regression analysis
play

Regression analysis Tamuno Alfred, PhD Biostatistician DataCamp - PowerPoint PPT Presentation

Nov 29, 2023 •307 likes •973 views

DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Regression analysis Tamuno Alfred, PhD Biostatistician DataCamp Designing and Analyzing Clinical Trials in R Explanatory variables DataCamp

  1. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Regression analysis Tamuno Alfred, PhD Biostatistician

  2. DataCamp Designing and Analyzing Clinical Trials in R Explanatory variables

  3. DataCamp Designing and Analyzing Clinical Trials in R Explanatory variables Also referred to as covariates, independent or predictor variables. Examples include: age sex baseline values disease severity lifestyle factors

  4. DataCamp Designing and Analyzing Clinical Trials in R Explanatory variables

  5. DataCamp Designing and Analyzing Clinical Trials in R Simple linear regression

  6. DataCamp Designing and Analyzing Clinical Trials in R Simple linear regression

  7. DataCamp Designing and Analyzing Clinical Trials in R Simple linear regression

  8. DataCamp Designing and Analyzing Clinical Trials in R Simple linear regression

  9. DataCamp Designing and Analyzing Clinical Trials in R Simple linear regression

  10. DataCamp Designing and Analyzing Clinical Trials in R Simple linear regression

  11. DataCamp Designing and Analyzing Clinical Trials in R Simple linear regression asthma.trial$group <- relevel(asthma.trial$group, ref="Placebo") asthma.reg1 <- lm(fev.change ~group, asthma.trial) summary(asthma.reg1)

  12. DataCamp Designing and Analyzing Clinical Trials in R Simple linear regression asthma.trial$group <- relevel(asthma.trial$group, ref="Placebo") asthma.reg1<-lm(fev.change ~group, asthma.trial) summary(asthma.reg1)

  13. DataCamp Designing and Analyzing Clinical Trials in R Simple linear regression t.test(fev.change~group, var.equal=TRUE, data=asthma.trial)

  14. DataCamp Designing and Analyzing Clinical Trials in R Multivariable linear regression

  15. DataCamp Designing and Analyzing Clinical Trials in R Multivariable linear regression asthma.reg2<-lm(fev.change ~group + age, asthma.trial) summary(asthma.reg2)

  16. DataCamp Designing and Analyzing Clinical Trials in R Logistic regression Binary outcomes

  17. DataCamp Designing and Analyzing Clinical Trials in R Logistic regression asthma.logreg1=glm(attack~group + age, family=binomial(link="logit"), asthma.trial) summary(asthma.logreg1) exp(coefficients(asthma.logreg1)[2]) exp(confint(asthma.logreg1)[2,])

  18. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Let's practice!

  19. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Analysis sets, subgroups and interactions Tamuno Alfred, PhD Biostatistician

  20. DataCamp Designing and Analyzing Clinical Trials in R Patient adherence

  21. DataCamp Designing and Analyzing Clinical Trials in R Intention-to-treat (ITT) Evaluate according to planned treatment Consider all patients irrespective of treatment received and compliance May lead to dilution of treatment effect estimate Reflects real-life conditions

  22. DataCamp Designing and Analyzing Clinical Trials in R Analysis sets Full Analysis Set (FAS): Generally all randomized patients May exclude patients who violate eligibility criteria or without post-baseline visits Primary efficacy analyses Follows ITT principle

  23. DataCamp Designing and Analyzing Clinical Trials in R Analysis sets Full Analysis Set (FAS): Generally all randomized patients May exclude patients who violate eligibility criteria or without post-baseline visits Primary efficacy analyses Follows ITT principle Per-Protocol Set (PP): Subset of ‘compliers’ May better reflect pure treatment effect

  24. DataCamp Designing and Analyzing Clinical Trials in R Analysis sets Conducted on 201 FAS patients asthma.fas<-lm(fev.change ~group , asthma.trial) summary(asthma.fas)

  25. DataCamp Designing and Analyzing Clinical Trials in R Analysis sets Conducted on 201 FAS patients Conducted on 183 PP patients asthma.fas<-lm(fev.change ~group , asthma.pp<-lm(fev.change ~group , asthma.trial) asthma.trial, subset = pp==1) summary(asthma.fas) summary(asthma.pp)

  26. DataCamp Designing and Analyzing Clinical Trials in R Subgroup analysis Evaluate treatment responses in different patient groups, e.g. Males only <50 years Non-obese patients

  27. DataCamp Designing and Analyzing Clinical Trials in R Subgroup analysis asthma.u65<-lm( fev.change ~group , asthma.o65<-lm( fev.change ~group , asthma.trial, subset = age<65) asthma.trial, subset = age>=65) summary(asthma.u65) summary(asthma.o65)

  28. DataCamp Designing and Analyzing Clinical Trials in R Subgroup analysis Rationale for subgroups Pre-specified in Statistical Analysis Plan Sufficient numbers of patients Consider impact of multiple testing

  29. DataCamp Designing and Analyzing Clinical Trials in R Interaction

  30. DataCamp Designing and Analyzing Clinical Trials in R Interaction asthma.ageg <- lm( fev.change ~group + agegroup , asthma.trial) summary(asthma.ageg)

  31. DataCamp Designing and Analyzing Clinical Trials in R Interaction asthma.ageint <- lm( fev.change ~ group*agegroup , asthma.trial) summary(asthma.ageint)

  32. DataCamp Designing and Analyzing Clinical Trials in R Interaction asthma.ageint<-lm( fev.change ~group*agegroup , asthma.trial) summary(asthma.ageint)

  33. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Now your turn!

  34. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Multiplicity of data Tamuno Alfred, PhD Biostatistician

  35. DataCamp Designing and Analyzing Clinical Trials in R Multiplicity of data Multiple subgroups of patients Multiple looks at data (interim analyses) Multiple outcomes of interest Multiple study visits

  36. DataCamp Designing and Analyzing Clinical Trials in R Multiple testing

  37. DataCamp Designing and Analyzing Clinical Trials in R Bonferroni correction Divide the overall significance level by the number of tests to generate new significance levels, or Multiply the p-value from the tests by the number of tests to generate the adjusted p-value

  38. DataCamp Designing and Analyzing Clinical Trials in R Bonferroni correction Divide the overall significance level by the number of tests to generate new significance levels, or Multiply the p-value from the tests by the number of tests to generate the adjusted p-value 0.05/4

  39. DataCamp Designing and Analyzing Clinical Trials in R Bonferroni correction Divide the overall significance level by the number of tests to generate new significance levels, or Multiply the p-value from the tests by the number of tests to generate the adjusted p-value 0.05/4 May be conservative Alternatives include Holm–Bonferroni method and false discovery rate

  40. DataCamp Designing and Analyzing Clinical Trials in R Subgroup analysis asthma.u65<-lm( fev.change ~group , asthma.o65<-lm( fev.change ~group , asthma.trial, subset = age<65) asthma.trial, subset = age>=65) summary(asthma.u65) summary(asthma.o65)

  41. DataCamp Designing and Analyzing Clinical Trials in R Subgroup analysis Large number of subgroups can be derived

  42. DataCamp Designing and Analyzing Clinical Trials in R Subgroup analysis Large number of subgroups can be derived Increase chance of observing a Type I error

  43. DataCamp Designing and Analyzing Clinical Trials in R Subgroup analysis Large number of subgroups can be derived Increase chance of observing a Type I error Lack of power within subgroups

  44. DataCamp Designing and Analyzing Clinical Trials in R Subgroup analysis Large number of subgroups can be derived Increase chance of observing a Type I error Lack of power within subgroups Imbalance of other patient characteristics within subgroups

  45. DataCamp Designing and Analyzing Clinical Trials in R Subgroup analysis Large number of subgroups can be derived Increase chance of observing a Type I error Lack of power within subgroups Imbalance of other patient characteristics within subgroups Recommendations: Limit number of subgroups Use external evidence Correct p-values Use interaction tests

  46. DataCamp Designing and Analyzing Clinical Trials in R Interim analysis Multiple looks at the data can increase overall Type I error Trial may stop too early

  47. DataCamp Designing and Analyzing Clinical Trials in R Interim analysis Multiple looks at the data can increase overall Type I error Trial may stop too early Recommendations: Limit the number of looks at the efficacy data Use an appropriate stopping rule, e.g. Pocock or O'Brien-Fleming library(gsDesign) Pocock <- gsDesign(k=3, test.type=2, sfu="Pocock") 2*(1-pnorm(Pocock$upper$bound))

  48. DataCamp Designing and Analyzing Clinical Trials in R Multiple endpoints Multiple endpoints of interest e.g. different assessments of lung function, occurrence of an asthma attack Recommendations: Prioritize endpoints Correction for multiple testing, e.g. Bonferroni

  49. DataCamp Designing and Analyzing Clinical Trials in R Composite endpoints Can combine certain endpoints May increase statistical power

  50. DataCamp Designing and Analyzing Clinical Trials in R Composite endpoints Can combine certain endpoints May increase statistical power

Recommend

Analysis of variance and regression Other types of regression models Other types of regression

Analysis of variance and regression Other types of regression models Other types of regression

Analysis of variance and regression Other types of regression models Other types of regression models Counts: Poisson models Ordinal data: Proportional odds models Survival analysis (censored, time-to-event data): Cox proportional

1.02k views • 58 slides
Business Statistics CONTENTS Multiple regression Dummy regressors Assumptions of regression

Business Statistics CONTENTS Multiple regression Dummy regressors Assumptions of regression

MULTIPLE REGRESSION ANALYSIS AND OTHER ISSUES Business Statistics CONTENTS Multiple regression Dummy regressors Assumptions of regression analysis Predicting with regression analysis Old exam question Further study MULTIPLE REGRESSION The

721 views • 24 slides
Introduction to Regression Analysis Modeling a Response A regression model describes how a

Introduction to Regression Analysis Modeling a Response A regression model describes how a

ST 430/514 Introduction to Regression Analysis/Statistics for Management and the Social Sciences II Introduction to Regression Analysis Modeling a Response A regression model describes how a dependent variable (or response ) Y is affected, on

575 views • 13 slides
Regression Analysis in Stata Hsueh-Sheng Wu CFDR Workshop Series February 18, 2019 1 Overview

Regression Analysis in Stata Hsueh-Sheng Wu CFDR Workshop Series February 18, 2019 1 Overview

Regression Analysis in Stata Hsueh-Sheng Wu CFDR Workshop Series February 18, 2019 1 Overview Introduction to regression Venn diagram of question, data, and regression analysis Steps of conducting regression analysis

584 views • 18 slides
Introduction to Regression and Correlation James H. Steiger Department of Psychology and Human

Introduction to Regression and Correlation James H. Steiger Department of Psychology and Human

Regression Analysis Some Examples Revisiting Basic Regression Results Anscombes Quartet Smoothing the Mean Function The Scatterplot Matrix Two Bivariate Regression Models Where from Here? Introduction to Regression and Correlation James

1.06k views • 57 slides
Statistics and Data Analysis Regression Analysis (1) Ling-Chieh Kung Department of Information

Statistics and Data Analysis Regression Analysis (1) Ling-Chieh Kung Department of Information

Introduction Least square approximation Model validation Variable transformation and selection Statistics and Data Analysis Regression Analysis (1) Ling-Chieh Kung Department of Information Management National Taiwan University Regression

501 views • 37 slides
Analysis of variance and regression November 27, 2007 Other types of regression models Counts

Analysis of variance and regression November 27, 2007 Other types of regression models Counts

Analysis of variance and regression November 27, 2007 Other types of regression models Counts (Poisson models) Ordinal data proportional odds models model control model interpretation Survival analysis Lene Theil

844 views • 65 slides
Statistics and Data Analysis Regression Analysis (2) Ling-Chieh Kung Department of Information

Statistics and Data Analysis Regression Analysis (2) Ling-Chieh Kung Department of Information

Ticket selling Indicator variables Interaction among variables Endogeneity Statistics and Data Analysis Regression Analysis (2) Ling-Chieh Kung Department of Information Management National Taiwan University Regression Analysis (2) 1 / 35

457 views • 35 slides
Residual Analysis Inferences about a regression model are valid only under assumptions about the

Residual Analysis Inferences about a regression model are valid only under assumptions about the

ST 430/514 Introduction to Regression Analysis/Statistics for Management and the Social Sciences II Residual Analysis Inferences about a regression model are valid only under assumptions about the random errors in the observations. Objectives:

706 views • 17 slides
Statistics and Data Analysis Regression Analysis (3) Ling-Chieh Kung Department of Information

Statistics and Data Analysis Regression Analysis (3) Ling-Chieh Kung Department of Information

Residual analysis Case study: bike rentals Statistics and Data Analysis Regression Analysis (3) Ling-Chieh Kung Department of Information Management National Taiwan University Regression Analysis (3) 1 / 21 Ling-Chieh Kung (NTU IM)

460 views • 21 slides
Regression Analysis Scott Richter UNCG-Statistical Consulting Center Department of Mathematics

Regression Analysis Scott Richter UNCG-Statistical Consulting Center Department of Mathematics

Regression Analysis Scott Richter UNCG-Statistical Consulting Center Department of Mathematics and Statistics UNCG Quantitative Methodology Series Regression Analysis Summer 2015 I. Simple linear regression i. Motivating example-runtime 3

1.31k views • 96 slides
Multiple Regression Analysis Independent Variables Mechanics and Interpretation of OLS

Multiple Regression Analysis Independent Variables Mechanics and Interpretation of OLS

Motivation for Multiple Regression The Model with k Multiple Regression Analysis Independent Variables Mechanics and Interpretation of OLS Interpreting the OLS Caio Vigo Regression Line The Expected Value of the OLS The University of

2.75k views • 89 slides
Theory of Regression Analysis with Applications T Padma Ragaleena National Institute of Science

Theory of Regression Analysis with Applications T Padma Ragaleena National Institute of Science

Multiple-linear regression model Theory of Regression Analysis with Applications T Padma Ragaleena National Institute of Science Education and Research Bhubaneswar 20 November 2019 T Padma Ragaleena NISER Theory of Regression Analysis with

778 views • 38 slides
Analysis of variance and regression May 13, 2008 Repeated measurements over time Presentation

Analysis of variance and regression May 13, 2008 Repeated measurements over time Presentation

Analysis of variance and regression May 13, 2008 Repeated measurements over time Presentation of data Traditional ways of analysis Variance component model (the dogs revisited) Random regression Baseline considerations Lene

1.03k views • 86 slides
Getting to Regression: The Workhorse of Quantitative Political Analysis Department of

Getting to Regression: The Workhorse of Quantitative Political Analysis Department of

Correlation Regression Getting to Regression: The Workhorse of Quantitative Political Analysis Department of Government London School of Economics and Political Science Correlation Regression 1 Correlation 2 Regression Correlation

959 views • 80 slides
The Foundation of Regression Analysis Bivariate Linear Regression James H. Steiger Department of

The Foundation of Regression Analysis Bivariate Linear Regression James H. Steiger Department of

The Classic Bivariate Least Squares Model Evaluating and Extending the Model The Foundation of Regression Analysis Bivariate Linear Regression James H. Steiger Department of Psychology and Human Development Vanderbilt University Multilevel

1.36k views • 112 slides
Chapter 7 Linear Regression 04/05/2016 Huamei Dong 1. Review Least square regression line 2.

Chapter 7 Linear Regression 04/05/2016 Huamei Dong 1. Review Least square regression line 2.

Chapter 7 Linear Regression 04/05/2016 Huamei Dong 1. Review Least square regression line 2. Example of linear regression analysis with R 1. Review of linear regression analysis In last lecture, we learned correlation coefficient R : We also

240 views • 8 slides
Nonlinear regression analysis Peter Dalgaard (orig. Lene Theil Skovgaard) Department of

Nonlinear regression analysis Peter Dalgaard (orig. Lene Theil Skovgaard) Department of

Nonlinear regression analysis Peter Dalgaard (orig. Lene Theil Skovgaard) Department of Biostatistics University of Copenhagen Variance &amp; Regression, May 2008 Nonlinear regression Simple kinetic model Compartment models

1.16k views • 75 slides
(U) A Method for Regression Analysis on Sparse Datasets Daniel Barkmeyer NRO CAAG June 2015

(U) A Method for Regression Analysis on Sparse Datasets Daniel Barkmeyer NRO CAAG June 2015

(U) A Method for Regression Analysis on Sparse Datasets Daniel Barkmeyer NRO CAAG June 2015 Background Traditional regression analysis, e.g. Zero-Bias Minimum Percent Error (ZMPE), can run into problems when important independent

370 views • 22 slides
I t Introduction to d ti t Partial Least Square Regression Dr. Leonardo Ciaccheri PhD

I t Introduction to d ti t Partial Least Square Regression Dr. Leonardo Ciaccheri PhD

Institute of Applied Physics Nello Carrara I t Introduction to d ti t Partial Least Square Regression Dr. Leonardo Ciaccheri PhD Regression analysis This lesson is focused on two popular regression tools Principal Component

394 views • 17 slides
Multiple Linear Regression Recall: a regression model describes how a dependent variable (or

Multiple Linear Regression Recall: a regression model describes how a dependent variable (or

ST 430/514 Introduction to Regression Analysis/Statistics for Management and the Social Sciences II Multiple Linear Regression Recall: a regression model describes how a dependent variable (or response ) Y is affected, on average, by one or more

526 views • 21 slides
Simple Linear Regression Recall: A regression model describes how a dependent variable (or

Simple Linear Regression Recall: A regression model describes how a dependent variable (or

ST 430/514 Introduction to Regression Analysis/Statistics for Management and the Social Sciences II Simple Linear Regression Recall: A regression model describes how a dependent variable (or response ) Y is affected, on average, by one or more

592 views • 20 slides
Case study of Cox regression: Regression approach to tire reliability analysis This paper focuses

Case study of Cox regression: Regression approach to tire reliability analysis This paper focuses

Case study of Cox regression: Regression approach to tire reliability analysis This paper focuses on a particular failure mode known as tread and belt separation (TBS). In the event of TBS, the whole (or a part of the) tread and the second

124 views • 10 slides
1 KULKUNYA PRAYARACH, PH.D. Multiple Regression Analysis I. Analysis of Data III. Dummy

1 KULKUNYA PRAYARACH, PH.D. Multiple Regression Analysis I. Analysis of Data III. Dummy

Multiple Regression Analysis I. Analysis of Data III. Dummy Variable II. Hypothesis Testing IV. Research &amp; Group Work 1 KULKUNYA PRAYARACH, PH.D. Multiple Regression Analysis I. Analysis of Data III. Dummy Variable II. Hypothesis

501 views • 24 slides

More recommend