In clinical trials, there always is the possibility to use data-driven adaptation at the end of a study. There prevails, however, concern on whether the type I error rate of the trial could be inflated with such design, thus, necessitating multiplicity adjustment. In this project, a simulation experiment was set up to assess type I error rate inflation associated with switching dose group as a function of dropout rate at the end of the study, where the primary analysis is in terms of a longitudinal outcome. This simulation is inspired by a clinical trial in Alzheimer's disease. The type I error rate was assessed under a number of scenarios, in terms of differing correlations between efficacy and tolerance, different missingness mechanisms, ...
Treatment effects are often evaluated by comparing change over time in outcome measures. However, va...
Background: A number of statistical models are available for analysing ordinal outcomes. Its applica...
Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated ...
In clinical trials, there always is the possibility to use data-driven adaptation at the end of a st...
When patients randomised to the control group of a randomised controlled trial are allowed to switch...
In this project, we compared different analysis methods following stratified permuted block design, ...
Background We investigate methods used to analyse the results of clinical trials with survival outc...
Adaptive enrichment designs involve rules for restricting enrollment to a subset of the population d...
Biomarker-stratified clinical trials assess the biomarker signature of subjects and split them into ...
Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim a...
When designing a clinical trial, there is usually some uncertainty about the variability of the prim...
Propensity scores are often used to adjust for between-group variation in covariates, when individua...
Objective: To investigate the influence of heterogeneity in disease progression for detecting treatm...
BackgroundThe multi-arm multi-stage (MAMS) design described by Royston et al. [Stat Med. 2003;22(14)...
OBJECTIVE: To investigate the influence of heterogeneity in disease progression for detecting treatm...
Treatment effects are often evaluated by comparing change over time in outcome measures. However, va...
Background: A number of statistical models are available for analysing ordinal outcomes. Its applica...
Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated ...
In clinical trials, there always is the possibility to use data-driven adaptation at the end of a st...
When patients randomised to the control group of a randomised controlled trial are allowed to switch...
In this project, we compared different analysis methods following stratified permuted block design, ...
Background We investigate methods used to analyse the results of clinical trials with survival outc...
Adaptive enrichment designs involve rules for restricting enrollment to a subset of the population d...
Biomarker-stratified clinical trials assess the biomarker signature of subjects and split them into ...
Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim a...
When designing a clinical trial, there is usually some uncertainty about the variability of the prim...
Propensity scores are often used to adjust for between-group variation in covariates, when individua...
Objective: To investigate the influence of heterogeneity in disease progression for detecting treatm...
BackgroundThe multi-arm multi-stage (MAMS) design described by Royston et al. [Stat Med. 2003;22(14)...
OBJECTIVE: To investigate the influence of heterogeneity in disease progression for detecting treatm...
Treatment effects are often evaluated by comparing change over time in outcome measures. However, va...
Background: A number of statistical models are available for analysing ordinal outcomes. Its applica...
Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated ...