### Abstract

Correct specification of the inverse probability weighting (IPW) model is necessary for consistent inference from a marginal structural Cox model (MSCM). In practical applications, researchers are typically unaware of the true specification of the weight model. Nonetheless, IPWs are commonly estimated using parametric models, such as the main-effects logistic regression model. In practice, assumptions underlying such models may not hold and data-adaptive statistical learning methods may provide an alternative. Many candidate statistical learning approaches are available in the literature. However, the optimal approach for a given dataset is impossible to predict. Super learner (SL) has been proposed as a tool for selecting an optimal learner from a set of candidates using cross-validation. In this study, we evaluate the usefulness of a SL in estimating IPW in four different MSCM simulation scenarios, in which we varied the specification of the true weight model specification (linear and/or additive). Our simulations show that, in the presence of weight model misspecification, with a rich and diverse set of candidate algorithms, SL can generally offer a better alternative to the commonly used statistical learning approaches in terms of MSE as well as the coverage probabilities of the estimated effect in an MSCM. The findings from the simulation studies guided the application of the MSCM in a multiple sclerosis cohort from British Columbia, Canada (1995–2008), to estimate the impact of beta-interferon treatment in delaying disability progression.

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

Pages (from-to) | 2032-2047 |

Number of pages | 16 |

Journal | Statistics in Medicine |

Volume | 36 |

Issue number | 13 |

DOIs | |

State | Published - Jun 15 2017 |

### Fingerprint

### Keywords

- inverse-probability weighting
- marginal structural models
- multiple sclerosis
- super learner
- time-dependent confounding

### ASJC Scopus subject areas

- Epidemiology
- Statistics and Probability

### Cite this

*Statistics in Medicine*,

*36*(13), 2032-2047. https://doi.org/10.1002/sim.7266

**Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context.** / The BeAMS study group.

Research output: Contribution to journal › Article

*Statistics in Medicine*, vol. 36, no. 13, pp. 2032-2047. https://doi.org/10.1002/sim.7266

}

TY - JOUR

T1 - Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context

AU - The BeAMS study group

AU - Karim, Mohammad Ehsanul

AU - Platt, Robert W.

AU - Shirani, A.

AU - Zhao, Y.

AU - Evans, C.

AU - Kingwell, E.

AU - van der Kop, M. L.

AU - Oger, J.

AU - Gustafson, P.

AU - Petkau, J.

AU - Tremlett, H.

PY - 2017/6/15

Y1 - 2017/6/15

N2 - Correct specification of the inverse probability weighting (IPW) model is necessary for consistent inference from a marginal structural Cox model (MSCM). In practical applications, researchers are typically unaware of the true specification of the weight model. Nonetheless, IPWs are commonly estimated using parametric models, such as the main-effects logistic regression model. In practice, assumptions underlying such models may not hold and data-adaptive statistical learning methods may provide an alternative. Many candidate statistical learning approaches are available in the literature. However, the optimal approach for a given dataset is impossible to predict. Super learner (SL) has been proposed as a tool for selecting an optimal learner from a set of candidates using cross-validation. In this study, we evaluate the usefulness of a SL in estimating IPW in four different MSCM simulation scenarios, in which we varied the specification of the true weight model specification (linear and/or additive). Our simulations show that, in the presence of weight model misspecification, with a rich and diverse set of candidate algorithms, SL can generally offer a better alternative to the commonly used statistical learning approaches in terms of MSE as well as the coverage probabilities of the estimated effect in an MSCM. The findings from the simulation studies guided the application of the MSCM in a multiple sclerosis cohort from British Columbia, Canada (1995–2008), to estimate the impact of beta-interferon treatment in delaying disability progression.

AB - Correct specification of the inverse probability weighting (IPW) model is necessary for consistent inference from a marginal structural Cox model (MSCM). In practical applications, researchers are typically unaware of the true specification of the weight model. Nonetheless, IPWs are commonly estimated using parametric models, such as the main-effects logistic regression model. In practice, assumptions underlying such models may not hold and data-adaptive statistical learning methods may provide an alternative. Many candidate statistical learning approaches are available in the literature. However, the optimal approach for a given dataset is impossible to predict. Super learner (SL) has been proposed as a tool for selecting an optimal learner from a set of candidates using cross-validation. In this study, we evaluate the usefulness of a SL in estimating IPW in four different MSCM simulation scenarios, in which we varied the specification of the true weight model specification (linear and/or additive). Our simulations show that, in the presence of weight model misspecification, with a rich and diverse set of candidate algorithms, SL can generally offer a better alternative to the commonly used statistical learning approaches in terms of MSE as well as the coverage probabilities of the estimated effect in an MSCM. The findings from the simulation studies guided the application of the MSCM in a multiple sclerosis cohort from British Columbia, Canada (1995–2008), to estimate the impact of beta-interferon treatment in delaying disability progression.

KW - inverse-probability weighting

KW - marginal structural models

KW - multiple sclerosis

KW - super learner

KW - time-dependent confounding

UR - http://www.scopus.com/inward/record.url?scp=85013409024&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85013409024&partnerID=8YFLogxK

U2 - 10.1002/sim.7266

DO - 10.1002/sim.7266

M3 - Article

C2 - 28219110

AN - SCOPUS:85013409024

VL - 36

SP - 2032

EP - 2047

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 13

ER -