香蕉视频

tibor.schuster [at] mcgill.ca (Tibor Schuster, Ph.D).

Associate Professor, Department of Family Medicine, 香蕉视频
Graduate Program Director for the MSc, PhD and Postdoctoral Fellows in Family Medicine [and Primary Care].
Canada Research Chair in Biostatistical Methods for Primary Health Care Research (Tier II)

Biography: Dr Schuster accomplished his early academic and professional education at the Ludwig Maximilian University (LMU) of Munich and the Institute for Medical Statistics and Epidemiology at the Technical University of Munich (TUM). He obtained his doctorate in Biostatistics from the Faculty of Mathematics, Informatics and Statistics at the LMU. Subsequently, he received a post-doctoral award from the Canadian Network of Observational Drug Effect Studies (CNODES) and carried out a post-doctoral fellowship in pharmacoepidemiology at the Department of Epidemiology, Biostatistics and Occupational Health, 香蕉视频 and the Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research in Montreal. He continued with a research fellowship at the Murdoch Childrens Research Institute in Melbourne where he was acting Director of Biostatistics at the newly established Melbourne Children鈥檚 Trial Centre in 2015. In August 2016, Dr Schuster started a tenure-track faculty position as Assistant Professor at the Department of Family Medicine. He is holder of a Tier II Canada Research Chair in Biostatistical Methods for Primary Care Research. In 2019, he's been the acting Director of the Methods Development Component of the Quebec SPOR-SUPPORT (Strategy for Patient-Oriented Research Support for People and Patient-Oriented Research and Trials) Unit and since July 2019, the Graduate Program Director for the Ph.D. program and Postdoctoral Fellows at the Department of Family Medicine.聽聽Dr Schuster taught biostatistical methods at renowned institutions in Germany, Canada and Australia. He acted and is acting as supervisor and mentor for graduate and doctoral students in the fields of biostatistics, epidemiology and bio-medical research.

Research Interests: Dr Schuster鈥檚 main methodological interests are in the development and application of causal inference methods for the design and analysis of cluster randomized controlled trials and observational research studies based on administrative or electronic medical / health record data.

Randomized controlled trials are considered to be the gold standard for inference on intervention effects in bio-medical research and health sciences. If rigorously conducted, such trials yield unbiased and consistent estimates of average intervention effects in relevant target populations.

However, systematic patient drop-out and missing data issues occur frequently and can lead to substantial bias in effect estimation if not considered appropriately. Furthermore, treatment cross-over, non-adherence or non-compliance as well as subsequent (often event-driven) changes of individual treatment protocols require sophisticated analysis strategies to enable estimation of meaningful population-level effects.

Recent methodological developments, in particular so called causal inference approaches, provide promising solutions to these problems. However, for an effective implementation, consideration of relevant data to be collected is compulsory at the design stage, which is a shortcoming of many past and currently ongoing research studies. Furthermore, the immense amount of emerging data due to modern electronic sources requires computational and algorithmic intelligence that goes beyond conventional statistical modelling. Dr Schuster therefore encourages the incorporation and application of modern Machine Learning techniques in conjunction with fundamental principles of Causal Inference.

His specific methodological interests are in:
- Design and analysis of Cluster Crossover Trials, in particular Stepped Wedge Designs
- Causal Inference methods such as Marginal Structural Models and Targeted Learning,
- Theory and applications of Personalized Medicine and Dynamic Treatment Regimens such as Sequential, Multiple Assignment, Randomized Trial (SMART) designs
- Bayesian adaptive and sequential study designs, in particular Internal Pilot Studies and so called Platform Trials
- Confounder selection and adjustment in high dimensional covariate settings
- Modern methods for Statistical and Machine Learning and Data Visualization

Keywords: Cluster Randomized Controlled Trials, Electronic Medical / Health Record Data, Causal Inference, (Pharmaco-) Epidemiology, Biostatistics, Data Science

Publication record: Dr. Schuster has contributed with numerous developments and applications of quantitative methods covering a broad range of diagnostic, clinical therapeutic and epidemiological studies in different disciplines such as cardiology, oncology, internal medicine, anaesthesiology, toxicology, psychology, psychiatrics, surgery, medical engineering and primary care. Over the last ten years, he has (co-) authored over 300 publications in peer-reviewed international scientific journals. A selection of recent contributions is listed below:

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Barnett TA, Koushik A, Schuster T.
Invited Commentary: Cross-Sectional Studies and Causal Inference-It's Complicated.
Am J Epidemiol. 2023 Apr 6;192(4):517-519. doi: 10.1093/aje/kwad026.

Aqib A, Lebouch茅 B, Engler K, Schuster T.
Feasibility of a Platform Trial Design for the Development of Mobile Health Applications: A Review.
Telemed J E Health. 2023 Apr;29(4):501-509. doi: 10.1089/tmj.2021.0620. Epub 2022 Aug 11.

Shrier I, Steele R, Schuster T, Schnitzer M.
Guest Editorial - Special Issue:聽鈥淩ebels with a Cause: Monologues from Heckman, Pearl, Robins, and Rubin鈥�
Observational Studies聽聽vol. 8 no. 2, 2022, p. 3-6.聽Project MUSE,听.

Long S, Loutfi D, Kaufman JS, Schuster T.
Limitations of Canadian COVID-19 data reporting to the general public.
J Public Health Policy. 2022 Jan 31:1-19. doi: 10.1057/s41271-022-00337-x.

Pang M, Platt RW, Schuster T, Abrahamowicz M.
Flexible extension of the accelerated failure time model to account for nonlinear and time-dependent effects of covariates on the hazard.
Stat Methods Med Res. 2021 Nov;30(11):2526-2542. doi: 10.1177/09622802211041759. Epub 2021 Sep 21.

Pang M, Platt RW, Schuster T, Abrahamowicz M.
Spline-based accelerated failure time model.
Stat Med. 2020 Oct 26. doi: 10.1002/sim.8786.

Zhang H, Wang W, Haggerty J, Schuster T.
Predictors of patient satisfaction and outpatient health services in China: evidence from the WHO SAGE survey.
Fam Pract. 2020 Sep 5;37(4):465-472. doi: 10.1093/fampra/cmaa011.

Sourial N, Vedel I, Le Berre M, Schuster T.
Testing group differences for confounder selection in nonrandomized studies: flawed practice
CMAJ聽October 28, 2019聽191聽(43)聽E1189-E1193

Aboutalebi H, Precup D, Schuster T.
Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials.
arXiv preprint arXiv:1903.01026. 2019 Mar 4

Ihbe-Heffinger A, Langebrake C, Hohmann C, Leichenberg K, Hilgarth H, Kunkel M Lueb M, Schuster T.
Prospective survey-based study on the categorization quality of hospital pharmacists' interventions using DokuPIK.
Int J Clin Pharm. 2019 Apr;41(2):414-423.聽doi: 10.1007/s11096-019-00785-8.聽PMID:30895502

Zhang H, Schuster T.
Questionnaire instrument development in primary health care research: A plea for the use of Bayesian inference.
Can Fam Physician. 2018 Sep;64(9):699-700. PMID:30209103

Sourial N, Longo C, Vedel I, Schuster T.
Daring to draw causal claims from non-randomized studies of primary care interventions.
Fam Pract. 2018 Apr 18. doi: 10.1093/fampra/cmy005. PMID:29912314

Byrne RA, Schuster T.
Biodegradable polymer drug-eluting stents: caveat emptor.
Lancet. 2017 Oct 21; 390(10105), 1814-1816. PMID:29082870

Schuster T, Lowe WK, Platt RW.聽
Variance inflation is not the same as variance overestimation.
J Clin Epidemiol. 2017; 88, 161-162. PMID:28529186

Kasza J, Wolfe R, Schuster T.
Assessing the impact of unmeasured confounding for binary outcomes using confounding functions.
Int J Epidemiol. 2017 Aug 1;46(4):1303-1311. doi: 10.1093/ije/dyx023. PMID:28338913

Pang M, Schuster T, Filion KB, Schnitzer ME, Eberg M, Platt RW.
Effect Estimation in Point-Exposure Studies with Binary Outcomes and High-Dimensional Covariate Data - A Comparison of Targeted Maximum Likelihood Estimation and Inverse Probability of Treatment Weighting.
Int J Biostat. 2016 Nov 1;12(2). PMID: 27889705.

Schuster T, Lowe WK, Platt RW.
Propensity score model overfitting led to inflated variance of estimated odds ratios.
J Clin Epidemiol. 2016 Dec;80:97-106. PMID: 27498378.

Pang M, Schuster T, Filion KB, Eberg M, Platt RW.
Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research.
Epidemiology. 2016 Jul;27(4):570-7. PMID: 27031037.

Davidson AJ, Disma N, de Graaff JC, Withington DE, Dorris L, Bell G, Stargatt R, Bellinger DC, Schuster T et al.
Neurodevelopmental outcome at 2 years of age after general anaesthesia and awake-regional anaesthesia in infancy (GAS): an international multicentre, randomised controlled trial.
Lancet. 2016 Jan 16;387(10015):239-50. PMID: 26507180.

Austin PC, Schuster T, Platt RW.
Statistical power in parallel group point exposure studies with time-to-event outcomes: an empirical comparison of the performance of randomized controlled trials and the inverse probability of treatment weighting (IPTW) approach.
BMC Med Res Methodol. 2015 Oct 15;15:87. PMID: 26472109.

Chevance A, Schuster T, Steele R, Tern猫s N, Platt RW.
Contour plot assessment of existing meta-analyses confirms robust association of statin use and acute kidney injury risk.
J Clin Epidemiol. 2015 Oct;68(10):1138-43. PMID: 26092287.

Schuster T, Pang M, Platt RW.
On the role of marginal confounder prevalence - implications for the high-dimensional propensity score algorithm.
Pharmacoepidemiol Drug Saf. 2015 Sep;24(9):1004-7. PMID: 25866189.