A sparse additive model for treatment effect-modifier selection

Summary Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.

How to Manage the Obese Patient With Cancer

Purpose Obesity (body mass index [BMI] ≥ 30 kg/m2) is common among patients with cancer. We reviewed management issues in the obese patient with cancer, focusing on how obesity influences treatment selection (including chemotherapy dosing), affects chemotherapy toxicity and surgical complications, and might be a treatment effect modifier. Methods The majority of evidence is drawn from observational studies and secondary analyses of trial data, typically analyzed in N × 3 BMI categories (normal weight, overweight, and obese) matrix structures. We propose a methodological framework for interpretation focusing on sample size and composition, nonlinearity, and unmeasured confounding. Results There is a common perception that obesity is associated with increased treatment-related toxicity. Accordingly, cytotoxic chemotherapy dose reduction is common in patients with elevated BMI. Contrary to this, there is some evidence that full dosing in obese patients does not result in increased toxicity. However, these data are from a limited number of regimens, and fail to fully capture cytotoxic drug pharmacodynamics and pharmacokinetic variability in obese patients. Among patients undergoing surgery, there is evidence that elevated BMI is associated with increased perioperative mortality and increased rates of infectious complications. A novel finding is that these relationships hold after surgery for malignancy, but not for benign indications. There are biologic plausibilities that obesity might be an effect modifier of treatment, but supporting evidence from clinical studies is inconsistent. Conclusion In line with the ASCO 2012 guidelines, chemotherapy dosing is probably best performed using actual body weight in obese patients. However, specific regimens known to be associated with increased toxicity in this group should be used with caution. There is no guidance on dose for obese patients treated with biologic agents. Currently, there are no specific recommendations for the surgical management of the obese patient with cancer.

Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention

In randomized controlled trials, the evaluation of an overall treatment effect is often followed by effect modification or subgroup analyses, where the possibility of a different magnitude or direction of effect for varying values of a covariate is explored. While studies of effect modification are typically restricted to pretreatment covariates, longitudinal experimental designs permit the examination of treatment effect modification by intermediate outcomes, where intermediates are measured after treatment but before the final outcome. We present a novel application of generalized structural mean models (GSMMs) for simultaneously assessing effect modification by post-treatment covariates and accounting for noncompliance to assigned treatment status. The proposed approach may also be used to identify post-treatment effect modifiers in the absence of noncompliance. The methods are evaluated using a simulation study that demonstrates that our approach retains consistent estimation of effect modification by intermediate variables that are affected by treatment and also predict outcomes. We illustrate the method using a randomized trial designed to promote re-employment through teaching skills to enhance self-esteem and inoculate job seekers against setbacks in the job search process. Our analysis provides some evidence that the intervention was much less successful among subjects that displayed higher levels of depression at intermediate post-treatment waves of the study. We also compare the assumptions of our approach and principal stratification as alternatives to account for differences in effects by intermediate variables.