Mark van der Laan

About

Mark Johannes van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley. He graduated in 1993 under the supervision of Richard Gill at Utrecht University in the Netherlands. Since starting a position in Biostatistics in 1994, he has been at UC Berkeley. His research contributions include work in survival analysis, semiparametric statistics, multiple testing, censored data, and causal inference. He developed the targeted maximum likelihood methodology and the general theory for super-learning. Van der Laan is a founding editor of the Journal of Causal Inference and the International Journal of Biostatistics. He has authored over 300 publications, written four books on targeted learning, censored data, and multiple testing, and has mentored 55 PhD students. His awards include the COPSS Presidents' Award in 2005, the Mortimer Spiegelman Award in 2004, and the van Dantzig Award in 2005.

Research topics

• Computer Science
• Data science
• Medicine
• Political Science
• Artificial Intelligence
• Business
• Management science
• Accounting
• Mathematics
• Statistics

Selected publications

• Nonparametric bootstrap inference for the targeted highly adaptive least absolute shrinkage and selection operator (LASSO) estimator

  The International Journal of Biostatistics · 2020 · 85 citations

  Senior authorCorresponding

  The Highly-Adaptive least absolute shrinkage and selection operator (LASSO) Targeted Minimum Loss Estimator (HAL-TMLE) is an efficient plug-in estimator of a pathwise differentiable parameter in a statistical model that at minimal (and possibly only) assumes that the sectional variation norm of the true nuisance functions (i.e., relevant part of data distribution) are finite. It relies on an initial estimator (HAL-MLE) of the nuisance functions by minimizing the empirical risk over the parameter…

  DOI

• The Current Landscape in Biostatistics of Real-World Data and Evidence: Causal Inference Frameworks for Study Design and Analysis

  Statistics in Biopharmaceutical Research · 2021 · 52 citations

  As real-world data (RWD) become more readily available, the regulatory agencies, medical product developers, and other key stakeholders have increasing interests in exploring the use of real-world evidence (RWE) to support regulatory decisions alternative to traditional clinical trials. To facilitate and promote statistical research in design, analysis, and interpretation of RWE studies for regulatory decision making, the ASA Biopharmaceutical Section established the RWE Scientific Working Group…

  DOI

• The Current Landscape in Biostatistics of Real-World Data and Evidence: Clinical Study Design and Analysis

  Statistics in Biopharmaceutical Research · 2021 · 40 citations

  Real-world data (RWD), such as electronic health records, reimbursement requests as adjudicated by health insurance companies, and health survey data as collected by government agencies or other research organizations, are increasingly used in drug development. Regulatory agencies, public-private partnerships, and professional organizations have initiated major programs and released guidance or guidelines to address challenges in the use of real-world evidence (RWE) to inform regulatory decision…

  DOI

• Use of Real‐World Data and Real‐World Evidence in Rare Disease Drug Development: A Statistical Perspective

  Clinical Pharmacology & Therapeutics · 2025-02-14 · 7 citations

  review

  Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in medical product development and regulatory decision-making, especially for rare diseases. After outlining the challenges and possible strategies to address the challenges in rare disease drug development (see the accompanying paper), the Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section reviews the roles of RWD and RWE in clinical trials for drugs…

  PublisherDOI

• Challenges and Possible Strategies to Address Them in Rare Disease Drug Development: A Statistical Perspective

  Clinical Pharmacology & Therapeutics · 2025-03-13 · 7 citations

  reviewOpen access

  Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phenotypes and genotypes within a disorder, and lack of appropriate surrogate endpoints to measure clinical benefits. The Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section has assembled…

  PublisherOA PDFDOI

Frequent coauthors

• Antoine Chambaz

  Centre National de la Recherche Scientifique

  86 shared

• Nicholas P. Jewell

  London School of Hygiene & Tropical Medicine

  78 shared

• James M. Robins

  Harvard University

  68 shared

• Alex Luedtke

  67 shared

• Daniel B. Rubin

  Massachusetts General Hospital

  65 shared

• Alessio Mi

  Université Paris Cité

  64 shared

• Moulinath Banerjee

  64 shared

• Ian W. McKeague

  Columbia University

  64 shared

Awards & honors

• COPSS Presidents' Award (2005)
• Mortimer Spiegelman Award (2004)
• van Dantzig Award (2005)

Similar researchers at University of California, Berkeley

• Maya Petersenh-49
• Lexin Lih-34
• Laura Balzerh-26