Table 3.
List of IDeAl added aspects, explanation in brackets
| adaptive combination | |
| adaptive graph-based multiple | |
| adaptive LASSO | |
| adaptive strategy | |
| adjusted significance level | |
| assessment of randomisation procedures | |
| Bayesian decision theory | |
| biased corrected test (likelihood ratio test) | |
| biasing policy | |
| BIC criterion | |
| blocked ANOVA | |
| bootstrap (constrained parametric bootstrap procedure) | |
| causal inference | |
| CorrMixed | |
| decision theoretic aspect (Bayesian decision theoretic) | |
| dOFV (delta objective function values) | |
| dose-response | |
| EffectTreat | |
| ERDO (evaluation of randomisation procedures for design optimisation) | |
| similarity of dose response | |
| FDR (false discovery rate) | |
| first-in-human | |
| Fisher information matrix | |
| SLOPE (group SLOPE, geneSLOPE) | |
| k-means | |
| linked assessment criterion | |
| many-to-one | |
| maximum likelihood estimation | |
| MC-AGQ | |
| MC-HMC | |
| MCPMod (closed MCPMod) | |
| confidence bands for difference of curves | |
| meta-analysis | |
| meta-analytic paradigma | |
| mixed effects model | |
| MIXFIM | |
| model averaging | |
| model selection | |
| monte-carlo | |
| stochastic simulation | |
| non-parametric | |
| open-label | |
| optimal-design (compound D-optimality criterion) | |
| parametric power estimation | |
| permutation test | |
| PESEL (penalized semi-integrated likelihood method) | |
| pharmacometrics | |
| prior information | |
| randomisation based inference | |
| randomizeR | |
| real world data | |
| Robustness | |
| SIR (sampling importance resampling) | |
| scepticism factor (scepticism) | |
| selection bias | |
| sequential analysis | |
| Simulation | |
| SPF (surrogate predictive function) | |
| stratification | |
| personalised medicine | |
| SURROGATE | |
| TestingSimilarity | |
| threshold-crossing | |
| time trend bias | |
| two-stage adaptive design | |
| type-I-error probability | |
| intersection-union principle |