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Compliance-adjusted intervention effects in survival data

Author

Listed:
  • Lois G. Kim

    (MRC Biostatistics Unit)

  • Ian R. White

    (MRC Biostatistics Unit)

Abstract

Survival data are most frequently analyzed by the intention-to-treat principle. However, presenting a compliance-adjusted analysis alongside the primary analysis can provide an insight into the effect of the treatment for those individuals actually complying with their randomized intervention. There are a number of methods for this type of analysis. Loeys and Goetghebeur (2003) use proportional hazards techniques to provide an estimate of the treatment effect for compliers when compliance is measured on an all-or-nothing scale. This methodology is here made available through a new Stata command, stcomply. Copyright 2004 by StataCorp LP.

Suggested Citation

  • Lois G. Kim & Ian R. White, 2004. "Compliance-adjusted intervention effects in survival data," Stata Journal, StataCorp LP, vol. 4(3), pages 257-264, September.
    • Handle: RePEc:tsj:stataj:v:4:y:2004:i:3:p:257-264
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    References listed on IDEAS

    as
    1. T. Loeys & E. Goetghebeur, 2003. "A Causal Proportional Hazards Estimator for the Effect of Treatment Actually Received in a Randomized Trial with All-or-Nothing Compliance," Biometrics, The International Biometric Society, vol. 59(1), pages 100-105, March.
    2. Ian R. White & Sarah Walker & Abdel Babiker, 2002. "strbee: Randomization-based efficacy estimator," Stata Journal, StataCorp LP, vol. 2(2), pages 140-150, May.
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