### Abstract

Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download.

Original language | English (US) |
---|---|

Pages (from-to) | 248-257 |

Number of pages | 10 |

Journal | Biometrics |

Volume | 68 |

Issue number | 1 |

DOIs | |

State | Published - Mar 2012 |

### Fingerprint

### Keywords

- Principal stratification
- Randomized trials
- Reproducibility

### ASJC Scopus subject areas

- Applied Mathematics
- Statistics and Probability
- Agricultural and Biological Sciences(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Medicine(all)

### Cite this

*Biometrics*,

*68*(1), 248-257. https://doi.org/10.1111/j.1541-0420.2011.01646.x

**Predicting Treatment Effect from Surrogate Endpoints and Historical Trials : An Extrapolation Involving Probabilities of a Binary Outcome or Survival to a Specific Time.** / Baker, Stuart G.; Sargent, Daniel J.; Buyse, Marc; Burzykowski, Tomasz.

Research output: Contribution to journal › Article

*Biometrics*, vol. 68, no. 1, pp. 248-257. https://doi.org/10.1111/j.1541-0420.2011.01646.x

}

TY - JOUR

T1 - Predicting Treatment Effect from Surrogate Endpoints and Historical Trials

T2 - An Extrapolation Involving Probabilities of a Binary Outcome or Survival to a Specific Time

AU - Baker, Stuart G.

AU - Sargent, Daniel J.

AU - Buyse, Marc

AU - Burzykowski, Tomasz

PY - 2012/3

Y1 - 2012/3

N2 - Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download.

AB - Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download.

KW - Principal stratification

KW - Randomized trials

KW - Reproducibility

UR - http://www.scopus.com/inward/record.url?scp=84858865962&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84858865962&partnerID=8YFLogxK

U2 - 10.1111/j.1541-0420.2011.01646.x

DO - 10.1111/j.1541-0420.2011.01646.x

M3 - Article

C2 - 21838732

AN - SCOPUS:84858865962

VL - 68

SP - 248

EP - 257

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 1

ER -