A simulation study of diagnostics for bias in non-probability samples

A non-probability sampling mechanism is likely to bias estimates of parameters with respect to a target population of interest. This bias poses a unique challenge when selection is \u27non-ignorable\u27, i.e. dependent upon the unobserved outcome of interest, since it is then undetectable and thus cannot be ameliorated. We extend a simulation study by Nishimura et al. [International Statistical Review, 84, 43--62 (2016)], adding a recently published statistic, the so-called \u27standardized measure of unadjusted bias\u27, which explicitly quantifies the extent of bias under the assumption that a specified amount of non-ignorable selection exists. Our findings suggest that this new sensitivity diagnostic is considerably correlated with, and more predictive of, the true, unknown extent of selection bias than other diagnostics, even when the underlying assumed level of non-ignorability is incorrect

Tests for Gene-Environment Interactions and Joint Effects with Exposure Misclassification

The number of methods for genome-wide testing of gene-environment interactions (GEI) continues to increase with the hope of discovering new genetic risk factors and obtaining insight into the disease-gene-environment relationship. The relative performance of these methods based on family-wise type 1 error rate and power depends on underlying disease-gene-environment associations, estimates of which may be biased in the presence of exposure misclassification. This simulation study expands on a previously published simulation study of methods for detecting GEI by evaluating the impact of exposure misclassification. We consider seven single step and modular screening methods for identifying GEI at a genome-wide level and seven joint tests for genetic association and GEI, for which the goal is to discover new genetic susceptibility loci by leveraging GEI when present. In terms of statistical power, modular methods that screen based on the marginal disease-gene relationship are more robust to exposure misclassification. Joints tests that include main/marginal effects of a gene display a similar robustness, confirming results from earlier studies. Our results offer an increased understanding of the strengths and limitations of methods for genome-wide search for GEI and joint tests in presence of exposure misclassification. KEY WORDS: case-control; genome-wide association; gene discovery, gene-environment independence; modular methods; multiple testing; screening test; weighted hypothesis test. Abbreviations: CC, case-control; CC(EXP), CC in the exposed subgroup; CO, case-only; CT, cocktail; DF, degree of freedom; D-G, disease-gene; EB, empirical Bayes; EB(EXP), EB in the exposed subgroup; EDGxE, joint marginal/association screening; FWER, family-wise error rate; G-E, gene-environment; GEI, gene-environment interaction; GEWIS, Gene Environment Wide Interaction Study; H2, hybrid two-step; LR, likelihood ratio; MA, marginal; OR, odds ratio; SE, sensitivity; SP, specificity; TS, two-step gene-environment screening

Indices of nonâ ignorable selection bias for proportions estimated from nonâ probability samples

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151805/1/rssc12371_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151805/2/rssc12371.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151805/3/rssc12371-sup-0001-SupInfo.pd

Propensity score‐based diagnostics for categorical response regression models

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102113/1/sim5940.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102113/2/sim5940-sup-0001-supplementary.pd

A single center phase II study of ixazomib in patients with relapsed or refractory cutaneous or peripheral T‐cell lymphomas

The transcription factor GATA‐3, highly expressed in many cutaneous T‐cell lymphoma (CTCL) and peripheral T‐cell lymphomas (PTCL), confers resistance to chemotherapy in a cell‐autonomous manner. As GATA‐3 is transcriptionally regulated by NF‐κB, we sought to determine the extent to which proteasomal inhibition impairs NF‐κB activation and GATA‐3 expression and cell viability in malignant T cells. Proteasome inhibition, NF‐κB activity, GATA‐3 expression, and cell viability were examined in patient‐derived cell lines and primary T‐cell lymphoma specimens ex vivo treated with the oral proteasome inhibitor ixazomib. Significant reductions in cell viability, NF‐κB activation, and GATA‐3 expression were observed preclinically in ixazomib‐treated cells. Therefore, an investigator‐initiated, single‐center, phase II study with this agent in patients with relapsed/refractory CTCL/PTCL was conducted. Concordant with our preclinical observations, a significant reduction in NF‐κB activation and GATA‐3 expression was observed in an exceptional responder following one month of treatment with ixazomib. While ixazomib had limited activity in this small and heterogeneous cohort of patients, inhibition of the NF‐κB/GATA‐3 axis in a single exceptional responder suggests that ixazomib may have utility in appropriately selected patients or in combination with other agents.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139920/1/ajh24895.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139920/2/ajh24895_am.pd

Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels

Radiation esophagitis (RE) is a common adverse event associated with radiotherapy for non-small cell lung cancer (NSCLC). While plasma cytokine levels have been correlated with other forms of radiation-induced toxicity, their association with RE has been less well studied. We analyzed data from 126 patients treated on 4 prospective clinical trials. Logistic regression models based on combinations of dosimetric factors [maximum dose to 2 cubic cm (D2cc) and generalized equivalent uniform dose (gEUD)], clinical variables, and pretreatment plasma levels of 30 cytokines were developed. Cross-validated estimates of area under the receiver operating characteristic curve (AUC) and log likelihood were used to assess prediction accuracy. Dose-only models predicted grade 3 RE with AUC values of 0.750 (D2cc) and 0.727 (gEUD). Combining clinical factors with D2cc increased the AUC to 0.779. Incorporating pretreatment cytokine measurements, modeled as direct associations with RE and as potential interactions with the dose-esophagitis association, produced AUC values of 0.758 and 0.773, respectively. D2cc and gEUD correlated with grade 3 RE with odds ratios (ORs) of 1.094/Gy and 1.096/Gy, respectively. Female gender was associated with a higher risk of RE, with ORs of 1.09 and 1.112 in the D2cc and gEUD models, respectively. Older age was associated with decreased risk of RE, with ORs of 0.992/year and 0.991/year in the D2cc and gEUD models, respectively. Combining clinical with dosimetric factors but not pretreatment cytokine levels yielded improved prediction of grade 3 RE compared to prediction by dose alone. Such multifactorial modeling may prove useful in directing radiation treatment planning