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Global Clinical Trials Innovation Summit Berlin 20-21 October 2016 BIOSTATISTICS A FEW ESSENTIALS: USE AND APPLICATION IN CLINICAL RESEARCH Berlin, 20 October 2016 Dr. Aamir Shaikh Founder, Assansa

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

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 Prevalence Surveys Risk-factors Clinical trials Screening Research instruments Diagnostics

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 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

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 Publication Clinical Practice 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)

Group Exercise Q Research Question And Study Type/Design Match the columns (Research question and study type) Research Question/Study description 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 B. I want to confirm if giving only half the recommended dose of this antibiotic will be safer with no loss of efficacy Study Type 1. Observational descriptive study 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 D. I want to find out if coffee consumption is significantly higher in patients with GERD compared to those without GERD E. I want to follow up patients with stroke and prospectively study the role of different risk factors in post-stroke morbidity 3. Interventional study 4. Observational cross-sectional study 5. Observational case-control study

Group Exercise Q & A Research Question And Study Type/Design Match the columns (Research question and study type) Research Question/Study description 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 B. 3 I want to confirm if giving only half the recommended dose of this antibiotic will be safer with no loss of efficacy Study Type 4. Observational cross-sectional study 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 D. 5 I want to find out if coffee consumption is significantly higher in patients with GERD compared to those without GERD E. 2 I want to follow up patients with stroke and prospectively study the role of different risk factors in post-stroke morbidity 1. Observational descriptive study 5. Observational case-control study 2. Observational cohort study

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) Numerical (Quantitative) Nominal Ordinal Discrete Continuous

Classification Of Data Types, With Examples Data Categorical (Qualitative) Numerical (Quantitative) Nominal Ordinal Discrete Continuous Gender Hair colour Disease outcome: Dead / Alive 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 Blood pressure Age Height Weight Temperature

Different Types Of Data For The Same Variable "Blood Pressure" Nominal Categories: Normal blood pressure High blood pressure Blood Pressure Categorical Ordinal Discrete Categories: Prehypertension Stage 1 hypertension Stage 2 hypertension Number of hypertensive crises in a lifetime Numerical Continuous 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

Analysis And Inference Location And Spread Of Data Use Of A Right Statistical Test Probability Confidence Statistical Significance vs Clinical Significance (Relevance)

Statistics Which Describe Data What Do We Want To Describe? 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

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 + 1 SD). ~ 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 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! 50% of values (4 values) 50% of values (4 values) 4 0 1 3 4 5 7 9 12 MEDIAN 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 Categorical Data Parametric Otherwise 2 groups > 2 groups 2 groups > 2 groups 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 Categorical Data Parametric Otherwise 2 groups > 2 groups 2 groups > 2 groups 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 Categorical Data Both Parametric Otherwise 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

Tests For Survival (Time To Event) Is there difference between time (survival) trends? Non-parametric 2 groups > 2 groups Mantel-Haenszel test Log rank test / Mantel-Cox test

Probablity p Value 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.

A Qualitative Interpretation of Probabilities Probability <0.01 Chances <1% The effect beneficial/trivial/harmful is not, is almost certainly not 0.01 0.05 1 5% 0.05 0.25 5 25% 0.25 0.75 25 75% is very unlikely to be is unlikely to be, is probably not is possibly (not), may (not) be 0.75 0.95 0.95 0.99 >0.99 75 95% 95 99% >99% is likely to be, is probably is very likely to be is, is almost certainly

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

Significance p value vs Confidence Interval (CI) Reduced Risk Confidence Interval Increased Risk Results consistent with chance 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 No Difference Relative Risk

An Important Consideration. Statistical vs Clinical Significance (Relevance) Old statisticians do not die they just lose their significance..

Statistical Surgeon - 1 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.

Nationally the mortality rate for this operation in this age group was 16/123 = 13%. Would you sack him now? No! Statistical Surgeon - 2 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%.

Statistical Surgeon - 3 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!

From Clinical Research To Clinical Practice 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)

Calculating RR And OR Outcome + -- Exposure + -- a X d a / (a + b) OR = ------------ RR = ------------------ b X c c / (c + d)

Properties Of RR And OR 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.

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 Placebo # of patients Total Event Drug # of patients Total Event Relative Risk RR 1 Relative Risk Reduction RRR 2 Absolute Risk Reduction ARR 3 Number Needed to Treat 4 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 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

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 RR 1 Relative Risk Reduction RRR 2 Absolute Risk Reduction ARR 3 Number Needed to Treat 4 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 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

Relative Risk (RR), Absolute Risk (AR), And Number Needed to Treat (NNT) Placebo # of patients Total Event * Drug # of patients Total Event Relative Risk RR 1 Relative Risk Reduction RRR 2 3178 1038 3810 854 854 / 3810 = 0.69 (1-0.69)x100 1038 / 3178 = 31% ** 2030 84 2051 56 56 / 2051 84 / 2030 = 0.66 (1-0.66)x100 = 34% Absolute Risk Reduction ARR 3 32.6% - 22.4% = 10.2% 4.1% - 2.7% = 1.4% Number Needed to Treat 4 NNT 100/10.2 = 10 100/1.4 = 71 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

Recommended Reading / Resource - 1 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 - 2 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

Recommended Reading / Resource - 3 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/howread-paper Book: How to Read a Paper: The Basics of Evidence-Based Medicine 4 th Edition Author: Trisha Greenhalgh Publisher: BMJ Publishing Group (2010)

Recommended Reading / Resource - 4 Book: Medical Statistics Made Easy 2 nd Edition Author: Michael Harris, Gordon Taylor Publisher: INFORMA UK LIMITED (2008)

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

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Global Clinical Trials Innovation Summit Berlin October 2016