Using mediation analysis to identify causal mechanisms in disease management interventions

Alwin, D.F., Hauser, R.M.: The decomposition of effects in path analysis. Am. Sociol. Rev. 40, 37–47 (1975)

Antonakis, J., Bendahan, S., Jacquart, P., Lalive, R.: On making causal claims: a review and recommendations. Leadersh. Q. 21, 1086–1120 (2010)

Baron, R.M., Kenny, D.A.: The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 118–1173 (1986)

Bauer, D.J., Preacher, K.J., Gil, K.M.: Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychol. Methods 11, 142–163 (2006)

Bodenheimer, T., Lorig, K., Holman, H., Grumbach, K.: Patient self-management of chronic disease in primary care. J. Am. Med. Assoc. 288, 2469–2475 (2002)

Bollen, K.A.: Structural equations with latent variables. Wiley, New York (1989)

Breen, R.B., Karlson, K.B., Holm, A.: Total, direct, and indirect in logit and probit models. Sociol. Methods Res. (Forthcoming)

Butterworth, S.W., Andersen, B.T.: Health Coaching Performance Assessment™ (HCPA): a new tool for benchmarking and improving effectiveness. HealthSciences Institute. http://healthsciences.org/health-coaching-performance-assessment-hcpa-white-paper(2011) . Accessed 13 Feb 2012

Butterworth, S., Linden, A., McClay, W.: Health coaching as an intervention in health management programs. Dis. Manag. Health Outcomes 15, 299–307 (2007)

Cheong, J., MacKinnon, D.P., Khoo, S.T.: Investigation of meditational process using parallel process latent growth curve modeling. Struct. Equ. Model. 10, 238–262 (2003)

Cole, D.A., Maxwell, S.E.: Testing meditational models with longitudinal data: questions and tips in the use of structural equation modeling. J. Abnorm. Psychol. 112, 558–577 (2003)

Congressional Budget Office: an analysis of the literature on disease management programs. Washington DC: Congressional Budget Office. http://www.cbo.gov/sites/default/files/cbofiles/ftpdocs/59xx/doc5909/10-13-diseasemngmnt.pdf(2004) . Accessed 19 Oct 2012

Cramer, J.S.: Logit models. From economics and other fields. Cambridge University Press, Cambridge (2003)

Duncan, O.D.: Path analysis: sociological examples. Am. J. Sociol. 72, 1–16 (1966)

Efron, B., Tibshirani, R.: An introduction to the bootstrap. Chapman and Hall, New York (1993)

Frangakis, C.E., Rubin, D.B.: Principal stratification in causal inference. Biometrics 58, 21–29 (2002)

Freedman, L.S., Schatzkin, A.: Sample size for studying intermediate endpoints within intervention trials of observational studies. Am. J. Epidemiol. 136, 1148–1159 (1992)

Gennetian, L.A., Magnuson, K., Morris, P.A.: From statistical associations to causation: what developmentalists can learn from instrumental variables techniques coupled with experimental data. Dev. Psychol. 44, 381–394 (2008)

Glynn, A.N.: The product and difference fallacies for indirect effects. Am. J. Political Sci. 56, 257–269 (2012)

Goetzel, R.Z., Ozminkowski, R.J., Villagra, V.G., Duffy, J.: Return on investment on disease management: a review. Health Care Financ. Rev. 26, 1–19 (2005)

Hafeman, D.M.: Confounding of indirect effects: a sensitivity analysis exploring the range of bias due to a cause common to both the mediator and the outcome. Am. J. Epidemiol. 174, 710–717 (2011)

Hafeman, D.M., Schwartz, S.: Opening the black box: a motivation for the assessment of mediation. Int. J. Epidemiol. 38, 838–845 (2009)

Hibbard, J.H., Stockard, J., Mahoney, E.R., Tusler, M.: Development of the patient activation measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv. Res. 39, 1026–1105 (2004)

Hicks, R., Tingley, D.: Casual mediation analysis. Stata J. 11, 605–619 (2011)

Hill, J., Waldfogel, J., Brooks-Gunn, J.: Sustained effects of high participation in an early intervention for low-birth-weight premature infants. Dev. Psychol. 39, 730–744 (2003)

Hirano, K., Imbens, G.W.: The propensity score with continuous treatments. In: Gelman, A., Meng, X.-L. (eds.) Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, pp. 73–84. Wiley InterScience, West Sussex (2004)

Holland, P.W.: Statistics and causal inference. J. Am. Stat. Assoc. 81, 945–960 (1986)

Holland, P.W.: Causal inference, path analysis, and recursive structural equation models. In: Clogg, C.C. (ed.) Sociological Methodology, pp. 449–484. American Sociological Association, Washington, DC (1988)

Hong, G.: Ratio of mediator probability weighting for estimating natural direct and indirect effects. In: 2010 Proceedings of the American Statistical Association, Biometrics Section, pp. 2401–2415. American Statistical Association, Alexandria (2010)

Imai, K., Keele, L., Tingley, D.: A general approach to causal mediation analysis. Psychol. Methods 15, 309–334 (2010a)

Imai, K., Keele, L., Yamamoto, T.: Identification, inference, and sensitivity analysis for causal mediation effects. Stat. Sci. 25, 51–71 (2010b)

Imai, K., Keele, L., Tingley, D., Yamamoto, T.: Advances in social science research using R. In: Vinod, H.D. (ed.) Causal Mediation Analysis Using R, pp. 129–154. Springer, New York (2010c)

Imai, K., Tingley, D., Yamamoto, T.: Experimental designs for identifying causal mechanisms. J. R. Stat. Soc. A 176(1), 5–51 (2013)

Imai, K., van Dyke, D.A.: Causal inference with general treatment regimes: generalizing the propensity score. J. Am. Stat. Assoc. 99, 854–866 (2004)

Jo, B.: Causal inference in randomized experiments with mediational processes. Psychol. Methods 13, 314–336 (2008)

Jo, B., Stuart, E.A.: Comments: causal interpretations of mediation effects. J. Res. Educ. Eff. 5, 250–253 (2012)

Jo, B., Stuart, E.A., MacKinnon, D.P., Vinokur, A.D.: The use of propensity scores in mediation analysis. Multivar. Behav. Res. 46, 425–452 (2011)

Jo, B., Vinokur, A.D.: Sensitivity analysis and bounding of causal effects with alternative identifying assumptions. J. Educ. Behav. Stat. 36, 415–440 (2011)

Joffe, M.M., Rosenbaum, P.R.: Invited commentary: propensity scores. Am. J. Epidemiol. 150, 327–333 (1999)

Judd, C.M., Kenny, D.A.: Process analysis: estimating mediation in treatment evaluations. Eval. Rev. 5, 602–619 (1981)

Karlson, K.B., Holm, A.: Decomposing primary and secondary effects: a new decomposition method. Res. Stratif. Soc. Mobil. 29, 221–237 (2011)

Karlson, K.B., Holm, A., Breen, R.: Comparing regression coefficients between models using logit and probit: a new method. Sociol. Methodol. 42, 274–301 (2012)

Kohler, U., Karlson, K.B., Holm, A.: Comparing coefficients of nested nonlinear probability models. Stata J. 11, 420–438 (2011)

Kraemer, H.C., Kiernan, M., Essex, M.J., Kupfer, D.J.: How and why criteria defining moderators and mediators differ between the Baron and Kenny and MacArthur approaches. Health Psychol. 27, 101–108 (2008)

Krull, J.L., MacKinnon, D.P.: Multilevel modeling of individual and group level mediated effects. Multivar. Behav. Res. 36, 249–277 (2001)

Linden, A., Adler-Milstein, J.: Medicare disease management in a policy context. Health Care Financ. Rev. 29, 1–11 (2008)

Linden, A., Adams, J.L.: Using propensity score-based weighting in the evaluation of health management programme effectiveness. J. Eval. Clin. Pract. 16, 175–179 (2010a)

Linden, A., Adams, J.L.: Evaluating health management programmes over time: application of propensity score-based weighting to longitudinal data. J. Eval. Clin. Pract. 16, 180–185 (2010b)

Linden, A., Roberts, N.: Disease management interventions: what’s in the black box? Dis. Manag. 7, 275–291 (2004)

Linden, A., Butterworth, S., Roberts, N.: Disease management interventions II: what else is in the black box? Dis. Manag. 9, 73–85 (2006)

Long, J.S.: Regression models for categorical and limited dependent variables. Sage, Thousand Oaks (1997)

Lorig, K.R., Holman, H.: Self-management education: history, definition, outcomes, and mechanisms. Ann. Behav. Med. 26, 1–7 (2003)

Lu, B., Zanutto, E., Hornik, R., Rosenbaum, P.R.: Matching with doses in an observational study of a media campaign against drug abuse. J. Am. Stat. Assoc. 96, 1245–1253 (2001)

MacKinnon, D.P.: Introduction to Statistical Mediation Analysis. Erlbaum, Mahwah, NJ (2008)

MacKinnon, D.P., Dwyer, J.H.: Estimation of mediated effects in prevention studies. Eval. Rev. 17, 144–158 (1993)

MacKinnon, D.P., Warsi, G., Dwyer, J.H.: A simulation study of mediated effect measures. Multivar. Behav. Res. 30, 41–62 (1995)

MacKinnon, D.P., Lockwood, C.M., Brown, C.H., Wang, W., Hoffman, J.M.: The intermediate endpoint effect in logistic and probit regression. Clin. Trials 4, 499–513 (2007)

MacKinnon, D.P., Lockwood, C.M., Hoffman, J.M., West, S.G., Sheets, V.: A comparison of methods to test mediation and other intervening variable effects. Psychol. Methods 7, 83–104 (2002)

Manski, C.F.: Identification of treatment response with social interactions. Econ. J. (Forthcoming)

Marks, R., Allegrante, J.P., Lorig, K.L.: A review and synthesis of research evidence for self-efficacy-enhancing interventions for reducing chronic disability: implications for health education practice (Part I). Health Promot. Pract. 6, 37–43 (2005)

Matheson, D., Wilkins, A., Psacharopoulos, D.: Realizing the promise of disease management: payer trends and opportunities in the United States. Boston Consulting Group, Boston (2006)

Mathieu, J.E., Taylor, S.R.: A framework for testing meso-mediational relationships in organizational behavior. J. Organ. Behav. 28, 141–172 (2007)

Mattke, S., Seid, M., Ma, S.: Evidence for the effect of disease management: is $1 billion a year a good investment? Am. J. Manag. Care 13, 670–676 (2007)

Maxwell, S.E., Cole, D.A.: Bias in cross-sectional analyses of longitudinal mediation. Psychol. Methods 12, 23–44 (2007)

Maxwell, S.E., Cole, D.A., Mitchell, M.A.: Bias in cross-sectional analyses of longitudinal mediation: partial and complete mediation under an autoregressive model. Multivar. Behav. Res. 46, 816–841 (2011)

Mays, G.P., Au, M., Claxton, G.: Convergence and dissonance: evolution in private-sector approaches to disease management and care coordination. Health Aff. 26, 1683–1691 (2007)

McKelvey, R.D., Zavoina, W.: A statistical model for the analysis of ordinal level dependent variables. J. Math. Sociol. 4, 103–120 (1975)

Miller, W.R., Rose, G.S.: Toward a theory of motivational interviewing. Am. Psychol. 64, 527–537 (2009)

Mirowsky, J., Ross, C.E.: Eliminating defense and agreement bias from measures of the sense of control: a 2 × 2 index. Soc. Psychol. Q. 54, 127–145 (1991)

Morgan, S.L., Todd, J.J.: A diagnostic routine for the detection of consequential heterogeneity of causal effects. Sociol. Methodol. 38, 231–281 (2008)

Nelson, L.: Lessons from medicare’s demonstration projects on disease management and care coordination. Congressional Budget Office Working Paper 2012-01. http://www.cbo.gov/ftpdocs/126xx/doc12664/WP2012-01_Nelson_Medicare_DMCC_Demonstrations.pdf.(2012) . Accessed 11 Feb 2012

Ofman, J.J., Badamgarav, E., Henning, J.M., Knight, K., Gano Jr, A.D., Levan, R.K., Gur-Arie, S., Richards, M.S., Hasselblad, V., Weingarten, S.R.: Does disease management improve clinical and economic outcomes in patients with chronic diseases? A systematic review. Am. J. Med. 117, 182–192 (2004)

Pearl, J.: Direct and indirect effects. In: Proceedings of the Seventeenth Conference on Uncertainty and Artificial Intelligence. pp. 411–420. Morgan Kaufmann, San Francisco (2001)

Pearl, J.: The mediation formula: a guide to the assessment of causal pathways in non-linear models. Technical report R-363, University of California, Los Angeles (2011)

Pearl, J.: The causal foundations of structural equation modeling. In: Hoyle, R.H. (ed.) Handbook of Structural Equation Modeling, pp. 68–91. Guilford Press, New York (2012)

Peterson, M.L., Sinisi, S.E., van der Laan, M.J.: Estimation of direct causal effects. Epidemiology 17, 276–284 (2006)

Robins, J.M.: Marginal structural models. In: 1997 Proceedings of the Section on Bayesian Statistical Science, pp. 1–10. American Statistical Association, Alexandria (1998)

Robins, J.M., Greenland, S.: Identifiability and exchangeability for direct and indirect effects. Epidemiology 3, 143–155 (1992)

Robins, J.M., Hernan, M.A., Brumback, B.: Marginal structural models and causal inference in epidemiology. Epidemiology 11, 550–560 (2000)

Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55 (1983)

Royston, P., Altman, D.G., Sauerbrei, W.: Dichotomizing continuous predictors in multiple regression: a bad idea. Stat. Med. 25, 127–141 (2006)

Rubin, D.B.: Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66, 688–701 (1974)

Rubin, D.B.: Bayesian inference for causal effects: the role of randomization. Ann Stat 6, 34–58 (1978)

Shrout, P., Bolger, N.: Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychol. Methods 7, 422–445 (2002)

Selig, J.P., Preacher, K.J.: Mediation models for longitudinal data in developmental research. Res. Hum. Dev. 6, 144–164 (2009)

Sobel, M.E.: Asymptotic confidence intervals for indirect effects in structural equation models. In: Leinhardt, S. (ed.) Sociological Methodology, pp. 290–312. American Sociological Association, Washington, DC (1982)

Sobel, M.E.: Identification of causal parameters in randomized studies with mediating variables. J. Educ. Behav. Stat. 33, 230–251 (2008)

Sousa, V.D., Zauszniewski, J.A., Bergquist-Beringer, S., Musil, C.M., Neese, J.B., Jaber, A.F.: Reliability, validity and factor structure of the Appraisal of Self-Care Agency Scale—Revised (ASAS-R). J. Eval. Clin. Pract. 16, 1031–1040 (2010)

Stolzenberg, R.M.: The measurement and decomposition of causal effects in nonlinear and nonadditive models. Sociol. Methodol. 11, 459–488 (1980)

VanderWeele, T.J.: Marginal structural models for the estimation of direct and indirect effects. Epidemiology 20, 18–26 (2009)

VanderWeele, T.J.: Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology 21, 540–551 (2010)

Winship, C., Mare, R.D.: Structural equations and path analysis for discrete data. Am. J. Sociol. 89, 54–110 (1983)

Winship, C., Mare, R.D.: Regression models with ordinal variables. Am. Sociol. Rev. 49, 512–525 (1984)

Wooldridge, J.M.: Econometric analysis of cross section and panel data. MIT Press, Cambridge (2002)

Zanutto, E., Lu, B., Hornik, R.: Using propensity score subclassification for multiple treatment doses to evaluate a national antidrug media campaign. J. Educ. Behav. Stat. 30, 59–73 (2005)

Zhang, Z., Zyphur, M.J., Preacher, K.J.: Testing multilevel mediation using hierarchical linear models: problems and solutions. Organ. Res. Methods 12, 695–719 (2009)