A flexible B-spline model for multiple longitudinal biomarkers and survival

Often when jointly modeling longitudinal and survival data, we are interested in a multivariate longitudinal measure that may not fit well by linear models. To overcome this problem, we propose a joint longitudinal and survival model that has a nonparametric model for the longitudinal markers. We use cubic B-splines to specify the longitudinal model and a proportional hazards model to link the longitudinal measures to the hazard. To fit the model, we use a Markov chain Monte Carlo algorithm. We select the number of knots for the cubic B-spline model using the Conditional Predictive Ordinate (CPO) and the Deviance Information Criterion (DIC). The method and model selection approach are validated in a simulation. We apply this method to examine the link between viral load, CD4 count, and time to event in data from an AIDS clinical trial. The cubic B-spline model provides a good fit to the longitudinal data that could not be obtained with simple parametric models.     

Individual risk prediction: Comparing random forests with Cox proportional-hazards model by a simulation study

With big data becoming widely available in healthcare, machine learning algorithms such as random forest (RF) that ignores time-to-event information and random survival forest (RSF) that handles right-censored data are used for individual risk prediction alternatively to the Cox proportional hazards (Cox-PH) model. We aimed to systematically compare RF and RSF with Cox-PH. RSF with three split criteria [log-rank (RSF-LR), log-rank score (RSF-LRS), maximally selected rank statistics (RSF-MSR)]; RF, Cox-PH, and Cox-PH with splines (Cox-S) were evaluated through a simulation study based on real data. One hundred eighty scenarios were investigated assuming different associations between the predictors and the outcome (linear/linear and interactions/nonlinear/nonlinear and interactions), training sample sizes (500/1000/5000), censoring rates (50%/75%/93%), hazard functions (increasing/decreasing/constant), and number of predictors (seven, 15 including noise variables). Methods' performance was evaluated with time-dependent area under curve and integrated Brier score. In all scenarios, RF had the worst performance. In scenarios with a low number of events (⩽70), Cox-PH was at least noninferior to RSF, whereas under linearity assumption it outperformed RSF. Under the presence of interactions, RSF performed better than Cox-PH as the number of events increased whereas Cox-S reached at least similar performance with RSF under nonlinear effects. RSF-LRS performed slightly worse than RSF-LR and RSF-MSR when including noise variables and interaction effects. When applied to real data, models incorporating survival time performed better. Although RSF algorithms are a promising alternative to conventional Cox-PH as data complexity increases, they require a higher number of events for training. In time-to-event analysis, algorithms that consider survival time should be used.     

Effect of Radiotransmitters on Northern Bobwhite Annual Survival

Abstract: Radiotelemetry has been widely used in northern bobwhite (Colinus virginianus) research to estimate survival rates and other demographic parameters. Biologists have used this knowledge to study bobwhite ecology, develop management theory, and base management actions. We tested the assumption that radiotransmitters do not bias survival rates of bobwhites by comparing survival rates of banded bobwhites with and without radiotransmitters on Tall Timbers Research Station (TTRS) from 1999 to 2004. We used Burnham's model in Program MARK and model-selection procedures to determine relative importance of year, gender, and radiotagged status on annual survival rates and recovery processes. Three plausible models (relative quasi-likelihood Akaike's Information Criterion [ΔQAIC_c] < 3) included year dependence in survival and an additive effect of gender but no radiotransmitter effect. Models including a radiotransmitter effect in survival were >8 ΔQAIC_c from the top models, had low Akaike model weights (w_i < 0.007), and low importance weight (S̀w_i(radio) = 0.01). We also compared band—recapture survival estimates from the QAIC_c minimizing model to staggered entry Kaplan—Meier (KM) survival estimates from 2000 to 2003. Annual KM survival estimates of male and female bobwhites were within the 95% confidence interval of band—recapture estimates in 7 of 8 comparisons. We conclude that radiotelemetry is a reliable technique for determining bobwhite survival. Managers should view information from properly conducted telemetry research as reliable and useful for management.     

Estimating the proportion cured of cancer: Some practical advice for users

BackgroundCure models can provide improved possibilities for inference if used appropriately, but there is potential for misleading results if care is not taken. In this study, we compared five commonly used approaches for modelling cure in a relative survival framework and provide some practical advice on the use of these approaches.Patients and methodsData for colon, female breast, and ovarian cancers were used to illustrate these approaches. The proportion cured was estimated for each of these three cancers within each of three age groups. We then graphically assessed the assumption of cure and the model fit, by comparing the predicted relative survival from the cure models to empirical life table estimates.ResultsWhere both cure and distributional assumptions are appropriate (e.g., for colon or ovarian cancer patients aged <75 years), all five approaches led to similar estimates of the proportion cured. The estimates varied slightly when cure was a reasonable assumption but the distributional assumption was not (e.g., for colon cancer patients ≥75 years). Greater variability in the estimates was observed when the cure assumption was not supported by the data (breast cancer).ConclusionsIf the data suggest cure is not a reasonable assumption then we advise against fitting cure models. In the scenarios where cure was reasonable, we found that flexible parametric cure models performed at least as well, or better, than the other modelling approaches. We recommend that, regardless of the model used, the underlying assumptions for cure and model fit should always be graphically assessed.     

Ethnic,racial and socioeconomic disparities in breast cancer survival in two Brazilian capitals between 1996 and 2012

ObjectiveTo study the impact of socio-economic status and ethno-racial strata on excess mortality hazard and net survival of women with breast cancer in two Brazilian state capitals.MethodWe conducted a survival analysis with individual data from population-based cancer registries including women with breast cancer diagnosed between 1996 and 2012 in Aracaju and Curitiba. The main outcomes were the excess mortality hazard (EMH) and net survival. The associations of age, year of diagnosis, disease stage, race/skin colour and socioeconomic status (SES) with the excess mortality hazard and net survival were analysed using multi-level spline regression models, modelled as cubic splines with knots at 1 and 5 years of follow-up.ResultsA total of 2045 women in Aracaju and 7872 in Curitiba were included in the analyses. The EMH was higher for women with lower SES and for black and brown women in both municipalities. The greatest difference in excess mortality was seen between the most deprived women and the most affluent women in Curitiba, hazard ratio (HR) 1.93 (95%CI 1.63–2.28). For race/skin colour, the greatest ratio was found in Curitiba (HR 1.35, 95%CI 1.09–1.66) for black women compared with white women. The most important socio-economic difference in net survival was seen in Aracaju. Age-standardised net survival at five years was 55.7% for the most deprived women and 67.2% for the most affluent. Net survival at eight years was 48.3% and 61.0%, respectively. Net survival in Curitiba was higher than in Aracaju in all SES groups.”ConclusionOur findings suggest the presence of contrasting breast cancer survival expectancy in Aracaju and Curitiba, highlighting regional inequalities in access to health care. Lower survival among brown and black women, and those in lower SES groups indicates that early detection, early diagnosis and timely access to treatment must be prioritized to reduce inequalities in outcome among Brazilian women.     

Bayesian regularization for flexible baseline hazard functions in Cox survival models

Fully Bayesian methods for Cox models specify a model for the baseline hazard function. Parametric approaches generally provide monotone estimations. Semi‐parametric choices allow for more flexible patterns but they can suffer from overfitting and instability. Regularization methods through prior distributions with correlated structures usually give reasonable answers to these types of situations. We discuss Bayesian regularization for Cox survival models defined via flexible baseline hazards specified by a mixture of piecewise constant functions and by a cubic B‐spline function. For those “semi‐parametric” proposals, different prior scenarios ranging from prior independence to particular correlated structures are discussed in a real study with microvirulence data and in an extensive simulation scenario that includes different data sample and time axis partition sizes in order to capture risk variations. The posterior distribution of the parameters was approximated using Markov chain Monte Carlo methods. Model selection was performed in accordance with the deviance information criteria and the log pseudo‐marginal likelihood. The results obtained reveal that, in general, Cox models present great robustness in covariate effects and survival estimates independent of the baseline hazard specification. In relation to the “semi‐parametric” baseline hazard specification, the B‐splines hazard function is less dependent on the regularization process than the piecewise specification because it demands a smaller time axis partition to estimate a similar behavior of the risk.     

Effects of the length of central cancer registry operations on identification of subsequent cancers and on survival estimates

BackgroundPopulation-based cancer survival analyses have traditionally been based on the first primary cancer. Recent studies have brought this practice into question, arguing that varying registry reference dates affect the ability to identify earlier cancers, resulting in selection bias. We used a theoretical approach to evaluate the extent to which the length of registry operations affects the classification of first versus subsequent cancers and consequently survival estimates.MethodsSequence number central was used to classify tumors from the New York State Cancer Registry, diagnosed 2001–2010, as either first primaries (value = 0 or 1) or subsequent primaries (≥2). A set of three sequence numbers, each based on an assumed reference year (1976, 1986 or 1996), was assigned to each tumor. Percent of subsequent cancers was evaluated by reference year, cancer site and age. 5-year relative survival estimates were compared under four different selection scenarios.ResultsThe percent of cancer cases classified as subsequent primaries was 15.3%, 14.3% and 11.2% for reference years 1976, 1986 and 1996, respectively; and varied by cancer site and age. When only the first primary was included, shorter registry operation time was associated with slightly lower 5-year survival estimates. When all primary cancers were included, survival estimates decreased, with the largest decreases seen for the earliest reference year.ConclusionsRegistry operation length affected the identification of subsequent cancers, but the overall effect of this misclassification on survival estimates was small. Survival estimates based on all primary cancers were slightly lower, but might be more comparable across registries.     

Correcting bias due to missing stage data in the non-parametric estimation of stage-specific net survival for colorectal cancer using multiple imputation

BackgroundPopulation-based net survival by tumour stage at diagnosis is a key measure in cancer surveillance. Unfortunately, data on tumour stage are often missing for a non-negligible proportion of patients and the mechanism giving rise to the missingness is usually anything but completely at random. In this setting, restricting analysis to the subset of complete records gives typically biased results. Multiple imputation is a promising practical approach to the issues raised by the missing data, but its use in conjunction with the Pohar-Perme method for estimating net survival has not been formally evaluated.MethodsWe performed a resampling study using colorectal cancer population-based registry data to evaluate the ability of multiple imputation, used along with the Pohar-Perme method, to deliver unbiased estimates of stage-specific net survival and recover missing stage information. We created 1000 independent data sets, each containing 5000 patients. Stage data were then made missing at random under two scenarios (30% and 50% missingness).ResultsComplete records analysis showed substantial bias and poor confidence interval coverage. Across both scenarios our multiple imputation strategy virtually eliminated the bias and greatly improved confidence interval coverage.ConclusionsIn the presence of missing stage data complete records analysis often gives severely biased results. We showed that combining multiple imputation with the Pohar-Perme estimator provides a valid practical approach for the estimation of stage-specific colorectal cancer net survival. As usual, when the percentage of missing data is high the results should be interpreted cautiously and sensitivity analyses are recommended.     

Probabilities of dying from cancer and other causes in French cancer patients based on an unbiased estimator of net survival: A study of five common cancers

BackgroundNet survival is the survival that would be observed if cancer were the only possible cause of death. Although it is an important epidemiological tool allowing temporal or geographical comparisons, it cannot inform on the “crude” probability of death of cancer patients; i.e., when taking into account other possible causes of deaths.MethodsIn this work, we provide estimates of the crude probabilities of death from cancer and from other causes as well as the probability of being alive up to ten years after cancer diagnosis according to the age and year of diagnosis. Based on a flexible excess hazard model providing unbiased estimates of net survival, our methodology avoids the pitfalls associated with the use of the cause of death. We used data from FRANCIM, the French network of cancer registries, and studied five common cancer sites: head and neck, breast, prostate, lung, and colorectal cancers.ResultsFor breast, prostate, and colorectal cancers, the impact of the other causes on the total probability of death increased with the age at diagnosis whereas it remained negligible for lung and head and neck cancers whatever the age. For breast, prostate, and colorectal cancer, the more recently was the cancer diagnosed, the less was the probability of death from cancer.ConclusionThe crude probability of death is an intuitive concept that may prove particularly useful in choosing an appropriate treatment, or refining the indication of a screening strategy by allowing the clinician to estimate the proportion of cancer patients who will die specifically from cancer.     

Survival and Recovery Rate of Canada Geese Staging in Interior Alaska

Abstract: Lesser Canada geese (Branta canadensis parvipes) are indistinguishable from other subspecies of small Canada geese on the wintering grounds using current survey methods. Consequently, managers are unable to adequately measure their abundance. Without direct estimates of abundance, researchers often use estimates of vital rates that influence abundance (e.g., annual survival) to monitor potential impact of harvest on the population. Based on capture and re-sighting data records of 567 geese marked from 1994 through 1998, we calculated annual survival and recovery rates for different age and sex classes of white-cheeked geese staging in interior Alaska. We compared those survival and recovery rates with those of other neck-collared white-cheeked geese. The best approximating model allowed survival to vary by age class while holding Seber's recovery probability (r̂) constant over sex, age class, and time. We estimated annual survival to be 0.49 (SE = 0.05) for hatch-year geese and 0.68 (SE = 0.03) for after-hatch-year geese based on the weighted average of all models with a change in Akaike's Information Criterion adjusted for small sample size and lack of fit < 4. Estimates of annual survival of white-cheeked geese in this study are among the lowest and recovery estimates are among the highest for migratory populations of neck-collared geese. Low survival estimates of Canada geese in our study suggest that harvest rates may be higher than in many other populations. Surveys to estimate abundance or other population parameters such as reproductive success and recruitment are necessary to determine whether this population is self-sustaining. Furthermore, we recommend monitoring abundance and harvest of small white-cheeked geese east and west of the Cascade Mountain Range separately to better determine harvest pressure on white-cheeked geese wintering east of the Cascades.     

Survival of resident and translocated greater sage-grouse in Strawberry Valley,Utah: A 13-year study

Survival of greater sage-grouse (Centrocercus urophasianus) has been well described in large populations across the species range. Very little published information exists, however, on survival rates of translocated sage-grouse or grouse from a long-term (>10 yr) study. Our objectives were to estimate seasonal and annual survival rates; assess differences in survival between resident and translocated, adult and yearling, and male and female sage-grouse; identify environmental and behavioral factors associated with survival; and assess the influence of mammalian predator control on survival rates of radio-marked sage-grouse in Strawberry Valley, Utah from 1998 to 2010. We used a 2-stage model selection approach using Akaike's Information Criterion corrected for sample size (AIC_c) with known-fate models in Program MARK to evaluate the influences of seasonal, annual, demographic, and behavioral effects on survival rates of sage-grouse. We captured and fitted 535 individual sage-grouse (male and female, resident and translocated) with radio transmitters over a 13-year period and monitored them weekly. The top model of survival, which accounted for 22% of the AIC_c weight, included 3 seasons that varied by year where rates were influenced by residency, sex, and whether a female initiated a nest. A group-level covariate for the number of canids killed each year received some support as this variable improved model fit compared to identical models without it, although confidence intervals around β estimates overlapped zero slightly. All other demographic or environmental variables showed little or no support. Annual estimates of survival for females ranged between 28% and 84% depending on year and translocation source. Survival was consistently highest during the fall–winter months with a mean monthly survival rate of 0.97 (95% CI = 0.96–0.98). The lack of a control site and other potential confounding factors limit the extent of our inference with respect to predator control. Nonetheless, we suggest managers consider enhancing nesting habitat, translocating sage-grouse, and possibly controlling predators to improve survival rates of sage-grouse. © The Wildlife Society, 2013     

Correcting observer effect bias in estimates of nesting success of a coastal bird,the White-fronted Plover Charadrius marginatus

Capsule A recently developed observer-effects model gives better estimates than the survival model.

Aims To compare the performance of the traditional survival model with that of an observer-effects model in estimating nesting success.

Methods We used survival data from 654 nests in a covariate-dependent extension of a maximum-likelihood estimator model to simultaneously estimate daily survival rate and observer effect on nesting success in four different sectors of a study site. Standard likelihood-ratio tests and Akaike's information criterion differences were used to compare model performances.

Results A significant (negative) observer effect was detected in one of four sectors of the study area, and was probably attributable to small mammal predators. Despite significantly higher predation risk during the laying period, there was a significant positive observer effect during the laying period, contrasting with a weak negative effect during the incubation period, suggesting that an observer's tracks around unattended nests reduced predation risk. Likelihood-ratio tests indicated a significantly better fit using the observer effects model versus the Mayfield survival model in four of six scenarios.

Conclusion The observer-effects model accounts for bias in estimates of nest survival rates, and is preferred over the survival model because the inclusion of observer effect as a variable does not over-parameterize the model.     

How Size and Condition Influence Survival and Cause-Specific Mortality of Female Elk

The size of animal populations fluctuates with number of births, rate of immigration, rate of emigration, and number of deaths. For many ungulate populations, adult female survival is the most important factor influencing population growth. Therefore, increased understanding of survival and causes of mortality for adult females is fundamental for conservation and management. The objectives of our study were to quantify survival rates of female elk (Cervus canadensis) and determine cause-specific mortality. We predicted that hunter harvest would be the leading cause of mortality. Further, we predicted that hunters would harvest animals that were in prime age (2–9 yr) and in better condition than elk predated by mountain lions (Puma concolor). From 2015 to 2017, we captured 376 female elk in central Utah, USA. We assessed body size and condition of captured elk, fitted each animal with a global positioning system-collar, and determined cause of death when we received mortality signals. We estimated survival using Kaplan-Meier estimates and Cox proportional hazard models within an Akaike's Information Criterion model selection framework to identify covariates that influenced survival. We analyzed differences in size and condition measurements between harvested elk and predated elk using analysis of variance tests. Our best model indicated consistent survival across years; mean survival was 78.3 ± 3.5% (SE) including hunter harvest and 95.5 ± 1.7% without hunter harvest. In decreasing order of importance, elk mortality occurred from hunter harvest (21.2%), mountain lion predation (3.7%), depredation removal (0.5%), automobile collision (0.3%), disease (0.3%), complications during calving (0.3%), and those characterized as undetermined (1.3%). Neck circumference and body length were negatively associated with survival, suggesting that larger animals in good condition had lower survival as a result of hunter harvest. Individuals that died because of cougar predation were smaller and had less loin muscle than the average animal. Hunters removed large, healthy, prime-aged females, individuals that likely have a greater effect on population growth than elk lost to other predators. If the proportion of larger, healthy females in the population begins to decline, hunting practices may require adjustment because hunters may be removing individuals with the greatest reproductive value. © 2021 The Wildlife Society.     

Factors Influencing Survival of Radiotagged and Banded Northern Bobwhites in Georgia

Abstract: Numerous studies of behavior and ecology of northern bobwhites (Colinus virginianus) have depended on radiotagging and telemetry for data collection. Excluding the presumably short-term effects of trapping, handling, and attaching radiotransmitters, researchers often assume that little bias is associated with estimating survival and behavioral parameters associated with this technique. However, researchers have not adequately examined these effects on organisms being investigated and have thus assumed demographic information obtained from such methods are valid. In light of this conjecture, it is imperative to evaluate methodological assumptions to ensure research is statistically valid and biologically meaningful. Therefore, we used Burnham's model and program MARK to analyze survival estimates of individually banded and radiotagged bobwhites during an 8-year period (1997–2004) consisting of 6,568 individuals (2,527 radiotagged) via combined analysis of mark—recapture, dead recovery (via harvest), and radiotelemetry data to test the effects of radiotransmitters on bobwhite survival. We also compared band—recapture survival estimates to Kaplan—Meier survival estimates, and we examined the effects of various other factors (e.g., temporal, spatial) on bobwhite survival. Based on Akaike's model selection criterion, the best model including the radiotransmitter covariate (Akaike's Information Criterion adjusted for small sample size bias and overdispersion relative value = 0.72) did not explain more of the variation in survival than models without this effect. Thus, we found the effect of radiotransmitters as negligible. Bobwhite survival varied relative to spatial (e.g., site), temporal (e.g., yr and season), and gender effects. Average annual survival for the 8-year period was 22.76% (1.50 SE) for banded-only and 21.72% (1.49 SE) for radiotagged birds. Survival rate varied annually, ranging from 12.42% (7.51 SE) to 37.16% (8.27 SE), and seasonally, ranging from 23.82% (2.71 SE) to 65.06% (3.23 SE); however, between group (banded-only, radiotagged) survival differences were still inconsequential. We conclude that for our study, radiotelemetry provided reliable survival estimates of an intensively managed bobwhite population, where supplemental food was provided, and this information provided useful data to make practical habitat management decisions. We believe that future radiotelemetry studies would benefit as a whole if researchers conducted similar analyses prior to presenting their results from radiotelemetry data, especially for populations that are more food limited.     

Survival of cancer patients in urban and rural areas of Germany—A comparison

BackgroundCancer care services including cancer prevention activities are predominantly localised in central cities, potentially causing a heterogeneous geographic access to cancer care. The question of an association between residence in either urban or rural areas and cancer survival has been analysed in other parts of the world with inconsistent results. This study aims at a comparison of age-standardised 5-year survival of cancer patients resident in German urban and rural regions using data from 11 population-based cancer registries covering a population of 33 million people.Material and methodsPatients diagnosed with cancers of the most frequent and of some rare sites in 1997–2006 were included in the analyses. Places of residence were assigned to rural and urban areas according to administrative district types of settlement structure. Period analysis and district type specific population life tables were used to calculate overall age-standardised 5-year relative survival estimates for the period 2002–2006. Poisson regression models for excess mortality (relative survival) were used to test for statistical significance.ResultsThe 5-year relative survival estimates varied little among district types for most of the common sites with no consistent trend. Significant differences were found for female breast cancer patients and male malignant melanoma patients resident in city core regions with slightly better survival compared to all other district types, particularly for patients aged 65 years and older.ConclusionWith regard to residence in urban or rural areas, the results of our study indicate that there are no severe differences concerning quality and accessibility of oncological care in Germany among different district types of settlement.     

Integrated survival analysis using an event‐time approach in a Bayesian framework

Event‐time or continuous‐time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval‐censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece‐wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤ 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at <5 days old and were lower for chicks with larger birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event‐time analyses can be used by eliminating the need for having completely known fate data.     

Preoperative prealbumin-to-fibrinogen ratio to predict survival outcomes in hepatocellular carcinoma patients after hepatic resection

BackgroundThis study aimed to evaluate the clinical application of the preoperative prealbumin-to-fibrinogen ratio (PFR) in the clinical diagnosis of hepatocellular carcinoma (HCC) patients and its prognostic value.MethodsThe clinical and laboratory data of 269 HCC patients undergoing surgical treatment from January 2012 to January 2017 in Taizhou Hospital were retrospectively analysed. The Cox regression model was used to analyse the correlation between the PFR and other clinicopathological factors in overall survival (OS) and disease-free survival (DFS).ResultsCox regression analysis showed that the PFR (hazard ratio (HR)=2.123; 95% confidence interval (95% CI), 1.271-3.547; P=0.004) was an independent risk factor affecting the OS of HCC patients. Furthermore, a nomogram was built based on these risk factors. The C-index for the OS nomogram was 0.715.ConclusionsNomograms based on the PFR can be recommended as the correct and actual model to evaluate the prognosis of patients with HCC.     

Black Brant Harvest,Density Dependence,and Survival: A Record of Population Dynamics

ABSTRACT We analyzed 53 years of banding and band recovery data along with estimates of harvest and population size to assess the role of harvest and density dependence in survival patterns and population dynamics of black brant (Branta bernicla nigricans) over the period 1950–2003. The black brant population has declined steadily since complete annual surveys began in 1960, so the role of harvest in the dynamics of this population is of considerable interest. We used Brownie models implemented in Program MARK to analyze banding data. In some models, we incorporated estimated sport harvest to test hypotheses about the role of harvest in survival. We also examined the hypothesis of density-dependent regulation of mortality by incorporating estimates of population size as a covariate into models of survival. For a shorter period (1985–2003), we also assessed hypotheses about the role of subsistence harvest and predation as sources of mortality. The best supported model of variation in survival and band recovery allowed survival rates to vary among 2 age classes (juv, second-yr plus ad brant) and the 2 sexes. We constrained survival probabilities to be constant within decades but allowed them to vary among decades. We also constrained band recovery rates to be constant within decades and to vary in parallel among age and sex classes. We were limited to decade-specific estimates of survival and band recovery rates because some years before 1984 lacked any banding, and banding in some other years was sparse. A competitive model constrained survival estimates to be the same for males and females. No model containing harvest or population size was competitive with models lacking these covariates (relative quasi-Akaike's Information Criterion adjusted for small sample size [βQAIC_c] > 13). In the best supported model, band recovery rates declined from 0.038 ± 0.0028 (F) and 0.040 ± 0.0031 (M) to 0.007 ± 0.0007 (F) and 0.007 ± 0.0007 (M) between the 1950s and 2000s, a clear indication that harvest rates declined over this period. Survival rates increased from 0.70 ± 0.02 and 0.71 ± 0.02 for adult males and females, respectively, in the 1950s to 0.88 ± 0.009 and 0.88 ± 0.01 for males and females, respectively, in the 1990s. Survival rates in the 1990s were among the highest estimated for brant and did not increase in the 2000s with additional reductions in sport harvest. For the shorter data set from 1985 to 2003, models containing covariates for either sport or subsistence harvest were less competitive than models lacking these terms (βQAIC_c > 3). For the best model containing subsistence harvest, the estimate of β linking subsistence harvest to survival, although imprecisely estimated, was near zero (β = −0.04 ± 0.30), consistent with the hypothesis that subsistence harvest had little impact on survival during this period. We conclude that while harvest likely influenced survival and population dynamics in earlier decades, it is most likely that continued population decline at least since 1990 is a result of low recruitment.     

Survival of White-Tailed Deer Fawns in Southern Illinois

Abstract: Survival of white-tailed deer (Odocoileus virginianus) fawns has been quantified throughout much of North America. However, few studies have assessed the influence of intrinsic factors (e.g., fawn age and birth mass) and habitat on fawn survival. During 2002-2004, we captured and radiocollared 166 fawns in southern Illinois, USA, to estimate survival rates, determine causes of mortality, and identify factors influencing fawn survival. We used a known fates model in program MARK to estimate survival rates and compare explanatory models based on Akaike's Information Criterion corrected for small sample sizes (AIC_c). We developed 2 candidate sets of a priori models to quantify factors influencing fawn survival: model set 1 included intrinsic factors and model set 2 focused on habitat variables. We recorded 64 mortalities and the overall survival rate was 0.59 (95% CI = 0.51-0.68). Predation was the leading source of mortality (64%) and coyotes (Canis latrans) were the most prominent predator. For model set 1, model {S_{age X year}} had the lowest AIC_c value suggesting that the age at mortality varied among capture years. For model set 2, model {S_{landscape+forest}} had the lowest AIC_c value and indicated that areas inhabited by surviving fawns were characterized by a few large (i.e., > 5 ha) irregular forest patches adjacent to several small nonforest patches, and survival areas also contained more edge habitat than mortality areas. Due to the magnitude of coyote predation, survival areas could have represented landscapes where coyotes were less effective at locating and capturing fawns when compared to mortality areas. This study was the first account of macrohabitat characteristics directly influencing fawn survival. Wildlife managers can use this information to determine how habitat management activities may affect deer populations.     

Socioeconomic status and cancer survival in Brazil: Analysis of population data from the municipalities of Aracaju and Curitiba, 1996–2012

IntroductionThe association between socioeconomic status and cancer prognosis has been demonstrated in several countries. Despite the existence of indirect evidence of this phenomenon in Brazil, few studies in this regard are available.ObjectivesThe objective of the present study is to analyse socioeconomic related survival gaps for patients diagnosed with breast, cervical, lung, prostate, and colorectal cancer in the cities of Aracaju (SE) and Curitiba (PR).MethodsUsing population-based data, we estimated net survival by tumour site, year of diagnosis, socioeconomic status and local of residence. Net survival estimation was done with multilevel parametric model allowing flexible spline functions do estimate excess mortality hazards.Results28,005 cases were included in survival analysis. Five-year net survival showed positive association with SES. Intermunicipal survival gaps favouring Aracaju where prominent for breast (reaching 16,1% in 5 years)ObjectivesStudy the impact of socioeconomic factors on cancer survival in two Brazilian capitals. Methods: Survival analysis using population-based cancer data including patients diagnosed with breast, lung, prostate, cervical and colorectal cancer between 1996 and 2012 in Aracaju and Curitiba. Outcomes were excessive mortality hazard (EMH) and 5- and 8-years net survival (NS). The association of race/skin color and socioeconomic level (SES) with EMH and net survival were analyzed using a multilevel regression model with flexible splines.Results28,005 cases were included, 6636 from Aracaju and 21,369 from Curitiba. NS for all diseases studied increased more prominently for Curitiba population. We observed NS gap between the populations of Aracaju and Curitiba that increased or remained stable during the study period, with emphasis on the growth of the difference in NS of lung and colon cancer (among men). Only for cervical cancer and prostate cancer there was a reduction in the intermunicipal gaps. 5-year NS for breast cancer in Aracaju ranged from 55.2% to 73.4% according to SES. In Curitiba this variation was from 66.5% to 83.8%.ConclusionThe results of the present study suggests widening of socioeconomic and regional inequalities in the survival of patients with colorectal, breast, cervical, lung and prostate cancers in Brazil during the 1990 s and 2000 s