SlideShare una empresa de Scribd logo
1 de 17
Analysis of Polyps
by using R

20106102 강민규
20106120 김형섭
20106122 류승우
Contents
• About Polyps
  ▫ Colonial Polyps(대장 용종)이란?

• Script Analysis
  ▫ 통계적 결과 해석

• 생화학적 기전

• 결과 및 결론
About Polyps
About Polyps
About Polyps
               •비스테로이드성 항염증제
               (NSAIDs Non-Steroidal
               Anti Imflammatory Drugs)

               •대장용종 예방에 효과적

               •대표적으로 아스피린
               (Aspirin)이 NSAIDs에 속한
               다
Script Analysis
• library("HSAUR2")
• ## HSAUR2 패키지 library에 등록,polyps자료 탐색

• summary(polyps)
• ## polyps 자료 요약

•        number treat       age
•   Min. : 1.00 placebo:11 Min. :13.00
•   1st Qu.: 3.75 drug : 9 1st Qu.:19.75
•   Median :21.00 Median :22.50
•   Mean :24.05 Mean :25.00
•   3rd Qu.:41.00 3rd Qu.:27.75
•   Max. :63.00 Max. :50.00

•   aggregate(polyps[,"number"],list(polyps$treat),summary)
•   ## polyps의 수와 대조, 조작군에 대한 summary
•   Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
•   1 placebo 7.000 21.500 40.000 35.640 48.000 63.000
•   2 drug 1.000 2.000 3.000 9.889 17.000 33.000
•   aggregate(polyps[,"age"],list(polyps$treat),summary)
•   ## polyps 환자의 나이와 대조,조작군에 대한 summary
•   Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
•   1 placebo 13.00 19.50 22.00 26.27 32.00 50.00
•   2 drug 16.00 22.00 23.00 23.44 23.00 42.00



•   jpeg("polyps.jpg",width=720,height=400)
•   ## jpeg device 활성
•   layout(matrix(1:2,ncol=2))
•   ## 2칸짜리 layout 형성
•   plot(number~treat, data=polyps)
•   ## polyps 수와 treat사이의 관계 boxplot
•   plot(age~treat, data=polyps)
•   ## polyps 환자의 age와 treat 사이의 관계 boxplot
•   Dev.off()
•   ## device 비활성
•   tapply(polyps$age, polyps$treat,stem)
•   ## 줄기, 잎 그림으로 비교
Boxplot 출력결과
•   The decimal point is 1 digit(s) to the right of the |
•   1 | 389
•   2 | 0227
•   3 | 044
•   4|
•   5|0
•   The decimal point is 1 digit(s) to the right of the |
•   1 | 67
•   2 | 223333
•   3|
•   4|2
•   $placebo
•   NULL
•   $drug
•   NULL

• polyps_glm.1<-glm(number~treat+age, data=polyps, family=poisson())
• ## polyps_glm.1 에 glm함수(poisson) objecting

• summary(polyps_glm.1)
• ## glm, poisson 에러 모형으로 적합test
• Call:
• glm(formula = number ~ treat + age, family = poisson(), data = polyps)
• Deviance Residuals:

•       Min 1Q Median         3Q Max
•     -4.2212 -3.0536 -0.1802 1.4459 5.8301
•   Coefficients:
•            Estimate Std. Error z value Pr(>|z|)
•   (Intercept) 4.529024 0.146872 30.84 < 2e-16 ***
•   treatdrug -1.359083 0.117643 -11.55 < 2e-16 ***
•   age      -0.038830 0.005955 -6.52 7.02e-11 ***

• ---

•   Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
•   (Dispersion parameter for poisson family taken to be 1)
•   Null deviance: 378.66 on 19 degrees of freedom
•   Residual deviance: 179.54 on 17 degrees of freedom
•   AIC: 273.88
•   Number of Fisher Scoring iterations: 5
• polyps_glm.2<-glm(number~treat+age, data=polyps, family=quasipoisson())
• ## polyps_glm.2 에 glm 함수(quasipoisson) objecting

• summary(polyps_glm.2)
• ## glm, quasi-poisson 에러 모형으로 적합

•   Call:
•   glm(formula = number ~ treat + age, family = quasipoisson(),
•   data = polyps)
•   Deviance Residuals:
•       Min 1Q Median 3Q                  Max
•     -4.2212 -3.0536 -0.1802 1.4459 5.8301
•   Coefficients:
•            Estimate      Std. Error t value Pr(>|t|)
•   (Intercept) 4.52902         0.48106       9.415 3.72e-08 ***
•   treatdrug -1.35908        0.38533       -3.527 0.00259 **
•   age       -0.03883       0.01951 -1.991           0.06284 .
•   ---
•   Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
•   (Dispersion parameter for quasipoisson family taken to be 10.72805)
•   Null deviance: 378.66 on 19 degrees of freedom
•   Residual deviance: 179.54 on 17 degrees of freedom
•   AIC: NA
•   Number of Fisher Scoring iterations: 5
작용 기전
• Eicosanoids are synthesized from
  arachidonic acid, which is formed from
  phospholipids.

• Arachidonic acid is converted to
  prostaglandin H2, catalyzed by
  cyclooxygenase.

• This enzyme is the target of aspirin and
  other nonsteroidal anti-inflammatory
  drugs (NSAIDs).
• Aspirin and NSAIDs have also been found to
  reduce the frequency of colon cancer, apparently
  by inhibiting synthesis of prostaglandins that
  stimulate cell proliferation

• Cox-1, cox-2 와 polyps, GI tract , asthma

• Vioxx, celebrex 의 항암작용

• Polyps와 NSAID의 실효성
결과 정리 및 요약
• R을 이용한 통계자료 분석 결과

 ▫ Drug(NSAIDs)가 placebo와는 다르게 유의한 효과가 있다

 ▫ Age는 treatment에 아무런 연관이 없다

 ▫ Poisson과 quasi-poisson model의 차이는 polyps 발생 숫자의
    overdispersion 에 의한 것이다

• 생화학적 사실에 근거한 분석 결과

 ▫ Prostaglandin 은 cell proliferation 을 activation 시켜
    polyps, 또는 cancer를 발생시킨다

 ▫ NSAIDs는 archidonic acid로부터 prostaglandin 을 유도하는 효소;
   Cyclooxigenase(COX) 를 저해한다
감사합니다 !

Más contenido relacionado

Destacado (12)

에버노트와 드롭박스 설치
에버노트와 드롭박스 설치에버노트와 드롭박스 설치
에버노트와 드롭박스 설치
 
PHP 사용하기
PHP 사용하기PHP 사용하기
PHP 사용하기
 
HTML Form과 배열
HTML Form과 배열HTML Form과 배열
HTML Form과 배열
 
10.단일표본 평균 모비율
10.단일표본 평균 모비율10.단일표본 평균 모비율
10.단일표본 평균 모비율
 
PHP 기초 문법
PHP 기초 문법PHP 기초 문법
PHP 기초 문법
 
03.기술통계 자료의 중심과 퍼진정도
03.기술통계 자료의 중심과 퍼진정도03.기술통계 자료의 중심과 퍼진정도
03.기술통계 자료의 중심과 퍼진정도
 
06.확률분포
06.확률분포06.확률분포
06.확률분포
 
Smart work 자료 1
Smart work 자료 1Smart work 자료 1
Smart work 자료 1
 
확률변수와 분포함수
확률변수와 분포함수확률변수와 분포함수
확률변수와 분포함수
 
14.범주형자료분석
14.범주형자료분석14.범주형자료분석
14.범주형자료분석
 
R 기초 : R Basics
R 기초 : R BasicsR 기초 : R Basics
R 기초 : R Basics
 
13.상관과 회귀
13.상관과 회귀13.상관과 회귀
13.상관과 회귀
 

Similar a Analysis of polyps by using R

Predicting Adverse Drug Reactions Using PubChem Screening Data
Predicting Adverse Drug Reactions Using PubChem Screening DataPredicting Adverse Drug Reactions Using PubChem Screening Data
Predicting Adverse Drug Reactions Using PubChem Screening Data
Yannick Pouliot
 
AAPS 2015_W3081_Biomarker Screening Poster_Russell
AAPS 2015_W3081_Biomarker Screening Poster_RussellAAPS 2015_W3081_Biomarker Screening Poster_Russell
AAPS 2015_W3081_Biomarker Screening Poster_Russell
Lawrence Hwang
 
Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D ...
Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D ...Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D ...
Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D ...
Rajarshi Guha
 
Apresentação do estudo poise 2
Apresentação do estudo poise 2Apresentação do estudo poise 2
Apresentação do estudo poise 2
Felipe Motta
 
Cytoscan_Copy_Number_Confirmation_with_SYBR_Green_qPCR_white_paper
Cytoscan_Copy_Number_Confirmation_with_SYBR_Green_qPCR_white_paperCytoscan_Copy_Number_Confirmation_with_SYBR_Green_qPCR_white_paper
Cytoscan_Copy_Number_Confirmation_with_SYBR_Green_qPCR_white_paper
Andrea Ujvari
 

Similar a Analysis of polyps by using R (20)

English: Dr. Raymond J. Dattwyler
English: Dr. Raymond J. DattwylerEnglish: Dr. Raymond J. Dattwyler
English: Dr. Raymond J. Dattwyler
 
Predicting Adverse Drug Reactions Using PubChem Screening Data
Predicting Adverse Drug Reactions Using PubChem Screening DataPredicting Adverse Drug Reactions Using PubChem Screening Data
Predicting Adverse Drug Reactions Using PubChem Screening Data
 
AAPS 2015_W3081_Biomarker Screening Poster_Russell
AAPS 2015_W3081_Biomarker Screening Poster_RussellAAPS 2015_W3081_Biomarker Screening Poster_Russell
AAPS 2015_W3081_Biomarker Screening Poster_Russell
 
Bioassay
BioassayBioassay
Bioassay
 
P6 2017 biopython2
P6 2017 biopython2P6 2017 biopython2
P6 2017 biopython2
 
a brief introduction to epistasis detection
a brief introduction to epistasis detectiona brief introduction to epistasis detection
a brief introduction to epistasis detection
 
Optovue Poster
Optovue PosterOptovue Poster
Optovue Poster
 
Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D ...
Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D ...Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D ...
Exploring Compound Combinations in High Throughput Settings: Going Beyond 1D ...
 
The common architecture of autoimmune disease
The common architecture of autoimmune diseaseThe common architecture of autoimmune disease
The common architecture of autoimmune disease
 
P6 2018 biopython2b
P6 2018 biopython2bP6 2018 biopython2b
P6 2018 biopython2b
 
Apresentação do estudo poise 2
Apresentação do estudo poise 2Apresentação do estudo poise 2
Apresentação do estudo poise 2
 
Cytoscan_Copy_Number_Confirmation_with_SYBR_Green_qPCR_white_paper
Cytoscan_Copy_Number_Confirmation_with_SYBR_Green_qPCR_white_paperCytoscan_Copy_Number_Confirmation_with_SYBR_Green_qPCR_white_paper
Cytoscan_Copy_Number_Confirmation_with_SYBR_Green_qPCR_white_paper
 
AML.pptx
AML.pptxAML.pptx
AML.pptx
 
Charles River Pathology Associates Capabilities
Charles River Pathology Associates CapabilitiesCharles River Pathology Associates Capabilities
Charles River Pathology Associates Capabilities
 
Wes Schweer - Sub-Therapeutic Growth-Promoting Antibiotic Alternatives - PORK...
Wes Schweer - Sub-Therapeutic Growth-Promoting Antibiotic Alternatives - PORK...Wes Schweer - Sub-Therapeutic Growth-Promoting Antibiotic Alternatives - PORK...
Wes Schweer - Sub-Therapeutic Growth-Promoting Antibiotic Alternatives - PORK...
 
Resistin
ResistinResistin
Resistin
 
Elis arrays
Elis arraysElis arrays
Elis arrays
 
AKII.pptx
AKII.pptxAKII.pptx
AKII.pptx
 
Nirs
NirsNirs
Nirs
 
151 performance of a localized fiber optic
151 performance of a localized fiber optic151 performance of a localized fiber optic
151 performance of a localized fiber optic
 

Más de Yoonwhan Lee

02.자료다루기
02.자료다루기02.자료다루기
02.자료다루기
Yoonwhan Lee
 
12.세표본 이상의 평균비교
12.세표본 이상의 평균비교12.세표본 이상의 평균비교
12.세표본 이상의 평균비교
Yoonwhan Lee
 
11.두표본의 평균비교
11.두표본의 평균비교11.두표본의 평균비교
11.두표본의 평균비교
Yoonwhan Lee
 
09.통계적가설검정
09.통계적가설검정09.통계적가설검정
09.통계적가설검정
Yoonwhan Lee
 
00.통계학입문
00.통계학입문00.통계학입문
00.통계학입문
Yoonwhan Lee
 
통계자료 분석을 위한 R
통계자료 분석을 위한 R통계자료 분석을 위한 R
통계자료 분석을 위한 R
Yoonwhan Lee
 

Más de Yoonwhan Lee (18)

02.자료다루기
02.자료다루기02.자료다루기
02.자료다루기
 
01.r 기초
01.r 기초01.r 기초
01.r 기초
 
12.세표본 이상의 평균비교
12.세표본 이상의 평균비교12.세표본 이상의 평균비교
12.세표본 이상의 평균비교
 
11.두표본의 평균비교
11.두표본의 평균비교11.두표본의 평균비교
11.두표본의 평균비교
 
09.통계적가설검정
09.통계적가설검정09.통계적가설검정
09.통계적가설검정
 
08.추정
08.추정08.추정
08.추정
 
07.표본분포
07.표본분포07.표본분포
07.표본분포
 
05.확률
05.확률05.확률
05.확률
 
00.통계학입문
00.통계학입문00.통계학입문
00.통계학입문
 
통계자료 분석을 위한 R
통계자료 분석을 위한 R통계자료 분석을 위한 R
통계자료 분석을 위한 R
 
PHP를 이용한 간단한 방명록 만들기
PHP를 이용한 간단한 방명록 만들기PHP를 이용한 간단한 방명록 만들기
PHP를 이용한 간단한 방명록 만들기
 
MySQL과 PHP
MySQL과 PHPMySQL과 PHP
MySQL과 PHP
 
MySQL 기초
MySQL 기초MySQL 기초
MySQL 기초
 
추정
추정추정
추정
 
쿠키를 통해 구현해보는 간단한 로그인 과정
쿠키를 통해 구현해보는 간단한 로그인 과정쿠키를 통해 구현해보는 간단한 로그인 과정
쿠키를 통해 구현해보는 간단한 로그인 과정
 
PHP에서 객체와 데이터 연결 유지
PHP에서 객체와 데이터 연결 유지PHP에서 객체와 데이터 연결 유지
PHP에서 객체와 데이터 연결 유지
 
표본들의 분포
표본들의 분포표본들의 분포
표본들의 분포
 
Android 기초 앱 사용
Android 기초 앱 사용Android 기초 앱 사용
Android 기초 앱 사용
 

Último

1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
QucHHunhnh
 

Último (20)

Python Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docxPython Notes for mca i year students osmania university.docx
Python Notes for mca i year students osmania university.docx
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 
Role Of Transgenic Animal In Target Validation-1.pptx
Role Of Transgenic Animal In Target Validation-1.pptxRole Of Transgenic Animal In Target Validation-1.pptx
Role Of Transgenic Animal In Target Validation-1.pptx
 
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
 
ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.
 
General Principles of Intellectual Property: Concepts of Intellectual Proper...
General Principles of Intellectual Property: Concepts of Intellectual  Proper...General Principles of Intellectual Property: Concepts of Intellectual  Proper...
General Principles of Intellectual Property: Concepts of Intellectual Proper...
 
Web & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfWeb & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdf
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
ICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptx
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-IIFood Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
 
Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.ppt
 
PROCESS RECORDING FORMAT.docx
PROCESS      RECORDING        FORMAT.docxPROCESS      RECORDING        FORMAT.docx
PROCESS RECORDING FORMAT.docx
 
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxUnit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptx
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 

Analysis of polyps by using R

  • 1. Analysis of Polyps by using R 20106102 강민규 20106120 김형섭 20106122 류승우
  • 2. Contents • About Polyps ▫ Colonial Polyps(대장 용종)이란? • Script Analysis ▫ 통계적 결과 해석 • 생화학적 기전 • 결과 및 결론
  • 5. About Polyps •비스테로이드성 항염증제 (NSAIDs Non-Steroidal Anti Imflammatory Drugs) •대장용종 예방에 효과적 •대표적으로 아스피린 (Aspirin)이 NSAIDs에 속한 다
  • 6. Script Analysis • library("HSAUR2") • ## HSAUR2 패키지 library에 등록,polyps자료 탐색 • summary(polyps) • ## polyps 자료 요약 • number treat age • Min. : 1.00 placebo:11 Min. :13.00 • 1st Qu.: 3.75 drug : 9 1st Qu.:19.75 • Median :21.00 Median :22.50 • Mean :24.05 Mean :25.00 • 3rd Qu.:41.00 3rd Qu.:27.75 • Max. :63.00 Max. :50.00 • aggregate(polyps[,"number"],list(polyps$treat),summary) • ## polyps의 수와 대조, 조작군에 대한 summary • Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max. • 1 placebo 7.000 21.500 40.000 35.640 48.000 63.000 • 2 drug 1.000 2.000 3.000 9.889 17.000 33.000
  • 7. aggregate(polyps[,"age"],list(polyps$treat),summary) • ## polyps 환자의 나이와 대조,조작군에 대한 summary • Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max. • 1 placebo 13.00 19.50 22.00 26.27 32.00 50.00 • 2 drug 16.00 22.00 23.00 23.44 23.00 42.00 • jpeg("polyps.jpg",width=720,height=400) • ## jpeg device 활성 • layout(matrix(1:2,ncol=2)) • ## 2칸짜리 layout 형성 • plot(number~treat, data=polyps) • ## polyps 수와 treat사이의 관계 boxplot • plot(age~treat, data=polyps) • ## polyps 환자의 age와 treat 사이의 관계 boxplot • Dev.off() • ## device 비활성 • tapply(polyps$age, polyps$treat,stem) • ## 줄기, 잎 그림으로 비교
  • 9. The decimal point is 1 digit(s) to the right of the | • 1 | 389 • 2 | 0227 • 3 | 044 • 4| • 5|0 • The decimal point is 1 digit(s) to the right of the | • 1 | 67 • 2 | 223333 • 3| • 4|2 • $placebo • NULL • $drug • NULL • polyps_glm.1<-glm(number~treat+age, data=polyps, family=poisson()) • ## polyps_glm.1 에 glm함수(poisson) objecting • summary(polyps_glm.1) • ## glm, poisson 에러 모형으로 적합test
  • 10. • Call: • glm(formula = number ~ treat + age, family = poisson(), data = polyps) • Deviance Residuals: • Min 1Q Median 3Q Max • -4.2212 -3.0536 -0.1802 1.4459 5.8301 • Coefficients: • Estimate Std. Error z value Pr(>|z|) • (Intercept) 4.529024 0.146872 30.84 < 2e-16 *** • treatdrug -1.359083 0.117643 -11.55 < 2e-16 *** • age -0.038830 0.005955 -6.52 7.02e-11 *** • --- • Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 • (Dispersion parameter for poisson family taken to be 1) • Null deviance: 378.66 on 19 degrees of freedom • Residual deviance: 179.54 on 17 degrees of freedom • AIC: 273.88 • Number of Fisher Scoring iterations: 5
  • 11. • polyps_glm.2<-glm(number~treat+age, data=polyps, family=quasipoisson()) • ## polyps_glm.2 에 glm 함수(quasipoisson) objecting • summary(polyps_glm.2) • ## glm, quasi-poisson 에러 모형으로 적합 • Call: • glm(formula = number ~ treat + age, family = quasipoisson(), • data = polyps) • Deviance Residuals: • Min 1Q Median 3Q Max • -4.2212 -3.0536 -0.1802 1.4459 5.8301 • Coefficients: • Estimate Std. Error t value Pr(>|t|) • (Intercept) 4.52902 0.48106 9.415 3.72e-08 *** • treatdrug -1.35908 0.38533 -3.527 0.00259 ** • age -0.03883 0.01951 -1.991 0.06284 . • --- • Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 • (Dispersion parameter for quasipoisson family taken to be 10.72805) • Null deviance: 378.66 on 19 degrees of freedom • Residual deviance: 179.54 on 17 degrees of freedom • AIC: NA • Number of Fisher Scoring iterations: 5
  • 13.
  • 14. • Eicosanoids are synthesized from arachidonic acid, which is formed from phospholipids. • Arachidonic acid is converted to prostaglandin H2, catalyzed by cyclooxygenase. • This enzyme is the target of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs).
  • 15. • Aspirin and NSAIDs have also been found to reduce the frequency of colon cancer, apparently by inhibiting synthesis of prostaglandins that stimulate cell proliferation • Cox-1, cox-2 와 polyps, GI tract , asthma • Vioxx, celebrex 의 항암작용 • Polyps와 NSAID의 실효성
  • 16. 결과 정리 및 요약 • R을 이용한 통계자료 분석 결과 ▫ Drug(NSAIDs)가 placebo와는 다르게 유의한 효과가 있다 ▫ Age는 treatment에 아무런 연관이 없다 ▫ Poisson과 quasi-poisson model의 차이는 polyps 발생 숫자의 overdispersion 에 의한 것이다 • 생화학적 사실에 근거한 분석 결과 ▫ Prostaglandin 은 cell proliferation 을 activation 시켜 polyps, 또는 cancer를 발생시킨다 ▫ NSAIDs는 archidonic acid로부터 prostaglandin 을 유도하는 효소; Cyclooxigenase(COX) 를 저해한다