Long-Term Effect of TIMP3 Gene Expression on Thyroid Cancer: A Cure Model Analysis.

BACKGROUND: Thyroid cancer is the most common endocrine malignancy. TIMP3, a metalloproteinase inhibitor, can inhibit angiogenesis, invasion, and metastasis in thyroid cancer. In this study, we investigated the long-term effect of TIMP3 gene expression and other associated factors on the survival rate and cure probability of thyroid cancer patients. METHODS: In this historical cohort study, clinical information was collected from 507 thyroid cancer patients and 59 control samples based on the TCGA database. The Kaplan-Meier curve and log-rank test were employed for group comparisons. Weibull mixture and non-mixture cure models were utilized to explore the association between TIMP3 gene expression and survival time, as well as cure status. All statistical analyses were conducted using the R language. RESULTS: There were 507 thyroid cancer patients and 59 normal tissue participants in the study with an average age of 47.93±15.96 and 47.06 ± 17.74 years respectively. A total of 26.8 percent of patients were male, 69.6 percent had high expression, and 3.16 percent died during the study. Compared with normal tissue participants, tumor-positive patients had significantly lower TIMP3 expression (p<0.001). After 2,000 days of follow-up, 78 percent of patients were cured based on Kaplan-Meier curves. The results show that the Weibull mixture cure model is superior to the non-mixture cure model. Moreover, after controlling for other factors, higher TIMP3 expression was associated with an increased chance of long-term recovery in patients. Specifically, the odds of cure in patients with higher TIMP3 expression were approximately 2.3 times greater than others. CONCLUSIONS: TIMP3 expression has a protective effect on cure probability in thyroid cancer patients, even though it does not appear to affect short-term survival. This study suggests that targeting TIMP3 may offer promise for thyroid cancer and may be a potential biomarker for thyroid cancer prognosis.

The Impact of Multiple Births on Fertility: Stopping and Spacing in the United States During the Demographic Transition.

Multiple births strain mothers' and families' resources in ways that should highlight preferences for family size, birth spacing, and parity-dependent stopping behavior. Couples with surviving twins reach their target family size sooner than other couples and should be more likely to practice family limitation. Twins are also a greater burden on the mother's time and health, which could lead to postponing the next birth, even among couples who want additional children. We examine these hypotheses by analyzing families with twins in the 1900 and 1910 U.S. Censuses. Using reconstructed birth histories for more than 7 million women in the IPUMS full-count 1900 and 1910 datasets and event-history methods (Kaplan-Meier curves, cure models), we find clear evidence of family limitation following a multiple birth. Couples who had twins or triplets were more likely to stop childbearing, and those who continued having children delayed their next birth. Responses to multiple births were larger in groups previously identified as leaders in the transition to smaller families, and roughly one third of couples stopped after one or two children. We find no evidence that some groups relied primarily on birth spacing to reduce family size while others relied primarily on stopping.

Social cure model: testing the link between identity centrality and body appreciation in diverse sexual orientation and gender identity groups.

BACKGROUND: The level of experienced sociocultural pressure to have an idealized body can vary depending on a person's gender identity and sexual orientation. The current study explored whether differences in levels of body appreciation among people with different sexual orientations and gender identities vary because of differing levels of experienced pressure by in-group members and varying levels of experienced hostile behaviors because of their looks or body. Thereby, the study tests the social cure model, according to which high levels of identity centrality are associated with better mental health. METHODS: An online cross-sectional questionnaire study was conducted with 1,587 people (51.3% cisgender women, 39.3% cisgender men, 9.5% non-binary; 52.9% identified as heterosexual, 27.7% identified as bisexual/pansexual, 11.2% identified as gay/lesbian, 8.2% identified as asexual/demi/queer; Mage = 32.9, SD = 12.6) from German-speaking countries. Variables were assessed with German-language versions of the Multidimensional and Multicomponent Measure of Social Identification, Body Appreciation Scale-2, the Perceived Stigmatization Questionnaire, and the Sociocultural Attitudes Towards Appearance Questionnaire-4, revised. A manifest-path model was calculated. RESULTS: Non-binary persons reported lower levels of body appreciation than did cisgender men and sexual minority persons reported lower levels of body appreciation than did heterosexual persons. Furthermore, sexual minority persons experienced more hostile behaviors directed towards them because of their looks or body than did heterosexual persons. Similarly, non-binary persons experienced more hostile behaviors than did men. Non-binary persons were subjected to lower levels of in-group pressure than were men. Gay/lesbian persons and asexual persons were subjected to lower levels of in-group pressure than were heterosexual persons. More hostile behaviors and stronger in-group pressure were related to lower body appreciation. In cisgender women and men indirect links revealed associations between strong identity centrality and low levels of body appreciation through the mediator of high in-group pressure. CONCLUSIONS: Data in sexual minority individuals or non-binary persons supported the social cure model, according to which persons can find support and validation for their looks and body from in-group members. In cisgender women and men, strong identification as a woman or man can be related to stronger in-group pressure and in turn to lower body appreciation.

Mixture and Non-Mixture Cure Models for the survival analysis of SARS-CoV-2 patients in Khyber Pakhtunkhwa, Pakistan.

Objective: To examine the potential difference in survival and risk of death between asymptomatic and symptomatic SARS-CoV-2 patients, controlled by age and gender for all the attendance in hospitals of Khyber Pakhtunkhwa (KP), Pakistan. Methods: In this retrospective study, the medical records of 6273 SARS-CoV-2 patients admitted to almost all hospitals in Khyber Pakhtunkhwa during the first wave of the coronavirus outbreak from March to June 2020 were analysed. The effects of gender, age, and being symptomatic on the survival of SARS-CoV-2 patients were assessed using cure-survival models as opposed to the conventional Cox proportional hazards model. Results: The prevalence of initially symptomatic patients was 55.8%, and the overall mortality rate was 11.8%. The fitted cure-survival models suggest that age affects the probability of death (incidence) but not the short-term survival time of patients (latency); symptomatic patients have a higher risk of death than their asymptomatic counterparts, but the survival time of symptomatic patients is longer on average; gender has no significant effect on the probability of death and survival time. Conclusion: The available data and statistical results suggest that asymptomatic and young patients are generally less susceptible to initial infection with SARS-CoV-2 and therefore have a lower risk of death. Our regression models show that uncured asymptomatic patients generally have poorer short-term survival than their uncured symptomatic counterparts. The association between gender and survival outcome was not significant.

Sample size calculation for mixture cure model with restricted mean survival time as a primary endpoint.

It is not uncommon for a substantial proportion of patients to be cured (or survive long-term) in clinical trials with time-to-event endpoints, such as the endometrial cancer trial. When designing a clinical trial, a mixture cure model should be used to fully consider the cure fraction. Previously, mixture cure model sample size calculations were based on the proportional hazards assumption of latency distribution between groups, and the log-rank test was used for deriving sample size formulas. In real studies, the latency distributions of the two groups often do not satisfy the proportional hazards assumptions. This article has derived a sample size calculation formula for a mixture cure model with restricted mean survival time as the primary endpoint, and did simulation and example studies. The restricted mean survival time test is not subject to proportional hazards assumptions, and the difference in treatment effect obtained can be quantified as the number of years (or months) increased or decreased in survival time, making it very convenient for clinical patient-physician communication. The simulation results showed that the sample sizes estimated by the restricted mean survival time test for the mixture cure model were accurate regardless of whether the proportional hazards assumptions were satisfied and were smaller than the sample sizes estimated by the log-rank test in most cases for the scenarios in which the proportional hazards assumptions were violated.

Evidence of being cured for nasopharyngeal carcinoma: results of a multicenter patient-based study in China.

Background: The survival rates of patients with nasopharyngeal carcinoma (NPC) have improved significantly, but there is no consensus on whether they can be considered cured. We aimed to determine whether a statistical cure could be achieved for patients with NPC in the contemporary therapeutic landscape. Methods: This retrospective multicenter study enrolled 6315 patients with nonmetastatic NPC from nonendemic and endemic regions of China from 2007 to 2020. We applied mixture and nonmixture cure models to estimate the cure probabilities and cure times by incorporating background mortality for the general population, matching by gender, age, and diagnosed year. Findings: With death as the uncured event, the probability of patients with NPC achieving a life expectancy at par with the general population was 78.1%. Considering progression as the uncured event, the likelihood of patients attaining a life expectancy without progression equivalent to that of the general population was 72.4%. For individuals, the probabilities of achieving cure were conditional and time-dependent, requiring approximately 7.1 and 4.7 years with 95% certainty, respectively. The corresponding cure times for uncured patients were 8.9 and 6.8 years, respectively. The cure probability was correlated with age, Eastern Cooperative Oncology Group score, TNM staging, Epstein-Barr virus DNA copies, and lactate dehydrogenase. The correlation was excellent between 5-year overall survival/progression-free survival and cure fractions. Interpretation: Statistical cure is potentially achievable among patients with NPC undergoing contemporary treatment modalities. The results hold significant potential implications for both clinical practice and patient perspectives. Funding: National High Level Hospital Clinical Research Funding; Beijing Xisike Clinical Oncology Research Foundation; Beijing hope run fund.

[A simulation study of the reliability and accuracy of Cox-TEL method for estimating hazard ratio and difference in proportions for long-term survival data containing cured patients].

OBJECTIVE: To explore the applicable conditions of the Cox-TEL (Cox PH-Taylor expansion adjustment for long-term survival data) method for analysis of survival data that contain cured patients. METHODS: The simulated survival data method based on Weibull distribution was used to simulate and generate the survival data with different cure rates, censored rates, and cure rate differences. The Cox-TEL method was used for analysis of the generated simulation data, and its performance was evaluated by calculating its type Ⅰ error and power. RESULTS: Almost all the type Ⅰ error of the hazard ratios (HRs) obtained by the Cox-TEL method under different conditions were slightly greater than 0.05, and this method showed a good test power for estimating the HRs for data with a large sample size and a large difference in proportions (DPs). For the data of cured patients, the type Ⅰ error of the DPs obtained by the Cox-TEL method was well around 0.05, and its test power was robust in most of the scenarios. CONCLUSION: The Cox-TEL method is effective for analyzing data of uncured patients and obtaining reliable HRs for most of the survival data with a sample size, a low censored rates, and a large difference in cure rates. The method is capable of accurately estimating the DPs regardless of the sample size, censored rates, or the cure rates.

Estimating cure and risk of death from other causes of cancer patients: EUROCARE-6 data on head & neck, colorectal, and breast cancers.

BACKGROUND: To estimate net survival and cancer cure fraction (CF), i.e. the proportion of patients no longer at risk of dying from cancer progression/relapse, a clear distinction needs to be made between mortality from cancer and from other causes. Conventionally, CF is estimated assuming no excess mortality compared to the general population. METHODS: A new modelling approach, that corrects for patients' extra risk of dying (RR) from causes other than the diagnosed cancer, was considered to estimate both indicators. We analysed EUROCARE-6 data on head and neck (H&N), colorectal, and breast cancer patients aged 40-79, diagnosed from 1998 to 2002 and followed-up to 31/12/2014, provided by 65 European cancer registries. FINDINGS: Young male H&N cancer patients have 4 times the risk of dying from other causes than their peers, this risk decreases with age to 1.6. Similar results were observed for female. We observed an absolute increase in CF of 30 % using the new model instead of the conventional one. For colorectal cancer, CF with the new model increased by a maximum of 3 % for older patients and the RR ranged from 1 to 1.2 for both sexes. CF of female breast cancer ranged from 73 % to 79 % using the new cure model, with RR between 1.2 and 1.4. INTERPRETATION: Not considering a RR> 1 leads to underestimate the proportion of patients not bound to die of their diagnosed cancer. Estimates of cancer mortality risk have an important impact on patients' quality of life.

A generalized Gompertz promotion time cure model and its fitness to cancer data.

The cure models based on standard distributions like exponential, Weibull, lognormal, Gompertz, gamma, are often used to analyze survival data from cancer clinical trials with long-term survivors. Sometimes, the data is simple, and the standard cure models fit them very well, however, most often the data are complex and the standard cure models don't fit them reasonably well. In this article, we offer a novel generalized Gompertz promotion time cure model and illustrate its fitness to gastric cancer data by three different methods. The generalized Gompertz distribution is as simple as the generalized Weibull distribution and is not computationally as intensive as the generalized F distribution. One detailed real data application is provided for illustration and comparison purposes.

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

CureAuxSP: An R package for estimating mixture cure models with auxiliary survival probabilities.

BACKGROUND AND OBJECTIVE: There is a rising interest in exploiting aggregate information from external medical studies to enhance the statistical analysis of a modestly sized internal dataset. Currently available software packages for analyzing survival data with a cure fraction ignore the potentially available auxiliary information. This paper aims at filling this gap by developing a new R package CureAuxSP that can include subgroup survival probabilities extracted outside into an interested internal survival dataset. METHODS: The newly developed R package CureAuxSP provides an efficient approach for information synthesis under the mixture cure models, including Cox proportional hazards mixture cure model and the accelerated failure time mixture cure model as special cases. It focuses on synthesizing subgroup survival probabilities at multiple time points and the underlying method development lies in the control variate technique. Evaluation of homogeneity assumption based on a test statistic can be automatically carried out by our package and if heterogeneity does exist, the original outputs can be further refined adaptively. RESULTS: The R package CureAuxSP provides a main function SMC.AxuSP() that helps us adaptively incorporate external subgroup survival probabilities into the analysis of an internal survival data. We also provide another function Print.SMC.AuxSP() for printing the results with a better presentation. Detailed usages are described, and implementations are illustrated with numerical examples, including a simulated dataset with a well-designed data generating process and a real breast cancer dataset. Substantial efficiency gain can be observed by our results. CONCLUSIONS: Our R package CureAuxSP can make the wide applications of utilizing auxiliary information possible. It is anticipated that the performance of mixture cure models can be improved for the survival data with a cure fraction, especially for those with small sample sizes.

Estimating the long-term survival of unresectable stage III non-small cell lung cancer based on cure model analysis.

BACKGROUND: The predictors of long-term survival and appropriate surrogate endpoints in unresectable stage III non-small cell lung cancer (NSCLC) treated with radiotherapy remain unclear, especially in the immune therapy era. METHODS: This study retrospectively analyzed a prospective cohort of 822 patients treated at the Chinese National Cancer Center from 2013 to 2022. Cure fractions, surrogates for long-term survival, and associated factors were assessed using a mixture cure model, with validation against a matched Surveillance, Epidemiology, and End Results (SEER) dataset. RESULTS: 27.3% of patients with unresectable stage III NSCLC can achieve long-term survival after treated by radiotherapy. 4-year PFS and 5-year OS, when 80% of patients were considered cured, showed significant correlations with cure rates based on background mortality-adjusted PFS and relative survival, with R-squared values exceeding 0.85. Independent predictors of long-term survival included non-squamous cell carcinoma (non-SCC) pathological type, N category, gross tumor volume, and treatment combination with immune checkpoint inhibitors (ICIs). CONCLUSIONS: Radiotherapy, especially when combined with ICIs, offers a potential cure for a proportion of patients with unresectable stage III NSCLC. Tumor burden and ICIs are key predictors of long-term survival. The study suggested 4-year PFS and 5-year OS as surrogate endpoints for cure and long-term survival assessment.

Cost-effectiveness analysis 3L of axicabtagene ciloleucel vs tisagenlecleucel and lisocabtagene maraleucel in Japan.

Aim: Cost-effectiveness analysis (CEA) was performed to compare axicabtagene ciloleucel (axi-cel) with tisagenlecleucel (tisa-cel) and lisocabtagene (liso-cel) for treatment of relapsed or refractory large B-cell lymphoma in adult patients after ≥2 lines of therapy in Japan. Materials & methods: Cost-effectiveness analysis was conducted using the partition survival mixture cure model based on the ZUMA-1 trial and adjusted to the JULIET and TRANSCEND trials using matching-adjusted indirect comparisons. Results & conclusion: Axi-cel was associated with greater incremental life years (3.13 and 2.85) and incremental quality-adjusted life-years (2.65 and 2.24), thus generated lower incremental direct medical costs (-$976.29 [-¥137,657] and -$242.00 [-¥34,122]), compared with tisa-cel and liso-cel. Axi-cel was cost-effective option compared with tisa-cel and liso-cel from a Japanese payer's perspective.

[Box: see text].

On variable selection in a semiparametric AFT mixture cure model.

In clinical studies, one often encounters time-to-event data that are subject to right censoring and for which a fraction of the patients under study never experience the event of interest. Such data can be modeled using cure models in survival analysis. In the presence of cure fraction, the mixture cure model is popular, since it allows to model probability to be cured (called the incidence) and the survival function of the uncured individuals (called the latency). In this paper, we develop a variable selection procedure for the incidence and latency parts of a mixture cure model, consisting of a logistic model for the incidence and a semiparametric accelerated failure time model for the latency. We use a penalized likelihood approach, based on adaptive LASSO penalties for each part of the model, and we consider two algorithms for optimizing the criterion function. Extensive simulations are carried out to assess the accuracy of the proposed selection procedure. Finally, we employ the proposed method to a real dataset regarding heart failure patients with left ventricular systolic dysfunction.

Cure models with adaptive activation for modeling cancer survival.

We propose a class of cure rate models motivated by analysis of colon cancer and triple-negative breast cancer survival data. This class is indexed by an adaptive activation parameter and a function. We establish that the class is stochastically ordered in the activation parameter and also establish two identifiability results for this class. The first- and last-activation models are members of this class whereas many cure rate models proposed in the literature are also part of this class. We illustrate that while first- and last-activation models may perform poorly under model misspecifications, the proposed model with adaptive activation provides appropriate inference in these cases. We apply the proposed approach to assess treatment-sex interaction on cure rate in a colon cancer study and to assess role of tumor heterogeneity and ethnic disparity in breast cancer.

An updated cost-effectiveness analysis of axicabtagene ciloleucel in second-line large B-cell lymphoma patients in the United States.

AIMS: This economic evaluation of axicabtagene ciloleucel (axi-cel) versus previous standard of care (SOC; salvage chemotherapy followed by high-dose therapy with autologous stem cell rescue) in the second line (2L) large B-cell lymphoma population is an update of previous economic models that contained immature survival data. METHODS: This analysis is based on primary overall survival (OS) ZUMA-7 clinical trial data (median follow-up of 47.2 months), from a United States (US) payer perspective, with a model time horizon of 50 years. Mixture cure models were used to extrapolate updated survival data; subsequent treatment data and costs were updated. Patients who remained in the event-free survival state by 5 years were assumed to have achieved long-term remission and not require subsequent treatment. RESULTS: Substantial survival and quality of life benefits were observed despite 57% of patients in the SOC arm receiving subsequent cellular therapy: median model-projected (ZUMA-7 trial Kaplan-Meier estimated) OS was 78 months (median not reached) for axi-cel versus 25 months (31 months) for SOC, resulting in incremental quality-adjusted life year (QALY) difference of 1.63 in favor of axi-cel. Incrementally higher subsequent treatment costs were observed in the SOC arm due to substantial crossover to cellular therapies, thus, when considering the generally accepted willingness to pay threshold of $150,000 per QALY in the US, axi-cel was cost-effective with an incremental cost-effectiveness ratio of $98,040 per QALY. CONCLUSIONS: Results remained consistent across a wide range of sensitivity and scenario analysis, including a crossover adjusted analysis, suggesting that the mature OS data has significantly reduced the uncertainty of axi-cel's cost-effectiveness in the 2L setting in the US. Deferring treatment with CAR T therapies after attempting a path to transplant may result in excess mortality, lower quality of life and would be an inefficient use of resources relative to 2L axi-cel.

A Bayesian proportional hazards mixture cure model for interval-censored data.

The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval-censored, the estimation of this model is challenging due to its complex data structure. In this article, we propose a computationally efficient semiparametric Bayesian approach, facilitated by spline approximation and Poisson data augmentation, for model estimation and inference with interval-censored data and a cure rate. The spline approximation and Poisson data augmentation greatly simplify the MCMC algorithm and enhance the convergence of the MCMC chains. The empirical properties of the proposed method are examined through extensive simulation studies and also compared with the R package "GORCure". The use of the proposed method is illustrated through analyzing a data set from the Aerobics Center Longitudinal Study.

Estimating the cure proportion of stage IA lung adenocarcinoma: a population-based study.

BACKGROUND: We aimed to investigate the factors influencing the cure, recurrence, and metastasis rates of stage IA lung adenocarcinoma, using a mixed cure model. METHODS: A total of 1,064 patients who underwent video-assisted thoracoscopic pulmonectomy were included. Variable screening was performed using the random forest algorithm and least absolute shrinkage and selection operator approaches. The mixed cure model was used to identify factors affecting patient cure and survival, and a sequential analysis was performed on 5%, 10%, and 20% of the presentational subtype concurrently. A receiver operating characteristics curve was used to determine the best model and construct a nomogram to predict the cure rate. RESULTS: The median follow-up time was 58 (range: 3-115) months. Results from the cure part of the mixed model indicated that the predominant subtype, presentational subtype, and tumor diameter were the main prognostic factors affecting cure rate. Therefore, the nomogram to predict the cure rate was constructed based on these factors. The survival part indicated that the predominant subtype was the only factor that influenced recurrence and metastasis. A sequential analysis of the presentational subtype showed it had no significant effect on survival (P > 0.05). Regardless of the recording mode, no significant improvement was observed in the model's discriminative ability. Only a few postoperative pathological specimens showed lymphovascular invasion (LVI); however, the survival curve suggested a significant effect on patient survival. CONCLUSIONS: After excluding the existence of long-term survivors, the predominant tumor subtype was determined to be the only factor influencing recurrence and metastasis. Although LVI is rare in stage IA lung adenocarcinoma, its significance cannot be discounted in terms of determining patient prognosis.

Functional proportional hazards mixture cure model with applications in cancer mortality in NHANES and post ICU recovery.

We develop a functional proportional hazards mixture cure model with scalar and functional covariates measured at the baseline. The mixture cure model, useful in studying populations with a cure fraction of a particular event of interest is extended to functional data. We employ the expectation-maximization algorithm and develop a semiparametric penalized spline-based approach to estimate the dynamic functional coefficients of the incidence and the latency part. The proposed method is computationally efficient and simultaneously incorporates smoothness in the estimated functional coefficients via roughness penalty. Simulation studies illustrate a satisfactory performance of the proposed method in accurately estimating the model parameters and the baseline survival function. Finally, the clinical potential of the model is demonstrated in two real data examples that incorporate rich high-dimensional biomedical signals as functional covariates measured at the baseline and constitute novel domains to apply cure survival models in contemporary medical situations. In particular, we analyze (i) minute-by-minute physical activity data from the National Health And Nutrition Examination Survey 2003-2006 to study the association between diurnal patterns of physical activity at baseline and all cancer mortality through 2019 while adjusting for other biological factors; (ii) the impact of daily functional measures of disease severity collected in the intensive care unit on post intensive care unit recovery and mortality event. Our findings provide novel epidemiological insights into the association between daily patterns of physical activity and cancer mortality. Software implementation and illustration of the proposed estimation method are provided in R.

Comparison of statistical methods for extrapolating survival in previously untreated diffuse large B-cell lymphoma: results based on the POLARIX study.

OBJECTIVE: The ongoing Phase III randomized POLARIX study (GO39942; NCT03274492) demonstrated significantly improved progression-free survival (PFS) with polatuzumab vedotin plus rituximab, cyclophosphamide, doxorubicin and prednisone (Pola-R-CHP) versus rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) in patients with previously untreated diffuse large B-cell lymphoma (DLBCL). We compared statistical methodologies to extrapolate long-term PFS data from POLARIX. MATERIALS AND METHODS: This analysis explored four different approaches to extrapolate the POLARIX data: standard parametric survival, mixture-cure, landmark, and spline models. The resulting extrapolation curves were validated via comparison with the corresponding Kaplan-Meier (KM) curves from POLARIX and the POLARIX-like population of the Phase III GOYA study (NCT01287741; R-CHOP arm). RESULTS: The R-CHOP PFS KM curve from the GOYA validation set was well aligned with the POLARIX KM curve. As we anticipated that PFS in POLARIX would evolve similarly to that of GOYA, the data from GOYA were used to externally validate the extrapolated modelling results. While all four statistical methods were able to fit the data to the POLARIX KM curve, the mixture-cure model was the most accurate in predicting long-term PFS in the GOYA external validation set. In the mixture-cure model, generalized gamma distribution estimated 64% (95% confidence intervals [CI]: 56-71%) of patients to have long-term remission in the R-CHOP arm of POLARIX and GOYA, and 75% (95% CI: 70-79%) in the Pola-R-CHP arm of POLARIX. A limitation of this study was the comparison of the statistical models only in the PFS KM curves, since it was not possible to determine which statistical method was more appropriate to extrapolate the overall survival KM curves. CONCLUSIONS: Within this analysis, the mixture-cure model provided the best prediction of long-term outcomes from the primary PFS analysis of the POLARIX study.