Psychosocial factors and survival of young women with breast cancer: a population-based prospective cohort study

Purpose: Most women with early-stage breast cancer believe that psychosocial factors are an important influence over whether their cancer will recur. Studies of the issue have produced conflicting results.

Patients and Methods: A population-based sample of 708 Australian women diagnosed before age 60 years with nonmetastatic breast cancer was observed for a median of 8.2 years. Depression and anxiety, coping style, and social support were assessed at a median of 11 months after diagnosis. Hazard ratios for distant disease-free survival (DDFS) and overall survival (OS) associated with psychosocial factors were estimated separately using Cox proportional hazards survival models, with and without adjustment for known prognostic factors.

Results: Distant recurrence occurred in 209 (33%) of 638 assessable patients, and 170 (24%) of 708 patients died during the follow-up period. There were no statistically significant associations between any of the measured psychosocial factors and DDFS or OS from the adjusted analyses. From unadjusted analyses, associations between greater anxious preoccupation and poorer DDFS and OS were observed (P = .02). These associations were no longer evident after adjustment for established prognostic factors; greater anxious preoccupation was associated with younger age at diagnosis (P = .03), higher tumor grade (P = .02), and greater number of involved axillary nodes (P = .008).

Conclusion: The findings do not support the measured psychosocial factors being an important influence on breast cancer outcomes. Interventions for adverse psychosocial factors are warranted to improve quality of life but should not be expected to improve survival.

SAS macros for point and interval estimation of area under the receiver operating characteristic curve for non-proportional and proportional hazards Weibull models

Aims and objectives  For prediction of risk of cardiovascular end points using survival models the proportional hazards assumption is often not met. Thus, non-proportional hazards models are more appropriate for developing risk prediction equations in such situations. However, computer program for evaluating the prediction performance of such models has been rarely addressed. We therefore developed SAS macro programs for evaluating the discriminative ability of a non-proportional hazards Weibull model developed by Anderson (1991) and that of a proportional hazards Weibull model using the area under receiver operating characteristic (ROC) curve.

Method  Two SAS macro programs for non-proportional hazards Weibull model using Proc NLIN and Proc NLP respectively and model validation using area under ROC curve (with its confidence limits) were written with SAS IML language. A similar SAS macro for proportional hazards Weibull model was also written.

Results  The computer program was applied to data on coronary heart disease incidence for a Framingham population cohort. The five risk factors considered were current smoking, age, blood pressure, cholesterol and obesity. The predictive ability of the non-proportional hazard Weibull model was slightly higher than that of its proportional hazard counterpart. An advantage of SAS Proc NLP in terms of the example provided here is that it provides significance level for the parameter estimates whereas Proc NLIN does not.

Conclusion  The program is very useful for evaluating the predictive performance of non-proportional and proportional hazards Weibull models.

Pro-oxidative synergic bactericidal effect of NO: kinetics and inhibition by nitroxides

NO plays diverse roles in physiological and pathological processes, occasionally resulting in opposing effects, particularly in cells subjected to oxidative stress. NO mostly protects eukaryotes against oxidative injury, but was demonstrated to kill prokaryotes synergistically with H2O2. This could be a promising therapeutic avenue. However, recent conflicting findings were reported describing dramatic protective activity of NO. The previous studies of NO effects on prokaryotes applied a transient oxidative stress while arbitrarily checking the residual bacterial viability after 30 or 60min and ignoring the process kinetics. If NO-induced synergy and the oxidative stress are time-dependent, the elucidation of the cell killing kinetics is essential, particularly for survival curves exhibiting a "shoulder" sometimes reflecting sublethal damage as in the linear-quadratic survival models. We studied the kinetics of NO synergic effects on H2O2-induced killing of microbial pathogens. A synergic pro-oxidative activity toward gram-negative and gram-positive cells is demonstrated even at sub-μM/min flux of NO. For certain strains, the synergic effect progressively increased with the duration of cell exposure, and the linear-quadratic survival model best fit the observed survival data. In contrast to the failure of SOD to affect the bactericidal process, nitroxide SOD mimics abrogated the pro-oxidative synergy of NO/H2O2. These cell-permeative antioxidants, which hardly react with diamagnetic species and react neither with NO nor with H2O2, can detoxify redox-active transition metals and catalytically remove intracellular superoxide and nitrogen-derived reactive species such as (•)NO2 or peroxynitrite. The possible mechanism underlying the bactericidal NO synergy under oxidative stress and the potential therapeutic gain are discussed.

Modelling grouped survival times in toxicological studies using generalized additive models

A method for combining a proportional-hazards survival time model with a bioassay model where the log-hazard function is modelled as a linear or smoothing spline function of log-concentration combined with a smoothing spline function of time is described. The combined model is fitted to mortality numbers, resulting from survival times that are grouped due to a common set of observation times, using Generalized Additive Models (GAMs). The GAM fits mortalities as conditional binomials using an approximation to the log of the integral of the hazard function and is implemented using freely-available, general software for fitting GAMs. Extensions of the GAM are described to allow random effects to be fitted and to allow for time-varying concentrations by replacing time with a calibrated cumulative exposure variable with calibration parameter estimated using profile likelihood. The models are demonstrated using data from a studies of a marine and a, previously published, freshwater taxa. The marine study involved two replicate bioassays of the effect of zinc exposure on survival of an Antarctic amphipod, Orchomenella pinguides. The other example modelled survival of the daphnid, Daphnia magna, exposed to potassium dichromate and was fitted by both the GAM and the process-based DEBtox model. The GAM fitted with a cubic regression spline in time gave a 61 % improvement in fit to the daphnid data compared to DEBtox due to a non-monotonic hazard function. A simulation study using each of these hazard functions as operating models demonstrated that the GAM is overall more accurate in recovering lethal concentration values across the range of forms of the underlying hazard function compared to DEBtox and standard multiple endpoint probit analyses.

Integrating acoustic telemetry into mark-recapture models to improve the precision of apparent survival and abundance estimates

Capture-mark-recapture models are useful tools for estimating demographic parameters but often result in low precision when recapture rates are low. Low recapture rates are typical in many study systems including fishing-based studies. Incorporating auxiliary data into the models can improve precision and in some cases enable parameter estimation. Here, we present a novel application of acoustic telemetry for the estimation of apparent survival and abundance within capture-mark-recapture analysis using open population models. Our case study is based on simultaneously collecting longline fishing and acoustic telemetry data for a large mobile apex predator, the broadnose sevengill shark (Notorhynchus cepedianus), at a coastal site in Tasmania, Australia. Cormack-Jolly-Seber models showed that longline data alone had very low recapture rates while acoustic telemetry data for the same time period resulted in at least tenfold higher recapture rates. The apparent survival estimates were similar for the two datasets but the acoustic telemetry data showed much greater precision and enabled apparent survival parameter estimation for one dataset, which was inestimable using fishing data alone. Combined acoustic telemetry and longline data were incorporated into Jolly-Seber models using a Monte Carlo simulation approach. Abundance estimates were comparable to those with longline data only; however, the inclusion of acoustic telemetry data increased precision in the estimates. We conclude that acoustic telemetry is a useful tool for incorporating in capture-mark-recapture studies in the marine environment. Future studies should consider the application of acoustic telemetry within this framework when setting up the study design and sampling program.

Amalgamation of health services in South-West Victoria: reinvention or survival?

In Australia 'the hospital' has long been considered the cornerstone of small, rural health services. However, this premise has been altered significantly by the introduction of casemix loading and diagnostic-related groups that promote a rationalised output-based model of management. In the light of these changes, many rural health services have struggled to reinvent themselves by establishing a range of service models such as Multi-purpose Service (MPS) and Health Streams, while maintaining traditional models (i.e. bush nursing centres, nursing homes and aged-care facilities). These changes are about survival. This paper analyses one such case in south-west Victoria, the Macarthur and District Community Outreach Service, and compares the outcomes with other similar Victorian rural health research projects. Particular attention is paid to the nature of the health services, the management of change and the proposed health outcomes for the local rural communities. In conclusion, it is argued that this study adds to the body of knowledge surrounding the construction of models of community health and development programming, These models impact upon future rural and remote area initiatives throughout Australia.

Parametric conditional frailty models for recurrent cardiovascular events in the lipid study

Background Analysis of recurrent event data is frequently needed in clinical and epidemiological studies. An important issue in such analysis is how to account for the dependence of the events in an individual and any unobserved heterogeneity of the event propensity across individuals.Methods We applied a number of conditional frailty and nonfrailty models in an analysis involving recurrent myocardial infarction events in the Long-Term Intervention with Pravastatin in Ischaemic Disease study. A multiple variable risk prediction model was developed for both males and females. Results A Weibull model with a gamma frailty term fitted the data better than other frailty models for each gender. Among nonfrailty models the stratified survival model fitted the data best for each gender. The relative risk estimated by the elapsed time model was close to that estimated by the gap time model. We found that a cholesterol-lowering drug, pravastatin (the intervention being tested in the trial) had significant protective effect against the occurrence of myocardial infarction in men (HR¼0.71, 95% CI0.60–0.83). However, the treatment effect was not significant in women due to smaller sample size (HR¼0.75, 95% CI 0.51–1.10). There were no significant interactions between the treatment effect and each recurrent MI event (p¼0.24 for men and p¼0.55 for women). The risk of developing an MI event for a male who had an MI event during follow-up was about 3.4 (95% CI 2.6–4.4) times the risk compared with those who did not have an MI event. The corresponding relative risk for a female was about 7.8 (95% CI 4.4–13.6). Limitations The number of female patients was relatively small compared with their male counterparts, which may result in low statistical power to find real differences in the effect of treatment and other potential risk factors.Conclusions The conditional frailty model suggested that after accounting for all the risk factors in the model, there was still unmeasured heterogeneity of the risk for myocardial infarction, indicating the effect of subject-specific risk factors. These risk prediction models can be used to classify cardiovascular disease patients into different risk categories and may be useful for the most effective targeting of preventive therapies for cardiovascular disease.

Survival analysis of time-to-event data in respiratory health research studies

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes.

Estimating cumulative exposure of wildlife to non-lethal disturbance using spatially explicit capture-recapture models

Impact assessments often focus on short-term behavioral responses of animals to human disturbance. However, the cumulative effects caused by repeated behavioral disruptions are of management concern because these effects have the potential to influence individuals' survival and reproduction. We need to estimate individual exposure rates to disturbance to determine cumulative effects. We present a new approach to estimate the spatial exposure of minke whales to whalewatching boats in Faxaflõi Bay, Iceland. We used recent advances in spatially explicit capture-recapture modeling to estimate the probability that whales would encounter a disturbance (i.e., whalewatching boat). We obtained spatially explicit individual encounter histories of individually identifiable animals using photo-identification. We divided the study area into 1-km2 grid cells and considered each cell a spatially distinct sampling unit. We used capture history of individuals to model and estimate spatial encounter probabilities of individual minke whales across the study area, accounting for heterogeneity in sampling effort. We inferred the exposure of individual minke whales to whalewatching vessels throughout the feeding season by estimating individual whale encounters with vessels using the whale encounter probabilities and spatially explicit whalewatching intensity in the same area, obtained from recorded whalewatching vessel tracks. We then estimated the cumulative time whales spent with whalewatching boats to assess the biological significance of whalewatching disturbances. The estimated exposure levels to boats varied considerably between individuals because of both temporal and spatial variations in the activity centers of whales and the whalewatching intensity in the area. However, although some whales were repeatedly exposed to whalewatching boats throughout the feeding season, the estimated cumulative time they spent with boats was very low. Although whalewatching boat interactions caused feeding disruptions for the whales, the estimated low cumulative exposure indicated that the whalewatching industry in its current state likely is not having any long-term negative effects on vital rates.

Childhood cancer in Argentina: survival 2000–2007

 Introduction: Information on the epidemiology of childhood cancer in Latin America is limited. The Argentinean Oncopaediatric Registry (ROHA) is a population-based registry active since 2000. This paper describes the 3-year survival experience of children diagnosed with cancer in Argentina during 2000–2007 by major morphological subgroup, age, sex, and geographical region of residence.
Methods: Newly diagnosed paediatric cancer cases are registered in ROHA (estimated coverage is 93% of the country’s cases). Three-year overall survival was estimated using Kaplan–Meier methods. Univariate Cox models were used to compare subgroup survival.
Results: Between 2000 and 2007, a total of 10,181 new cancer diagnoses in children aged 0–14 years were reported to the registry. Three-year overall survival (95%CI) for all cancers was 61.7% (60.7; 62.7). Specific survival for the most frequent morphological types was: leukaemias 63.3% (61.6; 64.9), lymphomas and related neoplasms 75.3% (72.7; 77.7), brain neoplasms 46.3% (43.9; 48.7), soft-tissue sarcomas 52.3% (48.0; 56.5), neuroblastomas 49.6% (44.6; 54.3), renal tumours 76.7% (72.2; 80.6), and malignant bone tumours 47.2% (42.3; 51.9). Overall survival was associated with age but not sex and varied by geographical region. Compared to other regions, patients who resided in the capital city had a significantly higher survival: 69.6% (65.8; 73.0) versus 63.5% (59.4; 67.4) in Patagonia, 63.2% (61.9; 64.5) in the central region, 58.0% (54.2; 61.7) in Cuyo, 55.6% (52.5; 58.6) in the north-east, and 55.4% (52.4; 58.2) in the north-west (all P values <0.005).
Conclusions: Of children diagnosed with cancer in Argentina, 62% survived at least 3 years after diagnosis. Even though this figure is lower than that reported for more developed countries, survival patterns by diagnosis, age and sex were quite similar. Survival was lower in the two northern regions, which are areas with higher poverty levels.

Evaluating environmental, demographic and genetic effects on population-level survival in an island endemic

The population dynamics of island species are considered particularly sensitive to variation in environmental, demographic and/or genetic processes. However, few studies have attempted to evaluate the relative importance of these processes for key vital rates in island endemics. We integrated the results of long-term capture–mark–recapture analysis, prey surveys, habitat quality assessments and molecular analysis to determine the causes of variation in the survival rates of Komodo dragons Varanus komodoensis at 10 sites on four islands in Komodo National Park, Indonesia. Using open population capture–mark–recapture methods, we ranked competing models that considered environmental, ecological, genetic and demographic effects on site-specific Komodo dragon survival rates. Site-specific survival rates ranged from 0.49 (95% CI: 0.33–0.68) to 0.92 (0.79–0.97) in the 10 study sites. The three highest-ranked models (i.e. ΔQAICc < 2) explained ∼70% of variation in Komodo dragon survival rates and identified interactions between inbreeding coefficients, prey biomass density and habitat quality as important explanatory variables. There was evidence of additive effects from ecological and genetic (e.g. inbreeding) processes affecting Komodo dragon survival rates. Our results indicate that maintaining high ungulate prey biomass and habitat quality would enhance the persistence of Komodo dragon populations. Assisted gene flow may also increase the genetic and demographic viability of the smaller Komodo dragon populations.

Drought survival strategies, dispersal potential and persistence of invertebrate species in an intermittent stream landscape

Intermittent stream systems create a mosaic of aquatic habitat that changes through time, potentially challenging freshwater invertebrate dispersal. Invertebrates inhabiting these mosaics may show stronger dispersal capacity than those in perennial stream systems. To relate different combinations of dispersal and drought survival strategies to species persistence, we compared the distribution and dispersal potential of six invertebrate species across all streams in a montane landscape where drying is becoming increasingly frequent and prolonged. Invertebrates were collected from seventeen streams in the Victoria Range, Grampians National Park, Victoria, Australia. The species analysed were as follows: the caddisflies Lectrides varians Moseley (Leptoceridae) and Agapetus sp. (Glossosomatidae); the mayflies Nousia AV1 and Koorrnonga AV3 (Leptophlebiidae); the water penny beetle Sclerocyphon sp. (Psephenidae); and a freshwater crayfish Geocharax sp. nov. 1 (Parastacidae). These species were widespread in the streams and varied in their dispersal and drought survival strategies. The distribution of each species across the Victoria Range, their drought responses and within-stream habitat associations were determined. Hypotheses of the dispersal capacity and population structure for each species were developed and compared to four models of gene flow: Death Valley Model (DVM), Stream Hierarchy Model (SHM), Headwater Model (HM) or panmixia (PAN). Molecular genetic methods were then used to infer population structure and dispersal capacity for each species. The large caddisfly Lectrides resisted drought through aestivation and was panmictic (PAN) indicating strong dispersal capacity. Conversely, the small caddisfly Agapetus relied on perennially flowing reaches and gene flow was limited to short distances among stream headwaters, resembling the HM. Both mayflies depended on perennial surface water during drying and showed evidence of gene flow among streams: Koorrnonga mainly dispersed along stream channels within catchments, resembling the SHM, whereas Nousia appeared to disperse across land by adult flight. Sclerocyphon relied on perennial water to survive drying and showed an unusual pattern of genetic structure that indicated limited dispersal but did not resemble any of the models. Geocharax survived drought through aestivation or residence in perennial pools, and high levels of genetic structure indicated limited dispersal among streams, resembling the DVM. Despite good knowledge of species' drought survival strategies, the population structure of four species differed from predictions. Dispersal capacity varied strongly among species; most species were poor dispersers and only one species showed panmixia. Therefore, intermittent stream species may not necessarily be better dispersers than those in perennial streams. Species showing strong drought resistance strategies differed in dispersal capacity. Knowledge of life-history characteristics, distribution and refuge use does not necessarily enable successful prediction of invertebrate dispersal pathways or population structure. Dispersal among intermittent streams may be restricted to relatively short distances (km) for most invertebrate species. Thus, frequent drought refuges (perennial water) that provide strong connectivity to subpopulations through stream flow (hydrological dispersal), or continuous terrestrial vegetation (flight dispersal), will be critical to maintain genetic diversity, adaptability and population persistence.