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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

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