Integrating the Patient Perspective to Validate a Measure of Disease Severity in Inflammatory Bowel Disease: Online Survey of Patients and Their Physicians.

BACKGROUND: The patient perspective is essential for assessing disease severity, but it is not always adequately considered. We describe how a comprehensive clinical disease severity index (DSI) for inflammatory bowel disease (IBD) correlates with patient global self-assessment (PGSA). METHODS: In an individually linked parallel online survey, physicians provided the DSI, and patients provided self-assessed severity using a global question and visual analog scale (0-100) (PGSA). Mean DSI values by PGSA were calculated with 95% confidence intervals. Pearson correlation (r) and the intraclass correlation coefficient were calculated for PGSA vs DSI. Positive predictive values for identifying severe disease with PGSA categories as a reference were based on a threshold >22 points. RESULTS: The primary analysis included 89 pairs (46 Crohn's disease [CD], 43 ulcerative colitis [UC]) with strict criteria and 147 pairs when less stringent. Common reasons for exclusion were missing values for albumin or colonoscopy. Mean DSI values showed no clear trend with increasing PGSA in CD but good discrimination between moderate, severe, and very severe PGSA in UC. For PGSA on the visual analog scale, r was 0.54 for CD and 0.59 for UC (difference in means: CD 27.7, UC 13.8; intraclass correlation coefficient: CD 0.48, UC 0.58). A high DSI predicted severe disease in 76.2% of CD and 65.2% of UC. CONCLUSIONS: The DSI showed good discrimination for patient-reported disease severity in UC but performed unsatisfactorily in CD. Correlations were moderate. Further refinement of the DSI is suggested to better reflect the patient perspective.

The performance of an inflammatory bowel disease severity score was compared with self-perceived severity based on an individually linked online survey of patients and their physicians. Agreement and prediction of severe disease were moderate and should be improved by integrating the patients' perspective.

Flexible semiparametric mode regression for time-to-event data.

The distribution of time-to-event outcomes is usually right-skewed. While for symmetric and moderately skewed data the mean and median are appropriate location measures, the mode is preferable for heavily skewed data as it better represents the center of the distribution. Mode regression has been introduced for uncensored data to model the relationship between covariates and the mode of the outcome. Starting from nonparametric kernel density based mode regression, we examine the use of inverse probability of censoring weights to extend mode regression to handle right-censored data. We add a semiparametric predictor to add further flexibility to the model and we construct a pseudo Akaike's information criterion to select the bandwidth and smoothing parameters. We use simulations to evaluate the performance of our proposed approach. We demonstrate the benefit of adding mode regression to one's toolbox for analyzing survival data on a pancreatic cancer data set from a prospectively maintained cancer registry.

Weighted expectile regression for right-censored data.

Expectile regression can be used to analyze the entire conditional distribution of a response, omitting all distributional assumptions. Among its benefits are computational simplicity, efficiency, and the possibility to incorporate a semiparametric predictor. Due to its advantages in full data settings, we propose an extension to right-censored data situations, where conventional methods typically focus only on mean effects. We propose to extend expectile regression with inverse probability weights. Estimates are easy to implement and computationally simple. Expectiles can be converted to more easily interpreted tail expectations, that is, the expected residual life. It provides a meaningful effect measure, similar to the hazard rate. The results from an extensive simulation study are presented, evaluating consistency and sensitivity to violations of assumptions. We use the proposed method to analyze survival times of colorectal cancer patients from a regional certified high volume cancer center.

TP53 co-mutations in EGFR mutated patients in NSCLC stage IV: A strong predictive factor of ORR, PFS and OS in EGFR mt+ NSCLC.

INTRODUCTION: The impact of TP53 co-mutations in EGFR mutated patients on PFS and OS is controversial. Different classifications of TP53 mutations with respect to functional and potential clinical impact have been published. Therefore, we retrospectively analyzed the impact of TP53 co-mutations on ORR, PFS and OS in a cohort of EGFR mutated NSCLC IV patients (UICC 7) using different classifications of TP53 mutations. METHODS: 75 EGFR mutated NSCLC IV patients homogeneously treated with 1st line EGFR TKI were analyzed for TP53 co-mutations. TP53 mutations were classified according to three different types of classifications. The endpoints ORR, PFS and OS were investigated. RESULTS: TP53 co-mutations were found in 29/59 patients (49.2%). TP53 co-mutations were a statistically significant independent negative predictive factor for ORR, PFS and OS. TP53 co-mutations were associated with inferior mPFS and mOS: mPFS/mOS 12 vs. 18/24 vs. 42 months for non-disruptive/disruptive mutations vs. WT (p < 0.004)/(p < 0.009), 11 vs. 17/23 vs. 42 months for pathogenic vs. non-pathogenic/WT (p < 0.001)/(p < 0.001), and 7 vs. 12 vs. 18/12 vs. 28 vs. 42 months for exon 8 vs. non-exon 8 vs. WT (p < 0.001)/(p < 0.002). CONCLUSIONS: TP53 co-mutations are frequent in EGFR mt+ NSCLC and have a strong negative impact on all clinical endpoints of TKI therapy.