BIOSTATISTICS A FEW ESSENTIALS: USE AND … · Biostatistics_Global CR Summit_Berlin 20 Oct 2016 Session Outline A Few Essentials - To Begin With... Use And Application In Clinical

Biostatistics_Global CR Summit_Berlin 20 Oct 2016

“Global Clinical Trials Innovation Summit”

Berlin 20-21 October 2016

Berlin, 20 October 2016

Dr. Aamir Shaikh Founder, Assansa

BIOSTATISTICS – A FEW ESSENTIALS:

USE AND APPLICATION IN CLINICAL RESEARCH


Here We Are Then, Day 1 – Early Morning Topic – Biostatistics In Clinical Research

Welcome!

Biostatistics

In Clinical Research…


Session Outline

A Few Essentials - To Begin With...

Use And Application In Clinical Research

Planning And Design

Analysis And Inference

From Clinical Research To Clinical Practice

Recommended Reading / Resource


Session Outline



Planning And Design





To Begin With… Statistics In Perspective

“There are three kinds of lies: lies, damned lies, and statistics – Benjamin Disraraeli

Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write – H G Wells

How to Lie with Statistics – Huff 1954/1993


What Is Medical / Biostatistics?

Statistics - The art and science of collecting, presenting, analyzing, and interpreting data Medicine - A “science of uncertainty” Medical Research is a quest for truth. In biomedical research, truth is probabilistic

Medical / Biostatistics is largely about handling and quantifying variation and uncertainty in living (medical/biological) systems……in order to make appropriate inferences (decisions) with some level of confidence


From Idea To New Medicine - A Long And Difficult Journey Along A Winding Road - Clinical Research (CR) Plays An Essential Role


Clinical Research Is More Than

Just Clinical Trials…

Incidence

Research instruments Screening

Risk-factors Surveys Clinical trials

Prevalence

Diagnostics


Be able to understand and design scientifically sound “experiments” - clinical research projects

Understand the results, and communicate the scientific inference / clinical relevance

Critically appraise published medical literature

Facilitate translation of clinical research to clinical practice

As CR Professionals, We Need To Be Reasonably Comfortable And Competent With Biostatistics To …

Statistics can be used to prove anything - even the truth.

Anonymous


Session Outline



Planning And Design





Planning And Design

CR – One Proposed Thinking Approach

CR Question vs Study Type / Design ?

Types Of Data - Which Data When?

Sample Size – How Many? Why?


CR – One Proposed The Thinking Approach

One Suggested Thinking Approach…. Why? Need, Rationale and Background

What? Objectives and Study design

How? Methodology and Assessments

Who? Participants – Inclusion & Exclusion criteria

By Whom? Sponsor and Investigator

When? Time Schedules

Where? Institutes, Study Sites, Central labs

All Together? Structure, Balance and Judgment

Idea / CR Concept

Protocol Synopsis

Final Protocol

CR Study Report

Clinical Practice

Publication

Health Benefits


CR Question vs Study Type / Design ?

Observational

Descriptive

Analytical – Cross-sectional

study

– Case-control study

– Cohort study

Interventional RCT

Non-RCT


The Type Of Question Will Determine The Research – 1

Example – Acute Coronary Syndrome "I want to find out what are the symptoms (e.g. chest pain, sweating etc) that patients experience during an MI and which ones are more common" Observational descriptive study

"I want to find out if more salt in the diet increases the risk of ACS" Observational study

"I want to do a short, quick study to get an initial understanding of this issue" Cross-sectional study

"I want to do a more detailed study, but don’t want to spend too much time on it" Case-control study

"I want a more definitive answer, and am willing to spend much time on it if needed. I would also like to find out about many other potential risk factors" Cohort study

"I want to know if doing yoga can prevent recurrence of MI" Interventional study (Clinical Trial)


Match the columns (Research question and study type)

Research Question/Study description Study Type

A. I want to do a very quick study and find out if there is any correlation at all between exposure to radioactive material and certain cancers

1. Observational descriptive study

B. I want to confirm if giving only half the recommended dose of this antibiotic will be safer with no loss of efficacy

2. Observational cohort study

C. We know very little about early MI in India. I want to do a basic study to find out its incidence and prevalence

3. Interventional study

D. I want to find out if coffee consumption is significantly higher in patients with GERD compared to those without GERD

4. Observational cross-sectional study

E. I want to follow up patients with stroke and prospectively study the role of different risk factors in post-stroke morbidity

5. Observational case-control study

Group Exercise – Q Research Question And Study Type/Design


Match the columns (Research question and study type)

Research Question/Study description Study Type

A. 4 I want to do a very quick study and find out if there is any correlation at all between exposure to radioactive material and certain cancers

4. Observational cross-sectional study

B. 3 I want to confirm if giving only half the recommended dose of this antibiotic will be safer with no loss of efficacy

3. Interventional study

C. 1 We know very little about early MI in India. I want to do a basic study to find out its incidence and prevalence

1. Observational descriptive study

D. 5 I want to find out if coffee consumption is significantly higher in patients with GERD compared to those without GERD

5. Observational case-control study

E. 2 I want to follow up patients with stroke and prospectively study the role of different risk factors in post-stroke morbidity

2. Observational cohort study

Group Exercise – Q & A Research Question And Study Type/Design


Sample vs Population

Population: The set of data (numerical or otherwise) corresponding to the entire collection of units about which information is sought

In most studies, it is difficult or impossible to obtain information from the entire population. We rely on samples to make estimates or inferences related to the population

Sample: A subset of the population data that are actually collected in the course of a study

ALL Indian patients with Type 2 DM

A sample of 500 patients with T2DM from 10 tertiary care

hospitals in 5 metro cities in India

POPULATION

SAMPLE


Classification Of Data Types

Data

Categorical

(Qualitative)

Nominal Ordinal

Numerical

(Quantitative)

Discrete Continuous


Classification Of Data Types, With Examples

Data

Categorical

(Qualitative)

Nominal Ordinal

Numerical

(Quantitative)

Discrete Continuous

•Blood pressure •Age •Height •Weight •Temperature

• Blood pressure: Prehypertension, Grade 1, Grade 2 • Degree of illness: Mild, moderate, severe

•Number of events •Number of episodes •Number of doctors •Number of completed days

•Gender •Hair colour •Disease outcome: Dead / Alive


Different Types Of Data For The Same Variable "Blood Pressure"

Blood Pressure

Categorical

Nominal

Ordinal

Numerical

Discrete

Continuous

Categories: • Normal blood pressure • High blood pressure

Categories: • Prehypertension • Stage 1 hypertension • Stage 2 hypertension

Number of hypertensive crises in a lifetime

Actual BP measurement (mm Hg)


Sample Size – How Many? Why? A Function Of Effect Size, Variability, Probability, Power

error (p<0.05) less than a 5% probability (chance) that the result obtained is due to chance error (0.10 or 10%) Power - {1- = power (1- 0.1 = 0.9 or 90%)} Variabilility - estimated variability of study parameters (S.D.) Effect size - magnitude of expected difference ( effect) Statistical vs clinical significance


Session Outline



Planning And Design






Location And Spread Of Data

Use Of A Right Statistical Test

Probability

Confidence

Statistical Significance vs Clinical Significance (Relevance)


What is the “location” or “center” of the data? (“measures of location / central tendency”)

Numerical - Mean, Median, Mode

Categorical - Proportions

How is the data spread out ? How do the data vary? (“measures of dispersion / variability”)

Numerical - Range, Inter Quartile range, Variance, Standard deviation, Coefficient of variation

Categorical - Inter Quartile range

Statistics Which Describe Data What Do We Want To Describe?


Parametric data Normal (Gaussian) Distribution Curve

Data whose distribution in the underlying population can be represented by the normal distribution (Gaussian) curve

“Bell shaped”, Symmetrical

Mean, median and mode are equal

A rule of thumb for interpreting SD:

~ 68% of all data points fall within one SD of the mean (i.e., mean + 1SD).

~ 95% of all data points are within two SDs of the mean (i.e., mean + 2 SD)

~ 99% of all data points are within three SDs of the mean (i.e., mean + 3 SD). .

Karl Friedrich Gauss (1777-1855)


Calculating And Using Sample Mean, Median, Mode

1 5 3 7 0 9 12 4

50% of values (4 values) 50% of values (4 values)

MEDIAN

4.

Example. Data set (i.e., 1,3, 5, 4, 7, 0, 9, 12, and 4)

Mean is 5, Median is 4, Mode is 4

If the number 12 is incorrectly recorded as 120. It is seen that the mean changes from 5 to 17, while median is unchanged!

Use - Mean Height vs Median Overall Survival !

4


Most Appropriate Measure Of Location

Data - “symmetric” or “skewed”.

Data - “unimodal” or more“multimodal”

If data are symmetric, report the mean (mean median, and mode will be ~ same)

If data are skewed, report the median.

If data are multimodal, report the mean, median and/or mode for each subgroup.


Most Appropriate Measure Of Dispersion

If data are symmetric, with no serious outliers, use range and standard deviation.

If data are skewed, and/or have serious outliers, use IQR.

If comparing variation across two data sets, use coefficient of variation.


Use Of A “Right” Statistical Test

A Logical Approach


Difference Between Unpaired Groups

Is there a difference between groups? - Unpaired

Numerical Data

Parametric

2 groups > 2 groups

Otherwise

2 groups > 2 groups

Categorical Data

2 groups > 2 groups

Unpaired t ANOVA Mann-Whitney Kruskal-Wallis •2 test •Fischer’s test

2 test

Note: Multiple group comparison tests need to be followed by post hoc tests


Difference Between Paired Groups

Is there a difference between groups? - Paired

Numerical Data

Parametric

2 groups > 2 groups

Otherwise

2 groups > 2 groups

Categorical Data

2 groups > 2 groups

Paired t Repeated measures ANOVA

Wilcoxon Friedman’s McNemar’s Cochran’s Q

Note: Multiple group comparison tests need to be followed by post hoc tests


Tests Of Association

Is there an association between 2 variables?

Numerical Data

Both Parametric

Otherwise

Categorical Data

2 x 2 data Otherwise

Pearson’s r •Spearman’s •Kendall’s

•Risk ratio •Odd’s ratio

•2 for trend •Logistic regression


Tests Of Agreement Between Assessments

Is there agreement between assessments?

(Screening tests / Diagnostic tests / Rater validation)

Numerical Data

Categorical Data

•Intraclass correlation coefficient •Bland-Altmann plot (graphical method)

•Cohen’s kappa statistic •Kendall’s coefficient of concordance


Is there difference between time (survival) trends?

Non-parametric

2 groups > 2 groups

Tests For Survival (Time To Event)

Mantel-Haenszel test Log rank test / Mantel-Cox test


P value is simply the probability (Chance) that the result obtained is merely due to chance

Conventionally set as 0.05; Equivalent to 5% or 1/20 chance

Difference is significant if p value is less than 0.05 (< 0.05)

P value is calculated after data is collected/analyzed

Need to describe outcomes in plain language.

Therefore need to describe probabilities that the effect is beneficial, trivial, and/or harmful.

Probablity – p Value


A Qualitative Interpretation of Probabilities

The effect… beneficial/trivial/harmful

is not…, is almost certainly not…

Probability

<0.01

Chances

<1%

is very unlikely to be… 0.01–0.05 1–5%

is unlikely to be…, is probably not… 0.05–0.25 5–25%

is possibly (not)…, may (not) be… 0.25–0.75 25–75%

is likely to be…, is probably…

is very likely to be…

is…, is almost certainly…

0.75–0.95

0.95–0.99

>0.99

75–95%

95–99%

>99%


More About "Confidence Interval”

It is an interval that tells the precision with which we have estimated a sample statistic.

Interpretation of 95% CI: “We are 95% sure that the TRUE parameter value is in the 95% confidence interval”

Uses Statistical significance

Clinical relevance

Study Design – superiority vs equivalence vs non-inferiority


0.6 0.8 1 1.2 1.4

Relative Risk

No Difference

Confidence Interval

Results consistent

with chance

Reduced Risk Increased Risk

0.7 0.9 1.1 1.3

Significance p value vs Confidence Interval (CI)


An Important Consideration….

Old statisticians do not die –

they just lose their significance…..

Statistical vs Clinical Significance (Relevance)


Recently a surgeon had a mortality of 60% for a heart operation in children less than one year old.

Would you sack him?

No! Need to know what rate to expect, such

as rate of other surgeons doing

comparable operations.

Statistical Surgeon - 1


Nationally the mortality rate for this operation in this age group was 16/123 = 13%.

Would you sack him now?

No!

He may be unlucky.

He may be operating on more severe cases.

He may only have done a few operations.

For example 2/3 is 66%.



There is no evidence babies were more ill.

The surgeon operated on 15 babies of whom 9 died.

The 95% confidence interval for the national rate (13%) is 11 % to 36%.

Would you now sack him? YES !



Session Outline



Planning And Design






Evaluating And Communicating Risk And Benefit

A Case: Lipid Lowering Drugs – What Do The Numbers Mean?


Evaluating And Communicating Risk

Relative Risk (RR)

Odds Ratios (OR)

Relative Risk Reduction (RRR)

Absolute Risk Reduction (ARR)

Number Needed to Treat (NNT)


a X d a / (a + b)

OR = ------------ RR = ------------------

b X c c / (c + d)

Calculating RR And OR

Exposure

Outcome

+

--

+ --


RR and OR have been used interchangeably.

By convention - RR is mostly used for cohort studies

By convention - OR is mostly used in case-control studies

RR and OR can be expressed with 95% Confidence Intervals.

If this interval does not include a value of 1.0, then the association between exposure and outcome is stronger.

If both limits > 1.0 exposure is favoring outcome

If both limits < 1.0 exposure is protecting against outcome

OR used to investigate uncommon (< 10% incidence) events.

If the event occurs commonly, OR tends to overestimate risk.

Properties Of RR And OR


A Case: Lipid Lowering Drugs – What Do The Numbers Mean?

Drug A Patients taking this drug for 5 years have 34% fewer heart attacks than patients taking placebo

Drug B 2.7% of the patients taking this drug for 5 years had a heart attack, comparing to 4.1% taking a placebo, a difference of 1.4%

Drug C If 71 patients took this drug for 5 years the drug would prevent one from having a heart attack (There in no way of knowing in advance which person that might be)

Source: Therapeutics Letter Issue 15, 1996


Relative Risk (RR), Absolute Risk (AR), and Number Needed to Treat (NNT) - Exercise

1) Relative Risk (RR) = Event Rate (Drug) / Event rate (Placebo)

2) % Relative Risk Reduction (RRR) = 1- relative risk x 100

3) % Absolute Risk reduction (ARR) = % Event rate (Placebo) - % Event rate (Drug)

4) Number needed to treat (NNT) = 100 / % ARR

Therapeutics Letter Issue 15, 1996

Placebo

# of patients

Total Event

Drug

# of patients

Total Event

Relative Risk

RR1

Relative Risk

Reduction

RRR2

Absolute Risk

Reduction

ARR3

Number

Needed to

Treat4 NNT

2030 (84) 2051 (56) 56 / 2051

84 / 2030 = 0.66 (1-0.66)x100

= 34%

4.1% - 2.7%

= 1.4%

100/1.4

= 71


Relative Risk (RR), Absolute Risk (AR), and Number Needed to Treat (NNT) - Exercise

Placebo

# of patients

Total Event

Drug

# of patients

Total Event

Relative Risk

RR1

Relative Risk

Reduction

RRR2

Absolute Risk

Reduction

ARR3

Number

Needed to

Treat4 NNT

3178 (1038) 3810 (854)

2030 (84) 2051 (56) 56 / 2051

84 / 2030 = 0.66 (1-0.66)x100

= 34%

4.1% - 2.7%

= 1.4%

100/1.4

= 71


2) % Relative Risk Reduction (RRR) = 1- relative risk x 100

3) % Absolute Risk reduction (ARR) = % Event rate (Placebo) - % Event rate (Drug)



Relative Risk (RR), Absolute Risk (AR), And Number Needed to Treat (NNT)


2) Relative Risk Reduction (RRR) = 1- relative risk x 100

3) %Absolute Risk reduction (ARR) = % Event rate (Placebo) - % Event rate (Drug)


Placebo

# of patients

Total Event

Drug

# of patients

Total Event

Relative Risk

RR1

Relative Risk

Reduction

RRR2

Absolute Risk

Reduction

ARR3

Number

Needed to

Treat4 NNT

3178 1038 3810 854 854 / 3810

1038 / 3178 = 0.69 (1-0.69)x100

= 31%

32.6% - 22.4%

= 10.2%

100/10.2

= 10

2030 84 2051 56 56 / 2051

84 / 2030 = 0.66 (1-0.66)x100

= 34%

4.1% - 2.7%

= 1.4%

100/1.4 = 71 **

*


Session Outline



Planning And Design





THE IUATLD booklet - 2001 TABLE OF CONTENTS 3. GETTING STARTED IN RESEARCH Research question and protocol 4. STRUCTURING RESEARCH: STUDY DESIGN Designs of study and study types 5. THE SUBJECT OF RESEARCH Population, sampling methods, sample size 6. MEASUREMENT IN EPIDEMIOLOGY Collection and management of data 7. CONDUCTING RESEARCH PRACTICAL STEPS Study conduct; checking, coding, entering data 8. INTERPRETING RESULTS Data analysis, interpretation, and report writing 9. OTHER ISSUES IN RESEARCH IPR and ethics

Recommended Reading / Resource - 1


Designing Clinical Research: An

Epidemiologic Approach

3rd Revised edition 2006

Author: Deborah G. Grady, Warren S.

Browner, Thomas B. Newman, Stephen B.

Hulley, Steven R. Cummings


http://www.flipkart.com/author/deborah-g-grady

http://www.flipkart.com/author/warren-s-browner

http://www.flipkart.com/author/warren-s-browner

http://www.flipkart.com/author/thomas-b-newman

http://www.flipkart.com/author/stephen-b-hulley

http://www.flipkart.com/author/stephen-b-hulley

http://www.flipkart.com/author/steven-r-cummings



Book: How to Read a Paper: The Basics of

Evidence-Based Medicine 4th Edition

Author: Trisha Greenhalgh

Publisher: BMJ Publishing Group (2010)

Series of concise articles by Trisha Greenhalgh on 'How to read a

paper„, published in the British Medical Journal in 1997

http://www.bmj.com/about-bmj/resources-readers/publications/how-

read-paper

http://www.bmj.com/about-bmj/resources-readers/publications/how-read-paper











Book: Medical Statistics Made Easy 2nd Edition

Author: Michael Harris, Gordon Taylor

Publisher: INFORMA UK LIMITED (2008)


If you tell me that this interaction was average, you are just being mean.

A Final Thought….


Share Knowledge! Support Open Access!

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BIOSTATISTICS A FEW ESSENTIALS: USE AND … · Biostatistics_Global CR Summit_Berlin 20 Oct 2016 Session Outline A Few Essentials - To Begin With... Use And Application In Clinical