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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2011)

Collaborative targeted maximum likelihood estimation is an extension to targeted maximum likelihood estimation (TMLE), first introduced by van der Laan and Rubin in 2006. TMLE is an efficient, double robust, semi-parametric methodology for estimating a pathwise differential parameter of a statistical distribution given censored data. The TMLE procedure involves a parametric fluctuation of an initial estimate of the relevant factor of the density of the observed data, Q, that involves estimating the nuisance portion of the likelihood---censoring mechanism, g. DR estimators are consistent when at least one of these is estimated consistently. Though the best approach to nuisance parameter estimation is a current topic of debate in the literature, methods typically rely on maximizing a likelihood for g, however, by establishing the collaborative double robustness of the efficient influence curve, van der Laan and Gruber (2010) provides a theoretical justification for moving away from the practice of external nuisance parameter estimation. That paper also presents the collaborative TMLE (C-TMLE), and provides an algorithm for constructing the estimator.

This dissertation explores collaborative double robustness to provide an understanding of the requirements for effective nuisance parameter estimation that are the foundation of C-TMLE, and presents the forward-selection C-TMLE algorithm. This approach to targeted maximum likelihood estimation is especially useful when the true censoring mechanism is unknown, and adjusting for a large number of correlated confounders leads to highly variable estimates. It is also particularly effective when there is sparsity in the data that renders a statistical parameter borderline identifiable. Variations on the C-TMLE estimator are presented, and their behavior is compared and contrasted with several TMLEs and other estimators in the literature using simulated and real data. There is an emphasis on finite sample performance under sparsity and model misspecification, and the incorporation of data-adaptive techniques while still preserving influence-curve based inference. A method for ensuring that TMLEs respect known global constraints on the model is presented, and a guide to the R package tmle, for TMLE estimation of binary point treatment effects is provided in the Appendix.

    Cover page: Collaborative Targeted Maximum Likelihood Estimation
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2023)
    BACKGROUND: The Targeted Learning roadmap provides a systematic guide for generating and evaluating real-world evidence (RWE). From a regulatory perspective, RWE arises from diverse sources such as randomized controlled trials that make use of real-world data, observational studies, and other study designs. This paper illustrates a principled approach to assessing the validity and interpretability of RWE. METHODS: We applied the roadmap to a published observational study of the dose-response association between ritodrine hydrochloride and pulmonary edema among women pregnant with twins in Japan. The goal was to identify barriers to causal effect estimation beyond unmeasured confounding reported by the studys authors, and to explore potential options for overcoming the barriers that robustify results. RESULTS: Following the roadmap raised issues that led us to formulate alternative causal questions that produced more reliable, interpretable RWE. The process revealed a lack of information in the available data to identify a causal dose-response curve. However, under explicit assumptions the effect of treatment with any amount of ritodrine versus none, albeit a less ambitious parameter, can be estimated from data. CONCLUSIONS: Before RWE can be used in support of clinical and regulatory decision-making, its quality and reliability must be systematically evaluated. The TL roadmap prescribes how to carry out a thorough, transparent, and realistic assessment of RWE. We recommend this approach be a routine part of any decision-making process.
      Cover page: Evaluating and improving real-world evidence with Targeted Learning.
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2014)
      This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpart.
        Cover page: Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2023)
        BACKGROUND: Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to assess and ensure that analyses meet their intended goals. METHODS: The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence. RESULTS: In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative - a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping. CONCLUSION: These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.
          Cover page: An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data.
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2023)
          Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.
            Cover page: A causal roadmap for generating high-quality real-world evidence.
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