
disease
 The probability of not having the condition given a negative test
 Important points
 Unlike sensitivity and specificity, PPV is dependent on the prevalence of the disease
 The higher the prevalence of a disease, the higher the positive predictive value of the
test

8. Disease
Table 1 + -
Test
+ A B
- C D
Sensitivity = A Specificity = D
______ ______
A + C B + D
PPV = A NPV= D
_______ _______
A + B C + D

9.  Meta-analysis
 Case-control study
 Cohort study
 Clinical trial

10.  Meta-analysis
 Pooling of data from several studies (often via a literature search) to achieve a greater statical power
 Main disadvantage – Cannot overcome limitations of individual studies or bias in study section
 Case-control study
 Observational study (Retrospective)
 Sample chosen on the basis of presence (cases) or absence (controls) of disease
 Information collected about risk factors
 Cohort study
 Observational study
 Sample chosen on the basis of presence or absence of risk factors
 Subjects are followed over time for development of disease
 Clinical trial
 Experimental study
 Compares benefits of 2 or more treatments
 Highest quality study = RANDOMIZED CONTROL TRIAL

11.  Statistical technique for combining results of several studies into a single
numerical estimate
 Validity of MA depends on the quality of the systematic review on which
it‘s based
 Results are usually displayed with C.I., p values and a Forest plot ‘

12. A forest plot (or blobbogram) is a graphical display designed to illustrate
the relative strength of treatment effects in multiple quantitative
scientific studies addressing the same question. It was developed for use
in medical research as a means of graphically representing a meta-
analysis of the results of randomized controlled trials

13.  A Bias is defined as when an outcome is more likely to occur than another
 Selection Bias
 Subjects choose group
 Recall Bias
 Knowledge of presence of disorder alters recall by subjects
 Sampling Bias
 Subjects are not representative
 Late look bias
 Information gathered at an inappropriate time

14.  Blind studies
 Placebo responses
 Crossover studies
 Randomization

15.  Phase 1: evaluates safety with
increasing dose
 Phase 2: early work on possible
benefits/ efficacy
 Phase 3: Formal evaluation (RCT)
 Phase 4: Safety reporting in use

16. Disease
Table 1 + -
Exposure
+ A B
- C D
RR = [ a / a+b]
________
[c / c + d]

17.  “PROSCAR more than halves the risk of developing acute urinary
retention and the need for surgery”’
 Urologists had different points of view regarding:
“the 48% to 57% relative risk reduction promoted and the 1.9% to 2.4%
absolute risk reductions actually observed in the median risk of AUR and
surgery, respectively” [PLESS; MTOPS]

18. Disease
Table 1 + -
Exposure
+ A B
- C D
Experimental event rate (EER) = A / A+B
Control event rate (CER) = C /C+D
Relative risk = EER /CER

19. Retention
Table 1 + -
Finasteride
+ 42 1471
(2.8%)
- 99 1404
(6.6%)
RRR = Risk difference = 2.8% = 57%
_____________ ____
Baseline difference 6.6%
ARR = CER – EER = 6.6 – 2.9 = 3.8

20. Retention
Table 1 + -
Finasteride
+ 42 1471
(2.8%)
- 99 1404
(6.6%)
NNT = 1 = 1 = 26
____________________ ________________
Absolute Risk Reduction 0.038

21.  Absolute risk of a disease is your risk of developing the disease over a time period. We all
have absolute risks of developing various diseases such as heart disease, cancer, stroke,
etc. The same absolute risk can be expressed in different ways. For example, say you have a
1 in 10 risk of developing a certain disease in your life. This can also be said to be a 10% risk,
or a 0.1 risk - depending if you use percentages or decimals.

Relative risk is used to compare the risk in two different groups of people. For example, the
groups could be smokers and non-smokers. All sorts of groups are compared to others in
medical research to see if belonging to a group increases or decreases your risk of
developing certain diseases. For example, research has shown that smokers have a higher
risk of developing heart disease compared to (relative to) non-smokers.

23.  Null (H0)
 Hypothesis of no difference
 E.g. . There is no association between the disease and the risk factor in the population
 Alternative (H1)
 Hypothesis that there is some difference
 E.g.. There is some association between the disease and the risk factor in the
population

24.  Type 1 (α)
 Stating that there is an effect or difference when none exists (to mistakenly accept the
experimental hypothesis but reject the null hypothesis)
 E.g. . You “saw” the difference that did not exist [Convict an innocent man]
 P value of < 0.5
 This indicates there is a less than a 5% chance that the data will show something that is
not really there

25.  Type 2 (β)
 Stating that there is NOT an effect or difference when one exists (to fail to reject the
null hypothesis when in fact the null hypothesis is false)
 E.g. . You “did not see” the difference that does exist [Setting a guilty man free]

26.  Probability of rejecting the null hypothesis when it is in fact false
 Power depends on
 Total number of the end points experience by the population
 Difference in compliance between treatment groups
 The power of a test is the probability that a study of a given size would detect as
statistically significant a real difference of a given magnitude
“If you increase the sample size, you increase the power. There is power in numbers”

27.  In statistical significance testing, the p-value is the probability of
obtaining a test statistic at least as extreme as the one that was actually
observed, assuming that the null hypothesis is true
 A measure of the effect of chance within a study
 It is not the probability that the result of the study is true or correct

29.  Normal = Gaussian distribution = Bell Shaped
 Bimodal
 Positive skew (Mean > Median > Mode)
 Negative skew (Mean < Median < Mode)

30.  It shows the trade-off between sensitivity and specificity (any increase in
sensitivity will be accompanied by a decrease in specificity)
 The closer the curve follows the left-hand border and then the top border
of the ROC space, the more accurate the test
 The closer the curve comes to the 45-degree diagonal of the ROC space,
the less accurate the test
 The area under the curve is a measure of test accuracy

32.  The Kaplan–Meier estimator also known as the product limit estimator, is
an estimator for estimating the survival function from life-time data
 The term "survival" is a bit misleading; you can use survival curves to
study times required to reach any well-defined endpoint (e.g., re-
occlusion of a grafted blood vessel, first metastasis, discharge from the
hospital).