The function `GoodPretreatContCont`

examines the plausibility of finding a good pretreatment predictor in the continuous-continuous setting. For details, see Alonso et al. (submitted).

1 | ```
GoodPretreatContCont(T0T0, T1T1, Delta, T0T1=seq(from=0, to=1, by=.01))
``` |

`T0T0` |
A scalar that specifies the variance of the true endpoint in the control treatment condition. |

`T1T1` |
A scalar that specifies the variance of the true endpoint in the experimental treatment condition. |

`Delta` |
A scalar that specifies an upper bound for the prediction mean squared error when predicting the individual causal effect of the treatment on the true endpoint based on the pretreatment predictor. |

`T0T1` |
A scalar or vector that contains the correlation(s) between the counterfactuals |

An object of class `GoodPretreatContCont`

with components,

`T0T1` |
A scalar or vector that contains the correlation(s) between the counterfactuals T0 and T1 that were considered (i.e., |

`Sigma.Delta.T` |
A scalar or vector that contains the standard deviations of the individual causal treatment effects on the true endpoint as a function of |

`Rho2.Min` |
A scalar or vector that contains the |

Wim Van der Elst, Ariel Alonso, & Geert Molenberghs

Alonso, A., Van der Elst, W., & Molenberghs, G. (submitted). Validating predictors of therapeutic success: a causal inference approach.

1 2 3 4 5 | ```
# Assess the plausibility of finding a good pretreatment predictor when
# sigma_T0T0 = sigma_T1T1 = 8 and Delta = 1
MinPred <- GoodPretreatContCont(T0T0 = 8, T1T1 = 8, Delta = 1)
summary(MinPred)
plot(MinPred)
``` |

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