Jong S. Kim

Nomogram for Predicting the Benefit of Adjuvant Chemoradiotherapy for Resected Gallbladder Cancer

PURPOSE Although adjuvant chemoradiotherapy for resected gallbladder cancer may improve survival for some patients, identifying which patients will benefit remains challenging because of the rarity of this disease. The specific aim of this study was to create a decision aid to help make individualized estimates of the potential survival benefit of adjuvant chemoradiotherapy for patients with resected gallbladder cancer. METHODS Patients with resected gallbladder cancer were selected from the Surveillance, Epidemiology, and End Results (SEER) -Medicare database who were diagnosed between 1995 and 2005. Covariates included age, race, sex, stage, and receipt of adjuvant chemotherapy or chemoradiotherapy (CRT). Propensity score weighting was used to balance covariates between treated and untreated groups. Several types of multivariate survival regression models were constructed and compared, including Cox proportional hazards, Weibull, exponential, log-logistic, and lognormal models. Model performance was compared using the Akaike information criterion. The primary end point was overall survival with or without adjuvant chemotherapy or CRT. RESULTS A total of 1,137 patients met the inclusion criteria for the study. The lognormal survival model showed the best performance. A Web browser-based nomogram was built from this model to make individualized estimates of survival. The model predicts that certain subsets of patients with at least T2 or N1 disease will gain a survival benefit from adjuvant CRT, and the magnitude of benefit for an individual patient can vary. CONCLUSION A nomogram built from a parametric survival model from the SEER-Medicare database can be used as a decision aid to predict which gallbladder patients may benefit from adjuvant CRT.

Prediction Model for Estimating the Survival Benefit of Adjuvant Radiotherapy for Gallbladder Cancer

PURPOSE The benefit of adjuvant radiotherapy (RT) for gallbladder cancer remains controversial because most published data are from small, single-institution studies. The purpose of this study was to construct a survival prediction model to enable individualized predictions of the net survival benefit of adjuvant RT for gallbladder cancer patients based on specific tumor and patient characteristics. METHODS A multivariate Cox proportional hazards model was constructed using data from 4,180 patients with resected gallbladder cancer diagnosed from 1988 to 2003 from the Surveillance, Epidemiology, and End Results database. Patient and tumor characteristics were included as covariates and assessed for association with overall survival (OS) with and without adjuvant RT. The model was internally validated for discrimination and calibration using bootstrap resampling. RESULTS On multivariate regression analysis, the model showed that age, sex, papillary histology, stage, and adjuvant RT were significant predictors of OS. The survival prediction model demonstrated good calibration and discrimination, with a bootstrap-corrected concordance index of 0.71. The model predicts that adjuvant RT provides a survival benefit in node-positive or >or= T2 disease. A nomogram and a browser-based software tool were built from the model that can calculate individualized estimates of predicted net survival gain attributable to adjuvant RT, given specific input parameters. CONCLUSION In the absence of large, prospective, randomized, clinical trial data, a regression model can be used to make individualized predictions of the expected survival improvement from the addition of adjuvant RT after gallbladder cancer resection.

Conditional survival in gastric cancer: a SEER database analysis

BackgroundGastric cancer survival is typically reported in terms of survival from the time of diagnosis. Conditional survival is a more relevant measure of prognosis for patients who have already survived 1 or more years since diagnosis.MethodsUsing the Surveillance, Epidemiology, and End Results (SEER 17) database from the National Cancer Institute, we analyzed data from 20 018 gastric cancer patients diagnosed between 1988 and 1998. Using the life-table method, we computed 5-year relative conditional survival, grouped by summary stage, age, sex, and ethnicity, for patients who had already survived up to 5 years from diagnosis.ResultsRelative conditional survival improves over time for all groups of gastric cancer patients who survive a period of time after diagnosis. The largest gains in conditional survival were seen in patients with advanced stage disease. In general, females showed better survival than males. When grouped by ethnicity, Asians continued to have improved survival compared to other ethnic categories, even at 5 years out from diagnosis.ConclusionFor gastric cancer patients who survive a period of time after diagnosis, the largest increases in conditional survival are seen for patients with advanced stage disease and for those less than 65 years old. Conditional survival can provide more relevant prognostic information than survival from the time of diagnosis for gastric cancer patients who survive a period of time after diagnosis.

Maximum likelihood estimation for the proportional hazards model with partly interval-censored data

Summary. The maximum likelihood estimator (MLE) for the proportional hazards model with partly interval‐censored data is studied. Under appropriate regularity conditions, the MLEs of the regression parameter and the cumulative hazard function are shown to be consistent and asymptotically normal. Two methods to estimate the variance–covariance matrix of the MLE of the regression parameter are considered, based on a generalized missing information principle and on a generalized profile information procedure. Simulation studies show that both methods work well in terms of the bias and variance for samples of moderate size. An example illustrates the methods.

Generalized Log-Rank Tests for Partly Interval-Censored Failure Time Data

In this paper, we consider incomplete survival data: partly interval-censored failure time data where observed data include both exact and interval-censored observations on the survival time of interest. We present a class of generalized log-rank tests for this type of survival data and establish their asymptotic properties. The method is evaluated using simulation studies and illustrated by a set of real data from a diabetes study.

Residential trajectories of participants in North Carolina's Willie-M. Program : A semi-parametric group based model

Abstract A semi-parametric mixture model was fit using data on 611 children with serious emotional disturbance who participated in North Carolinas Willie-M. Program from 1995 to 2000 to identify patterns of residential restrictiveness over time. Results revealed 4 distinct restrictiveness trajectories: low/stable, high/stable, increasing, and decreasing. Correlates of trajectory group membership included age, IQ, initial behavior, and region of the state. Number of diagnoses and change in behavior did not predict group membership. Interpretation of results is guided by a consideration of the goal of wraparound systems of care to provide services and supports in community based and normalizing settings.

Quantification and consequences of lung cancer CT overdiagnosis

The term “overdiagnosis” designates screen-identification of ancers which would otherwise have remained inevident within he individual’s lifetime. The authors of the current U.S. Prevenive Services Task Force guideline cite a CT-lung cancer screening verdiagnosis estimate of 10–12% (vs. CXR-screened controls) [1]. he chest radiographic (CXR) screening estimate, based on the diferential case number identified by CXR vs. null screening is 25% 2]. As many of the CXR trial control cases, considered as clinially relevant, were equally susceptible to overdiagnosis because hey were identified by an incidental or a trial-prescribed CXR, 25% ay be considered a conservative estimate. The Task Force did not etail their estimate methodology, but the 10–12% range brackets he 11% differential in the number of cases identified in the 6.5ear National Lung Screening Trial (NLST) in the CT screened (1060) s. the CXR screened control group (941) [3]. Net overdiagnosis is he sum of CT (vs. CXR) overdiagnosis, 11% + CXR (vs. null screen) verdiagnosis, 25% = 36%. Because this range excludes control group

The National Lung Screening Trial Premise of Null and Chest Radiography Equivalence Is Open to Question

OBJECTIVE Lung cancer screening guidelines are based on the National Lung Screening Trial (NLST) that used chest radiographic control subjects on the premise of the reported mortality equivalence in chest radiography versus unscreened persons in the NLST-eligible subgroup of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. The purpose of this article is to discuss concerns regarding the validity of the NLST premise of chest radiography and null screening equivalence. CONCLUSION Anomalous findings combined with the failure of CT trials using unscreened control subjects to replicate the benefits of the NLST open to question the validity of this premise.

Nomogram for Predicting Overall Survival and Salvage Abdominoperineal Resection for Patients with Anal Cancer.

BACKGROUND: Anal cancer treatment has evolved from abdominoperineal resection to chemoradiotherapy, which allows for sphincter preservation. OBJECTIVE: The aim of this study was to develop an accurate model and nomogram to predict overall survival and the probability of salvage abdominoperineal resection for anal cancer patients. DESIGN: This is a retrospective cohort study. SETTINGS: Data were gathered from National Cancer Database entries from 1998 to 2010. PATIENTS: Patients with de novo anal cancer were selected from the National Cancer Database in the years 1998 through 2010; 1778 patients were included, and their data were analyzed. MAIN OUTCOME MEASURES: Variables included time to death, censoring indicator, age, race, sex, tumor size, year of diagnosis, surgery status, nodal status, TNM stage, and chemoradiation therapy. A stratified Cox proportional hazards model for overall survival and a logistic regression model for salvage abdominoperineal resection were developed. Our final models were internally validated for discrimination and validation. RESULTS: Statistically significant variables in the salvage surgery model were tumor size and nodal status (p ⩽ 0.001). For overall survival model, statistically significant variables (all with p ⩽ 0.005), fitted across the strata of TNM clinical stage included age, sex, tumor size, nodal status, chemoradiotherapy treatment, and combination salvage surgery and chemoradiotherapy. Nomograms that predict events are based on our final models. LIMITATIONS: Limitations included clerical database errors and nonmeasured variables, such as HIV status. CONCLUSIONS: A nomogram can predict overall survival and salvage surgery for an individual with anal cancer. Such tools may be used as decision support aids to guide therapy and predict whether or not patients may need salvage surgery.

Diminished Disease-Free Survival After Lobectomy: Screening Implications

BACKGROUND The aim of this study was to estimate the effect of lobectomy on life expectancy in healthy smokers and consider the implications for lung cancer screening. MATERIALS AND METHODS In a retrospective cohort study that provided a minimum of 15 years of follow-up, we analyzed lung cancer survival, all-cause survival, and fatality (1-survival) of 261 persons with stage I non-small-cell lung cancer who underwent lobectomy at Portland Providence Medical Center between 1978 and 1994. We: (1) compared 5-year disease-free fatality (non-lung-cancer fatality) with lung cancer fatality; and (2) based on actuarial data that demonstrated life expectancy equivalence of the healthiest smokers (whom we assumed would be comparable with subjects judged eligible for lobectomy) in the US population, we compared their long-term, disease-free survival (our primary end point) with actuarial expectations by computing the Kaplan-Meier survival function of the differences between lifetimes since surgery in disease-free persons versus matched, expected remaining lifetimes in the US population. RESULTS (1) Five-year disease-free fatality (16.1%) was 58% as high as 5-year lung cancer fatality (27.6%); (2) disease-free survival was reduced by 6.9-years (95% confidence interval, 5.5-8.3), 41% of actuarial life expectancy (17 years). The divergence from expected survival took place largely after 6 years of follow-up. CONCLUSION Lobectomy materially diminishes long-term disease-free survival in the healthiest smokers--persons judged healthy enough to tolerate major surgery and to have sufficient pulmonary reserve to sustain loss of one-fifth of their lung tissue. In screened populations, diminished survival in overdiagnosed persons will offset, to an undetermined extent, the mortality benefit imparted by preemption of advanced lung cancer.