Lu Tian

SummarySuppose that we are interested in using new bio‐ or clinical markers, in addition to the conventional markers, to improve prediction or diagnosis of the patient's clinical outcome. The incremental value from the new markers is typically assessed by averaging across patients in the entire study population. However, when measuring the new markers is costly or invasive, an overall improvement does not justify measuring the new markers in all patients. A more practical strategy is to utilize the patient's conventional markers to decide whether the new markers are needed for improving prediction of his/her health outcomes. In this article, we propose inference procedures for the incremental values of new markers across various subgroups of patients classified by the conventional markers. The resulting point and interval estimates can be quite useful for medical decision makers seeking to balance the predictive or diagnostic value of new markers against their associated cost and risk. Our proposals are theoretically justified and illustrated empirically with two real examples.

SummaryIn the presence of high‐dimensional predictors, it is challenging to develop reliable regression models that can be used to accurately predict future outcomes. Further complications arise when the outcome of interest is an event time, which is often not fully observed due to censoring. In this article, we develop robust prediction models for event time outcomes by regularizing the Gehan's estimator for the accelerated failure time (AFT) model (Tsiatis, 1996,Annals of Statistics18, 305–328) with least absolute shrinkage and selection operator (LASSO) penalty. Unlike existing methods based on the inverse probability weighting and the Buckley and James estimator (Buckley and James, 1979,Biometrika66, 429–436), the proposed approach does not require additional assumptions about the censoring and always yields a solution that is convergent. Furthermore, the proposed estimator leads to a stable regression model for prediction even if the AFT model fails to hold. To facilitate the adaptive selection of the tuning parameter, we detail an efficient numerical algorithm for obtaining the entire regularization path. The proposed procedures are applied to a breast cancer dataset to derive a reliable regression model for predicting patient survival based on a set of clinical prognostic factors and gene signatures. Finite sample performances of the procedures are evaluated through a simulation study.

Recently, meta-analysis has been widely utilized to combine information across comparative clinical studies for evaluating drug efficacy or safety profile. When dealing with rather rare events, a substantial proportion of studies may not have any events of interest. Conventional methods either exclude such studies or add an arbitrary positive value to each cell of the corresponding 2×2 tables in the analysis. In this article, we present a simple, effective procedure to make valid inferences about the parameter of interest with all available data without artificial continuity corrections. We then use the procedure to analyze the data from 48 comparative trials involving rosiglitazone with respect to its possible cardiovascular toxicity.

Under a general regression setting, we propose an optimal unconditional prediction procedure for future responses. The resulting prediction intervals or regions have a desirable average coverage level over a set of covariate vectors of interest. When the working model is not correctly specified, the traditional conditional prediction method is generally invalid. On the other hand, one can empirically calibrate the above unconditional procedure and also obtain its crossvalidated counterpart. Various large and small sample properties of these unconditional methods are examined analytically and numerically. We find that the 𝒦-fold crossvalidated procedure performs exceptionally well even for cases with rather small sample sizes. The new proposals are illustrated with two real examples, one with a continuous response and the other with a binary outcome.

This paper introduces a novel approach to making inference about the regression parameters in the accelerated failure time model for current status and interval censored data. The estimator is constructed by inverting a Wald-type test for testing a null proportional hazards model. A numerically efficient Markov chain Monte Carlo based resampling method is proposed for obtaining simultaneously the point estimator and a consistent estimator of its variance-covariance matrix. We illustrate our approach with interval censored datasets from two clinical studies. Extensive numerical studies are conducted to evaluate the finite-sample performance of the new estimators.

We applaud Drs. Spiegelman and Hertzmark's idea of using SAS procedure PROC GENMOD to estimate the risk ratio or difference (1). However, we have reservations about 1) the claim that there is no good justification for fitting the logistic regression and estimating the odds ratio when the odds ratio is not a good approximation of the risk ratio, and 2) using Poisson regression (PROC GENMOD) to estimate the risk ratio when the log-binomial model fails to converge.

SummaryWe develop methods for assessing the association between a binary time‐dependent covariate process and a failure time endpoint when the former is observed only at a single time point and the latter is right censored, and when the observations are subject to truncation and competing causes of failure. Using a proportional hazards model for the effect of the covariate process on the failure time of interest, we develop an approach utilizing EM algorithm and profile likelihood for estimating the relative risk parameter and cause‐specific hazards for failure. The methods are extended to account for other covariates that can influence the time‐dependent covariate process and cause‐specific risks of failure. We illustrate the methods with data from a recent study on the association between loss of hepatitis B e antigen and the development of hepatocellular carcinoma in a population of chronic carriers of hepatitis B.