Estimation of dynamic treatment strategies for maintenance therapy of children with acute lymphoblastic leukaemia: an application of history-adjusted marginal structural models

2011 ◽  
Vol 31 (5) ◽  
pp. 470-488 ◽  
Author(s):  
S. Rosthøj ◽  
N. Keiding ◽  
K. Schmiegelow
Keyword(s):  
Maintenance Therapy ◽  
Acute Lymphoblastic Leukaemia ◽  
Lymphoblastic Leukaemia ◽  
Treatment Strategies ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Dynamic Treatment

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 Prediction of leukocyte counts during paediatric acute lymphoblastic leukaemia maintenance therapy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Santeri Karppinen ◽  
Olli Lohi ◽  
Matti Vihola
Keyword(s):  
Maintenance Therapy ◽  
Acute Lymphoblastic Leukaemia ◽  
Lymphoblastic Leukaemia ◽  
Leukocyte Count ◽  
Peripheral Blood Leukocyte ◽  
Reactive Protein ◽  
Leukocyte Counts ◽  
Nonlinear State Space Models ◽  
Blood Leukocyte ◽  
The Individual

AbstractMaintenance chemotherapy with oral 6-mercaptopurine and methotrexate remains a cornerstone of modern therapy for acute lymphoblastic leukaemia. The dosage and intensity of therapy are based on surrogate markers such as peripheral blood leukocyte and neutrophil counts. Dosage based leukocyte count predictions could provide support for dosage decisions clinicians face trying to find and maintain an appropriate dosage for the individual patient. We present two Bayesian nonlinear state space models for predicting patient leukocyte counts during the maintenance therapy. The models simplify some aspects of previously proposed models but allow for some extra flexibility. Our second model is an extension which accounts for extra variation in the leukocyte count due to a treatment adversity, infections, using C-reactive protein as a surrogate. The predictive performances of our models are compared against a model from the literature using time series cross-validation with patient data. In our experiments, our simplified models appear more robust and deliver competitive results with the model from the literature.

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.

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