Observed Survival Time Research Articles

Microbial Spectrum and Viability of Bacterial Contaminants on Banknotes

Background & Aim: Banknotes are potential fomites for transmission of infection in different environments between people of different health status, hygiene practices, and health awareness. The aim of this study was to determine the spectrum of bacterial contaminants on Ghanaian banknotes and their ability to remain viable. Methods: Different denominations were collected for physical examination. The viability of bacteria present on each banknote was determined over a storage period by culturing, isolating, and biochemical identification. Positive-culture banknotes were analyzed every 3 days for a period of 12 days. The antimicrobial resistance of the selected bacteria isolates was determined by the Kirby-Bauer disc diffusion test. Results: The banknotes in mint (4%), fairly clean (54%), and dirty (42%) physical conditions had print ages ranging from 23 to 72 months. Eighty-nine isolates of 6 species comprising 0 to 3 species per banknote were identified. These were Citrobacter sp. 32 (36.0 %), Bacillus sp. 25 (27.2 %), Staphylococcus aureus 16 (17.4 %), Citrobacter koseri 11 (12 %), Enterobacter sp. 3 (3.3 %), and Escherichia coli 2 (2.2 %). The median observed survival time was six days for 5 species. Conclusion: A high prevalence of pathogens may occur among bacterial contaminants on banknotes of different physical conditions, denominations, ages, and from different sampling sites. These can remain viable for more than 6 days and may potentially contribute to the transmission of antimicrobial resistant diseases.

Accuracy of oncologists' estimates of expected survival time in advanced cancer.

To evaluate the claim that oncologists overestimate expected survival time (EST) in advanced cancer. We pooled 7 prospective studies in which observed survival time (OST) was compared with EST (median survival in a group of similar patients estimated at baseline by the treating oncologist). We hypothesized that EST would be well calibrated (approximately 50% of EST longer than OST) and imprecise (<30% of EST within 0.67 to 1.33 of OST), and that multiples of EST would provide well-calibrated scenarios for survival time: worst-case (approximately 10% of OST <1/4 of EST), typical (approximately 50% of OST within half to double EST), and best-case (approximately 10% of OST >3 times EST). Associations between baseline characteristics and calibration of EST were assessed. Characteristics of 1,211 patients: median age 66 years, male 61%, primary site lung (40%) and upper gastrointestinal (16%). The median OST was 8 months, and EST was 9 months. Oncologists' estimates of EST were well calibrated (50% longer than OST) and imprecise (28% within 0.67 to 1.33 of OST). Scenarios for survival time based on simple multiples of EST were well calibrated: 8% of patients had an OST less than 1/4 their EST (worst-case), 56% had an OST within half to double their EST (typical), and 11% had an OST greater than 3 times their EST (best-case). Calibration was independent of age, sex, and cancer type. Oncologists were no more likely to overestimate survival time than to underestimate it. Simple multiples of EST provide well-calibrated estimates of worst-case, typical, and best-case scenarios for survival.

Analyzing Survival Data with Competing Risks Based on R-packages

When it comes to data in survival analysis, it is easy to think of one censored or observed survival time of an individual. In the longitudinal study such as a clinical trial, an individual may be simultaneously exposed to different types of events. It is easy to think of competing risks data as an extension of univariate survival data, but since competing risks events are dependent on each other, if the analysis method of univariate survival data is applied, inferences with bias can be made. In this paper, we briefly introduce the statistical methods developed to analyze competing risks data and apply them to the real examples using the built-in functions in the R package.

Abstract 3243: Synergistic effects of connexin 43 inhibitor and PI3K isoform-selective inhibitors in temozolomide-resistant glioblastoma

Abstract Glioblastoma is the most common central nervous system cancer and is responsible for approximately one-half of all malignant brain tumors. With a 5-year survival rate of 6.8% and a median observed survival time of only 8 months, recurrent glioblastoma is among the world’s most lethal malignancies. Temozolomide (TMZ) has become a standard chemotherapeutic for glioblastoma due to its alkylating effects and ability to cross the blood-brain barrier. Unfortunately, many patients with glioblastoma develop resistance to TMZ, severely limiting therapeutic options. Recent studies have found a link between ectopic connexin 43 (Cx43) and TMZ-resistant glioblastoma. Further studies have shown success in overcoming this resistance by combining TMZ with alpha carboxyl terminus 1 (αCT1) peptide, a clinically tested Cx43 carboxyl terminus mimetic peptide inhibitor; however, because of the short half-life of αCT1, therapeutic potential is limited, and new approaches are needed. In this study, we hypothesized that simultaneously targeting Cx43 with αCT1 and phosphoinositide 3-kinase (PI3K) using PI3K isoform-selective inhibitors would synergistically sensitize glioblastoma to TMZ treatment because p110β, one of the four PI3K catalytic subunits, is a prime survival factor for TMZ-resistant glioblastoma. We cultured newly dissected primary glioblastoma cells, glioblastoma cell lines, and glioblastoma stem cells and dosed them with various combinations of αCT1, TMZ, and one of two clinically tested p110β inhibitors (TGX-221 or GSK2636771). Cell viability was measured using an MTS viability assay. Glioblastoma cells that expressed high levels of Cx43 and p110β experienced a significant decrease in viability when dosed with a triple combination. The additive properties of these combinations were measured using an excess over Bliss (EOB) score derived from Bliss independence modeling, which showed synergistic effects as high as 81% for triple therapy combinations in TMZ-resistant cell lines. The EOB was significantly higher in triple therapies than the double or single therapies. To support our findings, we used the same combinations in primary glioblastoma cells and measured the activity of cleaved caspase 3/7 using a luminescence assay. There was a synergistic increase in caspase 3/7 activity in the triple therapy that was not seen in any of the other combinations. Identical treatment in non-pathological human astrocytes suggests that our drug combinations do not exacerbate non-selective TMZ toxicity in the brain. Collectively, our data demonstrate that simultaneous inhibition of Cx43 and PI3K is an effective approach for overcoming TMZ resistance in glioblastoma. Citation Format: Kasen R. Hutchings, Kevin J. Pridham, Min Liu, Joseph Owens, Zhi Sheng. Synergistic effects of connexin 43 inhibitor and PI3K isoform-selective inhibitors in temozolomide-resistant glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3243.

Parameter Estimation for Additive Hazard Model Recurrent Event Using Counting Process Approach

The Cox regression model is widely used for survival data analysis. The Cox model requires a proportional hazard. If the proportional hazard assumption is doubtful, then the additive hazard model can be used, where the covariates act in an additively to the baseline hazard function. If the observed survival time is more than once for one individual during the observation, it is called a recurrent event. The additive hazard model measures risk difference to the effect of a covariate in absolutely, while the proportional hazards model measure hazard ratio in relatively. The risk coefficients estimation in the additive hazard model mimics the multiplicative hazard model, using partial likelihood methods. The derivation of these estimators, outlined in the technical notes, is based on the counting process approach. The counting process approach was first developed by Aalen on 1975 which combines elements of stochastic integration, martingale theory and counting process theory. The method is applied to study about the effect of supplementation on infant growth and development. Based on the processing results, the factors that affect the growth and development of the infant are gender, treatment and mother's education.

Estimating time-varying treatment switching effect using accelerated failure time model with application to vascular access for hemodialysis

Vascular access for hemodialysis is of paramount importance. Although studies have found that central venous catheter (CVC) is often associated with poor outcomes and switching to arteriovenous fistula (AVF) and arteriovenous grafts (AVG) is beneficial, it has not been fully elucidated how the effect of switching of access on outcomes changes over time and whether the effect depends on switching time. In this article, we propose to relate the observed survival time for patients without access change and the counterfactual time for patients with access change using an AFT model with time-varying effects. The flexibility of AFT model allows us to account for baseline effect and the prognostic effect from covariates at access change while estimating the effect of access change. The effect of access change over time is modeled nonparametrically using a cubic spline function. Simulation studies show excellent performance. Our methods are applied to investigate the effect of vascular access change over time in dialysis patients. It is concluded that the benefit of switching from CVC to AVG depends on the time of switching, the sooner the better.

The predictive value of neurobiological measures for recidivism in delinquent male young adults.

BackgroundNeurobiological measures have been associated with delinquent behaviour, but little is known about the predictive power of these measures for criminal recidivism and whether they have incremental value over and above demographic and behavioural measures. This study examined whether selected measures of autonomic functioning, functional neuroimaging and electroencephalography predict overall and serious recidivism in a sample of 127 delinquent young adults.MethodsWe assessed demographics; education and intelligence; previous delinquency and drug use; behavioural traits, including aggression and psychopathy; and neurobiological measures, including heart rate, heart rate variability, functional brain activity during an inhibition task and 2 electroencephalographic measures of error-processing. We tested longitudinal associations with recidivism using Cox proportional hazard models and predictive power using C-indexes.ResultsPast offences, long-term cannabis use and reactive aggression were strongly associated with recidivism, as were resting heart rate and error-processing. In the predictive model, demographics, past delinquency, drug use and behavioural traits had moderate predictive power for overall and for serious recidivism (C-index over 30 months [fraction of pairs in the data, where the higher observed survival time was correctly predicted]: C30 = 0.68 and 0.75, respectively). Neurobiological measures significantly improved predictive power (C30 = 0.72 for overall recidivism and C30 = 0.80 for serious recidivism).LimitationsFindings cannot be generalized to females, and follow-up was limited to 4 years.ConclusionDemographic and behavioural characteristics longitudinally predicted recidivism in delinquent male young adults, and neurobiological measures improved the models. This led to good predictive function, particularly for serious recidivism. Importantly, the most feasible measures (autonomic functioning and electroencephalography) proved to be useful neurobiological predictors.

A mild heat stress increases resistance to heat of dFOXO Drosophila melanogaster mutants but less in wild-type flies.

While severe stresses have deleterious effects, mild stresses can have beneficial effects called hormetic effects. This study observed survival time at 37.5 °C and at 13–16 days of age of wild-type Drosophila melanogaster flies and dFOXO mutants, after they were subjected to 5 or 10 min daily at 37.5 °C for 5 days starting at 5 days of age. This mild stress increased survival time of the mutants, this effect being nearly not observed in wild-type flies. Previous studies showed that another mild stress, the cold, can increase survival time to heat of wild-type flies, but not of dFOXO mutants, while hypergravity increased survival time of mutants but not of wild-type flies. Therefore, three mild stresses, cold, hypergravity, and heat can increase resistance to heat but the pathways mediating this effect are seemingly different, as cold does not increase resistance in dFOXO mutants but increases it in wild-type flies, while hypergravity and heat have opposite effects. It appears that dFOXO may be needed or not to observe hormetic effects.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10522-021-09914-1.

Palliative systemic chemotherapy with or without pressurized intraperitoneal aerosol chemotherapy with cisplatin and doxorubicin (PIPAC C/D) for gastric cancer with peritoneal metastasis: A propensity score analysis.

380 Background: Gastric cancer (GC) with peritoneal metastasis (PM) has a dismal prognosis. Palliative systemic chemotherapy (SC), usually doublet combinations of platinum and fluoropyrimidines, is the standard of care. Pressurized IntraPeritoneal Aerosol Chemotherapy with Cisplatin and Doxorubicin (PIPAC C/D) yields promising results. Here we aimed to compare overall survival (OS) between SC + PIPAC C/D vs. SC alone in patients with PM from GC. Methods: Prospective cohort of 95 consecutive patients with PM from GC treated in palliative intent at our institution from 2010 to 2018. Of these patients, 69 received SC + PIPAC C/D („PIPAC“), 26 SC alone („control“). Choice of treatment was not dictated by medical criteria, but by (non-) availability of the single-use medical devices needed for PIPAC in Russia. All patients received doublet or triplet chemotherapy with platinum together with fluoropyrimidines or capecitabin. A Cox proportional hazard model based on propensity score (PS) was used to assess the effect of PIPAC on OS and account for confounding factors. Results: The HR adjusted for PS for PIPAC vs. control was 0.396 (CI 5- 95% = 0.224-0.700, p-value 0.001). In the simple (unadjusted) Kaplan-Meier, median survival in the control group was 7.0 months (CI: 4.51 - 9.49) and in the PIPAC group 14.0 months (CI: 11.46-16.54). In the control group, all 26 patients died after 1-25 months. In the PIPAC group, 36 of 69 patients died after 4 to 20 months. The longest observed survival time in the PIPAC group was 27 months. Significance for the log-rank test after Mantel-Cox (not adjusted) was p &lt; 0.0001. Conclusions: Compared with SC alone, intensified chemotherapy combining PIPAC C/D and SC doubled OS. These promising results need to be confirmed in a randomized trial.

Estimating survival in advanced cancer: a comparison of estimates made by oncologists and patients.

To compare estimates of expected survival time (EST) made by patients with advanced cancer and their oncologists. At enrolment patients recorded their "understanding of how long you may have to live" in best-case, most-likely, and worst-case scenarios. Oncologists estimated survival time for each of their patients as the "median survival of a group of identical patients". We hypothesized that oncologists' estimates of EST would be unbiased (~ 50% longer or shorter than the observed survival time [OST]), imprecise (< 33% within 0.67 to 1.33 times OST), associated with OST, and more accurate than patients' estimates of their own survival. Twenty-six oncologists estimated EST for 179 patients. The median estimate of EST was 6.0 months, and the median OST was 6.2 months. Oncologists' estimates were unbiased (56% longer than OST), imprecise (27% within 0.67 to 1.33 times OST), and significantly associated with OST (HR 0.88, 95% CI 0.82 to 0.93, p < 0.01). Only 41 patients (23%) provided a numerical estimate of their survival with 107 patients (60%) responding "I don't know". The median estimate by patients for their most-likely scenario was 12 months. Patient estimates of their most-likely scenario were less precise (17% within 0.67 to 1.33 times OST) and more likely to overestimate survival (85% longer than OST) than oncologist estimates. Oncologists' estimates were unbiased and significantly associated with survival. Most patients with advanced cancer did not know their EST or overestimated their survival time compared to their oncologist, highlighting the need for improved prognosis communication training. Trial registration ACTRN1261300128871.

Estimating survival time in older adults receiving chemotherapy for advanced cancer

PurposeWe determined the accuracy of oncologists' estimates of expected survival time (EST) for older adults with advanced cancer, and explored predictors of survival from a geriatric assessment (GA). MethodsPatients aged ≥65 years starting a new line of palliative chemotherapy were eligible. For each patient at enrolment, oncologists estimated EST and rated frailty (Canadian Study on Health and Aging Clinical Frailty Scale, 1 = very fit, to 7 = severely frail), and a researcher completed a GA. We anticipated estimates of EST to be: imprecise [<33% between 0.67 and 1.33 times the observed survival time (OST)]; unbiased (approximately 50% of participants living longer than their EST); and, useful for estimating individualised worst-case (10% living ≤¼ times their EST), typical (50% living half to double EST), and best-case (10% living ≥3 times EST) scenarios for survival time. Logistic regression was used to identify independent predictors of OST. ResultsThe 102 participants [median age 74 years, vulnerable to frail (4–7 on scale) 35%] had a median OST of 15 months. 30% of estimates of EST were within 0.67–1.33 times the OST. 54% of participants lived longer than their EST, 9% lived ≤1/4 of their EST and 56% lived half to double their EST. Follow-up was insufficient to observe those living ≥3 times their EST. Independent predictors of OST were frailty (HR 4.16, p < .0001) and cancer type (p = .003). ConclusionsOncologists' estimates of EST were imprecise, but unbiased and accurate for formulating scenarios for survival. A pragmatic frailty rating was identified as a potentially useful predictor of OST.

Hypergravity increases resistance to heat in dFOXO Drosophila melanogaster mutants and can lower FOXO translocation in wild-type males.

Severe stresses have deleterious effects, but mild stresses can have beneficial effects called hormetic effects. This study observed survival time of wild-type Drosophila melanogaster flies and dFOXO mutants exposed to 37°C, a severe stress for flies, after they lived or not for 2weeks in hypergravity (3 or 5g), a mild stress with hormetic effects in flies. Hypergravity increased survival time of the mutants, this effect being less observed in wild-type flies. The heat stress increased dFOXO translocation similarly in all gravity groups in a wild-type strain, and hypergravity decreased dFOXO translocation similarly in heat-stressed or not heat-stressed males, no clear effect of the gravity level being observed in females. Because hypergravity increases resistance to heat in dFOXO mutants and the translocation is not tightly dependent on the gravity level, one can conclude that dFOXO does not mediate the effect of hypergravity on resistance to heat. A previous study showed that another mild stress, the cold, can increase survival time at 37°C of wild-type D. melanogaster flies, but this was not observed in dFOXO mutants. Therefore, two mild stresses, cold and hypergravity, can increase resistance to heat but the pathways mediating this effect are seemingly different, as cold does not increase resistance in dFOXO mutants while hypergravity increases it.

Estimating survival time in older adults receiving chemotherapy for advanced cancer.

e23017 Background: Best-case, worst-case, and typical scenarios for survival time, based on an oncologist’s estimate of expected survival time (EST), have proven accurate in a range of advanced cancers. We sought the accuracy and prognostic significance of such estimates, and of a simple, pragmatic rating of frailty, in older adults starting chemotherapy. Methods: Participants (pts) were aged 65 or older and starting a new line of chemotherapy for advanced cancer. For each pt at baseline, their treating oncologist recorded an individualised estimate of EST (median survival in a group of similar patients), ECOG performance status (PS), and rating of frailty with the single-item, Clinical Frailty Scale from the Canadian Study of Health and Aging. We hypothesised that estimates of EST would be unbiased (approximately 50% of pts would live longer than their EST); imprecise ( &lt; 33% would live for 0.67 to 1.33 times their EST); and, that simple multiples of the EST would provide accurate individualised scenarios for survival time, i.e. approximately 10% of pts would die within ¼ of their EST, 10% would live longer than 3 times their EST, and 50% would live from half to double their EST. We identified independent predictors of observed survival time (OST) with multivariable Cox regression. Results: Baseline characteristics of the 102 pts were: median age 74 (range 65-86), 1st line chemotherapy in 67%, colorectal cancer in 33%, PS 0 or 1 in 80%, and frailty rating of vulnerable to frail in 35%. The median EST was 15 months (range 4-60), median follow-up time was 19 months (range 0-27), and median OST was 15 months (range 0.5-27+). As hypothesized, 54% of pts lived longer than their EST, 30% lived within 0.67 to 1.33 times their EST, 56% lived half to double their EST, and 9% lived ≤1/4 of their EST. Follow-up was too short to observe those who will live ≥3 times their EST. Independent predictors of OST were frailty (HR 2.8, 95%CI 1.6-4.9, p = 0.0004) and EST (HR 0.96, 95%CI 0.93-0.99, p = 0.03). Conclusions: Oncologists’ estimates of EST were unbiased, imprecise, and accurate for formulating scenarios for survival time. A simple, pragmatic rating of frailty by the treating oncologist was a strong predictor of OST even after accounting for their estimate of EST.

CEA response at four weeks as an early predictor for outcomes in patients (pts) with metastatic colorectal cancer (mCRC) treated with first-line cetuximab-based chemotherapy: A STEP-analysis in the JACCRO CC-05/06 trials.

543 Background: We have reported that carcinoembryonic antigen (CEA) response correlated with clinical outcomes of 1st-line cet-based therapy (Target Oncol 2017). Early tumor shrinkage (ETS) is considered to be an on-treatment biomarker for outcomes of chemotherapy; however, clinical biomarkers to predict outcomes earlier are warranted. Methods: This study included 69 pts who were assessable for CEA at baseline and 4 wks, and with observed survival time from 2 phase II trials of 1st-line therapy for KRAS exon2 wild-type mCRC; JACCRO CC-05 of cet plus FOLFOX (UMIN000004197) and CC-06 of cet plus SOX (UMIN000007022). We investigated the influence of baseline age, gender, PS, primary tumor sidedness (PTS), number of tumor sites, as well as the CEA decrease at 4 wks to the patient’s OS and PFS. Results: PTS and the CEA decrease at 4 wks were found to be important predictors to OS and PFS. Baseline CEA and CEA decrease at 4 wks were median of 31.0 (range, 1.0-20920.0) and median of 35% (range, -259%-97%), respectively. The STEP-analysis indicated that CEA response was most significantly associated with OS when a cut-off value of 50% for CEA-responder (HR 0.49, log-rank test p = 0.03). Median OS in responders (n = 25) and non-responders (n = 44) were 36.2 m and 21.5 m, respectively. When the same cut-off value was used, median PFS in responders and non-responders were 11.6 m and 6.5 m, respectively (HR 0.64, log-rank test p = 0.08). In addition, a multivariate Cox Proportional-Hazards Model with both PTS and CEA response as risk factors showed that PTS correlated with both OS and PFS. In pts with left-sided tumors, non-responders (n = 33) had shorter OS and PFS compared to responders (n = 23). In the above 2-covariate Cox proportional hazard model, adjusted HR for CEA 50% decrease is 0.55, p = 0.1; adjusted HR for PTS is 2.66, p = 0.008. Conclusions: Our analysis suggests 50% CEA decrease at 4 wks as an early on-treatment biomarker for 1st-line cet-based therapy in mCRC. It may potentially predict outcomes earlier compared to ETS. Also, CEA response at 4 wks may differentiate pts who receive more benefit from cet treatment in left-sided tumors. Clinical trial information: UMIN000004197 and UMIN000007022.

Using Large Data to Present Uncertainty for Risk Prediction in the Era of Precision Medicine: The RESPECT Algorithm for Predicted Death at End-of-Life

IntroductionIn Ontario, only 52% of people received palliative care in their last year of life, with only 20\% of those receiving it at home, which can improve the dying experience. Existing algorithms identifying people at end-of-life can potentially improve access to palliative care but are difficult for patients to understand.&#x0D; Objectives and ApproachTo predict and communicate risk of death for community dwelling older adults using a pre-specified and published approach (Trial registration NCT02779309). All assessments from community-dwelling Ontarians (N = 488,636) who received at least one home care assessment from the residential assessment instrument – home care (RAI HC) from 2007 to 2013 (N=1,331,273) were included. The algorithm used a two-step approach by rank ordering participants into 61 groups based on six-month probability of death (from Cox-proportional hazard models) and generated Kaplan-Meier five-year survival curves for each group. Median Survival time is reported with uncertainties expressed with 25th to 75th percentiles.&#x0D; ResultsThe median predicted probability of death within six-months was 0.1095 (0.1093-0.1097, 95% CI). Risk varied among the 61 groups from 0.0158 (0.0158-0.0159) to 0.9820 (0.9810-0.9830). Median observed survival time varied from 27 days (10 to 81 days, 25th and 75th percentile) in the highest risk group to 10 years (3655 days (2111 to &gt;3655 days)) in the lowest risk group. Discrimination and calibration were satisfactory between the derivation (2007-2012 assessments) and validation (2013 assessments) cohorts, with a C statistics of 0.77 and discrimination plot intercept 0.094, slope 0.914. The Kaplan-Meier five-year survival curves for each of the 61 groups will be visually represented in six different ways displaying the risk and uncertainty, and can be altered to yield information of interest specific to each patient/caregiver.&#x0D; Conclusion/ImplicationsRESPECT is adaptive and personalized, with instantaneous feedback as the user provides a response to each question. We will present RESPECT’s development and implementation processes and set up an interactive presentation of the calculator, demonstrating RESPECT’s ability to deliver patient-comprehensible end-of-life prognoses with uncertainty to patients and their caregivers.

Dimension reduction for right-censored survival regression: transformation approach

High-dimensional survival data with large numbers of predictors has become more common. The analysis of such data can be facilitated if the dimensions of predictors are adequately reduced. Recent studies show that a method called sliced inverse regression (SIR) is an effective dimension reduction tool in high-dimensional survival regression. However, it faces incapability in implementation due to a double categorization procedure. This problem can be overcome in the right-censoring type by transforming the observed survival time and censoring status into a single variable. This provides more flexibility in the categorization, so the applicability of SIR can be enhanced. Numerical studies show that the proposed transforming approach is equally good to (or even better) than the usual SIR application in both balanced and highly-unbalanced censoring status. The real data example also confirms its practical usefulness, so the proposed approach should be an effective and valuable addition to usual statistical practitioners.

Colorectal cancer in the elderly and the influence of lead time bias: better survival does not equate with improved life expectancy.

Poorer outcomes in those aged ≥80 years who undergo colorectal cancer surgery have been previously reported. Little is known about the natural history of those managed non-operatively. We explored outcomes in all patients with colorectal cancer aged ≥80 years at time of diagnosis based on treatment received. Patients ≥80 years diagnosed with colorectal cancer in one hospital trust between 1998 and 2011 were identified from a prospectively maintained database. Primary endpoints were age at diagnosis, age at death/censor and mortality at 30, 90 and 365 days. Six hundred sixty-eight patients were identified. Four hundred twelve (61.7%) underwent surgery, 44 (6.6%) received endoscopic therapy and 212 (31.7%) had no active treatment. Of those who underwent surgery, 359 (87.1%) had resectional surgery, 34 (8.3%) defunctioning only, 13 (3.2%) received bypass surgery and 6 (1.5%) had an open and close laparotomy. The mean age at diagnosis was younger in those who underwent surgical resection (83.7 years) compared to those having defunctioning surgery (84.9 years, P = 0.043), endoscopic therapy (85.1 years, P = 0.008) or no surgical intervention (85.6 years, P < 0.001). There was no significant difference in the mean age of death or censor between groups. There was no significant difference in age at death or censor between treatment groups among patients aged ≥80 years presenting with colorectal cancer, suggesting that differences in the observed survival time are heavily influenced by lead time bias. Age at death or censor should be reported in addition to survival times in this age group to enable fair comparison of outcomes.

0351 G-estimation: why does it work and what does it offer?

ObjectivesStandard data analysis procedures provide biassed answers to etiologic questions in occupational studies. G-estimation is an alternative that allows researchers to avoid healthy worker survivor bias, and its results can...

Semiparametric regression analysis for time-to-event marked endpoints in cancer studies

In cancer studies the disease natural history process is often observed only at a fixed, random point of diagnosis (a survival time), leading to a current status observation (Sun (2006). The statistical analysis of interval-censored failure time data. Berlin: Springer.) representing a surrogate (a mark) (Jacobsen (2006). Point process theory and applications: marked point and piecewise deterministic processes. Basel: Birkhauser.) attached to the observed survival time. Examples include time to recurrence and stage (local vs. metastatic). We study a simple model that provides insights into the relationship between the observed marked endpoint and the latent disease natural history leading to it. A semiparametric regression model is developed to assess the covariate effects on the observed marked endpoint explained by a latent disease process. The proposed semiparametric regression model can be represented as a transformation model in terms of mark-specific hazards, induced by a process-based mixed effect. Large-sample properties of the proposed estimators are established. The methodology is illustrated by Monte Carlo simulation studies, and an application to a randomized clinical trial of adjuvant therapy for breast cancer.

The Median Informs the Message: Accuracy of Individualized Scenarios for Survival Time Based on Oncologists' Estimates

To determine the accuracy and usefulness of oncologists' estimates of survival time in individual patients with advanced cancer. Twenty-one oncologists estimated the "median survival of a group of identical patients" for each of 114 patients with advanced cancer. Accuracy was defined by the proportions of patients with an observed survival time bounded by prespecified multiples of their estimated survival time. We expected 50% to live longer (or shorter) than their oncologist's estimate (calibration), 50% to live from half to double their estimate (typical scenario), 5% to 10% to live ≤ one quarter of their estimate (worst-case scenario), and 5% to 10% to live three or more times their estimate (best-case scenario). Estimates within 0.67 to 1.33 times observed survival were deemed precise. Discriminative value was assessed with Harrell's C-statistic and prognostic significance with proportional hazards regression. Median survival time was 11 months. Oncologists' estimates were relatively well-calibrated (61% shorter than observed), imprecise (29% from 0.67 to 1.33 times observed), and moderately discriminative (Harrell C-statistic 0.63; P = .001). The proportion of patients with an observed survival half to double their oncologist's estimate was 63%, ≤ one quarter of their oncologist's estimate was 6%, and three or more times their oncologist's estimate was 14%. Independent predictors of observed survival were oncologist's estimate (hazard ratio [HR] = 0.92; P = .004), dry mouth (HR = 5.1; P < .0001), alkaline phosphatase more than 101 U/L (HR = 2.8; P = .0002), Karnofsky performance status ≤ 70 (HR = 2.3; P = .007), prostate primary (HR = 0.23; P = .002), and steroid use (HR = 2.4; P = .02). Oncologists' estimates of survival time were relatively well-calibrated, moderately discriminative, independently associated with observed survival, and a reasonable basis for estimating worst-case, typical, and best-case scenarios for survival.