Fundamentals Tamuno Alfred, PhD Biostatistician DataCamp - - PowerPoint PPT Presentation

▶

fundamentals

Fundamentals Tamuno Alfred, PhD Biostatistician DataCamp - - PowerPoint PPT Presentation

Oct 12, 2022 370 likes •798 views

DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Fundamentals Tamuno Alfred, PhD Biostatistician DataCamp Designing and Analyzing Clinical Trials in R What are clinical trials? Clinical

slide-1

SLIDE 1

DataCamp Designing and Analyzing Clinical Trials in R

Fundamentals

DESIGNING AND ANALYZING CLINICAL TRIALS IN R

Tamuno Alfred, PhD

Biostatistician

slide-2

SLIDE 2

DataCamp Designing and Analyzing Clinical Trials in R

What are clinical trials?

Clinical trials are scientific experiments used to evaluate the safety and efficacy of

ne or more treatments in humans.

slide-3

SLIDE 3

DataCamp Designing and Analyzing Clinical Trials in R

Classifications

slide-4

SLIDE 4

DataCamp Designing and Analyzing Clinical Trials in R

Randomization

slide-5

SLIDE 5

DataCamp Designing and Analyzing Clinical Trials in R

Hierarchy of medical evidence

slide-6

SLIDE 6

DataCamp Designing and Analyzing Clinical Trials in R

Confounders

slide-7

SLIDE 7

DataCamp Designing and Analyzing Clinical Trials in R

Aim to achieve similar patient characteristics

slide-8

SLIDE 8

DataCamp Designing and Analyzing Clinical Trials in R

Blinding

slide-9

SLIDE 9

DataCamp Designing and Analyzing Clinical Trials in R

Let's practice!

DESIGNING AND ANALYZING CLINICAL TRIALS IN R

slide-10

SLIDE 10

DataCamp Designing and Analyzing Clinical Trials in R

Types of data and endpoints

DESIGNING AND ANALYZING CLINICAL TRIALS IN R

Tamuno Alfred, PhD

Biostatistician

slide-11

SLIDE 11

DataCamp Designing and Analyzing Clinical Trials in R

Conducting clinical trials

Guidelines Trial protocols

slide-12

SLIDE 12

DataCamp Designing and Analyzing Clinical Trials in R

Statistical Analysis Plans

slide-13

SLIDE 13

DataCamp Designing and Analyzing Clinical Trials in R

Endpoints

Prioritized into primary and secondary endpoints Safety and/or efficacy measures

slide-14

SLIDE 14

DataCamp Designing and Analyzing Clinical Trials in R

Continuous endpoints

slide-15

SLIDE 15

DataCamp Designing and Analyzing Clinical Trials in R

Continuous endpoints

library(ggplot2) ggplot(data=exercise, aes(x=sbp_change)) + geom_histogram(fill="white", color="black") + xlab("SBP Change, mmHg")

slide-16

SLIDE 16

DataCamp Designing and Analyzing Clinical Trials in R

Continuous endpoints

library(dplyr) library(magrittr) exercise %>% summarize(mean_sbp = mean(sbp_baseline), sd_spb = sd(sbp_baseline))

slide-17

SLIDE 17

DataCamp Designing and Analyzing Clinical Trials in R

Categorical endpoints

library(dplyr) library(magrittr) finaldata %>% filter(!is.na(response)) %>% count(response, treatment) %>% mutate(pct = 100 * n / sum(n)) table(finaldata$response, finaldata$treatment)

slide-18

SLIDE 18

DataCamp Designing and Analyzing Clinical Trials in R

Composite endpoints

Combine multiple outcomes Summarize as categorical variable

slide-19

SLIDE 19

DataCamp Designing and Analyzing Clinical Trials in R

Count endpoints

Non-normal distribution Discrete values

slide-20

SLIDE 20

DataCamp Designing and Analyzing Clinical Trials in R

Survival endpoints

slide-21

SLIDE 21

DataCamp Designing and Analyzing Clinical Trials in R

Other collected data

Dates of birth Dates of study visits Times of blood collection Ethnicity Gender Adverse events Year of diagnosis Concomitant medication …

slide-22

SLIDE 22

DataCamp Designing and Analyzing Clinical Trials in R

Let's practice!

DESIGNING AND ANALYZING CLINICAL TRIALS IN R

slide-23

SLIDE 23

DataCamp Designing and Analyzing Clinical Trials in R

Basic statistical analysis

DESIGNING AND ANALYZING CLINICAL TRIALS IN R

Tamuno Alfred, PhD

Biostatistician

slide-24

SLIDE 24

DataCamp Designing and Analyzing Clinical Trials in R

Statistical inference

slide-25

SLIDE 25

DataCamp Designing and Analyzing Clinical Trials in R

Hypothesis testing

slide-26

SLIDE 26

DataCamp Designing and Analyzing Clinical Trials in R

Hypothesis testing

slide-27

SLIDE 27

DataCamp Designing and Analyzing Clinical Trials in R

Hypothesis testing

slide-28

SLIDE 28

DataCamp Designing and Analyzing Clinical Trials in R

Hypothesis testing

slide-29

SLIDE 29

DataCamp Designing and Analyzing Clinical Trials in R

Hypothesis testing

Estimate treatment effect, e.g. difference in means Confidence interval, typically 95%

slide-30

SLIDE 30

DataCamp Designing and Analyzing Clinical Trials in R

Hypothesis testing

Estimate treatment effect, e.g. difference in means Confidence interval, typically 95% Test statistic p-value

slide-31

SLIDE 31

DataCamp Designing and Analyzing Clinical Trials in R

Hypothesis testing

Estimate treatment effect, e.g. difference in means Confidence interval, typically 95% Test statistic p-value Compare to significance level α, typically 0.05

slide-32

SLIDE 32

DataCamp Designing and Analyzing Clinical Trials in R

Continuous: comparison of means

Normal distribution Two-sample t-test

t.test(pct.change~group, var.equal=TRUE, data=bmd)

slide-33

SLIDE 33

DataCamp Designing and Analyzing Clinical Trials in R

Continuous: comparison of means

slide-34

SLIDE 34

DataCamp Designing and Analyzing Clinical Trials in R

Continuous: comparison of distributions

Non-normal distribution Wilcoxon Rank Sum test (aka Mann Whitney test)

wilcox.test(outcome.variable~ group.variable, data=dataset)

slide-35

SLIDE 35

DataCamp Designing and Analyzing Clinical Trials in R

Continuous: comparison of distributions

Non-normal distribution Wilcoxon Rank Sum test (aka Mann Whitney test) Null hypothesis Alternative hypothesis

wilcox.test(outcome.variable~ group.variable, data=dataset)

slide-36

SLIDE 36

DataCamp Designing and Analyzing Clinical Trials in R

Binary: comparison of proportions

Chi-squared test of independence Use Fisher’s Exact Test on small sample sizes

table1<-table(care.trial$group, care.trial$recover) prop.test(table1, correct=FALSE)

slide-37

SLIDE 37

DataCamp Designing and Analyzing Clinical Trials in R

Binary: comparison of proportions

slide-38

SLIDE 38

DataCamp Designing and Analyzing Clinical Trials in R

Assumptions

Independent groups Similar patient characteristics between groups

slide-39

SLIDE 39

DataCamp Designing and Analyzing Clinical Trials in R

Extensions

Repeated measures data Three or more treatment groups

slide-40

SLIDE 40

DataCamp Designing and Analyzing Clinical Trials in R

Let's practice!

DESIGNING AND ANALYZING CLINICAL TRIALS IN R

Core Hubs:

All Converters PDF Hub Video Hub Audio Hub Image Hub File Compressor