Identification of Causal Effects Using Instrumental Variables

Angrist, Joshua D.; Imbens, Guido W.; Rubin, Donald B.

doi:10.1080/01621459.1996.10476902

Cited by 4,172 publications

(3,734 citation statements)

References 40 publications

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“…In Chapter 3 we start by criticising the usual naive approach to evaluating the modifying effects of process measures (correlating their values with clinical outcomes in the treated group, with no reference to the controls) and then describe modern methods developed from the use of IVs 14 and principal stratification. 7,11 Chapter 4 extends the ideas from these two chapters to cover trials involving longitudinal data structures (repeated measures of the putative mediators and/or process variables, as well as of clinical outcomes). Chapter 5 considers the challenge of trial design in the context of the use of IV methods and principal stratification.…”

Section: Efficacy and Mechanism Evaluationmentioning

confidence: 99%

“…Randomisation, here, is an example of an IV (see Chapter 3) and the above expression for the CACE estimate is an example of what is known as an IV estimator. 7 Finally, the four latent classes of Angrist et al 7 (always treated, never treated, compliers and defiers) also provide a relatively simple and straightforward example of principal stratification, 11 an idea which will be described in some detail in Chapters 2 and 3.…”

Section: Efficacy and Mechanism Evaluationmentioning

confidence: 99%

“…The analysis of data from trials with a significant amount of non-compliance does need careful thought, however, particularly if non-compliance increases the risk of there being no follow-up data on outcome. Returning to our hypothetical trial with two types of non-compliance with allocated treatment (failure to turn up if you are in the therapy group, obtaining therapy if you are a control), we start by following Angrist et al 7 and postulate that a trial comprises up to four types or classes of patient:…”

Section: Efficacy and Mechanism Evaluationmentioning

confidence: 99%

“…It is reasonable to assume that under most circumstances there are no defiers 7 (the so-called monotonicity assumption), leaving us with three classes (the always treated, the never treated and compliers). However, we cannot always identify which class a particular participant should belong to; a patient who is allocated to the treatment group who then receives therapy is either always treated or a complier, and a participant who is allocated to the control group and who actually experiences the control condition is either never treated or a complier.…”

Section: Efficacy and Mechanism Evaluationmentioning

confidence: 99%

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Evaluation and validation of social and psychological markers in randomised trials of complex interventions in mental health: a methodological research programme

Dunn

Emsley

Liu

et al. 2015

Health Technol Assess

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BackgroundThe development of the capability and capacity to evaluate the outcomes of trials of complex interventions is a key priority of the National Institute for Health Research (NIHR) and the Medical Research Council (MRC). The evaluation of complex treatment programmes for mental illness (e.g. cognitive–behavioural therapy for depression or psychosis) not only is a vital component of this research in its own right but also provides a well-established model for the evaluation of complex interventions in other clinical areas. In the context of efficacy and mechanism evaluation (EME) there is a particular need for robust methods for making valid causal inference in explanatory analyses of the mechanisms of treatment-induced change in clinical outcomes in randomised clinical trials.ObjectivesThe key objective was to produce statistical methods to enable trial investigators to make valid causal inferences about the mechanisms of treatment-induced change in these clinical outcomes. The primary objective of this report is to disseminate this methodology, aiming specifically at trial practitioners.MethodsThe three components of the research were (1) the extension of instrumental variable (IV) methods to latent growth curve models and growth mixture models for repeated-measures data; (2) the development of designs and regression methods for parallel trials; and (3) the evaluation of the sensitivity/robustness of findings to the assumptions necessary for model identifiability. We illustrate our methods with applications from psychological and psychosocial intervention trials, keeping the technical details to a minimum, leaving the reporting of the more theoretical and mathematically demanding results for publication in appropriate specialist journals.ResultsWe show how to estimate treatment effects and introduce methods for EME. We explain the use of IV methods and principal stratification to evaluate the role of putative treatment effect mediators and therapeutic process measures. These results are extended to the analysis of longitudinal data structures. We consider the design of EME trials. We focus on designs to create convincing IVs, bearing in mind assumptions needed to attain model identifiability. A key area of application that has become apparent during this work is the potential role of treatment moderators (predictive markers) in the evaluation of treatment effect mechanisms for personalised therapies (stratified medicine). We consider the role of targeted therapies and multiarm trials and the use of parallel trials to help elucidate the evaluation of mediators working in parallel.ConclusionsIn order to demonstrate both efficacy and mechanism, it is necessary to (1) demonstrate a treatment effect on the primary (clinical) outcome, (2) demonstrate a treatment effect on the putative mediator (mechanism) and (3) demonstrate a causal effect from the mediator to the outcome. Appropriate regression models should be applied for (3) or alternative IV procedures, which account for unmeasured confounding, provided that a valid instrument can be identified. Stratified medicine may provide a setting where such instruments can be designed into the trial. This work could be extended by considering improved trial designs, sample size considerations and measurement properties.FundingThe project presents independent research funded under the MRC–NIHR Methodology Research Programme (grant reference G0900678).

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Section: Efficacy and Mechanism Evaluationmentioning

confidence: 99%

Section: Efficacy and Mechanism Evaluationmentioning

confidence: 99%

Section: Efficacy and Mechanism Evaluationmentioning

confidence: 99%

Section: Efficacy and Mechanism Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation and validation of social and psychological markers in randomised trials of complex interventions in mental health: a methodological research programme

Dunn

Emsley

Liu

et al. 2015

Health Technol Assess

View full text Add to dashboard Cite

show abstract

“…A valid instrumental variable has to affect the choice of living arrangement (relevance assumption) without having a direct influence on female labor supply (exclusion restriction assumption). The exclusion restriction means that any effect of the instrument on female labor supply operates via its effect on living arrangement (Angrist, Imbens, and Rubin 1996). We select three instrumental variables.…”

Section: Model Specificationmentioning

confidence: 99%