Case studies

Problem	Technique
4 Use of historical control data	nested random effects
5 Meta-analysis to estimate treatment effects	aggregate data modeling & varying exposure times of count data
6 Use of historical control data with stratification	meta-analysis with covariates & use of mixture priors
7 Probability of success from a single arm trial	prior elicitation and use of RBesT mixtures as priors
8 Dose finding	non-linear models
9 Oncology dose escalation	constrained parameters
10 Time-to-event modelling in Oncology dose escalation	piece-wise constant survival model with Poisson regression & non-linear link function
11 Multiple imputation	multi-variate outcome modeling
12 Longitudinal data	longitudinal modeling with different covariance structures (MMRM)
13 Bayesian Mixed effects Model for Repeated Measures	unstructured MMRM for a continuous endpoint
14 Time-to-event data	parametric time-to-event modeling with customized parametrization by using user-defined contrasts
15 Network meta-analysis	arm based network meta-analysis

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format:
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# Case studies {#sec-case-studies}



::: {.content-visible when-profile="public"}

```{r, echo = FALSE}
overview <- dplyr::tribble(
                       ~Problem, ~Technique,
                       "@sec-use-hist-control-data", "nested random effects",
                       "@sec-map-treat-effects", "aggregate data modeling & varying exposure times of count data",
                       "@sec-use-hist-control-data-strata", "meta-analysis with covariates & use of mixture priors",
                       "@sec-pos", "prior elicitation and use of RBesT mixtures as priors",
                       "@sec-dose-finding", "non-linear models",
                       "@sec-onc-escalation",  "constrained parameters",
                       "@sec-tte-oncology",  "piece-wise constant survival model with Poisson regression & non-linear link function",
                       "@sec-multiple-imputation", "multi-variate outcome modeling",
                       "@sec-longitudinal-data", "longitudinal modeling with different covariance structures (MMRM)",
                       "@sec-mmrm", "unstructured MMRM for a continuous endpoint",
                       "@sec-tte-data", "parametric time-to-event modeling with customized parametrization by using user-defined contrasts",
                       "@sec-network-meta", "arm based network meta-analysis"
                   )
knitr::kable(overview)
```

:::