Modeling longitudinal data with ordinal response by varying coefficients

This paper presents a smooth regression model for ordinal data with longitudinal dependence structure. A marginal model with cumulative logit link is applied to cope with the ordinal scale and the main and covariate effects in the model are allowed to vary with time. Local fitting is pursued and asymptotic properties of the estimates are discussed. In a second step, the longitudinal dependence of the observations is considered. Cumulative log odds ratios are fitted locally, which allows investigation of how the longitudinal dependence of the ordinal observations changes with time.     

The right to withdraw from controlled human infection studies: Justifications and avoidance

The right to withdraw from research without penalty is well established around the world. However, it has been challenged in some corners of bioethics based on concerns about various harms—to participants, to scientific integrity, and to research bystanders—that may stem from withdrawal. These concerns have become particularly salient in emerging debates about the ethics of controlled human infection (CHI) studies in which participants are intentionally infected with pathogens, often in inpatient settings with extensive follow-up. In this article, I provide support for preserving the right to withdraw from research without penalty and demonstrate that it is also typically justified in the specific context of CHI studies. The right is well aligned with individual freedoms outside the research setting, where autonomous individuals are permitted to engage in behaviors that will foreseeably cause them harm; where they cannot be compelled to satisfy contracts for their services, nor penalized for failure to do so; and where their behavior is not constrained by public health authorities except in extreme circumstances. These freedoms are supported by U.S. law, as well as by ethical analysis that is more globally relevant. The problems associated with the right to withdraw, however, remain. The best approach to addressing them is not to restrict the right but rather to avoid initiating research when withdrawal would be especially problematic. If research proceeds, steps can still be taken to minimize participant withdrawal without infringing the right. Investigators can avoid participant surprise through informed consent focused on a study’s most burdensome aspects and promote study completion through financial incentives. Should participants nonetheless seek to withdraw, investigators may attempt to persuade them not to do so by encouraging consideration of the range of potential harms that may result. Researchers conducting CHI studies and other research from which withdrawal might be especially problematic should prepare for the possibility of participant withdrawal, respect participant requests to withdraw without penalty, and incorporate various measures to avoid such requests.     

Modeling and testing treated tumor growth using cubic smoothing splines

Human tumor xenograft models are often used in preclinical study to evaluate the therapeutic efficacy of a certain compound or a combination of certain compounds. In a typical human tumor xenograft model, human carcinoma cells are implanted to subjects such as severe combined immunodeficient (SCID) mice. Treatment with test compounds is initiated after tumor nodule has appeared, and continued for a certain time period. Tumor volumes are measured over the duration of the experiment. It is well known that untreated tumor growth may follow certain patterns, which can be described by certain mathematical models. However, the growth patterns of the treated tumors with multiple treatment episodes are quite complex, and the usage of parametric models is limited. We propose using cubic smoothing splines to describe tumor growth for each treatment group and for each subject, respectively. The proposed smoothing splines are quite flexible in modeling different growth patterns. In addition, using this procedure, we can obtain tumor growth and growth rate over time for each treatment group and for each subject, and examine whether tumor growth follows certain growth pattern. To examine the overall treatment effect and group differences, the scaled chi-squared test statistics based on the fitted group-level growth curves are proposed. A case study is provided to illustrate the application of this method, and simulations are carried out to examine the performances of the scaled chi-squared tests.     

Smoothing spline-based score tests for proportional hazards models

We propose "score-type" tests for the proportional hazards assumption and for covariate effects in the Cox model using the natural smoothing spline representation of the corresponding nonparametric functions of time or covariate. The tests are based on the penalized partial likelihood and are derived by viewing the inverse of the smoothing parameter as a variance component and testing an equivalent null hypothesis that the variance component is zero. We show that the tests have a size close to the nominal level and good power against general alternatives, and we apply them to data from a cancer clinical trial.     

Semiparametric frailty models for clustered failure time data

We consider frailty models with additive semiparametric covariate effects for clustered failure time data. We propose a doubly penalized partial likelihood (DPPL) procedure to estimate the nonparametric functions using smoothing splines. We show that the DPPL estimators could be obtained from fitting an augmented working frailty model with parametric covariate effects, whereas the nonparametric functions being estimated as linear combinations of fixed and random effects, and the smoothing parameters being estimated as extra variance components. This approach allows us to conveniently estimate all model components within a unified frailty model framework. We evaluate the finite sample performance of the proposed method via a simulation study, and apply the method to analyze data from a study of sexually transmitted infections (STI).