Covariates of high-risk human papillomavirus (HPV) infections are distinct for incident CIN1, CIN2 and CIN3 as disclosed by competing-risks regression models

Background: In addition to the oncogenic human papillomavirus (HPV), several cofactors are needed in cervical carcinogenesis, but whether the HPV covariates associated with incident i) CIN1 are different from those of incident ii) CIN2 and iii) CIN3 needs further assessment. Objectives: To gain further insights into the true biological differences between CIN1, CIN2 and CIN3, we assessed HPV covariates associated with incident CIN1, CIN2, and CIN3. Study Design and Methods: HPV covariates associated with progression to CIN1, CIN2 and CIN3 were analysed in the combined cohort of the NIS (n = 3,187) and LAMS study (n = 12,114), using competing-risks regression models (in panel data) for baseline HR-HPV-positive women (n = 1,105), who represent a sub-cohort of all 1,865 women prospectively followed-up in these two studies. Results: Altogether, 90 (4.8%), 39 (2.1%) and 14 (1.4%) cases progressed to CIN1, CIN2, and CIN3, respectively. Among these baseline HR-HPV-positive women, the risk profiles of incident GIN I, CIN2 and CIN3 were unique in that completely different HPV covariates were associated with progression to CIN1, CIN2 and CIN3, irrespective which categories (non-progression, CIN1, CIN2, CIN3 or all) were used as competing-risks events in univariate and multivariate models. Conclusions: These data confirm our previous analysis based on multinomial regression models implicating that distinct covariates of HR-HPV are associated with progression to CIN1, CIN2 and CIN3. This emphasises true biological differences between the three grades of GIN, which revisits the concept of combining CIN2 with CIN3 or with CIN1 in histological classification or used as a common end-point, e.g., in HPV vaccine trials.

Longitudinal outcomes of high-risk human papillomavirus (HPV) infections as competing-risks events following cervical HPV test at baseline visit in the *NIS-LAMS** cohort

Background: The complex natural history of human papillomavirus (HPV) infections following a single HPV test can be modeled as competing-risks events (i.e., no-, transient- or persistent infection) in a longitudinal setting. The covariates associated with these compet ng events have not been previously assessed using competing-risks regression models. Objectives: To gain further insights in the outcomes of cervical HPV infections, we used univariate- and multivariate competing-risks regression models to assess the covariaies associated with these competing events. Study Design and Methods: Covariates associated with three competing outcomes (no-, transient- or persistent HR-HPV infection) were analysed in a sub-cohort of 1,865 women prospectively followed-up in the NIS (n = 3,187) and LAMS Study (n = 12,114). Results: In multivariate competing-risks models (with two other outcomes as competing events), permanently HR-HPV negative outcome was significantly predicted only by the clearance of ASCUS+Pap during FU, while three independent covariates predicted transient HR-HPV infections: i) number of recent (< 12 months) sexual partners (risk increased), ii) previous Pap screening history (protective), and history of previous CIN (increased risk). The two most powerful predictors of persistent HR-HPV infections were persistent ASCUS+Pap (risk increased), and previous Pap screening history (protective). In pair-wise comparisons, number of recent sexual partners and previous CIN history increase the probability of transient HR-HPV infection against the HR-HPV negative competing event, while previous Pap screening history is protective. Persistent ASCUS+Pap during FU and no previous Pap screening history are significantly associated with the persistent HR-HPV outcome (compared both with i) always negative, and ii) transient events), whereas multiparity is protective. Conclusions: Different covariates are associated with the three main outcomes of cervical HPV infections. The most significant covariates of each competing events are probably distinct enough to enable constructing of a risk-profile for each main outcome.

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright (C) 2003 John Wiley Sons, Ltd.

On modifications to the long-term survival mixture model in the presence of competing risks

A mixture model for long-term survivors has been adopted in various fields such as biostatistics and criminology where some individuals may never experience the type of failure under study. It is directly applicable in situations where the only information available from follow-up on individuals who will never experience this type of failure is in the form of censored observations. In this paper, we consider a modification to the model so that it still applies in the case where during the follow-up period it becomes known that an individual will never experience failure from the cause of interest. Unless a model allows for this additional information, a consistent survival analysis will not be obtained. A partial maximum likelihood (ML) approach is proposed that preserves the simplicity of the long-term survival mixture model and provides consistent estimators of the quantities of interest. Some simulation experiments are performed to assess the efficiency of the partial ML approach relative to the full ML approach for survival in the presence of competing risks.

Health investment complementarities under competing risks

Applying the competing--risks model to multi--cause mortality,this paper provides a theoretical and empirical investigation of the positive complementarities that occur between disease--specific policy interventions. We argue that since an individual cannot die twice, competing risks imply that individuals will not waste resources on causes that are not the most immediate, but will make health investments so as to equalize cause--specific mortality. However, equal mortality risk from a variety of diseases does not imply that disease--specific public health interventions are a waste. Rather, a cause--specific intervention produces spillovers to other disease risks, so that the overall reduction in mortality will generally be larger than the direct effect measured on the targeted disease. The assumption that mortality from non--targeted diseases remains the same after a cause--specific intervention under--estimates the true effect of such programs, since the background mortality is also altered as a result of intervention. Analyzing data from one of the most important public health programs ever introduced, the Expanded Program on Immunization (EPI) of the United Nations, we find evidence for the existence of such complementarities, involving causes that are not biomedically, but behaviorally, linked.

Competing risks to breast cancer mortality in Catalonia

Background: Breast cancer mortality has experienced important changes over the last century. Breast cancer occurs in the presence of other competing risks which can influence breast cancer incidence and mortality trends. The aim of the present work is: 1) to assess the impact of breast cancer deaths among mortality from all causes in Catalonia (Spain), by age and birth cohort and 2) to estimate the risk of death from other causes than breast cancer, one of the inputs needed to model breast cancer mortality reduction due to screening or therapeutic interventions. Methods: The multi-decrement life table methodology was used. First, all-cause mortality probabilities were obtained by age and cohort. Then mortality probability for breast cancer was subtracted from the all-cause mortality probabilities to obtain cohort life tables for causes other than breast cancer. These life tables, on one hand, provide an estimate of the risk of dying from competing risks, and on the other hand, permit to assess the impact of breast cancer deaths on all-cause mortality using the ratio of the probability of death for causes other than breast cancer by the all-cause probability of death. Results: There was an increasing impact of breast cancer on mortality in the first part of the 20th century, with a peak for cohorts born in 1945–54 in the 40–49 age groups (for which approximately 24% of mortality was due to breast cancer). Even though for cohorts born after 1955 there was only information for women under 50, it is also important to note that the impact of breast cancer on all-cause mortality decreased for those cohorts. Conclusion: We have quantified the effect of removing breast cancer mortality in different age groups and birth cohorts. Our results are consistent with US findings. We also have obtained an estimate of the risk of dying from competing-causes mortality, which will be used in the assessment of the effect of mammography screening on breast cancer mortality in Catalonia.

‘Modeling and Analysis of Competing Risks Data’

there has been much research on analyzing various forms of competing risks data. Nevertheless, there are several occasions in survival studies, where the existing models and methodologies are inadequate for the analysis competing risks data. ldentifiabilty problem and various types of and censoring induce more complications in the analysis of competing risks data than in classical survival analysis. Parametric models are not adequate for the analysis of competing risks data since the assumptions about the underlying lifetime distributions may not hold well. Motivated by this, in the present study. we develop some new inference procedures, which are completely distribution free for the analysis of competing risks data.

Impact of age and comorbidities on long-term survival of patients with high-risk prostate cancer treated with radical prostatectomy: a multi-institutional competing-risks analysis

Survival after surgical treatment using competing-risk analysis has been previously examined in patients with prostate cancer (PCa). However, the combined effect of age and comorbidities has not been assessed in patients with high-risk PCa who might have heterogeneous rates of competing mortality despite the presence of aggressive disease.

Semiparametric Methods for Semi-competing Risks Problem with Censoring and Truncation

Studies of chronic life-threatening diseases often involve both mortality and morbidity. In observational studies, the data may also be subject to administrative left truncation and right censoring. Since mortality and morbidity may be correlated and mortality may censor morbidity, the Lynden-Bell estimator for left truncated and right censored data may be biased for estimating the marginal survival function of the non-terminal event. We propose a semiparametric estimator for this survival function based on a joint model for the two time-to-event variables, which utilizes the gamma frailty specification in the region of the observable data. Firstly, we develop a novel estimator for the gamma frailty parameter under left truncation. Using this estimator, we then derive a closed form estimator for the marginal distribution of the non-terminal event. The large sample properties of the estimators are established via asymptotic theory. The methodology performs well with moderate sample sizes, both in simulations and in an analysis of data from a diabetes registry.

The role of adjuvant chemotherapy for lymph node-positive upper tract urothelial carcinoma following radical nephroureterectomy: a retrospective study.

OBJECTIVE: To evaluate the effect of adjuvant chemotherapy (AC) on mortality after radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC) with positive lymph nodes (LNs) and to identify patient subgroups that are most likely to benefit from AC. PATIENTS AND METHODS: We retrospectively analysed data of 263 patients with LN-positive UTUC, who underwent full surgical resection. In all, 107 patients (41%) received three to six cycles of AC, while 156 (59.3%) were treated with RNU alone. UTUC-related mortality was evaluated using competing-risks regression models. RESULTS: In all patients (Tall N+), administration of AC had no significant impact on UTUC-related mortality on univariable (P = 0.49) and multivariable (P = 0.11) analysis. Further stratified analyses showed that only N+ patients with pT3-4 disease benefited from AC. In this subgroup, AC reduced UTUC-related mortality by 34% (P = 0.019). The absolute difference in mortality was 10% after the first year and increased to 23% after 5 years. On multivariable analysis, administration of AC was associated with significantly reduced UTUC-related mortality (subhazard ratio 0.67, P = 0.022). Limitations of this study are the retrospective non-randomised design, selection bias, absence of a central pathological review and different AC protocols. CONCLUSIONS: AC seems to reduce mortality in patients with pT3-4 LN-positive UTUC after RNU. This subgroup of LN-positive patients could serve as target population for an AC prospective randomised trial.

La néphrectomie partielle chez les patients atteints du cancer du rein de stade T1b

Objectif : La néphrectomie partielle est reconnue actuellement comme le traitement de choix des tumeurs de moins de 7 cm. Le but de notre étude est de comparer le taux de mortalité lié au cancer du rein suite au traitement par néphrectomie partielle ou radicale chez les patients de stade T1b, de présenter la tendance temporelle du taux d'intervention par néphrectomie partielle pour les tumeurs de stade T1b et d’identifier les facteurs sociodémographiques et tumoraux qui influencent le choix thérapeutique entre les deux types de traitement chirurgical. Méthode : Il s’agit d’une étude épidémiologique de type rétrospective. La population de patients provient de la base de donnée SEER (Surveillance, Epidemiology, and End Results) qui regroupe une grande proportion de la population nord-américaine. Dans notre étude, nous avons utilisé l’analyse par régression logistique pour identifier les facteurs sociodémographiques associés à l'intervention par néphrectomie partielle. Dans un deuxième temps, nous avons comparé la mortalité liée au cancer entre les deux options chirurgicales, après association par score de tendance pour diminuer les différences de base entre les deux populations. Nos critères étaient l’âge, la race, le sexe, l’état civil, le niveau socioéconomique, la taille tumorale, le grade nucléaire, l’histologie et la localité du centre hospitalier. L’analyse des données a été faite par le logiciel SPSS. Résultats : Le taux d'interventions par néphrectomie partielle a augmenté de 1,2% en 1988 à 15,9% en 2008 (p <0,001). Les jeunes patients, les tumeurs de petite taille, les patients de race noire, ainsi que les hommes sont plus susceptibles d'être traités par néphrectomie partielle (tous les p < 0,002). Parmi le groupe ciblé, le taux de mortalité lié au cancer à 5 ans et à 10 ans est de 4,4 et de 6,1% pour les néphrectomies partielles et de 6,0 et 10,4% pour les néphrectomies radicales (p = 0,03). Après ajustement de toutes les autres variables, les analyses de régression montrent que le choix entre les deux types de néphrectomie n’est pas associé à la mortalité lié au cancer (hazard ratio: 0,89, p = 0,5). Conclusion : Malgré un contrôle oncologique équivalent, le taux d'intervention par néphrectomie partielle chez les patients ayant un cancer du rein T1b est faible en comparaison à la néphrectomie radicale.

Somatostatin-based radiopeptide therapy with [177Lu-DOTA]-TOC versus [90Y-DOTA]-TOC in neuroendocrine tumours

PURPOSE Somatostatin-based radiopeptide treatment is generally performed using the β-emitting radionuclides (90)Y or (177)Lu. The present study aimed at comparing benefits and harms of both therapeutic approaches. METHODS In a comparative cohort study, patients with advanced neuroendocrine tumours underwent repeated cycles of [(90)Y-DOTA]-TOC or [(177)Lu-DOTA]-TOC until progression of disease or permanent adverse events. Multivariable Cox regression and competing risks regression were employed to examine predictors of survival and adverse events for both treatment groups. RESULTS Overall, 910 patients underwent 1,804 cycles of [(90)Y-DOTA]-TOC and 141 patients underwent 259 cycles of [(177)Lu-DOTA]-TOC. The median survival after [(177)Lu-DOTA]-TOC and after [(90)Y-DOTA]-TOC was comparable (45.5 months versus 35.9 months, hazard ratio 0.91, 95% confidence interval 0.63-1.30, p = 0.49). Subgroup analyses revealed a significantly longer survival for [(177)Lu-DOTA]-TOC over [(90)Y-DOTA]-TOC in patients with low tumour uptake, solitary lesions and extra-hepatic lesions. The rate of severe transient haematotoxicities was lower after [(177)Lu-DOTA]-TOC treatment (1.4 vs 10.1%, p = 0.001), while the rate of severe permanent renal toxicities was similar in both treatment groups (9.2 vs 7.8%, p = 0.32). CONCLUSION The present results revealed no difference in median overall survival after [(177)Lu-DOTA]-TOC and [(90)Y-DOTA]-TOC. Furthermore, [(177)Lu-DOTA]-TOC was less haematotoxic than [(90)Y-DOTA]-TOC.

Natural history of surgically treated high-risk prostate cancer

BACKGROUND No data exist on the patterns of biochemical recurrence (BCR) and their effect on survival in patients with high-risk prostate cancer (PCa) treated with surgery. The aim of our investigation was to evaluate the natural history of PCa in patients treated with radical prostatectomy (RP) alone. MATERIALS AND METHODS Overall, 2,065 patients with high-risk PCa treated with RP at 7 tertiary referral centers between 1991 and 2011 were identified. First, we calculated the probability of experiencing BCR after surgery. Particularly, we relied on conditional survival estimates for BCR after RP. Competing-risks regression analyses were then used to evaluate the effect of time to BCR on the risk of cancer-specific mortality (CSM). RESULTS Median follow-up was 70 months. Overall, the 5-year BCR-free survival rate was 55.2%. Given the BCR-free survivorship at 1, 2, 3, 4, and 5 years, the BCR-free survival rates improved by+7.6%,+4.1%,+4.8%,+3.2%, and+3.7%, respectively. Overall, the 10-year CSM rate was 14.8%. When patients were stratified according to time to BCR, patients experiencing BCR within 36 months from surgery had higher 10-year CSM rates compared with those experiencing late BCR (19.1% vs. 4.4%; P<0.001). At multivariate analyses, time to BCR represented an independent predictor of CSM (P<0.001). CONCLUSIONS Increasing time from surgery is associated with a reduction of the risk of subsequent BCR. Additionally, time to BCR represents a predictor of CSM in these patients. These results might help provide clinicians with better follow-up strategies and more aggressive treatments for early BCR.