In vivo CT dosimetry during CT colonography.

OBJECTIVE: The purpose of this study was to develop a method of measuring rectal radiation dose in vivo during CT colonography (CTC) and assess the accuracy of size-specific dose estimates (SSDEs) relative to that of in vivo dose measurements. MATERIALS AND METHODS: Thermoluminescent dosimeter capsules were attached to a CTC rectal catheter to obtain four measurements of the CT radiation dose in 10 volunteers (five men and five women; age range, 23-87 years; mean age, 70.4 years). A fixed CT technique (supine and prone, 50 mAs and 120 kVp each) was used for CTC. SSDEs and percentile body habitus measurements were based on CT images and directly compared with in vivo dose measurements. RESULTS: The mean absorbed doses delivered to the rectum ranged from 8.8 to 23.6 mGy in the 10 patients, whose mean body habitus was in the 27th percentile among American adults 18-64 years old (range, 0.5-67th percentile). The mean SSDE error was 7.2% (range, 0.6-31.4%). CONCLUSION: This in vivo radiation dose measurement technique can be applied to patients undergoing CTC. Our measurements indicate that SSDEs are reasonable estimates of the rectal absorbed dose. The data obtained in this pilot study can be used as benchmarks for assessing dose estimates using other indirect methods (e.g., Monte Carlo simulations).

Prognostic value of patient-reported symptom interference in patients with late-stage lung cancer.

PURPOSE: Patient-reported outcomes (PROs) have been found to be significant predictors of clinical outcomes such as overall survival (OS), but the effect of demographic and clinical factors on the prognostic ability of PROs is less understood. Several PROs derived from the 12-item Short-Form Health Survey (SF-12) and M. D. Anderson Symptom Inventory (MDASI) were investigated for association with OS, with adjustments for other factors, including performance status. METHODS: A retrospective analysis was performed on data from 90 patients with stage IV non-small cell lung cancer. Several baseline PROs were added to a base Cox proportional hazards model to examine the marginal significance and improvement in model fit attributable to the PRO: mean MDASI symptom interference level; mean MDASI symptom severity level for five selected symptoms; SF-12 physical and mental component summaries; and the SF-12 general health item. Bootstrap resampling was used to assess the robustness of the findings. RESULTS: The MDASI mean interference level had a significant effect on OS (p = 0.007) when the model was not adjusted for interactions with other prognostic factors. Further exploration suggested the significance was due to an interaction with performance status (p = 0.001). The MDASI mean symptom severity level and the SF-12 physical component summary, mental component summary, and general health item did not have a significant effect on OS. CONCLUSIONS: Symptom interference adds prognostic information for OS in advanced lung cancer patients with poor performance status, even when demographic and clinical prognostic factors are accounted for.

Goodness-of-fit diagnostics for Bayesian hierarchical models.

This article proposes methodology for assessing goodness of fit in Bayesian hierarchical models. The methodology is based on comparing values of pivotal discrepancy measures (PDMs), computed using parameter values drawn from the posterior distribution, to known reference distributions. Because the resulting diagnostics can be calculated from standard output of Markov chain Monte Carlo algorithms, their computational costs are minimal. Several simulation studies are provided, each of which suggests that diagnostics based on PDMs have higher statistical power than comparable posterior-predictive diagnostic checks in detecting model departures. The proposed methodology is illustrated in a clinical application; an application to discrete data is described in supplementary material.

Perfusion CT assessment of tissue hemodynamics following hepatic arterial infusion of increasing doses of angiotensin II in a rabbit liver tumor model.

PURPOSE: To investigate the effects of increasing doses of angiotensin II on hepatic hemodynamics in the normal rabbit liver and in hepatic VX2 tumors by using dynamic contrast material-enhanced perfusion computed tomography (CT). MATERIALS AND METHODS: This study was approved by the institutional animal care and use committee. Solitary hepatic VX2 tumors were implanted into 12 rabbits. In each animal, perfusion CT of the liver was performed before (at baseline) and after hepatic arterial infusion of varying doses (0.1-50.0 µg/mL) of angiotensin II. Images were acquired continuously for 80 seconds after the start of the intravenous contrast material administration. Blood flow (BF), blood volume (BV), mean transit time (MTT), and capillary permeability-surface area product were calculated for the tumor and the adjacent and distant normal liver tissue. Generalized linear mixed models were used to estimate the effects of angiotensin II dose on outcome measures. RESULTS: Angiotensin II infusion increased contrast enhancement of the tumor and distal liver vessels. Tumor BF increased in a dose-dependent manner after administration of 0.5-25.0 µg/mL angiotensin II, but only the 2.5 µg/mL dose induced a significant increase in tumor BF compared with BF in the adjacent (68.0 vs 26.3 mL/min/100 g, P < .0001) and distant (68.0 vs 28.3 mL/min/100 g, P = .02) normal liver tissue. Tumor BV varied with angiotensin II dose but was greater than the BV of the adjacent and distant liver tissue at only the 2.5 µg/mL (4.8 vs 3.5 mL/100 g for adjacent liver [P < .0001], 4.8 vs 3.3 mL/100 g for distant liver [P = .0006]) and 10.0 µg/mL (4.9 vs 4.4 mL/100 g for adjacent liver [P = .007], 4.9 vs 4.3 mL/100 g for distant liver [P = .04]) doses. Tumor MTT was significantly shorter than the adjacent liver tissue MTT at angiotensin II doses of 2.5 µg/mL (9.7 vs 15.8 sec, P = .001) and 10.0 µg/mL (5.1 vs 13.2 sec, P = .007) and significantly shorter than the distant liver tissue MTT at 2.5 µg/mL only (9.7 vs 15.3 sec, P = .0006). The capillary permeability-surface area product for the tumor was higher than that for the adjacent liver tissue at the 2.5 µg/mL angiotensin II dose only (11.5 vs 8.1 mL/min/100 g, P = .01). CONCLUSION: Perfusion CT enables a mechanistic understanding of angiotensin II infusion in the liver and derivation of the optimal effective dose. The 2.5 µg/mL angiotensin II dose increases perfusion in hepatic VX2 tumors versus that in adjacent and distant normal liver tissue primarily by constricting normal distal liver vessels and in turn increasing tumor BF and BV.

A Bayesian chi-squared goodness-of-fit test for censored data models.

We propose a Bayesian chi-squared model diagnostic for analysis of data subject to censoring. The test statistic has the form of Pearson's chi-squared test statistic and is easy to calculate from standard output of Markov chain Monte Carlo algorithms. The key innovation of this diagnostic is that it is based only on observed failure times. Because it does not rely on the imputation of failure times for observations that have been censored, we show that under heavy censoring it can have higher power for detecting model departures than a comparable test based on the complete data. In a simulation study, we show that tests based on this diagnostic exhibit comparable power and better nominal Type I error rates than a commonly used alternative test proposed by Akritas (1988, Journal of the American Statistical Association 83, 222-230). An important advantage of the proposed diagnostic is that it can be applied to a broad class of censored data models, including generalized linear models and other models with nonidentically distributed and nonadditive error structures. We illustrate the proposed model diagnostic for testing the adequacy of two parametric survival models for Space Shuttle main engine failures.

Statistical analysis of the National Institutes of Health peer review system.

A statistical model is proposed for the analysis of peer-review ratings of R01 grant applications submitted to the National Institutes of Health. Innovations of this model include parameters that reflect differences in reviewer scoring patterns, a mechanism to account for the transfer of information from an application's preliminary ratings and group discussion to final ratings provided by all panel members and posterior estimates of the uncertainty associated with proposal ratings. Application of this model to recent R01 rating data suggests that statistical adjustments to panel rating data would lead to a 25% change in the pool of funded proposals. Viewed more broadly, the methodology proposed in this article provides a general framework for the analysis of data collected interactively from expert panels through the use of the Delphi method and related procedures.