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Mao F, Cheung LC, Cook RJ. Two-phase designs with failure time processes subject to nonsusceptibility. Biometrics 2024; 80:ujad038. [PMID: 38446442 DOI: 10.1093/biomtc/ujad038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/05/2023] [Accepted: 12/21/2023] [Indexed: 03/07/2024]
Abstract
Epidemiological studies based on 2-phase designs help ensure efficient use of limited resources in situations where certain covariates are prohibitively expensive to measure for a full cohort. Typically, these designs involve 2 steps: In phase I, data on an outcome and inexpensive covariates are acquired, and in phase II, a subsample is chosen in which the costly variable of interest is measured. For right-censored data, 2-phase designs have been primarily based on the Cox model. We develop efficient 2-phase design strategies for settings involving a fraction of long-term survivors due to nonsusceptibility. Using mixture models accommodating a nonsusceptible fraction, we consider 3 regression frameworks, including (a) a logistic "cure" model, (b) a proportional hazards model for those who are susceptible, and (c) regression models for susceptibility and failure time in those susceptible. Importantly, we introduce a novel class of bivariate residual-dependent designs to address the unique challenges presented in scenario (c), which involves 2 parameters of interest. Extensive simulation studies demonstrate the superiority of our approach over various phase II subsampling schemes. We illustrate the method through applications to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.
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Affiliation(s)
- Fangya Mao
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Rockville, MD 20852, United States
| | - Li C Cheung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Rockville, MD 20852, United States
| | - Richard J Cook
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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2
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Shepherd BE, Han K, Chen T, Bian A, Pugh S, Duda SN, Lumley T, Heerman WJ, Shaw PA. Multiwave validation sampling for error-prone electronic health records. Biometrics 2023; 79:2649-2663. [PMID: 35775996 PMCID: PMC10525037 DOI: 10.1111/biom.13713] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/16/2022] [Indexed: 11/29/2022]
Abstract
Electronic health record (EHR) data are increasingly used for biomedical research, but these data have recognized data quality challenges. Data validation is necessary to use EHR data with confidence, but limited resources typically make complete data validation impossible. Using EHR data, we illustrate prospective, multiwave, two-phase validation sampling to estimate the association between maternal weight gain during pregnancy and the risks of her child developing obesity or asthma. The optimal validation sampling design depends on the unknown efficient influence functions of regression coefficients of interest. In the first wave of our multiwave validation design, we estimate the influence function using the unvalidated (phase 1) data to determine our validation sample; then in subsequent waves, we re-estimate the influence function using validated (phase 2) data and update our sampling. For efficiency, estimation combines obesity and asthma sampling frames while calibrating sampling weights using generalized raking. We validated 996 of 10,335 mother-child EHR dyads in six sampling waves. Estimated associations between childhood obesity/asthma and maternal weight gain, as well as other covariates, are compared to naïve estimates that only use unvalidated data. In some cases, estimates markedly differ, underscoring the importance of efficient validation sampling to obtain accurate estimates incorporating validated data.
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Affiliation(s)
- Bryan E. Shepherd
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA
| | - Kyunghee Han
- Depart. of Mathematics, Statistics, and Computer Science; Univ. of Illinois at Chicago
| | - Tong Chen
- Department of Statistics, University of Auckland
| | - Aihua Bian
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA
| | - Shannon Pugh
- Department of Emergency Medicine, Vanderbilt University Medical Center
| | - Stephany N. Duda
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | | | | | - Pamela A. Shaw
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute
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3
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Mao F, Cook RJ. Two-phase designs with current status data. Stat Med 2023; 42:1207-1232. [PMID: 36690474 DOI: 10.1002/sim.9666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 11/01/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023]
Abstract
We consider the design and analysis of two-phase studies aiming to assess the relation between a fixed (eg, genetic) marker and an event time under current status observation. We consider a common setting in which a phase I sample is comprised of a large cohort of individuals with outcome (ie, current status) data and a vector of inexpensive covariates. Stored biospecimens for individuals in the phase I sample can be assayed to record the marker of interest for individuals selected in a phase II sub-sample. The design challenge is then to select the phase II sub-sample in order to maximize the precision of the marker effect on the time of interest under a proportional hazards model. This problem has not been examined before for current status data and the role of the assessment time is highlighted. Inference based on likelihood and inverse probability weighted estimating functions are considered, with designs centered on score-based residuals, extreme current status observations, or stratified sampling schemes. Data from a registry of patients with psoriatic arthritis is used in an illustration where we study the risk of diabetes as a comorbidity.
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Affiliation(s)
- Fangya Mao
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
| | - Richard J Cook
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
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4
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Ryan B, Nirmalkanna A, Cigsar C, Yilmaz YE. Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies. STATISTICS IN BIOSCIENCES 2023. [DOI: 10.1007/s12561-023-09369-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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5
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Soave D, Lawless JF. Regularized regression for two phase failure time studies. Comput Stat Data Anal 2023. [DOI: 10.1016/j.csda.2023.107703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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6
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Zhou J, Dai Y, Zuo Z, Liu T, Li S. Famine Exposure during Early Life and Risk of Cancer in Adulthood: A Systematic Review and Meta-Analysis. J Nutr Health Aging 2023; 27:550-558. [PMID: 37498102 DOI: 10.1007/s12603-023-1947-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/20/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVES Emerging evidences have explored the association between famine exposure during early life and cancer risk in adulthood, but the results remain controversial and inconsistent. This study aimed to provide a comprehensive evidence on the relation of famine exposure to later cancer risk. DESIGN Systematic review and meta-analysis. METHODS Relevant reports published up to March, 2022 were identified by searching PubMed, Embase, Web of sciences and Medline databases. Pooled relative ratios (RRs) with 95% confidence intervals (CIs) were used to evaluate the effect famine exposure on cancer risk. RESULTS Totally, 18 published articles with 6,061,147 subjects were included in this study. Compared with unexposed group, early life famine exposure dramatically increased the risk of cancer in adulthood (RR=1.13, 95% CI: 1.04-1.22). The pooled RRs were different in terms of sex, exposure severity, exposure period, famine type, study design type and cancer location. A remarkably elevated risk for cancer was discerned in women exposed to famine (RR=1.09, 95% CI: 1.00-1.18), severe exposure (RR=1.12, 95% CI: 1.02-1.22) and adolescence exposure (RR=1.76, 95% CI: 1.02-2.50), Chinese famine exposure (RR=1.55, 95% CI: 1.29-1.82) and cohort studies (RR=1.28, 95% CI: 1.13-1.42). Moreover, a significant association of early-life famine exposure with increased risk of breast (RR=1.16, 95% CI: 1.05-1.27) and stomach cancers (RR=1.89, 95% CI: 1.24-2.54) was observed. CONCLUSION This meta-analysis suggests that exposure to famine during early life may increase the risk of cancer in adulthood. The above-mentioned association is pronounced in women exposed to famine, severe exposure, adolescence exposure, Chinese famine, cohort studies, breast and stomach cancers. It is essential for decision-makers to take targeted measures for improving population awareness regarding the long-term effect of early life nutritional status.
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Affiliation(s)
- J Zhou
- Suyi Li, Department of Oncology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of the University of Science and Technology of China, No. 107 Huanhu East Road, Hefei, Anhui, China. ; Ting Liu, Department of Pharmaceutics, School of Pharmaceutical Sciences, Capital Medical University, Beijing 100069, China.
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Le Guen Y, Belloy ME, Napolioni V, Eger SJ, Kennedy G, Tao R, He Z, Greicius MD. A novel age-informed approach for genetic association analysis in Alzheimer's disease. Alzheimers Res Ther 2021; 13:72. [PMID: 33794991 PMCID: PMC8017764 DOI: 10.1186/s13195-021-00808-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/11/2021] [Indexed: 01/17/2023]
Abstract
BACKGROUND Many Alzheimer's disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery. METHODS Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases). RESULTS Modeling variable AD risk across age results in 5-10% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3, and TAOK2 genes. CONCLUSION Our AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.
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Affiliation(s)
- Yann Le Guen
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94304, USA.
| | - Michael E Belloy
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94304, USA
| | - Valerio Napolioni
- School of Biosciences and Veterinary Medicine, University of Camerino, 62032, Camerino, Italy
| | - Sarah J Eger
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94304, USA
| | - Gabriel Kennedy
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94304, USA
| | - Ran Tao
- Department of Biostatistics and Vanderbilt Genetic Institute, Vanderbilt University, Nashville, TN, 37203, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94304, USA
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, 94304, USA
| | - Michael D Greicius
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94304, USA
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Han K, Lumley T, Shepherd BE, Shaw PA. Two-phase analysis and study design for survival models with error-prone exposures. Stat Methods Med Res 2020; 30:962280220978500. [PMID: 33327876 PMCID: PMC8715910 DOI: 10.1177/0962280220978500] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
Increasingly, medical research is dependent on data collected for non-research purposes, such as electronic health records data. Health records data and other large databases can be prone to measurement error in key exposures, and unadjusted analyses of error-prone data can bias study results. Validating a subset of records is a cost-effective way of gaining information on the error structure, which in turn can be used to adjust analyses for this error and improve inference. We extend the mean score method for the two-phase analysis of discrete-time survival models, which uses the unvalidated covariates as auxiliary variables that act as surrogates for the unobserved true exposures. This method relies on a two-phase sampling design and an estimation approach that preserves the consistency of complete case regression parameter estimates in the validated subset, with increased precision leveraged from the auxiliary data. Furthermore, we develop optimal sampling strategies which minimize the variance of the mean score estimator for a target exposure under a fixed cost constraint. We consider the setting where an internal pilot is necessary for the optimal design so that the phase two sample is split into a pilot and an adaptive optimal sample. Through simulations and data example, we evaluate efficiency gains of the mean score estimator using the derived optimal validation design compared to balanced and simple random sampling for the phase two sample. We also empirically explore efficiency gains that the proposed discrete optimal design can provide for the Cox proportional hazards model in the setting of a continuous-time survival outcome.
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Affiliation(s)
- Kyunghee Han
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Pennsylvania, PA, USA
| | - Thomas Lumley
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Bryan E Shepherd
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Pamela A Shaw
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Pennsylvania, PA, USA
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Shepherd BE, Shaw PA. Errors in multiple variables in human immunodeficiency virus (HIV) cohort and electronic health record data: statistical challenges and opportunities. STATISTICAL COMMUNICATIONS IN INFECTIOUS DISEASES 2020; 12:20190015. [PMID: 35880997 PMCID: PMC9204761 DOI: 10.1515/scid-2019-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 08/21/2020] [Indexed: 06/15/2023]
Abstract
Objectives: Observational data derived from patient electronic health records (EHR) data are increasingly used for human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) research. There are challenges to using these data, in particular with regards to data quality; some are recognized, some unrecognized, and some recognized but ignored. There are great opportunities for the statistical community to improve inference by incorporating validation subsampling into analyses of EHR data.Methods: Methods to address measurement error, misclassification, and missing data are relevant, as are sampling designs such as two-phase sampling. However, many of the existing statistical methods for measurement error, for example, only address relatively simple settings, whereas the errors seen in these datasets span multiple variables (both predictors and outcomes), are correlated, and even affect who is included in the study.Results/Conclusion: We will discuss some preliminary methods in this area with a particular focus on time-to-event outcomes and outline areas of future research.
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Affiliation(s)
- Bryan E. Shepherd
- Biostatistics, Vanderbilt University, 2525 West End, Suite 11000, 37203Nashville, Tennessee, USA
| | - Pamela A. Shaw
- Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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10
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Abstract
The two-phase design is a cost-effective sampling strategy to evaluate the effects of covariates on an outcome when certain covariates are too expensive to be measured on all study subjects. Under such a design, the outcome and inexpensive covariates are measured on all subjects in the first phase and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase. Previous research on two-phase studies has focused largely on the inference procedures rather than the design aspects. We investigate the design efficiency of the two-phase study, as measured by the semiparametric efficiency bound for estimating the regression coefficients of expensive covariates. We consider general two-phase studies, where the outcome variable can be continuous, discrete, or censored, and the second-phase sampling can depend on the first-phase data in any manner. We develop optimal or approximately optimal two-phase designs, which can be substantially more efficient than the existing designs. We demonstrate the improvements of the new designs over the existing ones through extensive simulation studies and two large medical studies.
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Affiliation(s)
- Ran Tao
- Department of Biostatistics and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232.,Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599
| | - Donglin Zeng
- Department of Biostatistics and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232.,Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599
| | - Dan-Yu Lin
- Department of Biostatistics and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232.,Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599
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11
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Lawless JF, Cook RJ. A new perspective on loss to follow‐up in failure time and life history studies. Stat Med 2019; 38:4583-4610. [DOI: 10.1002/sim.8318] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 05/07/2019] [Accepted: 06/20/2019] [Indexed: 11/11/2022]
Affiliation(s)
- Jerald F. Lawless
- Department of Statistics and Actuarial ScienceUniversity of Waterloo Waterloo Ontario Canada
| | - Richard J. Cook
- Department of Statistics and Actuarial ScienceUniversity of Waterloo Waterloo Ontario Canada
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12
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Jiang S, Cook RJ. Score tests based on a finite mixture model of Markov processes under intermittent observation. Stat Med 2019; 38:3013-3025. [PMID: 30972787 DOI: 10.1002/sim.8155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 01/07/2019] [Accepted: 03/08/2019] [Indexed: 11/09/2022]
Abstract
A mixture model is described, which accommodates different Markov processes governing disease progression in a finite set of latent classes. We give special attention to the setting in which individuals are examined intermittently and transition times are consequently interval censored. A score test is developed to identify genetic markers associated with class membership. Simulation studies are conducted to validate the algorithm, assess the finite sample properties of the estimators, and assess the frequency properties of the score tests. A permutation test is recommended for settings when there is concern that the asymptotic approximation to the score test is poor. An application involving progression in joint damage in psoriatic arthritis (PsA) provides illustration and identifies human leukocyte antigen markers associated with unilateral and bilateral sacroiliac damage in individuals with PsA.
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Affiliation(s)
- Shu Jiang
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
| | - Richard J Cook
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
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13
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Abstract
Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this paper, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms, even when the imputation model is misspecified. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic with a consistent variance estimator using sieve approximation theory and empirical process theory. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study.
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Affiliation(s)
- Kin Yau Wong
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - D Y Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
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14
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Lin DY. Discussion of the Paper by R. L. Prentice and Y. Huang - Optimal Designs and Efficient Inference for Biomarker Studies. STATISTICAL THEORY AND RELATED FIELDS 2018; 2:21-22. [PMID: 30662976 PMCID: PMC6333203 DOI: 10.1080/24754269.2018.1493630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Affiliation(s)
- D Y Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599-7420, U.S.A
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Schaubel DE, Nan B. Special issue dedicated to Jack Kalbfleisch. LIFETIME DATA ANALYSIS 2018; 24:1-2. [PMID: 29264748 PMCID: PMC5875455 DOI: 10.1007/s10985-017-9416-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Affiliation(s)
- Douglas E Schaubel
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Bin Nan
- Department of Statistics, University of California-Irvine, Irvine, CA, 92697, USA
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16
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Espin-Garcia O, Craiu RV, Bull SB. Two-phase designs for joint quantitative-trait-dependent and genotype-dependent sampling in post-GWAS regional sequencing. Genet Epidemiol 2017; 42:104-116. [PMID: 29239496 PMCID: PMC5814750 DOI: 10.1002/gepi.22099] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Revised: 10/23/2017] [Accepted: 10/23/2017] [Indexed: 11/09/2022]
Abstract
We evaluate two‐phase designs to follow‐up findings from genome‐wide association study (GWAS) when the cost of regional sequencing in the entire cohort is prohibitive. We develop novel expectation‐maximization‐based inference under a semiparametric maximum likelihood formulation tailored for post‐GWAS inference. A GWAS‐SNP (where SNP is single nucleotide polymorphism) serves as a surrogate covariate in inferring association between a sequence variant and a normally distributed quantitative trait (QT). We assess test validity and quantify efficiency and power of joint QT‐SNP‐dependent sampling and analysis under alternative sample allocations by simulations. Joint allocation balanced on SNP genotype and extreme‐QT strata yields significant power improvements compared to marginal QT‐ or SNP‐based allocations. We illustrate the proposed method and evaluate the sensitivity of sample allocation to sampling variation using data from a sequencing study of systolic blood pressure.
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Affiliation(s)
- Osvaldo Espin-Garcia
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Radu V Craiu
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Shelley B Bull
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
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