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Gepubliceerd in: Quality of Life Research 2/2009

01-03-2009

Mediators, moderators, and modulators of causal effects in clinical trials––Dynamically Modified Outcomes (DYNAMO) in health-related quality of life

Auteurs: Gary W. Donaldson, Yoshio Nakamura, Carol Moinpour

Gepubliceerd in: Quality of Life Research | Uitgave 2/2009

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Abstract

Purpose

There is more than one effect of a randomized intervention, and more than one response to a treatment. The mean group difference in the pre-specified outcome of a randomized controlled trial (RCT) estimates the average causal effect of treatment across causal mechanisms that may be distinct. To understand the comprehensive impact of an intervention, we propose identifying and separating the direct causal effects of the intervention from mediating, moderating, and modulating (individual differences) influences of uncontrolled variables.

Methods

Relational outcomes and moderated interventions describe two common mechanisms by which treatment interactions with uncontrolled variables expand or qualify the causal inferences to be drawn from an RCT, signifying treatment impact beyond that captured by conventional mean differences. Relational outcomes are associations between post-randomization measures. The treatment intervention may affect relational outcomes, and individual differences may modulate them. With moderated interventions, the effect of treatment on the outcome of interest depends on personal attributes or pre-randomization uncontrolled variables.

Results

Awareness and measurement of both types of mechanisms can greatly improve interpretation of the results of a clinical trial.

Conclusion

The integrated formal system of Dynamically Modified Outcomes (DYNAMO) provides comprehensive analysis of the diverse causal influences and interactions operating in a clinical trial.
Voetnoten
1
In the language of time series, pain and anxiety are stationary in that their overall levels and variances do not change over long time periods.
 
2
To simplify presentation of the concepts, we represent anxiety as having abrupt, immediate effects on pain, with no reciprocal causation. Considerations are similar, though more complex, when onset and offset are gradual, when pain also causes anxiety, and with varying degrees of random error in the relationships.
 
3
It is of course highly desirable to reduce levels of pain and anxiety over time, so that the lines would trend downward, but the difficulty in doing this is precisely what makes these conditions chronic. Clinical management therefore emphasizes control and functioning, intrinsically relational concepts, in addition to reduction of intensity.
 
4
For simplicity of presentation, we assume both X and W are binary variables coded 0 or 1, but the model generalizes naturally to polytomous and continuous variables.
 
5
We retain this orientation for hypothetical manipulations throughout without further comment. Completely equivalent, though less intuitive, are hypothetical manipulations that replace the treatment intervention with the control intervention.
 
6
For reference, the regression line of Y on Z for Controls [E(Y|Z, X = 0)] is indicated; this line is relevant for individual mediating and causal effects but does not figure in the calculation of the ACE.
 
7
Intercept terms, which change with the addition or modification of explanatory variables, can easily obscure the conceptual meanings presented in Table 1. It is possible to incorporate correct adjustments for intercepts, but slopes and mean levels have more natural interpretations in this context. To estimate the parameters of Table 1 with standard software, it is most efficient to combine slope estimates (only) from regression modules with descriptive estimates of population means.
 
8
Literally, “contrary to fact,” yet the literal meaning fails to convey the scope of the concept. Counterfactual reasoning is at the heart of scientific explanation, at two levels [1]. Scientific laws express general hypothetical relationships describing what would happen were certain conditions to be modified (given that they are not so modified now), and specific counterfactual reasoning allows us to predict what would have happened had a particular outcome been otherwise than we observe.
 
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Metagegevens
Titel
Mediators, moderators, and modulators of causal effects in clinical trials––Dynamically Modified Outcomes (DYNAMO) in health-related quality of life
Auteurs
Gary W. Donaldson
Yoshio Nakamura
Carol Moinpour
Publicatiedatum
01-03-2009
Uitgeverij
Springer Netherlands
Gepubliceerd in
Quality of Life Research / Uitgave 2/2009
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-008-9439-x

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