Net survival, the one that would be observed if cancer were the only cause of death, is the most appropriate indicator to compare cancer mortality between areas or countries. Several parametric and non-parametric methods have been developed to estimate net survival, particularly when the cause of death is unknown. These methods are based either on the relative survival ratio or on the additive excess hazard model, the latter using the general population mortality hazard to estimate the excess mortality hazard (the hazard related to net survival). The present work used simulations to compare estimator abilities to estimate net survival in different settings such as the presence/absence of an age effect on the excess mortality hazard or on the potential time of follow-up, knowing that this covariate has an effect on the general population mortality hazard too. It showed that when age affected the excess mortality hazard, most estimators, including specific survival, were biased. Only two estimators were appropriate to estimate net survival. The first is based on a multivariable excess hazard model that includes age as covariate. The second is non-parametric and is based on the inverse probability weighting. These estimators take differently into account the informative censoring induced by the expected mortality process. The former offers great flexibility whereas the latter requires neither the assumption of a specific distribution nor a model-building strategy. Because of its simplicity and availability in commonly used software, the nonparametric estimator should be considered by cancer registries for population-based studies.

Generalized pairwise comparisons have been proposed to permit a comprehensive assessment of several prioritized outcomes between two groups of observations. This procedure estimates Δ, the net chance of a better outcome with treatment than with control by comparing the patients outcomes among all possible pairs taking one patient from the treatment group and one patient from the control group. For time to event outcomes, the standard procedure of generalized pairwise comparisons is analogous to the Gehan's modification of the Mann-Whitney test which is biased in presence of censored observation and less powerful than Efron's modification of this test. We adapt Efron's modification to generalized pairwise comparisons. We show how a pairwise contribution to Δ can be calculated from the estimates of the survival function in the presence of right-censored data. We performed a simulation study to assess the bias, the type I error and the power of the new procedure. The estimate of Δ with the new procedure is only slightly biased even in presence of heavy censoring. We also show how this bias can be corrected when only one time-to-event outcome is analyzed. The new procedure has higher power in most cases compared to the standard procedure.

The net chance of a longer survival is useful whether or not the assumption of proportional hazards is met in the analysis of survival end points and may be helpful as a measure of treatment benefit that has direct relevance to patients and health care professionals.

The excess hazard regression model is an approach developed for the analysis of cancer registry data to estimate net survival, that is, the survival of cancer patients that would be observed if cancer was the only cause of death. Cancer registry data typically possess a hierarchical structure: individuals from the same geographical unit share common characteristics such as proximity to a large hospital that may influence access to and quality of health care, so that their survival times might be correlated. As a consequence, correct statistical inference regarding the estimation of net survival and the effect of covariates should take this hierarchical structure into account. It becomes particularly important as many studies in cancer epidemiology aim at studying the effect on the excess mortality hazard of variables, such as deprivation indexes, often available only at the ecological level rather than at the individual level. We developed here an approach to fit a flexible excess hazard model including a random effect to describe the unobserved heterogeneity existing between different clusters of individuals, and with the possibility to estimate non-linear and time-dependent effects of covariates. We demonstrated the overall good performance of the proposed approach in a simulation study that assessed the impact on parameter estimates of the number of clusters, their size and their level of unbalance. We then used this multilevel model to describe the effect of a deprivation index defined at the geographical level on the excess mortality hazard of patients diagnosed with cancer of the oral cavity. Copyright © 2016 John Wiley & Sons, Ltd.

Net survival, the survival which might occur if cancer was the only cause of death, is a major epidemiological indicator required for international or temporal comparisons. Recent findings have shown that all classical methods used for routine estimation of net survival from cancer-registry data, sometimes called ''relative-survival methods,'' provide biased estimates. Meanwhile, an unbiased estimator, the Pohar-Perme estimator (PPE), was recently proposed. Using real data, we investigated the magnitude of the errors made by four ''relative-survival'' methods (Ederer I, Hakulinen, Ederer II and a univariable regression model) vs. PPE as reference and examined the influence of time of follow-up, cancer prognosis, and age on the errors made. The data concerned seven cancer sites (2,51,316 cases) collected by FRANCIM cancer registries. Net survivals were estimated at 5, 10 and 15 years postdiagnosis. At 5 years, the errors were generally small. At 10 years, in good-prognosis cancers, the errors made in nonstandardized estimates with all classical methods were generally great (12.7 to 19% points in prostate cancer) and increased in age-class estimations (vs. 5-year ones). At 15 years, in bad-or average-prognosis cancers, the errors were often substantial whatever the nature of the estimation. In good-prognosis cancers, the errors in nonstandardized estimates of all classical methods were great and sometimes very important. With all classical methods, great errors occurred in age-class estimates resulting in errors in age-standardized estimates (10.4 to 13.2% points in breast cancer). In estimating net survival, cancer registries should abandon all classical methods and adopt the new Pohar-Perme estimator.

؊4 ) among each other. Significant differences were also observed within each group of HIV-1 genotype. The global disparity was higher for CRF02_AG than for B subtypes. This study indicates a risk of viral load misestimating or discrepancies between techniques, depending on the HIV-1 subtype, and speaks in favor of using the same assay for the monitoring of HIV-1-infected patients.

A CAD system may improve the characterization of prostate lesions at multiparametric MR imaging by increasing reading specificity.

Summary Describing the dynamics of patient mortality hazard is a major concern for cancer epidemiologists. In addition to time and age, other continuous covariates have often to be included in the model. For example, survival trend analyses and socio‐economic studies deal respectively with the year of diagnosis and a deprivation index. Taking advantage of a recent theoretical framework for general smooth models, the paper proposes a penalized approach to hazard and excess hazard models in time‐to‐event analyses. The baseline hazard and the functional forms of the covariates were specified by using penalized natural cubic regression splines with associated quadratic penalties. Interactions between continuous covariates and time‐dependent effects were dealt with by forming a tensor product smooth. The smoothing parameters were estimated by optimizing either the Laplace approximate marginal likelihood criterion or the likelihood cross‐validation criterion. The regression parameters were estimated by direct maximization of the penalized likelihood of the survival model, which avoids data augmentation and the Poisson likelihood approach. The implementation proposed was evaluated on simulations and applied to real data. It was found to be numerically stable, efficient and useful for choosing the appropriate degree of complexity in overall survival and net survival contexts; moreover, it simplified the model building process.