Development of an FRα Companion Diagnostic Immunohistochemical Assay for Mirvetuximab Soravtansine.

CONTEXT.­: Folate receptor-α (FRα, encoded by the FOLR1 gene) is overexpressed in several solid tumor types, including epithelial ovarian cancer (EOC), making it an attractive biomarker and target for FRα-based therapy in ovarian cancer. OBJECTIVE.­: To describe the development, analytic verification, and clinical performance of the VENTANA FOLR1 Assay (Ventana Medical Systems Inc) in EOC. DESIGN.­: We used industry standard studies to establish the analytic verification of the VENTANA FOLR1 Assay. Furthermore, the VENTANA FOLR1 Assay was used in the ImmunoGen Inc-sponsored SORAYA study to select patients for treatment with mirvetuximab soravtansine (MIRV) in platinum-resistant EOC. RESULTS.­: The VENTANA FOLR1 Assay is highly reproducible, demonstrated by a greater than 98% overall percent agreement (OPA) for repeatability and intermediate precision studies, greater than 93% OPA for interreader and greater than 96% for intrareader studies, and greater than 90% OPA across all observations in the interlaboratory reproducibility study. The performance of the VENTANA FOLR1 Assay in the SORAYA study was evaluated by the overall staining acceptability rate, which was calculated using the number of patient specimens that were tested with the VENTANA FOLR1 Assay that had an evaluable result. In the SORAYA trial, data in patients who received MIRV demonstrated clinically meaningful efficacy, and the overall staining acceptability rate of the assay was 98.4%, demonstrating that the VENTANA FOLR1 Assay is safe and effective for selecting patients who may benefit from MIRV. Together, these data showed that the assay is highly reliable, consistently producing evaluable results in the clinical setting. CONCLUSIONS.­: The VENTANA FOLR1 Assay is a robust and reproducible assay for detecting FRα expression and identifying a patient population that derived clinically meaningful benefit from MIRV in the SORAYA study.

Model-based standardization to adjust for unmeasured cluster-level confounders with complex survey data.

Model-based standardization uses a statistical model to estimate a standardized, or unconfounded, population-averaged effect. With it, one can compare groups had the distribution of confounders been identical in both groups to that of the standard population. We develop two methods for model-based standardization with complex survey data that accommodate a categorical confounder that clusters the individual observations into a very large number of subgroups. The first method combines a random-intercept generalized linear mixed model with a conditional pseudo-likelihood estimator of the fixed effects. The second method combines a between-within generalized linear mixed model with census data on the cluster-level means of the individual-level covariates. We conduct simulation studies to compare the two approaches. We apply the two methods to the 2008 Florida Behavioral Risk Factor Surveillance System survey data to estimate standardized proportions of people who drink alcohol, within age groups, adjusting for measured individual-level and unmeasured cluster-level confounders.

Methods of estimating or accounting for neighborhood associations with health using complex survey data.

Reasons for health disparities may include neighborhood-level factors, such as availability of health services, social norms, and environmental determinants, as well as individual-level factors. Investigating health inequalities using nationally or locally representative data often requires an approach that can accommodate a complex sampling design, in which individuals have unequal probabilities of selection into the study. The goal of the present article is to review and compare methods of estimating or accounting for neighborhood influences with complex survey data. We considered 3 types of methods, each generalized for use with complex survey data: ordinary regression, conditional likelihood regression, and generalized linear mixed-model regression. The relative strengths and weaknesses of each method differ from one study to another; we provide an overview of the advantages and disadvantages of each method theoretically, in terms of the nature of the estimable associations and the plausibility of the assumptions required for validity, and also practically, via a simulation study and 2 epidemiologic data analyses. The first analysis addresses determinants of repeat mammography screening use using data from the 2005 National Health Interview Survey. The second analysis addresses disparities in preventive oral health care using data from the 2008 Florida Behavioral Risk Factor Surveillance System Survey.

Conditional pseudolikelihood methods for clustered ordinal, multinomial, or count outcomes with complex survey data.

In order to adjust individual-level covariate effects for confounding due to unmeasured neighborhood characteristics, we have recently developed conditional pseudolikelihood methods to estimate the parameters of a proportional odds model for clustered ordinal outcomes with complex survey data. The methods require sampling design joint probabilities for each within-neighborhood pair. In the present article, we develop a similar methodology for a baseline category logit model for clustered multinomial outcomes and for a loglinear model for clustered count outcomes. All of the estimators and asymptotic sampling distributions we present can be conveniently computed using standard logistic regression software for complex survey data, such as sas proc surveylogistic. We demonstrate validity of the methods theoretically and also empirically by using simulations. We apply the new method for clustered multinomial outcomes to data from the 2008 Florida Behavioral Risk Factor Surveillance System survey in order to investigate disparities in frequency of dental cleaning both unadjusted and adjusted for confounding by neighborhood.