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

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

As Urologist we must be aware of a number of different ‘biases’ present in current literature which include

Media

Pharmaceutical Industry

Colleagues

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)

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”

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

Table 1 + -

+ A B

- C D

Disease Te

st

Sensitivity = A Specificity = D ______ ______ A + C B + D PPV = A NPV= D _______ _______ A + B C + D

Meta-analysis

Case-control study

Cohort study

Clinical trial

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

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 ‘

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

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

Blind studies

Placebo responses

Crossover studies

Randomization

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

Table 1 + -

+ A B

- C D

Disease E

xpo

sure

RR = [ a / a+b] ________ [c / c + d]

“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]

Table 1 + -

+ A B

- C D

Disease E

xpo

sure

Experimental event rate (EER) = A / A+B Control event rate (CER) = C /C+D Relative risk = EER /CER

Table 1 + -

+ 42 (2.8%)

1471

- 99 (6.6%)

1404

Retention F

inas

teri

de

RRR = Risk difference = 2.8% = 57% _____________ ____ Baseline difference 6.6% ARR = CER – EER = 6.6 – 2.9 = 3.8

Table 1 + -

+ 42 (2.8%)

1471

- 99 (6.6%)

1404

Retention F

inas

teri

de

NNT = 1 = 1 = 26 ____________________ ________________ Absolute Risk Reduction 0.038

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.

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

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

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]

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”

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

Normal = Gaussian distribution = Bell Shaped

Bimodal

Positive skew (Mean > Median > Mode)

Negative skew (Mean < Median < Mode)

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

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).