On asymptotically optimal tests under loss of identifiability in semiparametric models

We consider tests of hypotheses when the parameters are not identifiable under the null in semiparametric models, where regularity conditions for profile likelihood theory fail. Exponential average tests based on integrated profile likelihood are constructed and shown to be asymptotically optimal under a weighted average power criterion with respect to a prior on the nonidentifiable aspect of the model. These results extend existing results for parametric models, which involve more restrictive assumptions on the form of the alternative than do our results. Moreover, the proposed tests accommodate models with infinite dimensional nuisance parameters which either may not be identifiable or may not be estimable at the usual parametric rate. Examples include tests of the presence of a change-point in the Cox model with current status data and tests of regression parameters in odds-rate models with right censored data. Optimal tests have not previously been studied for these scenarios. We study the asymptotic distribution of the proposed tests under the null, fixed contiguous alternatives and random contiguous alternatives. We also propose a weighted bootstrap procedure for computing the critical values of the test statistics. The optimal tests perform well in simulation studies, where they may exhibit improved power over alternative tests.Comment: Published in at http://dx.doi.org/10.1214/08-AOS643 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Robust Inference for Univariate Proportional Hazards Frailty Regression Models

We consider a class of semiparametric regression models which are one-parameter extensions of the Cox [J. Roy. Statist. Soc. Ser. B 34 (1972) 187-220] model for right-censored univariate failure times. These models assume that the hazard given the covariates and a random frailty unique to each individual has the proportional hazards form multiplied by the frailty. The frailty is assumed to have mean 1 within a known one-parameter family of distributions. Inference is based on a nonparametric likelihood. The behavior of the likelihood maximizer is studied under general conditions where the fitted model may be misspecified. The joint estimator of the regression and frailty parameters as well as the baseline hazard is shown to be uniformly consistent for the pseudo-value maximizing the asymptotic limit of the likelihood. Appropriately standardized, the estimator converges weakly to a Gaussian process. When the model is correctly specified, the procedure is semiparametric efficient, achieving the semiparametric information bound for all parameter components. It is also proved that the bootstrap gives valid inferences for all parameters, even under misspecification. We demonstrate analytically the importance of the robust inference in several examples. In a randomized clinical trial, a valid test of the treatment effect is possible when other prognostic factors and the frailty distribution are both misspecified. Under certain conditions on the covariates, the ratios of the regression parameters are still identifiable. The practical utility of the procedure is illustrated on a non-Hodgkin's lymphoma dataset.Comment: Published by the Institute of Mathematical Statistics (http://www.imstat.org) in the Annals of Statistics (http://www.imstat.org/aos/) at http://dx.doi.org/10.1214/00905360400000053

A Flexible Framework for Incorporating Patient Preferences Into Q-Learning

In real-world healthcare problems, there are often multiple competing outcomes of interest, such as treatment efficacy and side effect severity. However, statistical methods for estimating dynamic treatment regimes (DTRs) usually assume a single outcome of interest, and the few methods that deal with composite outcomes suffer from important limitations. This includes restrictions to a single time point and two outcomes, the inability to incorporate self-reported patient preferences and limited theoretical guarantees. To this end, we propose a new method to address these limitations, which we dub Latent Utility Q-Learning (LUQ-Learning). LUQ-Learning uses a latent model approach to naturally extend Q-learning to the composite outcome setting and adopt the ideal trade-off between outcomes to each patient. Unlike previous approaches, our framework allows for an arbitrary number of time points and outcomes, incorporates stated preferences and achieves strong asymptotic performance with realistic assumptions on the data. We conduct simulation experiments based on an ongoing trial for low back pain as well as a well-known completed trial for schizophrenia. In all experiments, our method achieves highly competitive empirical performance compared to several alternative baselines.Comment: Under Revie

Biclustering with heterogeneous variance

In cancer research, as in all of medicine, it is important to classify patients into etiologically and therapeutically relevant subtypes to improve diagnosis and treatment. One way to do this is to use clustering methods to find subgroups of homogeneous individuals based on genetic profiles together with heuristic clinical analysis. A notable drawback of existing clustering methods is that they ignore the possibility that the variance of gene expression profile measurements can be heterogeneous across subgroups, and methods that do not consider heterogeneity of variance can lead to inaccurate subgroup prediction. Research has shown that hypervariability is a common feature among cancer subtypes. In this paper, we present a statistical approach that can capture both mean and variance structure in genetic data. We demonstrate the strength of our method in both synthetic data and in two cancer data sets. In particular, our method confirms the hypervariability of methylation level in cancer patients, and it detects clearer subgroup patterns in lung cancer data

Data for cancer comparative effectiveness research: Past, present, and future potential

Comparative effectiveness research (CER) can efficiently and rapidly generate new scientific evidence and address knowledge gaps, reduce clinical uncertainty, and guide health care choices. Much of the potential in CER is driven by the application of novel methods to analyze existing data. Despite its potential, several challenges must be identified and overcome so that CER may be improved, accelerated, and expeditiously implemented into the broad spectrum of cancer care and clinical practice

A framework for understanding cancer comparative effectiveness research data needs

Randomized controlled trials remain the gold standard for evaluating cancer intervention efficacy. Randomized trials are not always feasible, practical, or timely, and often don’t adequately reflect patient heterogeneity and real-world clinical practice. Comparative effectiveness research can leverage secondary data to help fill knowledge gaps randomized trials leave unaddressed; however, comparative effectiveness research also faces shortcomings. The goal of this project was to develop a new model and inform an evolving framework articulating cancer comparative effectiveness research data needs

Skeletal Muscle–Derived Cell Implantation for the Treatment of Fecal Incontinence: A Randomized, Placebo-Controlled Study

Background and Aims: Fecal incontinence (FI) improvement following injection of autologous skeletal muscle–derived cells has been previously suggested. This study aimed to test the efficacy and safety of said cells through a multicenter, placebo-controlled study, to determine an appropriate cell dose, and to delineate the target patient population that can most benefit from cell therapy. Methods: Patients experiencing FI for at least 6 months were randomized to receive a cell-free medium or low or high dose of cells. All patients received pelvic floor electrical stimulation before and after treatment. Incontinence episode frequency (IEF), FI quality of life, FI burden assessed on a visual analog scale, Wexner score, and parameters reflecting anorectal physiological function were all assessed for up to 12 months. Results: Cell therapy improved IEF, FI quality of life, and FI burden, reaching a preset level of statistical significance in IEF change compared with the control treatment. Post hoc exploratory analyses indicated that patients with limited FI duration and high IEF at baseline are most responsive to cells. Effects prevailed or increased in the high cell count group from 6 to 12 months but plateaued or diminished in the low cell count and control groups. Most physiological parameters remained unaltered. No unexpected adverse events were observed. Conclusions: Injection of a high dose of autologous skeletal muscle–derived cells followed by electrical stimulation significantly improved FI, particularly in patients with limited FI duration and high IEF at baseline, and could become a valuable tool for treatment of FI, subject to confirmatory phase 3 trial(s). (ClinicalTrialRegister.eu; EudraCT Number: 2010-021463-32)

Detection of gene pathways with predictive power for breast cancer prognosis

<p>Abstract</p> <p>Background</p> <p>Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.</p> <p>Results</p> <p>The new method advances beyond existing alternatives along the following aspects. First, it can assess the predictive power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant predictive power for prognosis. Important pathways missed by alternative methods are identified.</p> <p>Conclusions</p> <p>The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.</p