Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes

@article{Frangakis1999AddressingCO,
  title={Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes},
  author={Constantine Frangakis and Donald B. Rubin},
  journal={Biometrika},
  year={1999},
  volume={86},
  pages={365-379}
}
SUMMARY We study the combined impact that all-or-none compliance and subsequent missing outcomes can have on the estimation of the intention-to-treat effect of assignment in randomised studies. In this setting, a standard analysis, which drops subjects with missing outcomes and ignores compliance information, can be biased for the intention-to-treat effect. To address all-or-none compliance that is followed by missing outcomes, we construct a new estimation procedure for the intention-to-treat… 

Figures and Tables from this paper

Parametric Potential-Outcome Survival Models for Causal Inference

Estimating causal effects in clinical trials is often complicated by treatment noncompliance and missing outcomes. In time-to-event studies, estimation is further complicated by censoring. Censoring

Test Equality and Sample Size Calculation Based on Risk Difference in a Randomized Clinical Trial with Noncompliance and Missing Outcomes

TLDR
This work develops an asymptotic test procedure for testing equality of two treatment effects among compliers and derives a sample size calculation formula accounting for both noncompliance and missing outcomes for a desired power 1 - beta at a nominal alpha-level.

Likelihood Methods for Treatment Noncompliance and Subsequent Nonresponse in Randomized Trials

TLDR
A maximum likelihood estimator (MLE) of the causal effect of treatment assignment for a two-armed randomized trial assuming all-or-none treatment noncompliance and allowing for subsequent nonresponse is constructed.

Estimating the causal effects of treatment

  • G. Dunn
  • Economics
    Epidemiologia e Psichiatria Sociale
  • 2002
TLDR
A relatively non-technical review of recent statistical research on the analysis and interpretation of the results of randomised controlled trials in which there are possibly all three types of protocol violation: non-adherence to allocated treatment, contamination and attrition.

Bias Mechanisms in Intention-to-Treat Analysis With Data Subject to Treatment Noncompliance and Missing Outcomes

  • B. Jo
  • Psychology
    Journal of educational and behavioral statistics : a quarterly publication sponsored by the American Educational Research Association and the American Statistical Association
  • 2007
TLDR
An analytical approach was employed to compare sensitivity of causal effect estimates with different assumptions on treatment noncompliance and non-response behaviors in the Johns Hopkins school intervention trial, to fully clarify bias mechanisms of considered models and to connect these models based on common parameters.

Handling Missing Data in Randomized Experiments with Noncompliance

TLDR
This paper shows that identification and estimation of causal treatment effects considering both noncompliance and missing outcomes can be relatively easily conducted under various missing data assumptions, including the latent ignorability proposed by Frangakis and Rubin.

Compliance with Treatment Allocation

TLDR
This work obtains a valid estimate of the average effect of random allocation on outcomes in the subgroup of participants who receive treatment if and only if they are randomized to its receipt, known as the complier-average causal effect of treatment.

Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance

We develop new methods for analyzing randomized experiments with noncompliance and, by extension, instrumental variable settings, when the often controversial, but key, exclusion restriction

Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials

TLDR
Estimators for the CACE are developed using multiple imputation methods, which have been successfully applied to a wide variety of missing data problems, but have not yet been applied to the potential outcomes setting of causal inference.

Series 2-19-2009 Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials

This working paper is hosted by The Berkeley Electronic Press (bepress) and may not be commercially reproduced without the permission of the copyright holder. Summary. Randomized clinical trials are
...

References

SHOWING 1-10 OF 30 REFERENCES

A Bayesian framework for intent-to-treat analysis with missing data.

TLDR
A Bayesian approach to fitting a similar two-piece linear spline model to a random effects model and shows how the model can be applied to data that have no off-treatment observations.

Intent-to-treat analysis for longitudinal studies with drop-outs.

TLDR
This work proposes a method that involves multiple imputation of the missing values following drop-out based on an "as treated" model, using actual dose after drop- out if this is known, or imputed doses that incorporate a variety of plausible alternative assumptions if unknown.

Identification of Causal Effects Using Instrumental Variables: Comment

TLDR
It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.

Identification of Causal Effects Using Instrumental Variables

TLDR
It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.

Estimating causal effects of treatments in randomized and nonrandomized studies.

A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating

Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome

This paper proposes a simple technique for assessing the range of plausible causal con- clusions from observational studies with a binary outcome and an observed categorical covariate. The technique

Bayesian inference for causal effects in randomized experiments with noncompliance

For most of this century, randomization has been a cornerstone of scientific experimentation, especially when dealing with humans as experimental units. In practice, however, noncompliance is

Analysis of clinical trials by treatment actually received: is it really an option?

TLDR
The problem of the definition of actual treatment is investigated in the context of a recent clinical trial, which provided results that were at times inconsistent or counter-intuitive, and which neither helped to confirm nor further explain the intention to treat analysis.

Analysis of Survival Data from a Randomized Trial with All-or-None Compliance: Estimating the Cost-Effectiveness of a Cancer Screening Program

TLDR
Estimating cost-effectiveness as dollars per life year saved requires an extension to the analysis of yearly survival data and involves modeling both the hazard for death from cancer and death from competing risk.

Statistical Analysis with Missing Data

TLDR
This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.