chapter 1 general overview

BiostatisticsBonnie Wang

王博涵Wang Bo Han

[email protected]. of Preventive Medicine

School of Public Health Administration Liaoning Medical University

Reference Book ：Fundamentals of Biostatistics(7th, Edition)

Author ： Bernard Rosner, Harvard University

Teaching plan：Chapter 1 ： General OverviewChapter 2 ： Descriptive StatisticsChapter 3 ： The normal curveChapter 4 ： Hypothesis Testing and t-testChapter 5 ： Analysis of variance (ANOVA)Chapter 6 ： Chi square testChapter 7 ： Nonpapametric testsChapter 8 ： CorrelationChapter 9 ： Simple linear regression

Why medical students should understand biostatistics ?

Basic requirement of medical research

To avoid misleading

To do our medical work smarter

Chapter 1 ： General Overview

Statistics is a branch of science that deals with the art of collecting, classifying, displaying, analyzing, and interpreting results from research.

The field of statistics has two main areas: mathematical statistics and applied statistics.

Mathematical statistics concerns the development of new methods of statistical inference and requires detailed knowledge of abstract mathematics for its implementation.

Applied statistics involves applying the methods of mathematical statistics to specific subject areas, such as economics, psychology, and public health.

Biostatistics

a portmanteau word made from biology and statistics

The application of statistics to a wide range of topics in biology.

Biostatistics is the branch of applied statistics that applies statistical methods to medical and biological problems.

It is the science which deals with development and application of the most appropriate methods for the:

Collection of data.

Presentation of the collected data.

Analysis and interpretation of the results.

Making decisions on the basis of such analysis

The history of biostatistics

Pierre-Simon Laplace (1749-1827)

Probability theory will be widely used in medical.

Pierre-Charles-Alexandre Louis(1787-1872)

Collected some patients information to find the treatment of typhoid fever.

Karl Pearson (1857-1936)

Pearson's work was all-embracing in the wide application and development of mathematical statistics, and encompassed the fields of biology, epidemiology, anthropometry, medicine and social history.In 1901, with Weldon and Galton, he founded the journal Biometrika whose object was the development of statistical theory. He edited this journal until his death.

Ronald A. Fisher （ 1890 ～ 1962 ） An English statistician, evolutionary

biologist, eugenicist, and geneticist. Among other things, Fisher is well known for his contributions to statistics by creating ANOVA (analysis of variance), Fisher's exact test and Fisher's equation. Anders Hald called him "a genius who almost single-handedly created the foundations for modern statistical science", while Richard Dawkins named him "the greatest biologist since Darwin".

Challenge of modern statistical science

Public health:Chronic non-communicable diseases---(survival analysis 、 analysis of risk factors)Genetics and statisticsPopulation pharmacokinetics and statisticsCost-effectiveness analysis……

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What is BioStatistics?

MathematicsMedicine

Statistics

BiostatisticsBiostatistics

Collaborative ScienceCollaborative Science

BasicClinical

others

Role of statisticians

To guide the design of an experiment or survey prior to data collection

To analyze data using proper statistical procedures and techniques

To present and interpret the results to researchers and other decision makers

BY C. J. Chang

Physiology of Research

Truth in theUniverse

Truth in theStudy

Findingsin the Study

inferenceinference

InternalValidity

ExternalValidity

Investigator’s View:

BY C. J. Chang

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Biostatistician’s + Investigator’s View:

InternalValidity

ExternalValidity

ResearchQuestion

StudyPlan

ActualStudydesign implement

BY C. J. Chang


Truth in theUniverse

Truth in theStudy

Findingsin the Study

inferenceinference

How Does it Work?

InternalValidity

ExternalValidity

ResearchQuestion

StudyPlan

ActualStudydesign implement

BY C. J. Chang

Collaborating Team Work

Principal Investigator

BiostatisticianBiostatistician

Lab Scientist

Project Director

Research Nurse

Data Programmer

etc.BY C. J. Chang

Sample Size CalculationSample Size Calculation Power AnalysisPower Analysis

Before Designing the Study:

N=2987?!

What Do Biostatisticians Do?

BY C. J. Chang

During the Study:

TEAM work Questionnaire Design Data Base Management Quality Assurance/Quality Control Monthly, Semi Annual, Annual Reports Interim Data Analysis etc...


BY C. J. Chang

At the End of the Study:

Always start with descriptive statistics Scatter plot Use of appropriate variable Use simple, efficient, and practical methods Time independent vs. time dependent covariates Not necessary advanced methods Use most popular, acceptable statistical packages Use universal recognizable data format

Tips in Statistical analysis


BY C. J. Chang

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Making Conclusions and Interpretation

How to control type-I, II errors One answer for one hypothesis Statistical vs. clinical significance Over powered vs. under powered Multiple comparisons Over stretch statistical method


BY C. J. Chang

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Example: KSBPSKKidney SStone - BBlood PPressure SStudy

A study of the effects of Extracoporeal Shock Wave Lithotripsy (ESWL) on Hypertension

Funded by National Electronic Manufacture Association(NEMA) and approved by FDA in 1990

PI : Michael Alderman, M.D. Co-PI : Chee Jen Chang, Ph.D.Co-PI : Jonathan Tobin, Ph.D.

Albert Einstein College of Medicine

BY C. J. Chang

KSBPSKKidney SStone - BBlood PPressure SStudy

Experimental study : ESWL （ Extracorporeal Shock Wave Lithotripsy ）vs. open surgery

Non randomized and non masked design study

Post marketing surveillance study

Phase IV study

Prospective two year follow up study for 4 years

Multi centers study

Original Protocol:

BY C. J. Chang

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Sample size :900 control, 900 ESWL patients

Primary variables : SBP/DBP at baseline, 6, 12, and 24 months

Secondary variables: Renal functions at 0, 6, 12, and 24 months

Covariates:Demographic variables, stone burden

Study Design:

BY C. J. Chang

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Hypothesis :

DBP difference between controland ESWL groups is not greater2.5 mmHg at 24 month

BY C. J. Chang

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Difficulties: Patients selection biasPatient’s follow-up visit interest is LOWRenal function collection is costly and unchangedDifferent device typeDifferent technology in device typeNew technology developed during the study

BY C. J. Chang

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Statistical solutions to some difficulties:

Patient’s follow-up visit interest is LOW:

reduce sample size, increase reduce sample size, increase

effect sizeeffect size

Patients selection bias :

statistical analysis controlling statistical analysis controlling

covariatescovariates

BY C. J. Chang

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Renal function collection is costlyand unchanged:

bioequivalence testbioequivalence test to revise protocol

Different device type and technologyin device type:

stratified data analysisstratified data analysis


Statistical solutions to some difficulties:

BY C. J. Chang

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Can Statistics solve all the difficulties?

New technology developed duringthe study

Participants’ willingness

No !No !

BY C. J. Chang

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In September, 1997, FDA issues:

““Although an increase in blood pressureAlthough an increase in blood pressuremay result after Lithotripsy for the treatmentmay result after Lithotripsy for the treatmentof kidney stones, a 2 years prospective studyof kidney stones, a 2 years prospective studysuggests that treatment with Lithotripsy willsuggests that treatment with Lithotripsy willnot result in a difference in diastolic bloodnot result in a difference in diastolic bloodpressure of pressure of 4 mmHg as compared to other 4 mmHg as compared to otherkidney stone therapies.”kidney stone therapies.”

BY C. J. Chang

Methodology developmentConsultantHealth- and bio- informaticsResearch protocols review Committee of Clinical Investigation (CCI) Internal Review Board (IRB)Regulatory review

What Do Biostatistician Do?then, now and future

BY C. J. Chang

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Teaching (university, medical school)Research (institute, foundation)Government Agency (NIH, CDC)Regulatory Agency (FDA)Industry (Pharmaceutical, Biotech.) Consulting (CRO)

What Do Biostatistician Do?then, now and future

BY C. J. Chang

Basic conception

Populations and Samples

Homogeneity and variability

Type of the variable

Parameters and statistics

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Population

It is the largest collection of It is the largest collection of valuesvalues of a of a ranrandom variabledom variable for which we have an for which we have an interest at a particular time. interest at a particular time.

For example: The weights of all the children enrolled in a

certain elementary school. Populations may be finite or infinite.

Text Book : Basic Concepts and Methodology for the Health

Sciences 38

SampleSampleIt is a part of a population. It is a part of a population.

For example: The weights of only a fraction of

these children.

Types of data Constant Variables: Types of variables Quantitative variables Quantitative continuous Quantitative descrete Qualitative variables Qualitative nominal Qualitative ordinal

Text Book : Basic Concepts and Methodology for the Health

Sciences 40

VariableIt is a characteristic that takes on different values

in different persons, places, or things.

For example:- heart rate,

- the heights of adult males,

- the weights of preschool children,

- the ages of patients seen in a dental clinic.

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Quantitative VariablesIt can be measured inthe usual sense.For example: - the heights of adult

males, - the weights of

preschool children,- the ages of patients

seen in a - dental clinic.

Qualitative Variables Many characteristics are

not capable of being measured. Some of them can be ordered or ranked.

For example:- classification of people into

socio-economic groups,

- social classes based on income, education, etc.

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A discrete variable is characterized by gaps

or interruptions in the values that it can assume.

For example:- The number of daily

admissions to a general hospital,

- The number of decayed, missing or filled teeth per child in an elementary school.

A continuous variable can assume any value within a

specified relevant interval of values assumed by the variable.

For example:- Height, - weight, - skull circumference. No matter how close together the

observed heights of two people, we can find another person whose height falls somewhere in between.

Statistics: It is a descriptive measure computed from the

data of a sample.

Parameters: It is a descriptive measure computed from the

data of a population. Since it is difficult to measure a parameter from the

population, a sample is drawn of size n, whose values are 1 , 2 , …, n. From this data, we measure the statistic.

chapter 1 general overview

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