Curriculum Vitae, Lawrence McCandless SFU
Latest research updates on my twitter account @LCMcCandless
Also check out my Google Scholar account
Faculty of Health Sciences (primary appointment)
Department of Statstics and Actuarial Sciences (associate member)
Simon Fraser University
Burnaby BC V5A 1S6
2014 - Present, Associate Professor, Simon Fraser Unversity
2015 - 2016, Visiting Professor, Department of Epidemiology, Biostatistics and Occupational Health, McGill University
2008 - 2014, Assistant Professor, Simon Fraser Unversity
2007  PhD, Department of Statistics, University of British Columbia
Supervisors: Paul Gustafson and Peter Austin
2008 Postdoctoral Fellow, Department of Epidemiology and Biostatistics, Imperial College London
Supervisors: Sylvia Richardson and Nicky Best
Bayesian statistics, epidemiology, causal inference
1. Analysis of environmental chemical exposures and child health outcomes in the HOME Study
With Bruce Lamphear and Joseph Braun.
2. Bayesian longitudinal data analysis with missing data in the At Home / Chez Soi randomized controlled trial
With Joan Hu and Julian Somers
Funding as principal investigator
CIHR Catalyst Grant, Biostatistical methods for estimating the cumulative impact of environmental contaminant exposures on preterm birth, 2016-2018, $198,330
NSERC Discovery Grant, Bayesian bias modelling for causal inference in statistics, 2015-2020, $80,000
CIHR Planning Grant, Prenatal exposure to environmental contaminants and fetal growth: How to account for multiplicity when testing multiple statistical hypotheses?, 2015, $12,500
NSERC Discovery Grant, Bayesian bias modelling for analysis of observational data, 2009-2015, $65,000
Janice Mung-Yi Hu (PhD 2017-present)
Harry Zhuang (PhD 2017-present)
Brendan Bernardo (MSc 2016-present)
Tian Li (MSc 2015-2017)
Janice Mung-Yi Hu (MSc 2014-2016)
Meghan Woods (MPH 2014-2016)
Emily Rempel (MSc 2012-2014)
Service to academic community
Associate editor of Statistics in Medicine (2013-2017)
Statistical Society of Canada (SSC): Board of directors (2013-2015); Student travel awards chair (2014-2017)
Recent Conference Talks
Beyond p-values and 95% confidence intervals: Using Bayesian statistics to estimate weak effects in environmental health. Cascadia Occupational, Environmental and Population Health conference, 2018 Abbotsford
Bayesian sensitivity analysis for unmeasured confounding in causal mediation analysis. ICSA 2017 Chicago
Sensitivity analysis for several unmeasured confounders. ACIC 2015 Philedelphia
A Bayesian perspective on unmeasured confounding in large administrative databases. ISCB 2014 Vienna
Causal inference in epidemiology using Bayesian methods: The example of meta-analysis of statins and fracture risk. JSM 2013 Montreal
Publications in Biostatistics and Epidemiology (** Students under my supervision)
Gustafson P, McCandless LC. (2018) When is a sensitivity parameter exactly that? Statistical Science. 33:86-95.
McCandless LC, Somers JM. (2017) Bayesian sensitivity analysis for unmeasured confounding in causal mediation analysis. Statistical Methods in Medical Research. xx-xx.
**Woods MM, Lanphear BP, Braun JM, McCandless LC (2017) Gestational exposure to endocrine disrupting chemicals in relation to infant birth weight: a Bayesian analysis of the HOME Study. Environmental Health. 16:115 (12 pages).
McCandless LC, Gustafson P. (2017) A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding. Statistics in Medicine. 36:2887-2901.
McCandless LC, Patterson ML, Currie LB, Moniruzzaman A, Somers JM. (2016) Bayesian estimation of the size of a street-dwelling homeless population. Journal of Modern Applied Statistical Methods 15:1 (25 pages).
McCandless LC, Stewart LC, Rempel ES, Venners SA, Somers JM. (2015) Criminal justice system contact and mortality among offenders with mental illness in British Columbia: an assessment of mediation. Journal of Epidemiology and Community Health. 69:460-6.
**Rempel ES, Somers JM, Calvert JR, McCandless LC (2015) Diagnosed alcohol dependence and criminal sentencing among British Columbia Aboriginal offenders Drug Alcohol Dependence. 154:192-8.
Gustafson P, McCandless LC. (2014) Commentary: Priors, Parameters, and Probability: A Bayesian Perspective on Sensitivity Analysis. Epidemiology 25:910-12.
Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S (2014) Good practices for quantitative bias analysis. International Journal of Epidemiology 43:1969-85.
McCandless LC. (2013) Statins and fracture risk: Can we quantify the healthy-user effect? Epidemiology 24:743-52. Runner up for Rothman Prize for best paper published in Epidemiology in 2013.
McCandless LC, Richardson S, Best N. (2012) Adjustment for missing
confounders using external validation data and propensity scores.
Journal of the American Statistical Association 107:40-51.
McCandless LC, Gustafson P, Levy AR, Richardson S. (2012) Hierarchical
priors for bias parameters in Bayesian sensitivity analysis for
unmeasured confounding. Statistics in Medicine 31:383-96.
McCandless LC. (2012) Meta-analysis of observational studies with
unmeasured confounders. The International Journal of Biostatistics.
8:2, Article 5 (33 pages).
McCandless LC. (2012) Discussion of " Bayesian effect estimation
accounting for adjustment uncertainty," by Wang C, Parmigiani and
Dominici F. Biometrics 68, 678-80.
Gustafson P, McCandless LC, Levy AR and S. Richardson (2010) Simplified
Bayesian Sensitivity Analysis for Mismeasured and Unobserved
Confounders. Biometrics 4:1129-37.
Gustafson P, McCandless LC (2010). Probabilistic Approaches to Better
Quantifying the Results of Epidemiologic Studies. International Journal
of Environmental Research and Public Health 7:1520-39.
McCandless LC, Douglas IJ, Evans SJ, Smeeth L (2010). Cutting Feedback
in Bayesian Regression Adjustment for the Propensity Score. The
International Journal of Biostatistics 6:2, 16. (24 pages)
McCandless LC, Gustafson P, Austin PC. (2009) Bayesian propensity score
analysis for observational data. Statistics in Medicine 15:94-112.
McCandless LC, Gustafson P, Levy AR (2008). A sensitivity analysis
using information about measured confounders yielded improved
assessments of uncertainty from unmeasured confounding. Journal of
Clinical Epidemiology 61:247-55.
McCandless LC, Gustafson P, Levy AR. (2007) Bayesian sensitivity
analysis for unmeasured confounding in observational studies.
Statistics in Medicine. 26:2331--47.
Gustafson P, McCandless LC (2005). Comment on Multiple-bias modelling
for analysis of observational data." by Sander Greenland. Journal of
the Royal Statistical Society, Series A 168:267-306.
Gustafson P, Hossain S, McCandless LC. (2005) Innovative Bayesian
methods for biostatistics and epidemiology. In Handbook of Statistics,
Vol. 25 on Bayesian Statistics (D. Dey and C.R. Rao, Eds.), Elsevier,