Curriculum Vitae, Lawrence McCandless SFU
Latest research updates at @LCMcCandless and Google Scholar
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
Lawrence McCandless is an accomplished scholar in the field of epidemiology and biostatistics. His research focuses on the fields of environmental epidemiology, child health research and mental health. He is the principal investigator on a CIHR grant investigating the effect of environmental in utero exposures (e.g. pesticides and heavy metals) on adverse pregnancy outcomes using data from the MIREC Study and the HOME Study. Dr. McCandless's methodological expertise lies in the area of epidemiological data analysis and Bayesian statistics. He collaborates extensively both nationally and internationally. In addition to his position as associate professor in the Faculty of Health Sciences, Dr. McCandless is also an associate member of the Department of Statistics and Actuarial Sciences at SFU. From 2013-2017, Dr McCandless was an Associate Editor for Statistics in Medicine.
Dr. McCandless is passionate about teaching and training students in quantitative methods. He teaches courses in epidemiology, biostatistics, and scientific computing using the R programming language. He is the primary instructor for HSCI805 Intermediate Epidemiology, and HSCI410 Exploratory Data Analysis. He mentors a team of MSc, MPH and PhD students who are supported by operating grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Canadian Institutes for Health Research (CIHR).
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
Selhedin Essa (MSc 2017-present)
Josh Alampi (Ugrad 2019-present)
Janice Mung-Yi Hu (PhD 2017-present)
Harry Zhuang (PhD 2017-present)
Brendan Bernardo (MSc 2016-2018)
Tian Li (MSc 2015-2017)
Janice Mung-Yi Hu (MSc 2014-2016)
Meghan Woods (MPH 2014-2016)
Emily Rempel (MSc 2012-2014)
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
Selected publications in Biostatistics and Environmental Epidemiology (** Students under my supervision)
**Li T, Somers JM, Hu XJ, McCandless LC (2019). Bayesian sensitivity analysis for non-ignorable missing data in longitudinal studies. Statistics in Biosciences. doi.org/10.1007/s12561-019-09234-6.
**Bernardo BA, Lanphear BP, Venners SA, Arbuckle TA, Braun JM, Muckle G, Fraser WD, McCandless LC (2019). Assessing the Relation between Plasma PCB Concentrations and Elevated Autistic Behaviours using Bayesian Predictive Odds Ratios. International Journal of Environmental Research and Public Health (Special Issue: Methodological Innovations and Reflections). doi:10.3390/ijerph16030457.
Patel NB, Xu Y, McCandless LC, Chen A, Yolton K, Braun J, Jones RL, Dietrich KN, Lanphear BP. (2019) Very low-level prenatal mercury exposure and behaviors in children: the HOME Study. Environ Health. 18(1) doi: 10.1186/s12940-018-0443-5.
Cheraghi Z, Nedjat S, Mirmiran P, Moslehi N, Mansournia N, Etminan M, Mansournia MA, McCandless LC. (2019) Effects of food items and related nutrients on metabolic syndrome using Bayesian multilevel modelling using the Tehran Lipid and Glucose Study (TLGS): a cohort study. BMJ Open. 18(12) doi: 10.1136/bmjopen-2017-020642.
McCandless LC, Somers JM. (2019) Bayesian sensitivity analysis for unmeasured confounding in causal mediation analysis. Statistical Methods in Medical Research. 28:515-531.
Moradzadeh R, Mansournia MA, Baghfalaki T, Nadrian H, Gustafson P, McCandless LC. (2018) The impact of maternal smoking during pregnancy on childhood asthma: adjusted for exposure misclassification; Results from the National Health and Nutrition Examination Survey, 2011-2012. Annals of Epidemiology. Oct;28(10):697-703.
Kalloo G, Wellenius GA, McCandless L, Calafat AM, Sjodin A, Karagas M, Chen A, Yolton K, Lanphear BP, Braun JM. (2018) Profiles and Predictors of Environmental Chemical Mixture Exposure among Pregnant Women: The Health Outcomes and Measures of the Environment Study. Environmental Science and Technology Sep 4;52(17):10104-10113.
Yuchi W, Gombojav E, Boldbaatar B, Galsuren J, Enkhmaa S, Beejin B, Naidan G, Ochir C, Legtseg B, Byambaa T, Barn P, Henderson SB, Janes CR, Lanphear BP, McCandless LC, Takaro TK, Venners SA, Webster GM, Allen RW. (2018) Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city. Environmental Pollution. Nov 16;245:746-753.
Barn P, Gombojav E, Ochir C, Boldbaatar B, Beejin B, Naidan G, Galsuren J, Legtseg B, Byambaa T, Hutcheon JA, Janes C, Janssen PA, Lanphear BP, McCandless LC, Takaro TK, Venners SA, Webster GM, Allen RW. (2018) The effect of portable HEPA filter air cleaner use during pregnancy on fetal growth: The UGAAR randomized controlled trial. Environment International. Dec;121(Pt 1):981-989.
**Hu JMY, **Zhuang LH, **Bernardo BA, McCandless LC. (2018) Statistical Challenges in the Analysis of Biomarkers of Environmental Chemical Exposures for Perinatal Epidemiology Current Epidemiology Reports. doi.org/10.1007/s40471-018-0156-x
Gustafson P, McCandless LC. (2018) When is a sensitivity parameter exactly that? Statistical Science. 33:86-95.
**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, Levy AR (2009). Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure patients. Epidemiologic Perspectives and Innovations 6:5, 16. (11 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,
... and some interests outside of research: Hiking, Trail running, Tennis and my mountain bike