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Using mediation analysis to identify causal mechanisms in disease management interventions

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Abstract

For over two decades, disease management (DM) has been touted as an intervention capable of producing large scale cost savings for health care purchasers. However, the preponderance of scientific evidence suggests that these programs do not save money. This finding is not surprising given that the theorized causal mechanism by which the intervention supposedly influences the outcome has not been systematically assessed. Mediation analysis is a statistical approach to identifying causal pathways by testing the relationships between the treatment, the outcome, and an intermediate variable that is posited to mediate the relationship between the treatment and outcome. This analysis can therefore help identify how to make DM interventions effective by determining the causal mechanisms between intervention components and the desired outcome. DM interventions can then be optimized by eliminating those activities that are ineffective or even counter-productive. In this article we seek to promote the application of mediation analysis to DM program evaluation by describing the two principal frameworks generally followed in causal mediation analysis; structural equation modeling and potential outcomes. After comparing several approaches within these frameworks using real and simulated data, we find that some methods perform better than others under the conditions imposed upon the models. We conclude that mediation analysis can assist DM programs in developing and testing the causal pathways that enable interventions to be effective in achieving desired outcomes.

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Notes

  1. Linden and Adler-Milstein (2008) highlight many additional factors that explain why the standard DM approach is not effective, based on the best available evidence from the literature.

  2. In observational studies the assumption of no residual confounding or no selection bias cannot be tested, and so causal effects are only identified under this assumption.

  3. “Similar” refers to comparability on both observed and unobserved characteristics, because we assume no residual confounding (ignorability) once we have conditioned on pre-treatment covariates and treatment.

  4. Mediators or outcomes may also be ordered, such as rating of perceived health status or satisfaction on a Likert-type scale (e.g., 1 through 5), which can be modeled using ordered logit or probit models (which are natural extensions of the logit or probit models).

  5. Breen et al. (Forthcoming) develop the method further and suggest some further identities we refer to here.

  6. An additional assumption required here for causal inference is that each individual’s potential outcomes are unrelated to the treatment status of any other individual under study (Rubin 1978; Manski, Forthcoming). In an evaluation of a DM intervention, this assumption could be violated if members of the same household were enrolled in the intervention, possibly influencing the outcomes of one another.

  7. This holds under the no interaction assumption in which direct and indirect effects are assumed to be identical between treatment and control groups (see Imai et al. 2010, p. 312).

  8. See Morgan and Todd (2008) for a discussion on assessing the effects of the ATT versus ATC estimators.

  9. The method is implemented in both the R Language (Imai et al. 2010) and in Stata (Hicks and Tingley 2011).

  10. See Linden and Adams (2010a, b) for a description of these techniques used in the DM context.

  11. However, in the software (Hicks and Tingley 2011), the reported “percent mediated” is the median of a simulated distribution of “percent mediated,” and thus may not provide the same result as that derived by dividing Eq. 10 by Eq. 11. We return to this point in our Monte Carlo study.

  12. MacKinnon et al. (1995) demonstrate that mediation percentages are unstable for smaller sample sizes. We nevertheless choose to report these percentages here, because they are widely used in applied research and because they provide a sensible metric for comparing results in nonlinear probability models in which point estimates of total, direct, and effects are identified up to an arbitrary scale.

  13. We dichotomize these variables strictly to illustrate the modeling approach. In practice, converting continuous variables to dichotomous or categorical variables should be avoided, as it leads to a loss of information and reduces power (Royston et al. 2006).

  14. We first estimated an interaction model which produced a non-significant interaction effect (p = 0.230, CI: −0.044, 0.18), followed by a review of the contrasts between groups at each level of the mediator which supported the no-interaction effect assumption.

  15. Because the true mediation effect cannot be analytically derived in this setup, we obtain the true percent mediated using a Monte Carlo study with 100 replications and 1,000,000 observations per draw, which essentially provides us with a population estimate.

  16. We report both mean and median given the skewed distribution of the mediation percentages across the 72 scenarios. Although the level of bias differs between the two central tendency measures, the overall pattern of results is very similar whether one uses the mean or the median as the basis of evaluation. We nevertheless report both central tendencies in order for the reader to properly assess the results.

  17. Comparing the method by Imai et al. (2010a, b, c) using a logit link for the outcome and a linear model for the mediator and the method by Karlson et al. (2012) in a Monte Carlo study, Breen et al. (Forthcoming) found the methods to yield highly similar results; corroborating the results we report here.

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Acknowledgments

We thank Dustin Tingley, Raymond Hicks, Danella Hafeman, and Adam Glynn for clarifications of the modeling approaches used in their respective papers, to John Antonakis for evocative discussions pertaining to concerns of endogeneity in mediation analysis, and to Julia Adler-Milstein for her invaluable review and edits. We are indebted to the editor and two anonymous reviewers for providing excellent comments which substantially improved the manuscript.

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  1. Linden Consulting Group, LLC, 1301 North Bay Drive, Ann Arbor, MI, 48103, USA

    Ariel Linden

  2. Department of Health Management & Policy, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Ariel Linden

  3. SFI, The Danish National Centre for Social Research, Copenhagen, Denmark

    Kristian Bernt Karlson

  4. Department of Education, Aarhus University, Aarhus, Denmark

    Kristian Bernt Karlson

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  1. Ariel Linden
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Linden, A., Karlson, K.B. Using mediation analysis to identify causal mechanisms in disease management interventions. Health Serv Outcomes Res Method 13, 86–108 (2013). https://doi.org/10.1007/s10742-013-0106-5

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