Pharmacokinetic and pharmacodynamic properties of calaspargase pegol Escherichia coli L-asparaginase in the treatment of patients with acute lymphoblastic leukemia: results from Children's Oncology Group Study AALL07P4.

PURPOSE: Asparaginase is a critical agent used to treat acute lymphoblastic leukemia (ALL). Pegaspargase (SS-PEG), a pegylated form of Escherichia coli L-asparaginase with a succinimidyl succinate (SS) linker, is the first-line asparaginase product used in Children's Oncology Group (COG) ALL trials. Calaspargase pegol (SC-PEG) replaces the SS linker in SS-PEG with a succinimidyl carbamate linker, creating a more stable molecule. COG AALL07P4 was designed to determine the pharmacokinetic and pharmacodynamic comparability of SC-PEG to SS-PEG in patients with newly diagnosed high-risk (HR) B-cell ALL. PATIENTS AND METHODS: A total of 165 evaluable patients were randomly assigned at a 2:1 ratio to receive SC-PEG at 2,100 (SC-PEG2100; n = 69) or 2,500 IU/m(2) (SC-PEG2500; n = 42) versus SS-PEG 2,500 IU/m(2) (SS-PEG2500; n = 54) as part of an otherwise identical chemotherapy regimen. The groups were similar demographically, except more female patients received SC-PEG2500. RESULTS: The mean half-life of plasma asparaginase activity for both SC-PEG doses was approximately 2.5× longer than that of SS-PEG2500. The total systemic exposure, as defined by induction area under the curve from time 0 to 25 days, was greater with SC-PEG2500 than with SS-PEG2500 or SC-PEG2100. The proportion of patients with plasma asparaginase activity ≥ 100 mIU/mL and ≥ 400 mIU/mL was higher in patients who received SC-PEG as compared with SS-PEG2500. After one dose of pegylated asparaginase on induction day 4, plasma asparagine was undetectable for 11 days for SS-PEG2500 and 18 days for both SC-PEG groups. CONCLUSION: SC-PEG2500 achieves a significantly longer period of asparaginase activity above defined thresholds and asparagine depletion compared with SS-PEG2500 and has a comparable toxicity profile in children with HR B-cell ALL.

Adjusting for confounding by neighborhood using generalized linear mixed models and complex survey data.

When investigating health disparities, it can be of interest to explore whether adjustment for socioeconomic factors at the neighborhood level can account for, or even reverse, an unadjusted difference. Recently, we proposed new methods to adjust the effect of an individual-level covariate for confounding by unmeasured neighborhood-level covariates using complex survey data and a generalization of conditional likelihood methods. Generalized linear mixed models (GLMMs) are a popular alternative to conditional likelihood methods in many circumstances. Therefore, in the present article, we propose and investigate a new adaptation of GLMMs for complex survey data that achieves the same goal of adjusting for confounding by unmeasured neighborhood-level covariates. With the new GLMM approach, one must correctly model the expectation of the unmeasured neighborhood-level effect as a function of the individual-level covariates. We demonstrate using simulations that even if that model is correct, census data on the individual-level covariates are sometimes required for consistent estimation of the effect of the individual-level covariate. We apply the new methods to investigate disparities in recency of dental cleaning, treated as an ordinal outcome, using data from the 2008 Florida Behavioral Risk Factor Surveillance System (BRFSS) survey. We operationalize neighborhood as zip code and merge the BRFSS data with census data on ZIP Code Tabulated Areas to incorporate census data on the individual-level covariates. We compare the new results to our previous analysis, which used conditional likelihood methods. We find that the results are qualitatively similar.

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.

Doubly robust testing and estimation of model-adjusted effect-measure modification with complex survey data.

Model-based standardization enables adjustment for confounding of a population-averaged exposure effect on an outcome. It requires either a model for the probability of the exposure conditional on the confounders (an exposure model) or a model for the expectation of the outcome conditional on the exposure and the confounders (an outcome model). The methodology can also be applied to estimate averaged exposure effects within categories of an effect modifier and to test whether these effects differ or not. Recently, we extended that methodology for use with complex survey data, to estimate the effects of disability status on cost barriers to health care within three age categories and to test for differences. We applied the methodology to data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey (BRFSS). The exposure modeling and outcome modeling approaches yielded two contrasting sets of results. In the present paper, we develop and apply to the BRFSS example two doubly robust approaches to testing and estimating effect modification with complex survey data; these approaches require that only one of these two models be correctly specified. Furthermore, assuming that at least one of the models is correctly specified, we can use the doubly robust approaches to develop and apply goodness-of-fit tests for the exposure and outcome models. We compare the exposure modeling, outcome modeling, and doubly robust approaches in terms of a simulation study and the BRFSS example.

Adjusting for confounding by neighborhood using a proportional odds model and complex survey data.

In social epidemiology, an individual's neighborhood is considered to be an important determinant of health behaviors, mediators, and outcomes. Consequently, when investigating health disparities, researchers may wish to adjust for confounding by unmeasured neighborhood factors, such as local availability of health facilities or cultural predispositions. With a simple random sample and a binary outcome, a conditional logistic regression analysis that treats individuals within a neighborhood as a matched set is a natural method to use. The authors present a generalization of this method for ordinal outcomes and complex sampling designs. The method is based on a proportional odds model and is very simple to program using standard software such as SAS PROC SURVEYLOGISTIC (SAS Institute Inc., Cary, North Carolina). The authors applied the method to analyze racial/ethnic differences in dental preventative care, using 2008 Florida Behavioral Risk Factor Surveillance System survey data. The ordinal outcome represented time since last dental cleaning, and the authors adjusted for individual-level confounding by gender, age, education, and health insurance coverage. The authors compared results with and without additional adjustment for confounding by neighborhood, operationalized as zip code. The authors found that adjustment for confounding by neighborhood greatly affected the results in this example.

Testing and estimating model-adjusted effect-measure modification using marginal structural models and complex survey data.

Recently, it has been shown how to estimate model-adjusted risks, risk differences, and risk ratios from complex survey data based on risk averaging and SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina). The authors present an alternative approach based on marginal structural models (MSMs) and SAS (SAS Institute, Inc., Cary, North Carolina). The authors estimate the parameters of the MSM using inverse weights that are the product of 2 terms. The first term is a survey weight that adjusts the sample to represent the unstandardized population. The second term is an inverse-probability-of-exposure weight that standardizes the population in order to adjust for confounding; it must be estimated using the survey weights. The authors show how to use the MSM parameter estimates and contrasts to test and estimate effect-measure modification; SAS code is provided. They also explain how to program the previous risk-averaging approach in SAS. The 2 methods are applied and compared using data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey to assess effect modification by age of the difference in risk of cost barriers to health care between persons with disability and persons without disability.