Biomedical subjects

Jiameng Zhang

Publications and source records attributed to Jiameng Zhang.

4 recordsLinked to original sources

PubMed2007

Impact of nonignorable coarsening on Bayesian inference.

The coarse data model of Heitjan and Rubin (1991) generalizes the missing data model of Rubin (1976) to cover other forms of incompleteness such as censoring and grouping. The model has 2 components: an ideal data model describing the distribution of the quantity of interest and a coarsening mechanism that describes a distribution over degrees of coarsening given the ideal data. The coarsening mechanism is said to be nonignorable when the degree of coarsening depends on an incompletely observed ideal outcome, in which case failure to properly account for it can spoil inferences. A theme in recent research is to measure sensitivity to nonignorability by evaluating the effect of a small departure from ignorability on the maximum likelihood estimate (MLE) of a parameter of the ideal data model. One such construct is the "index of local sensitivity to nonignorability" (ISNI) (Troxel and others, 2004), which is the derivative of the MLE with respect to a nonignorability parameter evaluated at the ignorable model. In this paper, we adapt ISNI to Bayesian modeling by instead defining it as the derivative of the posterior expectation. We propose the application of ISNI as a first step in judging the robustness of a Bayesian analysis to nonignorable coarsening. We derive formulas for a range of models and apply the method to evaluate sensitivity to nonignorable coarsening in 2 real data examples, one involving missing CD4 counts in an HIV trial and the other involving potentially informatively censored relapse times in a leukemia trial.

Bayes Theorem↗

PubMed2006

A simple local sensitivity analysis tool for nonignorable coarsening: application to dependent censoring.

Right- and interval-censored data are common special cases of coarsened data (Heitjan and Rubin, 1991, Annals of Statistics19, 2244-2253). As with missing data, standard statistical methods that ignore the random nature of the coarsening mechanism may lead to incorrect inferences. We extend a simple sensitivity analysis tool, the index of local sensitivity to nonignorability (Troxel, Ma, and Heitjan, 2004, Statistica Sinica14, 1221-1237), to the evaluation of nonignorability of the coarsening process in the general coarse-data model. By converting this index into a simple graphical display one can easily assess the sensitivity of key inferences to nonignorable coarsening. We illustrate the validity of the method with a simulated example, and apply it to right-censored data from an observational study of cardiac transplantation and to interval-censored data on time to detectable viral load from a clinical trial in HIV disease.

Biometry↗

PubMed2005

A population-based study of lung carcinoma in Pennsylvania: comparison of Veterans Administration and civilian populations.

BACKGROUND: Lung carcinoma remains the major cause of cancer death in North America and is even more common among military veterans. The objective of this study was to determine whether there were differences in the characteristics and survival of Pennsylvania patients with lung carcinoma in the Veterans Administration (VA) hospital system compared with patients in the rest of the state. METHODS: The Pennsylvania Cancer Registry was used to identify all patients who were diagnosed with lung carcinoma in the State of Pennsylvania from 1995 to 1999. Patients who were treated within the Veterans Administration Health Care Network were identified by hospital code. Survival from the date of diagnosis of lung carcinoma was determined by using the Pennsylvania state mortality files from 1995 to 2001. RESULTS: From 1995 to 1999, 48,994 patients were newly diagnosed with lung carcinoma in Pennsylvania (41.2% women), including 856 patients in the VA system (6 women). The current analysis was restricted to male patients (n = 28,798 men). There was no major difference in age of VA patients compared with non-VA patients, and the proportions of patients who had localized or regional stage disease were similar (49% of VA patients vs. 48% of non-VA patients). The proportion of black patients was much higher in the VA population (23%) compared with the non-VA population (9%). The median survival was 6.3 months for VA patients compared with 7.9 months for patients in the rest of the state, and the 5-year overall survival rate was 12% for VA patients compared with 15% for patients in the rest of the state. When survival was analyzed according to race, there was a significant difference in the age-adjusted survival of white patients in the VA system compared with patients in the rest of the state (P = 0.0007), but no significant difference was observed among black patients (P = 0.92). CONCLUSIONS: The overall survival of VA patients with lung carcinoma in Pennsylvania was inferior to that of patients in the remainder of the state and this was due primarily to differences in survival among the white patients. Further investigation will be necessary to determine whether this disparity was caused by differences in socioeconomic status or comorbidities or whether there are systematic differences in the diagnosis, staging, or treatment of lung carcinoma between VA patients and civilian patients.

Aged↗

PubMed2005

Nonignorable censoring in randomized clinical trials.

BACKGROUND: In a clinical trial, survival may be censored by the end of the study, especially for subjects who enter later in the enrollment period. If there is a trend toward better survival over time then longer survivors experience shorter censoring times (heavier censoring). In such a case, the censoring and survival times are correlated, and thus the censoring is nonignorable in the sense that standard survival models that assume independent censoring could yield incorrect inferences. We will demonstrate a graphical method for analyzing sensitivity of estimates of survival model parameters to small departures from nonignorable censoring. METHODS: We assume a parametric model of survival together with a scaled beta model for the censoring process that incorporates the dependence of censoring time on survival time. We assess sensitivity using an index of local sensitivity to nonignorability (Troxel et al.). High sensitivity indicates a large impact of nonignorable censoring on the parameter of interest and a need for additional modeling. RESULTS: A simulation study shows that the approach is valid for practical use. We apply our method to a clinical trial evaluating the survival benefit of a surgically implanted left ventricular assist device in subjects with end-stage heart failure. Sensitivity is somewhat larger in estimates of the mean survival in the device arm, where survival is better and the fraction censored is therefore larger. The degree of nonignorability required to substantially affect estimates is larger than seems plausible, however. CONCLUSIONS: Our results illustrate how one can apply sensitivity analysis to evaluate the reliability of survival parameter estimates in a clinical trial.

Clinical Trials as Topic↗