Patterns of treatment effects in subsets of patients in clinical trials.

We discuss the practice of examining patterns of treatment effects across overlapping patient subpopulations. In particular, we focus on the case in which patient subgroups are defined to contain patients having increasingly larger (or smaller) values of one particular covariate of interest, with the intent of exploring the possible interaction between treatment effect and that covariate. We formalize these subgroup approaches (STEPP: subpopulation treatment effect pattern plots) and implement them when treatment effect is defined as the difference in survival at a fixed time point between two treatment arms.

Tailored computer-based cancer risk communication: correcting colorectal cancer risk perception.

We developed a computer-based tailored risk presentation and risk communication aid for colorectal cancer prevention. To evaluate the effectiveness of the tool, we randomized 353 participants to receive different risk presentation formats (relative plus absolute risk vs. absolute risk only vs.

Cancer prevention for working class, multi-ethnic populations through health centers: the healthy directions study.

Background: This paper presents the study design and baseline data from Healthy Directions-Health Centers (HCs), a study designed to address social contextual factors in cancer prevention interventions for working class, multi-ethnic populations. This study is part of the Harvard Cancer Prevention Program Project.

Methods: Ten community HCs were paired and randomly assigned to intervention or control.

Measuring explained variation in linear mixed effects models.

We generalize the well-known R(2) measure for linear regression to linear mixed effects models. Our work was motivated by a cluster-randomized study conducted by the Eastern Cooperative Oncology Group, to compare two different versions of informed consent document. We quantify the variation in the response that is explained by the covariates under the linear mixed model, and study three types of measures to estimate such quantities.

Estimating average regression effect under non-proportional hazards.

We present an estimator of average regression effect under a non-proportional hazards model, where the regression effect of the covariates on the log hazard ratio changes with time. In the absence of censoring, the new estimate coincides with the usual partial likelihood estimate, both estimates being consistent for a parameter having an interpretation as an average population regression effect. In the presence of an independent censorship, the new estimate is still consistent for this same population parameter, whereas the partial likelihood estimate will converge to a different quantity that depends on censoring.

Estimation with correlated censored survival data with missing covariates.

Incomplete covariate data are a common occurrence in studies in which the outcome is survival time. Further, studies in the health sciences often give rise to correlated, possibly censored, survival data. With no missing covariate data, if the marginal distributions of the correlated survival times follow a given parametric model, then the estimates using the maximum likelihood estimating equations, naively treating the correlated survival times as independent, give consistent estimates of the relative risk parameters Lipsitz et al.

Media and marijuana: A longitudinal analysis of news media effects on adolescents' marijuana use and related outcomes, 1977-1999.

This study examined how aggregate levels of news coverage about marijuana have impacted adolescents' marijuana behavior generally, and through the intervening variables of personal disapproval and perceived harmfulness of marijuana, two variables that existing research has identified as significant predictors of adolescent marijuana use at the aggregate level. It was hypothesized that news coverage of reasons why people should not use marijuana would cause increase in aggregate marijuana abstinence, perceived harmfulness, and personal disapproval. Conversely, news coverage of positive aspects of marijuana use would cause decreases in marijuana abstinence, perceived harmfulness, and personal disapproval.

A comparison of parametric versus permutation methods with applications to general and temporal microarray gene expression data.

Motivation: In analyses of microarray data with a design of different biological conditions, ranking genes by their differential 'importance' is often desired so that biologists can focus research on a small subset of genes that are most likely related to the experiment conditions. Permutation methods are often recommended and used, in place of their parametric counterparts, due to the small sample sizes of microarray experiments and possible non-normality of the data. The recommendations, however, are based on classical knowledge in the hypothesis test setting.

Survival analysis with time-varying regression effects using a tree-based approach.

Nonproportional hazards often arise in survival analysis, as is evident in the data from the International Non-Hodgkin's Lymphoma Prognostic Factors Project. A tree-based method to handle such survival data is developed for the assessment and estimation of time-dependent regression effects under a Cox-type model. The tree method approximates the time-varying regression effects as piecewise constants and is designed to estimate change points in the regression parameters.

Modeling spatial survival data using semiparametric frailty models.

We propose a new class of semiparametric frailty models for spatially correlated survival data. Specifically, we extend the ordinary frailty models by allowing random effects accommodating spatial correlations to enter into the baseline hazard function multiplicatively. We prove identifiability of the models and give sufficient regularity conditions.

A semiparametric estimate of treatment effects with censored data.

A semiparametric estimate of an average regression effect with right-censored failure time data has recently been proposed under the Cox-type model where the regression effect beta(t) is allowed to vary with time. In this article, we derive a simple algebraic relationship between this average regression effect and a measurement of group differences in k-sample transformation models when the random error belongs to the G(rho) family of Harrington and Fleming (1982, Biometrika 69, 553-566), the latter being equivalent to the conditional regression effect in a gamma frailty model. The models considered here are suitable for the attenuating hazard ratios that often arise in practice.

Bayesian semiparametric models for survival data with a cure fraction.

We propose methods for Bayesian inference for a new class of semiparametric survival models with a cure fraction. Specifically, we propose a semiparametric cure rate model with a smoothing parameter that controls the degree of parametricity in the right tail of the survival distribution. We show that such a parameter is crucial for these kinds of models and can have an impact on the posterior estimates.

Monte Carlo EM for missing covariates in parametric regression models.

We propose a method for estimating parameters for general parametric regression models with an arbitrary number of missing covariates. We allow any pattern of missing data and assume that the missing data mechanism is ignorable throughout. When the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769).

On Bayesian inference for proportional hazards models using noninformative priors.

In this article, we investigate the properties of the posterior distribution under the uniform improper prior for two commonly used proportional hazards models; the Weibull regression model and the extreme value regression model. We allow the observations to be right censored. We obtain sufficient conditions for the existence of the posterior moment generating function of the regression coefficients.

A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data.

We introduce the subpopulation treatment effect pattern plot (STEPP) method, designed to facilitate the interpretation of estimates of treatment effect derived from different but potentially overlapping subsets of clinical trial data. In particular, we consider sequences of subpopulations defined with respect to a covariate, and obtain confidence bands for the collection of treatment effects (here obtained from the Cox proportional hazards model) associated with the sequences. The method is aimed at determining whether the magnitude of the treatment effect changes as a function of the values of the covariate.

Inference using conditional logistic regression with missing covariates.

When there are many nuisance parameters in a logistic regression model, a popular method for eliminating these nuisance parameters is conditional logistic regression. Unfortunately, another common problem in a logistic regression analysis is missing covariate data. With many nuisance parameters to eliminate and missing covariates, many investigators exclude any subject with missing covariates and then use conditional logistic regression, often called a complete-case analysis.

A jackknife estimator of variance for Cox regression for correlated survival data.

Studies in the health sciences often give rise to correlated survival data. Wei, Lin, and Weissfeld (1989, Journal of the American Statistical Association 84, 1065-1073) and Lee, Wei, and Amato (1992, in Survival Analysis: State of the Art) showed that, if the marginal distributions of the correlated survival times follow a proportional hazards model, then the estimates from Cox's partial likelihood (Cox, D.R.