Person: Murray, Eleanor
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Murray
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Eleanor
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Murray, Eleanor
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Publication Agent-Based Models for Causal Inference(2016-05-03) Murray, Eleanor; Hernan, Miguel A.; Robins, James M.; Seage III, George R.; Freedberg, Kenneth A.Sound clinical decision making requires evidence-based estimates of the impact of different treatment strategies. In the absence of randomized trials, two potential approaches are agent-based models (ABMs) and the parametric g-formula. Although these methods are mathematically similar, they have generally been considered in isolation. In this dissertation, we bridge the gap between ABMs and the parametric g-formula, in order to improve the use of ABMs for causal inference. In Chapter 1, we describe bias that can occur when ABM inputs or estimates are extrapolated to new populations, and demonstrate the impact of this bias by comparison with the parametric g-formula. We describe the assumptions that are required for extrapolation of an ABM and show that violations of these assumptions produce biased estimates of the risk and causal effect. In Chapter 2, we describe an approach to provide calibration targets for ABMs, and to identify the set of parameters of the ABM that interfere with transportability of the model results to a particular population. We illustrate this approach by comparing the estimates from an existing ABM, the Cost-Effectiveness of Preventing AIDS Complications (CEPAC) model, to estimates from the parametric g-formula applied to a prospective clinical data of HIV-positive individuals under different treatment initiation strategies. In Chapter 3, we focus on the core problem of causal inference from ABMs: how to define and estimate the parameters described in Chapter 2 in light of the bias described in Chapter 1. To illustrate this problem, we consider CEPAC input parameters for opportunistic diseases. We formally define the effect of interest, describe the conditions under which this effect is or is not identifiable, and describe the assumptions required for transportability of this effect. Finally, we show that the estimation of these parameters via a naïve regression analysis approach provides implausible estimates.Publication Improved adherence adjustment in the Coronary Drug Project(BioMed Central, 2018) Murray, Eleanor; Hernan, MiguelBackground: The survival difference between adherers and non-adherers to placebo in the Coronary Drug Project has been used to support the thesis that adherence adjustment in randomized trials is not generally possible and, therefore, that only intention-to-treat analyses should be trusted. We previously demonstrated that adherence adjustment can be validly conducted in the Coronary Drug Project using a simplistic approach. Here, we re-analyze the data using an approach that takes full advantage of recent methodological developments. Methods: We used inverse-probability weighted hazards models to estimate the 5-year survival and mortality risk when individuals in the placebo arm of the Coronary Drug Project adhere to at least 80% of the drug continuously or never during the 5-year follow-up period. Results: Adjustment for post-randomization covariates resulted in 5-year mortality risk difference estimates ranging from − 0.7 (95% confidence intervals (CI), − 12.2, 10.7) to 4.5 (95% CI, − 6.3, 15.3) percentage points. Conclusions: Our analysis confirms that appropriate adjustment for post-randomization predictors of adherence largely removes the association between adherence to placebo and mortality originally described in this trial. Trial registration ClinicalTrials.gov, Identifier: NCT00000482. Registered retrospectively on 27 October 1999. Electronic supplementary material The online version of this article (10.1186/s13063-018-2519-5) contains supplementary material, which is available to authorized users.Publication Adherence adjustment in the Coronary Drug Project: A call for better per-protocol effect estimates in randomized trials(SAGE Publications, 2016) Murray, Eleanor; Hernan, MiguelBACKGROUND: In many randomized controlled trials, patients and doctors are more interested in the per-protocol effect than in the intention-to-treat effect. However, valid estimation of the per-protocol effect generally requires adjustment for prognostic factors associated with adherence. These adherence adjustments have been strongly questioned in the clinical trials community, especially after 1980 when the Coronary Drug Project team found that adherers to placebo had lower 5-year mortality than non-adherers to placebo. METHODS: We replicated the original Coronary Drug Project findings from 1980 and re-analyzed the Coronary Drug Project data using technical and conceptual developments that have become established since 1980. Specifically, we used logistic models for binary outcomes, decoupled the definition of adherence from loss to follow-up, and adjusted for pre-randomization covariates via standardization and for post-randomization covariates via inverse probability weighting. RESULTS: The original Coronary Drug Project analysis reported a difference in 5-year mortality between adherers and non-adherers in the placebo arm of 9.4 percentage points. Using modern approaches, we found that this difference was reduced to 2.5 (95% confidence interval: -2.1 to 7.0). CONCLUSION: Valid estimation of per-protocol effects may be possible in randomized clinical trials when analysts use appropriate methods to adjust for post-randomization variables.Publication Using Observational Data to Calibrate Simulation Models(SAGE Publications, 2017) Murray, Eleanor; Robins, James; Seage, George; Lodi, Sara; Hyle, Emily; Reddy, Krishna; Freedberg, Kenneth; Hernan, MiguelBACKGROUND: Individual-level simulation models are valuable tools for comparing the impact of clinical or public health interventions on population health and cost outcomes over time. However, a key challenge is ensuring that outcome estimates correctly reflect real-world impacts. Calibration to targets obtained from randomized trials may be insufficient if trials do not exist for populations, time periods, or interventions of interest. Observational data can provide a wider range of calibration targets but requires methods to adjust for treatment-confounder feedback. We propose the use of the parametric g-formula to estimate calibration targets and present a case-study to demonstrate its application. METHODS: We used the parametric g-formula applied to data from the HIV-CAUSAL Collaboration to estimate calibration targets for 7-y risks of AIDS and/or death (AIDS/death), as defined by the Center for Disease Control and Prevention under 3 treatment initiation strategies. We compared these targets to projections from the Cost-Effectiveness of Preventing AIDS Complications (CEPAC) model for treatment-naïve individuals presenting to care in the following year ranges: 1996 to 1999, 2000 to 2002, or 2003 onwards. RESULTS: The parametric g-formula estimated a decreased risk of AIDS/death over time and with earlier treatment. The uncalibrated CEPAC model successfully reproduced targets obtained via the g-formula for baseline 1996 to 1999, but over-estimated calibration targets in contemporary populations and failed to reproduce time trends in AIDS/death risk. Calibration to g-formula targets improved CEPAC model fit for contemporary populations. CONCLUSION: Individual-level simulation models are developed based on best available information about disease processes in one or more populations of interest, but these processes can change over time or between populations. The parametric g-formula provides a method for using observational data to obtain valid calibration targets and enables updating of simulation model inputs when randomized trials are not available.