### Abstract

Recently, it has been shown how to estimate model-adjusted risks, risk differences, and risk ratios from complex survey data based on risk averaging and SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina). The authors present an alternative approach based on marginal structural models (MSMs) and SAS (SAS Institute, Inc., Cary, North Carolina). The authors estimate the parameters of the MSM using inverse weights that are the product of 2 terms. The first term is a survey weight that adjusts the sample to represent the unstandardized population. The second term is an inverse-probability-of-exposure weight that standardizes the population in order to adjust for confounding; it must be estimated using the survey weights. The authors show how to use the MSM parameter estimates and contrasts to test and estimate effect-measure modification; SAS code is provided. They also explain how to program the previous risk-averaging approach in SAS. The 2 methods are applied and compared using data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey to assess effect modification by age of the difference in risk of cost barriers to health care between persons with disability and persons without disability.

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

Pages (from-to) | 1085-1091 |

Number of pages | 7 |

Journal | American Journal of Epidemiology |

Volume | 172 |

Issue number | 9 |

DOIs | |

State | Published - Nov 1 2010 |

Externally published | Yes |

### Fingerprint

### Keywords

- health surveys
- heterogeneity
- interaction
- logistic regression
- models statistical
- probability weighting
- standardization
- survey analysis

### ASJC Scopus subject areas

- Epidemiology

### Cite this

*American Journal of Epidemiology*,

*172*(9), 1085-1091. https://doi.org/10.1093/aje/kwq244

**Testing and estimating model-adjusted effect-measure modification using marginal structural models and complex survey data.** / Brumback, Babette A.; Bouldin, Erin D.; Zheng, Hao W.; Cannell, Michael B.; Andresen, Elena.

Research output: Contribution to journal › Article

*American Journal of Epidemiology*, vol. 172, no. 9, pp. 1085-1091. https://doi.org/10.1093/aje/kwq244

}

TY - JOUR

T1 - Testing and estimating model-adjusted effect-measure modification using marginal structural models and complex survey data

AU - Brumback, Babette A.

AU - Bouldin, Erin D.

AU - Zheng, Hao W.

AU - Cannell, Michael B.

AU - Andresen, Elena

PY - 2010/11/1

Y1 - 2010/11/1

N2 - Recently, it has been shown how to estimate model-adjusted risks, risk differences, and risk ratios from complex survey data based on risk averaging and SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina). The authors present an alternative approach based on marginal structural models (MSMs) and SAS (SAS Institute, Inc., Cary, North Carolina). The authors estimate the parameters of the MSM using inverse weights that are the product of 2 terms. The first term is a survey weight that adjusts the sample to represent the unstandardized population. The second term is an inverse-probability-of-exposure weight that standardizes the population in order to adjust for confounding; it must be estimated using the survey weights. The authors show how to use the MSM parameter estimates and contrasts to test and estimate effect-measure modification; SAS code is provided. They also explain how to program the previous risk-averaging approach in SAS. The 2 methods are applied and compared using data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey to assess effect modification by age of the difference in risk of cost barriers to health care between persons with disability and persons without disability.

AB - Recently, it has been shown how to estimate model-adjusted risks, risk differences, and risk ratios from complex survey data based on risk averaging and SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina). The authors present an alternative approach based on marginal structural models (MSMs) and SAS (SAS Institute, Inc., Cary, North Carolina). The authors estimate the parameters of the MSM using inverse weights that are the product of 2 terms. The first term is a survey weight that adjusts the sample to represent the unstandardized population. The second term is an inverse-probability-of-exposure weight that standardizes the population in order to adjust for confounding; it must be estimated using the survey weights. The authors show how to use the MSM parameter estimates and contrasts to test and estimate effect-measure modification; SAS code is provided. They also explain how to program the previous risk-averaging approach in SAS. The 2 methods are applied and compared using data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey to assess effect modification by age of the difference in risk of cost barriers to health care between persons with disability and persons without disability.

KW - health surveys

KW - heterogeneity

KW - interaction

KW - logistic regression

KW - models statistical

KW - probability weighting

KW - standardization

KW - survey analysis

UR - http://www.scopus.com/inward/record.url?scp=77958553423&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77958553423&partnerID=8YFLogxK

U2 - 10.1093/aje/kwq244

DO - 10.1093/aje/kwq244

M3 - Article

C2 - 20801863

AN - SCOPUS:77958553423

VL - 172

SP - 1085

EP - 1091

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

SN - 0002-9262

IS - 9

ER -