Application of the Marginal Structural Model to Account for Suboptimal Adherence in a Randomized Controlled Trial

James Rochon, Manjushri Bhapkar, Carl F Pieper, William E Kraus, James Rochon, Manjushri Bhapkar, Carl F Pieper, William E Kraus

Abstract

Background: There is considerable interest in adjusting for suboptimal adherence in randomized controlled trials. A per-protocol analysis, for example removes individuals who fail to achieve a minimal level of adherence. One can also reassign non-adherers to the control group, censor them at the point of non-adherence, or cross them over to the control. However, there are biases inherent in each of these methods. Here, we describe an application of causal modeling to address this issue.

Methods: The marginal structural model with inverse-probability weighting was implemented using a weighted generalized estimating equation model. Two ancillary models were developed to derive the weights. First, stepwise linear regression was used to model the observed percent weight loss, while stepwise logistic regression model was applied to model early discontinuation from the intervention. From these, participant- and time-specific weights were calculated.

Discussion: This model is complicated and requires careful attention to detail. Which variables to force into the ancillary models, how to construct interaction terms, and how to address time-dependent covariates must be considered. Nevertheless, it can be used to great effect to predict intervention effects at full adherence. Moreover, by contrasting these results against intention-to-treat results, insights can be gained into the intrinsic physiologic effect of the intervention.

Trial registration: ClinicalTrials.gov Identifier NCT00427193.

Keywords: adherence; aging; calorie restriction; causal modeling; marginal structural model; weighted GEE model.

Conflict of interest statement

None

Figures

Fig. 1
Fig. 1
Box-and-whisker plot of the percent weight change in the CR intervention over the four follow-up intervals.
Fig. 2
Fig. 2
Box-and-Whisker plots of the final stabilized weights, by time point, in the analysis of the adjusted resting metabolic rate.
Fig. 3
Fig. 3
Scatterplot of the adjusted resting metabolic rate against percent weight loss in the CR group at months 12 and 24 overlaid with the fitted quadratic curve. The vertical reference line corresponds to the targeted weight loss level.

References

    1. Peduzzi P., Wittes J., Detre K. Analysis as-randomized and the problem of non-adherence: an example from the veterans affairs randomized trial of coronary bypass surgery. Stat. Med. 1993;12:1185–1195.
    1. Robins J.M., Hernán M.A., Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–560.
    1. Hernán M.A., Brumback B., Robins J.M. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology. 2000;11:561–570.
    1. Kahn K.L., Adams J.L., Weeks J.C. Adjuvant chemotherapy use and adverse events among older patients with stage III colon cancer. JAMA. 2010;303:1037–1045.
    1. Palella F.J., Jr., Armon C., Buchacz K. The association of HIV susceptibility testing with survival among HIV-infected patients receiving antiretroviral therapy: a cohort study. Ann. Intern. Med. 2009;151:73–84.
    1. Sterne J.A., Hernán M.A., Ledergerber B. Long-term effectiveness of potent antiretroviral therapy in preventing AIDS and death: a prospective cohort study. Lancet. 2005;366:378–384.
    1. Toh S., Hernández-Díaz S., Logan R. Coronary heart disease in postmenopausal recipients of estrogen plus progestin therapy: does the increased risk ever disappear? A randomized trial. Ann. Intern. Med. 2010;152:211–217.
    1. Yamaguchi T., Ohashi Y. Adjusting for differential proportions of second line treatment in cancer clinical trials. Part I: structural nested models and marginal structural models to test and estimate treatment arm effects. Stat. Med. 2004;23:1991–2003.
    1. Toh S., Hernández-Díaz S., Logan R. Estimating absolute risks in the presence of nonadherence: an application to a follow-up study with baseline randomization. Epidemiology. 2010;21:528–539.
    1. Cain L.E., Cole S.R. Inverse probability-of-censoring weights for the correction of time-varying noncompliance in the effect of randomized highly active antiretroviral therapy on incident AIDS or death. Stat. Med. 2009;28:1725–1738.
    1. Robins J.M., Finkelstein D.M. Correcting for non-compliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics. 2000;56:779–788.
    1. Holland P.W. Statistics and causal inference (with discussion) J. Am. Stat. Assoc. 1986;81:945–970.
    1. Zeger S.L., Liang K.-Y. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42:121–130.
    1. Rosenbaum P.R., Rubin D.B. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55.
    1. Fontana L., Partridge L., Longo V.D. Extending healthy life span – from yeast to humans. Science. 2010;328:321–326.
    1. Willcox D.C., Willcox B.J., He Q. They really are that old: a validation study of centenarian prevalence in Okinawa. J. Gerontol. A Biol. Sci. Med. Sci. 2008;63:338–349.
    1. Meyer T.E., Kovacs S.J., Ehsani A.A. Long-term caloric restriction ameliorates the decline in diastolic function in humans. J. Am. Coll. Cardiol. 2006;47:398–402.
    1. Heilbronn L.K., de Jonge L., Frisard M.I. Effect of 6-month calorie restriction on biomarkers of longevity, metabolic adaptation, and oxidative stress in overweight individuals: a randomized controlled trial. JAMA. 2006;295:1539–1548.
    1. Das S.K., Gilhooly C.H., Golden J.K. Long-term effects of 2 energy-restricted diets differing in glycemic load on dietary adherence, body composition, and metabolism in CALERIE: a 1-y randomized controlled trial. Am. J. Clin. Nutr. 2007;85:1023–1030.
    1. Racette S.B., Weiss E.P., Villareal D.T. One year of caloric restriction in humans: feasibility and effects on body composition and abdominal adipose tissue. J. Gerontol. A Biol. Sci. Med. Sci. 2006;61:943–950.
    1. Rochon J., Bales C.W., Ravussin E. Design and conduct of the CALERIE study: comprehensive assessment of the long-term effects of reducing intake of energy. J. Gerontol. A Biol. Sci. Med. Sci. 2011;66:97–108.
    1. Rickman A.D., Williamson D.A., Martin C.K. The CALERIE Study: design and methods of an innovative 25% caloric restriction intervention. Contemp. Clin. Trials. 2011;32:874–881.
    1. Wadden T.A., Butryn M.L., Byrne K.J. Efficacy of lifestyle modification for long-term weight control. Obes. Res. 2004;12(Suppl.):151S–162S.
    1. Howarth N.C., Saltzman E., Roberts S.B. Dietary fiber and weight regulation. Nutr. Rev. 2001;59:129–139.
    1. Pieper C., Redman L., Racette S. Development of adherence metrics for caloric restriction interventions. Clin. Trials. 2011;8:155–164.
    1. Jennrich R.I., Schluchter M.D. Unbalanced repeated-measures models with structured covariance matrices. Biometrics. 1986;42:805–820.
    1. Diggle P.J., Heagerty P.J., Liang K.-Y., Zeger S.L. second ed. Oxford University Press; New York: 2002. Analysis of Longitudinal Data.
    1. Ravussin E., Redman L.M., Rochon J. A two-year randomized controlled trial of human caloric restriction: feasibility and effects on predictors of health span and longevity. J. Gerontol. A Biol. Sci. Med. Sci. 2015;70:1097–1104.
    1. Efron E. Logistic regression, survival analysis, and the Kaplan-Meier curve. J. Am. Stat. Assoc. 1988;83:414–425.
    1. Anderson D.A., Williamson D.A., Duchmann E.G. Development and validation of a multifactorial treatment outcome measure for eating disorders. Assessment. 1999;6:7–20.
    1. Beck A.T., Beamesderfer A. Assessment of depression: the depression inventory. Mod. Probl. Pharmacopsychiatry. 1974;7:151–169.
    1. Royall R.M. Model robust confidence intervals using maximum likelihood estimators. Int. Stat. Rev. 1986;54:221–226.
    1. Faries D.E., Kadziola Z.A. Analysis of longitudinal data using marginal structural models. In: Faries D.E., Leon A.C., Haro J.M., editors. Analysis of Observational Heath Care Data Using SAS. SAS Institute; Cary, NC: 2010. pp. 211–230.

Source: PubMed

3
Abonnieren