title: Simpson's paradox - aggregating and partitioning populations in health disparities of lung cancer patients.
abstract: It is well known that non-small cell lung cancer (NSCLC) is a heterogeneous group of diseases. Previous studies have demonstrated genetic variation among different ethnic groups in the epidermal growth factor receptor (EGFR) in NSCLC. Research by our group and others has recently shown a lower frequency of EGFR mutations in African Americans with NSCLC, as compared to their White counterparts. In this study, we use our original study data of EGFR pathway genetics in African American NSCLC as an example to illustrate that univariate analyses based on aggregation versus partition of data leads to contradictory results, in order to emphasize the importance of controlling statistical confounding. We further investigate analytic approaches in logistic regression for data with separation, as is the case in our example data set, and apply appropriate methods to identify predictors of EGFR mutation. Our simulation shows that with separated or nearly separated data, penalized maximum likelihood (PML) produces estimates with smallest bias and approximately maintains the nominal value with statistical power equal to or better than that from maximum likelihood and exact conditional likelihood methods. Application of the PML method in our example data set shows that race and EGFR-FISH are independently significant predictors of EGFR mutation.
authors.affiliation: Seidman Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA. pxf16@po.cwru.edu.
authors.forename: P
authors.initials: P
authors.lastname: Fu
authors.affiliation: Seidman Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
authors.forename: A
authors.initials: A
authors.lastname: Panneerselvam
authors.affiliation: Seidman Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
authors.forename: B
authors.initials: B
authors.lastname: Clifford
authors.affiliation: Seidman Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
authors.forename: A
authors.initials: A
authors.lastname: Dowlati
authors.affiliation: Cleveland Clinic, Taussig Cancer Institute, Cleveland, OH, USA.
authors.forename: P C
authors.initials: PC
authors.lastname: Ma
authors.affiliation: College of Education, Texas A &M University - Corpus Christi, Corpus Christi, TX, USA.
authors.forename: G
authors.initials: G
authors.lastname: Zeng
authors.affiliation: Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA.
authors.forename: B
authors.initials: B
authors.lastname: Halmos
authors.affiliation: Seidman Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
authors.forename: R S
authors.initials: RS
authors.lastname: Leidner
mesh_terms: D001741:Black or African American / Q000235:genetics / Q000706:statistics & numerical data; D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D002289:Carcinoma, Non-Small-Cell Lung / Q000453:epidemiology / Q000235:genetics*; D003627:Data Interpretation, Statistical; D005260:Female; D018773:Genes, erbB-1 / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics; D054624:Health Status Disparities; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D008175:Lung Neoplasms / Q000453:epidemiology / Q000235:genetics*; D008297:Male; D008875:Middle Aged; D009154:Mutation / Q000235:genetics; D044465:White People / Q000235:genetics / Q000706:statistics & numerical data