scholarly journals The causal effect of dynamic fertility treatment strategies on the probability of pregnancy: a novel application of marginal structural models (MSMs)

2019 ◽  
Vol 112 (3) ◽  
pp. e178
Author(s):  
Soudeh Ansari ◽  
Michael P. LaValley ◽  
Sara Lodi ◽  
Brooke Hayward ◽  
Gilbert L. Mottla ◽  
...  
Keyword(s):  
Causal Effect ◽  
Treatment Strategies ◽  
Structural Models ◽  
Fertility Treatment ◽  
Marginal Structural Models

scholarly journals Causal inference in continuous time: an example on prostate cancer therapy

Biostatistics ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 172-185 ◽  
Author(s):  
Pål Christie Ryalen ◽  
Mats Julius Stensrud ◽  
Sophie Fosså ◽  
Kjetil Røysland
Keyword(s):  
Prostate Cancer ◽  
Continuous Time ◽  
Causal Effect ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Cancer Treatments ◽  
Discrete Parameter ◽  
Inverse Probability Weights ◽  
Using Data ◽  
Prostate Cancer Treatments

Abstract In marginal structural models (MSMs), time is traditionally treated as a discrete parameter. In survival analysis on the other hand, we study processes that develop in continuous time. Therefore, Røysland (2011. A martingale approach to continuous-time marginal structural models. Bernoulli 17, 895–915) developed the continuous-time MSMs, along with continuous-time weights. The continuous-time weights are conceptually similar to the inverse probability weights that are used in discrete time MSMs. Here, we demonstrate that continuous-time MSMs may be used in practice. First, we briefly describe the causal model assumptions using counting process notation, and we suggest how causal effect estimates can be derived by calculating continuous-time weights. Then, we describe how additive hazard models can be used to find such effect estimates. Finally, we apply this strategy to compare medium to long-term differences between the two prostate cancer treatments radical prostatectomy and radiation therapy, using data from the Norwegian Cancer Registry. In contrast to the results of a naive analysis, we find that the marginal cumulative incidence of treatment failure is similar between the strategies, accounting for the competing risk of other death.

Estimating the Causal Effect of Some Exposure From Nonrandomized Studies: The Example of Reduced Intensity Conditioning (RIC) in Hematological Diseases.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3365-3365
Author(s):  
Matthieu Resche-Rigon ◽  
Marie Robin ◽  
Regis Peffault de Latour ◽  
Sylvie Chevret ◽  
Gerard P Socie
Keyword(s):  
Quantitative Methods ◽  
Causal Effect ◽  
Autologous Transplantation ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Reduced Intensity Conditioning ◽  
Hematological Diseases ◽  
Causal Pathway ◽  
The Impact ◽  
Reduced Intensity

Abstract Abstract 3365 Poster Board III-253 Introduction: Although allogeneic SCT with RIC has now gained wide acceptance, its eventual benefit again non-transplant approach is largely unknown (outside the setting of large randomized trials). When evaluating the impact on survival of reduced intensity conditioning in malignant hematological diseases, standard estimations based on Cox regression from observational databases could be biased because they ignore covariates that confound treatment decision. In this setting, we applied and compared two different statistical methods that were developed to control for confounding in estimating exposure (or treatment) effect from epidemiological studies. Patients and Methods: The statistical challenge was that allograft tended to be given when a patient was in advanced phase of his/her hematological malignancy, so that treatment was confounded by performance indicators, which in turn lie on the causal pathway between treatment and outcome. Thus, comparison of outcome first used propensity score (PS) analyses that attempt to create a comparison group of non-treated patients that closely resembles the group of treated patients by matching for the likelihood that a given patient has received the treatment. Then, we used marginal structural models (MSMs) that consist in creating, by using inverse probability of treatment weights, a pseudo-population in which the probability of treatment does no longer depend on covariates, and the effect of treatment on outcome is the same as in the original population. Result: Reduced intensity conditioning allograft was performed in 82 patients with chemotherapy-sensitive patients relapsing after autologous transplantation. Patients with myeloma (MM, 23 pts), follicular lymphoma (FL, 28 pts) or Hodgkin disease (HD, 31 pts), were compared to 276 patients who relapsed after autologous transplantation but did not underwent allogeneic stem cell transplantation (142 MM, 115 FL and 19 HD). From original datasets, 21 (91%) matched pairs could be constituted from MM patients, as compared to 19 (68%) of the FL patients, down to 15 (48%) of the HD patients. Based on these PS-matched samples, a significant benefit of reduced intensity conditioning as compared with non allografted patients was observed in MM, with estimated hazard ratio (HR) of death at 0.34 (95% confidence interval, CI: 0.14-0.88), as well as in FL (HR= 0.78, 95%CI: 0.27;2.30) and in HD (HR= 0.24; 95%CI: 0.09-0.62). MSM-based analyses that applied to the reweighted populations confirmed these trends towards survival benefits in FL, though partially erased in MM and HD. Conclusions: We reported the application of marginal structural models, a new class of causal models to estimate the effect of nonrandomized treatments as an alternative to PS based approaches in small samples. We expect that an increasing number of physicians involved in clinical cohorts become familiar with these novel and appealing quantitative methods when assessing innovative treatment effects. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Doubly Robust and Efficient Estimation of Marginal Structural Models for the Hazard Function

2016 ◽  
Vol 12 (1) ◽  
pp. 233-252 ◽  
Author(s):  
Wenjing Zheng ◽  
Maya Petersen ◽  
Mark J. van der Laan
Keyword(s):  
Maximum Likelihood ◽  
Hazard Function ◽  
Causal Effect ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Targeted Maximum Likelihood ◽  
Dynamic Treatment ◽  
Doubly Robust

Abstract In social and health sciences, many research questions involve understanding the causal effect of a longitudinal treatment on mortality (or time-to-event outcomes in general). Often, treatment status may change in response to past covariates that are risk factors for mortality, and in turn, treatment status may also affect such subsequent covariates. In these situations, Marginal Structural Models (MSMs), introduced by Robins (1997. Marginal structural models Proceedings of the American Statistical Association. Section on Bayesian Statistical Science, 1–10), are well-established and widely used tools to account for time-varying confounding. In particular, a MSM can be used to specify the intervention-specific counterfactual hazard function, i. e. the hazard for the outcome of a subject in an ideal experiment where he/she was assigned to follow a given intervention on their treatment variables. The parameters of this hazard MSM are traditionally estimated using the Inverse Probability Weighted estimation Robins (1999. Marginal structural models versus structural nested models as tools for causal inference. In: Statistical models in epidemiology: the environment and clinical trials. Springer-Verlag, 1999:95–134), Robins et al. (2000), (IPTW, van der Laan and Petersen (2007. Causal effect models for realistic individualized treatment and intention to treat rules. Int J Biostat 2007;3:Article 3), Robins et al. (2008. Estimaton and extrapolation of optimal treatment and testing strategies. Statistics in Medicine 2008;27(23):4678–721)). This estimator is easy to implement and admits Wald-type confidence intervals. However, its consistency hinges on the correct specification of the treatment allocation probabilities, and the estimates are generally sensitive to large treatment weights (especially in the presence of strong confounding), which are difficult to stabilize for dynamic treatment regimes. In this paper, we present a pooled targeted maximum likelihood estimator (TMLE, van der Laan and Rubin (2006. Targeted maximum likelihood learning. The International Journal of Biostatistics 2006;2:1–40)) for MSM for the hazard function under longitudinal dynamic treatment regimes. The proposed estimator is semiparametric efficient and doubly robust, offering bias reduction over the incumbent IPTW estimator when treatment probabilities may be misspecified. Moreover, the substitution principle rooted in the TMLE potentially mitigates the sensitivity to large treatment weights in IPTW. We compare the performance of the proposed estimator with the IPTW and a on-targeted substitution estimator in a simulation study.

Instrumental variable estimation of the marginal structural Cox model for time-varying treatments

Biometrika ◽  
2021 ◽  
Author(s):  
Y Cui ◽  
H Michael ◽  
F Tanser ◽  
E Tchetgen Tchetgen
Keyword(s):  
Instrumental Variable ◽  
Causal Effect ◽  
Cox Model ◽  
Sufficient Conditions ◽  
Structural Models ◽  
Model Parameters ◽  
Marginal Structural Models ◽  
Time Varying ◽  
Unmeasured Confounding ◽  
Sequential Randomization

Summary Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of marginal structural model parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. The marginal structural Cox model is one of the most popular marginal structural models to evaluate the causal effect of time-varying treatments on a censored failure time outcome. In this paper, we establish sufficient conditions for identification of marginal structural Cox model parameters with the aid of a time-varying instrumental variable, when sequential randomization fails to hold due to unmeasured confounding. Our instrumental variable identification condition rules out any interaction between an unmeasured confounder and the instrumental variable in its additive effects on the treatment process, the longitudinal generalization of the identifying condition of Wang & Tchetgen Tchetgen (2018). We describe a large class of weighted estimating equations that give rise to consistent and asymptotically normal estimators of the marginal structural Cox model, thereby extending the standard inverse probability of treatment weighted estimation of marginal structural models to the instrumental variable setting. Our approach is illustrated via extensive simulation studies and an application to estimate the effect of community antiretroviral therapy coverage on HIV incidence.

scholarly journals Marginal Structural Models with Counterfactual Effect Modifiers

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Wenjing Zheng ◽  
Zhehui Luo ◽  
Mark J van der Laan
Keyword(s):  
Maximum Likelihood ◽  
Causal Inference ◽  
Influence Function ◽  
Structural Model ◽  
Causal Effect ◽  
Structural Models ◽  
Efficient Estimation ◽  
Robust Estimator ◽  
Marginal Structural Models ◽  
Targeted Maximum Likelihood

Abstract In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics. In longitudinal settings, time-varying or post-intervention effect modifiers are also of interest. In this work, we investigate the robust and efficient estimation of the Counterfactual-History-Adjusted Marginal Structural Model (van der Laan MJ, Petersen M. Statistical learning of origin-specific statically optimal individualized treatment rules. Int J Biostat. 2007;3), which models the conditional intervention-specific mean outcome given a counterfactual modifier history in an ideal experiment. We establish the semiparametric efficiency theory for these models, and present a substitution-based, semiparametric efficient and doubly robust estimator using the targeted maximum likelihood estimation methodology (TMLE, e.g. van der Laan MJ, Rubin DB. Targeted maximum likelihood learning. Int J Biostat. 2006;2, van der Laan MJ, Rose S. Targeted learning: causal inference for observational and experimental data, 1st ed. Springer Series in Statistics. Springer, 2011). To facilitate implementation in applications where the effect modifier is high dimensional, our third contribution is a projected influence function (and the corresponding projected TMLE estimator), which retains most of the robustness of its efficient peer and can be easily implemented in applications where the use of the efficient influence function becomes taxing. We compare the projected TMLE estimator with an Inverse Probability of Treatment Weighted estimator (e.g. Robins JM. Marginal structural models. In: Proceedings of the American Statistical Association. Section on Bayesian Statistical Science, 1-10. 1997a, Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology. 2000;11:561–570), and a non-targeted G-computation estimator (Robins JM. A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Math Modell. 1986;7:1393–1512.). The comparative performance of these estimators is assessed in a simulation study. The use of the projected TMLE estimator is illustrated in a secondary data analysis for the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial where effect modifiers are subject to missing at random.

scholarly journals A graphical perspective of marginal structural models: An application for the estimation of the effect of physical activity on blood pressure

2016 ◽  
Vol 27 (8) ◽  
pp. 2428-2436
Author(s):  
Denis Talbot ◽  
Amanda M Rossi ◽  
Simon L Bacon ◽  
Juli Atherton ◽  
Geneviève Lefebvre
Keyword(s):  
Physical Activity ◽  
Blood Pressure ◽  
Repeated Measures ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Causal Effect ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Time Varying ◽  
Causal Graph

Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models are a relatively new class of causal models that effectively deal with the estimation of the effects of time-varying exposures. Marginal structural models have traditionally been embedded in the counterfactual framework to causal inference. In this paper, we use the causal graph framework to enhance the implementation of marginal structural models. We illustrate our approach using data from a prospective cohort study, the Honolulu Heart Program. These data consist of 8006 men at baseline. To illustrate our approach, we focused on the estimation of the causal effect of physical activity on blood pressure, which were measured at three time points. First, a causal graph is built to encompass prior knowledge. This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using marginal structural models for repeated measures and guided the implementation of the models with the causal graph. By employing the causal graph framework, we also show the validity of fitting conditional marginal structural models for repeated measures in the context implied by our data.

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