Survival of cancer survivors with a new pancreatic cancer diagnosis.

BACKGROUND: Persons newly diagnosed with pancreas cancer and who have survived a previous cancer are often excluded from clinical trials, despite limited evidence about their prognosis. We examined the association between previous cancer and overall survival. METHODS: This US population-based cohort study included older adults (aged ≥66 years) diagnosed with pancreas cancer between 2005 and 2015 in the linked Surveillance, Epidemiology, and End Results-Medicare data. We used Cox proportional hazards models to estimate stage-specific effects of previous cancer on overall survival, adjusting for sociodemographic, treatment, and tumor characteristics. RESULTS: Of 32,783 patients, 18.7% were previously diagnosed with another cancer. The most common previous cancers included prostate (29.0%), breast (18.9%), or colorectal (9.7%) cancer. More than half of previous cancers (53.9%) were diagnosed 5 or more years prior to pancreas cancer diagnosis or at an in situ or localized stage (47.8%). The proportions of patients surviving 1, 3, and 5 years after pancreas cancer were nearly identical for those with and without previous cancer. Median survival in months was as follows for those with and without previous cancer respectively: 7 versus 8 (Stage 0/I), 10 versus 10 (Stage II), 7 versus 7 (Stage III), and 3 versus 2 (Stage IV). Cox models indicated that patients with previous cancer had very similar or statistically equivalent survival to those with no previous cancer. CONCLUSIONS: Given nearly equivalent survival compared to those without previous cancer, cancer survivors newly diagnosed with pancreas cancer should be considered for inclusion in pancreas cancer clinical trials.

PSA-Net: Deep learning-based physician style-aware segmentation network for postoperative prostate cancer clinical target volumes.

PURPOSE: Automatic segmentation of medical images with deep learning (DL) algorithms has proven highly successful in recent times. With most of these automation networks, inter-observer variation is an acknowledged problem that leads to suboptimal results. This problem is even more significant in segmenting postoperative clinical target volumes (CTV) because they lack a macroscopic visible tumor in the image. This study, using postoperative prostate CTV segmentation as the test case, tries to determine 1) whether physician styles are consistent and learnable, 2) whether physician style affects treatment outcome and toxicity, and 3) how to explicitly deal with different physician styles in DL-assisted CTV segmentation to facilitate its clinical acceptance. METHODS: A dataset of 373 postoperative prostate cancer patients from UT Southwestern Medical Center was used for this study. We used another 83 patients from Mayo Clinic to validate the developed model and its adaptability. To determine whether physician styles are consistent and learnable, we trained a 3D convolutional neural network classifier to identify which physician had contoured a CTV from just the contour and the corresponding CT scan. Next, we evaluated whether adapting automatic segmentation to specific physician styles would be clinically feasible based on a lack of difference between outcomes. Here, biochemical progression-free survival (BCFS) and grade 3+ genitourinary and gastrointestinal toxicity were estimated with the Kaplan-Meier method and compared between physician styles with the log rank test and subsequently with a multivariate Cox regression. When we found no statistically significant differences in outcome or toxicity between contouring styles, we proposed a concept called physician style-aware (PSA) segmentation by developing an encoder-multidecoder network with perceptual loss to model different physician styles of CTV segmentation. RESULTS: The classification network captured the different physician styles with 87% accuracy. Subsequent outcome analysis showed no differences in BCFS and grade 3+ toxicity among physicians. With the proposed physician style-aware network (PSA-Net), Dice similarity coefficient (DSC) accuracy for all physicians was 3.4% higher on average than with a general model that does not differentiate physician styles. We show that these stylistic contouring variations also exist between institutions that follow the same segmentation guidelines, and we show the proposed method's effectiveness in adapting to new institutional styles. We observed an accuracy improvement of 5% in terms of DSC when adapting to the style of a separate institution. CONCLUSION: The performance of the classification network established that physician styles are learnable, and the lack of difference between outcomes among physicians shows that the network can feasibly adapt to different styles in the clinic. Therefore, we developed a novel PSA-Net model that can produce contours specific to the treating physician, thus improving segmentation accuracy and avoiding the need to train multiple models to achieve different style segmentations. We successfully validated this model on data from a separate institution, thus supporting the model's generalizability to diverse datasets.

Estimating the optimal timing of surgery by imputing potential outcomes.

Hypoplastic left heart syndrome is a congenital anomaly that is uniformly fatal in infancy without immediate treatment. The standard treatment consists of an initial Norwood procedure (stage 1) followed some months later by stage 2 palliation (S2P). The ideal timing of the S2P is uncertain. The Single Ventricle Reconstruction Trial (SVRT) randomized the procedure used in the initial Norwood operation, leaving the timing of the S2P to the discretion of the surgical team. To estimate the causal effect of the timing of S2P, we propose to impute the potential post-S2P survival outcomes using statistical models under the Rubin Causal Model framework. With this approach, it is straightforward to estimate the causal effect of S2P timing on post-S2P survival by directly comparing the imputed potential outcomes. Specifically, we consider a lognormal model and a restricted cubic spline model, evaluating their performance in Monte Carlo studies. When applied to the SVRT data, the models give somewhat different imputed values, but both support the conclusion that the optimal time for the S2P is at 6 months after the Norwood procedure.

Survival of patients newly diagnosed with colorectal cancer and with a history of previous cancer.

Patients with previous cancer are often excluded from clinical trials despite limited evidence about their prognosis. We examined the effect of previous cancer on overall and colorectal cancer (CRC)-specific survival of patients newly diagnosed with CRC. This population-based cohort study from the U.S.A. included patients aged ≥66 years and diagnosed with CRC between 2005 and 2015 in linked Surveillance, Epidemiology, and End Results-Medicare data. We estimated the stage-specific effects of a previous cancer on overall survival using Cox regression and on CRC-specific survival using competing risk regression. We also examined the effect of previous cancer type, timing, and stage on overall survival. Of 112,769 patients, 14.1% were previously diagnosed with another cancer--commonly prostate (32.9%) or breast (19.4%) cancer, with many (47.1%) diagnosed <5 years of CRC. For all CRC stages except IV, in which there was no difference, patients with previous cancer (vs. without) had worse overall survival. However, patients with previous cancer had improved CRC-specific survival. Overall survival for those with stage 0-III CRC varied by previous cancer type, timing, and stage; for example, patients with previous melanoma had overall survival equivalent to those with no previous cancer. Our results indicate that, in general, CRC patients with previous cancer have worse overall survival but superior CRC-specific survival. Given their equivalent survival to those without previous cancer, patients with previous melanoma and those with stage IV CRC with any type of previous cancer should be eligible to participate in clinical trials.

COVID-19 associated mortality and cardiovascular disease outcomes among US women veterans.

The burden of COVID-19 has been noted to be disproportionately greater in minority women, a population that is nevertheless still understudied in COVID-19 research. We conducted an observational study to examine COVID-19-associated mortality and cardiovascular disease outcomes after testing (henceforth index) among a racially diverse adult women veteran population. We assembled a retrospective cohort from a Veterans Affairs (VA) national COVID-19 shared data repository, collected between February and August 2020. A case was defined as a woman veteran who tested positive for SARS-COV-2, and a control as a woman veteran who tested negative. We used Kaplan-Meier curves and the Cox proportional hazards model to examine the distribution of time to death and the effects of baseline predictors on mortality risk. We used generalized linear models to examine 60-day cardiovascular disease outcomes. Covariates studied included age, body mass index (BMI), and active smoking status at index, and pre-existing conditions of diabetes, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), and a history of treatment with antiplatelet or anti-thrombotic drug at any time in the 2 years prior to the index date. Women veterans who tested positive for SARS-CoV-2 had 4 times higher mortality risk than women veterans who tested negative (Hazard Ratio 3.8, 95% Confidence Interval CI 2.92 to 4.89) but had lower risk of cardiovascular events (Odds Ratio OR 0.78, 95% CI 0.66 to 0.92) and developing new heart disease conditions within 60 days (OR 0.67, 95% CI 0.58 to 0.77). Older age, obesity (BMI > 30), and prior CVD and COPD conditions were positively associated with increased mortality in 60 days. Despite a higher infection rate among minority women veterans, there was no significant race difference in mortality, cardiovascular events, or onset of heart disease. SARS-CoV-2 infection increased short-term mortality risk among women veterans similarly across race groups. However, there was no evidence of increased cardiovascular disease incidence in 60 days. A longer follow-up of women veterans who tested positive is warranted.

Survival of women diagnosed with breast cancer and who have survived a previous cancer.

PURPOSE: Many women diagnosed with breast cancer have survived previous cancer; yet little is known about the impact of previous cancer on overall and cancer-specific survival. METHODS: This population-based cohort study using SEER-Medicare data included women (age ≥ 66 years) diagnosed with breast cancer between 2005 and 2015. Separately by breast cancer stage, we estimated effect of previous cancer on overall survival using Cox regression and on cause-specific survival using competing risk regression; all survival analyses adjusted for covariates. RESULTS: Of 138,576 women diagnosed with breast cancer, 8% had a previous cancer of another organ site, most commonly colorectal or uterine cancer or melanoma. Many of these women (46.3%) were diagnosed within 5 years of breast cancer. For all breast cancer stages except IV wherein there was no difference, women with vs. without previous cancer had worse overall survival. This survival disadvantage was driven by deaths due to the previous cancer and other causes. In contrast, women with previous cancer generally had favorable breast-cancer-specific survival, although this varied by stage. Overall survival varied by previous cancer type, timing, and stage; previous lung cancer, cancer diagnosed within 1 year of incident breast cancer, and previous cancer at a distant stage were associated with the worst survival. In contrast, women with a previous melanoma had equivalent overall survival to women without previous cancer. CONCLUSION: We observed variable impact of previous cancer on overall and breast-cancer-specific survival depending on breast cancer stage at diagnosis and the type, timing, and stage of previous cancer.

Estimating the optimal timing of surgery from observational data.

Infants with hypoplastic left heart syndrome require an initial Norwood operation, followed some months later by a stage 2 palliation (S2P). The timing of S2P is critical for the operation's success and the infant's survival, but the optimal timing, if one exists, is unknown. We attempt to identify the optimal timing of S2P by analyzing data from the Single Ventricle Reconstruction Trial (SVRT), which randomized patients between two different types of Norwood procedure. In the SVRT, the timing of the S2P was chosen by the medical team; thus with respect to this exposure, the trial constitutes an observational study, and the analysis must adjust for potential confounding. To accomplish this, we propose an extended propensity score analysis that describes the time to surgery as a function of confounders in a discrete competing-risk model. We then apply inverse probability weighting to estimate a spline hazard model for predicting survival from the time of S2P. Our analysis suggests that S2P conducted at 6 months after the Norwood gives the patient the best post-S2P survival. Thus, we place the optimal time slightly later than a previous analysis in the medical literature that did not account for competing risks of death and heart transplantation.

Approach to Managing a Postmenopausal Patient.

CASE AND PRINCIPLES OF MANAGEMENT: The case of a symptomatic, postmenopausal woman is presented and a full discussion of the approach to her management is discussed. Pertinent guidelines and scientific evidence are emphasized as support for the recommendations.

Flexible, rule-based dose escalation: The cohort-sequence design.

Phase I oncology trials seek to acquire preliminary information on the safety of novel treatments. In current practice, most such trials employ rule-based designs that determine whether to escalate the dose using data from the current dose only. The most popular of these, the 3 + 3, is simple and familiar but inflexible and inefficient. We propose a rule-based design that addresses these deficiencies. Our method, which we denote the cohort-sequence design, is defined by a sequence of J increasing cohort sizes n = ( n 1 , … , n J ) and corresponding critical values b = ( b 1 , … , b J ) . The idea is to begin with a small cohort size n 1 and escalate through the planned doses, increasing the cohort size when we encounter toxicities. By selection of J and a safety threshold tuning parameter θ, one can create a design that will efficiently identify a target toxicity rate, potentially including a built-in dose-expansion cohort. We compared our designs to the 3 + 3 under a range of toxicity scenarios, observing that our approach generally rapidly identifies an MTD without enrolling patients unnecessarily at low doses where both toxicity and response rates are likely to be low. We have implemented the design in the R package cohortsequence.

Underlying Breast Cancer Risk and Menopausal Hormone Therapy.

The recent Collaborative Group on Hormonal Factors in Breast Cancer (CGHFBC) publication calculated the attributable risk of breast cancer from use of estrogen alone and estrogen plus a synthetic progestogen for less than 5 to 15 or more years of use. This CGHFB report calculated attributable risk based on their findings of relative risk from pooled data from 58 studies. Notably, neither the CGHFBC nor other previous studies have examined the effect of underlying risk of breast cancer on attributable risk. This omission prompted us to determine the magnitude of the effect of underlying risk on attributable risk in this perspective. Meaningful communication of the potential risk of menopausal hormonal therapy requires providing women with the estimated risk above their existing underlying risk (ie, attributable risk). Therefore, we have estimated attributable risks from the data published by the CGHFBC, taking into account varying degrees of underlying risk. Based on the Endocrine Society Guideline on Menopausal Hormone Therapy (MHT), we divided groups into 3 categories of risk: low (1.5%), intermediate (3.0%), and high (6.0%) underlying risk of breast cancer over 5 years. In women taking estrogen plus a synthetic progestogen for 5 to 9 years, the attributable risks of MHT increased from 12, to 42, to 85 additional women per 1000 in the low-, intermediate-, and high-risk groups, respectively. The attributable risks for estrogen alone were lower but also increased based on underlying risk. Notably, the attributable risks were amplified with duration of MHT use, which increased both relative risk and breast cancer incidence.

Estimating cost-effectiveness from claims and registry data with measured and unmeasured confounders.

The analysis of observational data to determine the cost-effectiveness of medical treatments is complicated by the need to account for skewness, censoring, and the effects of measured and unmeasured confounders. We quantify cost-effectiveness as the Net Monetary Benefit (NMB), a linear combination of the treatment effects on cost and effectiveness that denominates utility in monetary terms. We propose a parametric estimation approach that describes cost with a Gamma generalized linear model and survival time (the canonical effectiveness variable) with a Weibull accelerated failure time model. To account for correlation between cost and survival, we propose a bootstrap procedure to compute confidence intervals for NMB. To examine sensitivity to unmeasured confounders, we derive simple approximate relationships between naïve parameters, assuming only measured confounders, and the values those parameters would take if there was further adjustment for a single unmeasured confounder with a specified distribution. A simulation study shows that the method returns accurate estimates for treatment effects on cost, survival, and NMB under the assumed model. We apply our method to compare two treatments for Stage II/III bladder cancer, concluding that the NMB is sensitive to hypothesized unmeasured confounders that represent smoking status and personal income.

Effect of Differences in Metabolic Activity of Melanoma Models on Response to Lonidamine plus Doxorubicin.

Lonidamine (LND), a metabolic modulator, sensitizes DB-1 human melanoma to doxorubicin (DOX) chemotherapy by acidifying and de-energizing the tumor. This report compares the effects of LND on two human melanoma lines, DB-1 and WM983B, which exhibit different metabolic properties. Using liquid chromatography mass spectrometry and Seahorse analysis, we show that DB-1 was more glycolytic than WM983B in vitro. 31P magnetic resonance spectroscopy (MRS) indicates that LND (100 mg/kg, i.p.) induces similar selective acidification and de-energization of WM983B xenografts in immunosuppressed mice. Over three hours, intracellular pH (pHi) of WM983B decreased from 6.91 ± 0.03 to 6.59 ± 0.10 (p = 0.03), whereas extracellular pH (pHe) of this tumor changed from 7.03 ± 0.05 to 6.89 ± 0.06 (p = 0.19). A decline in bioenergetics (ß-NTP/Pi) of 55 ± 5.0% (p = 0.03) accompanied the decline in pHi of WM983B. Using 1H MRS with a selective multiquantum pulse sequence and Hadamard localization, we show that LND induced a significant increase in tumor lactate levels (p < 0.01). LND pre-treatment followed by DOX (10 mg/kg, i.v.) produced a growth delay of 13.7 days in WM983B (p < 0.01 versus control), a growth delay significantly smaller than the 25.4 days that occurred with DB-1 (p = 0.03 versus WM983B). Differences in relative levels of glycolysis may produce differential therapeutic responses of DB-1 and WM983B melanomas.

Glucocorticoid Receptor Mutations and Hypersensitivity to Endogenous and Exogenous Glucocorticoids.

Background: The glucocorticoid receptor (GR) consists of two alternatively spliced isoforms: GRα, which activates gene transcription, and GRß, a dominant-negative receptor. Theoretically, inactivating variants of GRß could result in glucocorticoid hypersensitivity. Design: A 46-year-old woman presented for evaluation of adrenal insufficiency prompted by low plasma cortisol levels and multiple unexplained symptoms but without clinical evidence of glucocorticoid insufficiency. To explain these findings, extensive clinical, genetic, and molecular studies were performed. Methods: Standard clinical methods assessed the patient's hypothalamic-pituitary-adrenal axis. Validated molecular techniques were used for receptor sequencing, stable transfections, stimulation of candidate genes, cDNA arrays, Ingenuity Pathway Analysis, volcano analysis, and isolation and analysis of the patient's mononuclear cells. Results: Clinical studies excluded primary or secondary adrenal insufficiency, established consistently low basal cortisol levels, and demonstrated hypersensitivity to ultra-low-dose dexamethasone. Receptor sequencing identified two variants of GR9ß (A3669G and G3134T) as well as the known Bcl1 polymorphism. Reductionist studies using stable osteosarcoma cell lines transfected with the GRß variants demonstrated glucocorticoid hypersensitivity of transcribed genes on cDNA array analysis. The patient's monocytes responded to hydrocortisone with exaggerated stimulation of the candidate genes GILZ and FKBP5. Conclusion: Two variants of the dominant-negative GRß, in conjunction with a common Bcl1 intron variant, resulted in hypersensitivity to endogenous and exogenous glucocorticoids and, as a reflection of severity, low circulating cortisol levels without clinical evidence of glucocorticoid insufficiency. This prismatic case exemplifies the unique effects of variants of a dominant-negative receptor.

Statistical justification of expansion cohorts in phase 1 cancer trials.

BACKGROUND: Phase I cancer trials increasingly incorporate dose-expansion cohorts (DECs), reflecting a growing demand to acquire more information about investigational drugs. Protocols commonly fail to provide a sample-size justification or analysis plan for the DEC. In this study, we develop a statistical framework for the design of DECs. METHODS: We assume the maximum tolerated dose (MTD) for the investigational drug has been identified in the dose-escalation stage of the trial. We use the 80% lower confidence bound and the 90% upper confidence bound for the response and toxicity rates, respectively, as decision thresholds for the dose-expansion stage. We calculate the operating characteristics with reference to prespecified minimum effective response rates and maximum safe DLT rates. RESULTS: We apply our framework to specify a system of DEC plans. The design comprises three components: 1) the number of subjects enrolled at the MTD, 2) the minimum number of responses necessary to indicate provisional drug efficacy, and 3) the maximum number of dose-limiting toxicities (DLTs) permitted to indicate drug safety. We demonstrate our method in an application to a cancer immunotherapy trial. CONCLUSIONS: Our simple and practical tool enables creation of DEC designs that appropriately address the safety and efficacy objectives of the trial.

Estimating lead-time bias in lung cancer diagnosis of patients with previous cancers.

Surprisingly, survival from a diagnosis of lung cancer has been found to be longer for those who experienced a previous cancer than for those with no previous cancer. A possible explanation is lead-time bias, which, by advancing the time of diagnosis, apparently extends survival among those with a previous cancer even when they enjoy no real clinical advantage. We propose a discrete parametric model to jointly describe survival in a no-previous-cancer group (where, by definition, lead-time bias cannot exist) and in a previous-cancer group (where lead-time bias is possible). We model the lead time with a negative binomial distribution and the post-lead-time survival with a linear spline on the logit hazard scale, which allows for survival to differ between groups even in the absence of bias; we denote our model Logit-Spline/Negative Binomial. We fit Logit-Spline/Negative Binomial to a propensity-score matched subset of the Surveillance, Epidemiology, and End Results-Medicare linked data set, conducting sensitivity analyses to assess the effects of key assumptions. With lung cancer-specific death as the end point, the estimated mean lead time is roughly 11 months for stage I&II patients; with overall survival, it is roughly 3.4 months in stage I&II. For patients with higher-stage lung cancers, the mean lead time is 1 month or less for both outcomes. Accounting for lead-time bias reduces the survival advantage of the previous-cancer group when one exists, but it does not nullify it in all cases.

Adaptive parametric prediction of event times in clinical trials.

BACKGROUND: In event-based clinical trials, it is common to conduct interim analyses at planned landmark event counts. Accurate prediction of the timing of these events can support logistical planning and the efficient allocation of resources. As the trial progresses, one may wish to use the accumulating data to refine predictions. PURPOSE: Available methods to predict event times include parametric cure and non-cure models and a nonparametric approach involving Bayesian bootstrap simulation. The parametric methods work well when their underlying assumptions are met, and the nonparametric method gives calibrated but inefficient predictions across a range of true models. In the early stages of a trial, when predictions have high marginal value, it is difficult to infer the form of the underlying model. We seek to develop a method that will adaptively identify the best-fitting model and use it to create robust predictions. METHODS: At each prediction time, we repeat the following steps: (1) resample the data; (2) identify, from among a set of candidate models, the one with the highest posterior probability; and (3) sample from the predictive posterior of the data under the selected model. RESULTS: A Monte Carlo study demonstrates that the adaptive method produces prediction intervals whose coverage is robust within the family of selected models. The intervals are generally wider than those produced assuming the correct model, but narrower than nonparametric prediction intervals. We demonstrate our method with applications to two completed trials: The International Chronic Granulomatous Disease study and Radiation Therapy Oncology Group trial 0129. LIMITATIONS: Intervals produced under any method can be badly calibrated when the sample size is small and unhelpfully wide when predicting the remote future. Early predictions can be inaccurate if there are changes in enrollment practices or trends in survival. CONCLUSIONS: An adaptive event-time prediction method that selects the model given the available data can give improved robustness compared to methods based on less flexible parametric models.

Adjuvant Chemotherapy vs Postoperative Observation Following Preoperative Chemoradiotherapy and Resection in Gastroesophageal Cancer: A Propensity Score-Matched Analysis.

IMPORTANCE: Distant recurrence following preoperative chemoradiotherapy and resection in patients with gastroesophageal adenocarcinoma is common. Adjuvant chemotherapy may improve survival. OBJECTIVE: To compare adjuvant chemotherapy with postoperative observation following preoperative chemoradiotherapy and resection in patients with gastroesophageal adenocarcinoma. DESIGN, SETTING, AND PARTICIPANTS: Propensity score-matched analysis using the National Cancer Database. We included adult patients who received a diagnosis between 2006 and 2013 of clinical stage T1N1-3M0 or T2-4N0-3M0 adenocarcinoma of the distal esophagus or gastric cardia who were treated with preoperative chemoradiotherapy and curative-intent resection. Patients receiving adjuvant chemotherapy were matched by propensity score to patients undergoing postoperative observation. EXPOSURES: Adjuvant chemotherapy and postoperative observation. MAIN OUTCOMES AND MEASURES: Overall survival. RESULTS: We identified 10 086 patients (8840 [88%] male; mean [SD] age, 61 [9.5] years), 9272 in the postoperative observation group and 814 in the adjuvant chemotherapy group. Patients receiving adjuvant chemotherapy were younger (18-54 years: 252 [31%] vs 1989 [21%]; P < .001) and were more likely to have advanced disease (ypT3/4: 458 [62%] vs 3531 [46%]; P < .001; ypN+: 572 [72%] vs 3428 [39%]; P < .001), as well as shorter postoperative inpatient stays (>2 weeks: 94 [13%] vs 1589 [20%]; P < .001). A total of 732 patients in the adjuvant chemotherapy group were matched by propensity score to 3660 patients in the postoperative observation group. Adjuvant chemotherapy was associated with improved overall survival compared with postoperative observation (median survival: 40 months; 95% CI, 36-46 months vs 34 months; 95% CI, 32-35 months; stratified log-rank P < .001; hazard ratio, 0.79; 95% CI, 0.72-0.88). Overall survival at 1, 3, and 5 years was 88%, 47%, and 34% in the observation group, and 94%, 54%, and 38% in the adjuvant chemotherapy group, respectively. Adjuvant chemotherapy was associated with a survival benefit compared with postoperative observation in most patient subgroups. CONCLUSIONS AND RELEVANCE: For patients with locally advanced gastroesophageal adenocarcinoma treated with preoperative chemoradiotherapy and resection, adjuvant chemotherapy was associated with improved overall survival. Our findings have important implications for the postoperative treatment of this patient group for which few data are available.

Cure modeling in real-time prediction: How much does it help?

Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals.

Proximity and gravity: modeling heaped self-reports.

Self-reported daily cigarette counts typically exhibit a preponderance of round numbers, a phenomenon known as heaping or digit preference. Heaping can be a substantial nuisance, as scientific interest lies in the distribution of the underlying true values rather than that of the heaped data. In principle, we can estimate parameters of the underlying distribution from heaped data if we know the conditional distribution of the heaped count given the true count, denoted the heaping mechanism (analogous to the missingness mechanism for missing data). In general, it is not possible to estimate the heaping mechanism robustly from heaped data only. A doubly-coded smoking cessation trial data set that includes daily cigarette count as both a conventional heaped retrospective recall measurement and a precise instantaneous measurement offers the rare opportunity to directly estimate the heaping mechanism. We propose a novel model that describes the conditional probability of the self-reported count as a function of its proximity to the truth and its intrinsic attractiveness, denoted its gravity. We apply variations of the model to the cigarette count data, illuminating the cognitive processes that influence self-reporting behaviors. The principal application of the model will be to enabling the correct analysis of heaped-only data sets. Copyright © 2017 John Wiley & Sons, Ltd.

Pilot and Feasibility Trial Evaluating Immuno-Gene Therapy of Malignant Mesothelioma Using Intrapleural Delivery of Adenovirus-IFNα Combined with Chemotherapy.

PURPOSE: "In situ vaccination" using immunogene therapy has the ability to induce polyclonal antitumor responses directed by the patient's immune system. EXPERIMENTAL DESIGN: Patients with unresectable malignant pleural mesothelioma (MPM) received two intrapleural doses of a replication-defective adenoviral vector containing the human IFNα2b gene (Ad.IFN) concomitant with a 14-day course of celecoxib followed by chemotherapy. Primary outcomes were safety, toxicity, and objective response rate; secondary outcomes included progression-free and overall survival. Biocorrelates on blood and tumor were measured. RESULTS: Forty subjects were treated: 18 received first-line pemetrexed-based chemotherapy, 22 received second-line chemotherapy with pemetrexed (n = 7) or gemcitabine (n = 15). Treatment was generally well tolerated. The overall response rate was 25%, and the disease control rate was 88%. Median overall survival (MOS) for all patients with epithelial histology was 21 months versus 7 months for patients with nonepithelial histology. MOS in the first-line cohort was 12.5 months, whereas MOS for the second-line cohort was 21.5 months, with 32% of patients alive at 2 years. No biologic parameters were found to correlate with response, including numbers of activated blood T cells or NK cells, regulatory T cells in blood, peak levels of IFNα in blood or pleural fluid, induction of antitumor antibodies, nor an immune-gene signature in pretreatment biopsies. CONCLUSIONS: The combination of intrapleural Ad.IFN, celecoxib, and chemotherapy proved safe in patients with MPM. OS rate was significantly higher than historical controls in the second-line group. Results of this study support proceeding with a multicenter randomized clinical trial of chemo-immunogene therapy versus standard chemotherapy alone. Clin Cancer Res; 22(15); 3791-800. ©2016 AACR.