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Relative survival methods used to estimate the excess mortality of cancer patients rely on the background (or expected) mortality derived from general population life tables. These methods are based on splitting the observed mortality into the excess mortality and the background mortality. By assuming a regression model for the excess mortality, usually a Cox-type model, one may investigate the effects of certain covariates on the excess mortality. Some covariates are cancer-specific whereas others are variables that may influence the background mortality as well. The latter should be taken into account in the background mortality to avoid biases in estimating their effects on the excess mortality. Unfortunately, the available life table might not include such variables and, consequently, might provide inaccurate values of the background mortality. We propose a model that uses multiplicative parameters to correct potentially inaccurate background mortality. The model can be seen as an extension of the frequently used Estève model because we assume a Cox-type model for the excess mortality with a piecewise constant baseline function and introduce additional parameters that multiply the background mortality. The original and the extended model are compared, first in a simulation study, then in an application to colon cancer registry data. [ABSTRACT FROM AUTHOR]

This study aims to provide QoL level for DLBCL patients at diagnosis and after 1 year, and to assess the occurrence of a RS effect. B Introduction: b Few quality of life (QoL) data are available for diffuse large B-cell lymphoma (DLBCL) patients during the first year of diagnosis. Quality of life and response shift effect of diffuse large B-cell lymphoma French patients included in prospective real-life REALYSA cohort in the first year after diagnosis. [Extracted from the article]

HE4 and CA-125 are used for epithelial ovarian cancer (EOC) screening, diagnosis, and follow-up. Our objective was to study HE4 and CA-125 kinetics in patients treated for recurrent EOC. Serum samples were prospectively collected before the first chemotherapy cycle and every 3 months until disease progression. Data from 89/101 patients could be analyzed. At baseline, the median CA-125 and HE4 concentrations were 210 IU/L (7-10,310) and 184 pM (31-4,836). Among the 12 patients (13%) with normal CA-125 (<35 IU/L) concentration, eight had HE4 concentration ≥75 pM, and among the 16 patients with normal HE4 concentration (18%), 12 had increased CA-125 concentration. The median nadir concentrations were 31 IU/L (3-8,744) for CA-125 and 75 pM (20-4,836) for HE4. The median times to nadir were 14 (0-130) weeks for CA-125 and 12 (0-52) weeks for HE4. In multivariate analysis, CA-125 and HE4 nadir concentrations (<35 IU/L, HR 0.35, 95% CI: 0.17-0.72 and<75 pM, HR 0.40, 95% CI: 0.20-0.79) and time to CA-125 and HE4 nadir (>14 weeks, HR 0.37, 95% CI: 0.20-0.70 and >12 weeks, HR 0.43, 95% CI: 0.23-0.83) were prognostic factors of progression-free survival. More investigations on HE4 kinetics could help to better monitor patients with CA-125 concentration within normal values., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Fabbro, Lamy, Touraine, Floquet, Ray-Coquard and Mollevi.)

Introduction: Preoperative anxiety is a frequent problem that can lead to complications both during anaesthesia and in the postoperative period, especially in oncology. Studies have shown that it can be managed using non-pharmacological approaches, but few works have evaluated psychoeducational programmes. The aim of the COHErence Cardiaque (COHEC) II Study is to evaluate the combination of medical hypnosis (MH) and cardiac coherence (CC) training to manage preoperative anxiety in patients with cancer., Methods and Analysis: COHEC II is an ongoing multicentre randomised clinical trial carried out in three French comprehensive cancer centres. In total, 296 patients who will undergo surgery for cancer will be recruited during 18 months and will be randomised in the control arm or the intervention arm. Patients in the intervention arm will follow a daily programme that combines MH and CC, starting 7 days before surgery. The control arm will receive the standard treatment to manage preoperative anxiety. The primary endpoint is the anxiety level on surgery day, measured using a Visual Analogue Scale. Secondary endpoints are patient adherence to the programme, satisfaction and postsurgery recovery quality., Ethics and Dissemination: The study protocol was approved by the French Ethics Committee (Comité de Protection des Personnes EST-II) on 24 November 2021 and will be carried out following the good practice guidelines and the Declaration of Helsinki. Results will be published in peer-reviewed journals and presented at conferences., Trial Registration Number: NCT05197972., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Late fibrosis can occur in breast cancer patients treated with curative-intent radiotherapy. Predicting this toxicity is of clinical interest in order to adapt the irradiation dose delivered. Radiation-induced CD8 T-lymphocyte apoptosis (RILA) had been proven to be associated with less grade ≥2 late radiation-induced toxicities in patients with miscellaneous cancers. Tobacco smoking status and adjuvant hormonotherapy were also identified as potential factors related to late-breast-fibrosis-free survival. This article evaluates the predictive performance of the RILA using a ROC curve analysis that takes into account the dynamic nature of fibrosis occurrence. This time-dependent ROC curve approach is also applied to evaluate the ability of the RILA combined with the other previously identified factors. Our analysis includes a Monte Carlo cross-validation procedure and the calculation of an expected cost of misclassification, which provides more importance to patients who have no risk of late fibrosis in order to be able to treat them with the maximal irradiation dose. Performance evaluation was assessed at 12, 24, 36 and 50 months. At 36 months, our results were comparable to those obtained in a previous study, thus underlying the predictive power of the RILA. Based on specificity and cost, RILA alone seemed to be the most performant, while its association with the other factors had better negative predictive value results.

Purpose: A joint modeling approach is recommended for analysis of longitudinal health-related quality of life (HRQoL) data in the presence of potentially informative dropouts. However, the linear mixed model modeling the longitudinal HRQoL outcome in a joint model often assumes a linear trajectory over time, an oversimplification that can lead to incorrect results. Our aim was to demonstrate that a more flexible model gives more reliable and complete results without complicating their interpretation., Methods: Five dimensions of HRQoL in patients with esophageal cancer from the randomized clinical trial PRODIGE 5/ACCORD 17 were analyzed. Joint models assuming linear or spline-based HRQoL trajectories were applied and compared in terms of interpretation of results, graphical representation, and goodness of fit., Results: Spline-based models allowed arm-by-time interaction effects to be highlighted and led to a more precise and consistent representation of the HRQoL over time; this was supported by the martingale residuals and the Akaike information criterion., Conclusion: Linear relationships between continuous outcomes (such as HRQoL scores) and time are usually the default choice. However, the functional form turns out to be important by affecting both the validity of the model and the statistical significance., Trial Registration: This study is registered with ClinicalTrials.gov, number NCT00861094., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Patient-reported outcomes such as health-related quality of life (HRQoL) are increasingly used as endpoints in randomized cancer clinical trials. However, the patients often drop out so that observation of the HRQoL longitudinal outcome ends prematurely, leading to monotone missing data. The patients may drop out for various reasons including occurrence of toxicities, disease progression, or may die. In case of informative dropout, the usual linear mixed model analysis will produce biased estimates. Unbiased estimates cannot be obtained unless the dropout is jointly modeled with the longitudinal outcome, for instance by using a joint model composed of a linear mixed (sub)model linked to a survival (sub)model. Our objective was to investigate in a clinical trial context the consequences of using the most frequently used linear mixed model, the random intercept and slope model, rather than its corresponding joint model., Methods: We first illustrate and compare the models on data of patients with metastatic pancreatic cancer. We then perform a more formal comparison through a simulation study., Results: From the application, we derived hypotheses on the situations in which biases arise and on their nature. Through the simulation study, we confirmed and complemented these hypotheses and provided general explanations of the bias mechanisms., Conclusions: In particular, this article reveals how the linear mixed model fails in the typical situation where poor HRQoL is associated with an increased risk of dropout and the experimental treatment improves survival. Unlike the joint model, in this situation the linear mixed model will overestimate the HRQoL in both arms, but not equally, misestimating the difference between the HRQoL trajectories of the two arms to the disadvantage of the experimental arm., (© 2023. The Author(s).)

Purpose: Health-related quality of life (HRQoL) is an important endpoint in cancer clinical trials. Analysis of HRQoL longitudinal data is plagued by missing data, notably due to dropout. Joint models are increasingly receiving attention for modelling longitudinal outcomes and the time-to-dropout. However, dropout can be informative or non-informative depending on the cause., Methods: We propose using a joint model that includes a competing risks sub-model for the cause-specific time-to-dropout. We compared a competing risks joint model (CR JM) that distinguishes between two causes of dropout with a standard joint model (SJM) that treats all the dropouts equally. First, we applied the CR JM and SJM to data from 267 patients with advanced oesophageal cancer from the randomized clinical trial PRODIGE 5/ACCORD 17 to analyse HRQoL data in the presence of dropouts unrelated and related to a clinical event. Then, we compared the models using a simulation study., Results: We showed that the CR JM performed as well as the SJM in situations where the risk of dropout was the same whatever the cause. In the presence of both informative and non-informative dropouts, only the SJM estimations were biased, impacting the HRQoL estimated parameters., Conclusion: The systematic collection of the reasons for dropout in clinical trials would facilitate the use of CR JMs, which could be a satisfactory approach to analysing HRQoL data in presence of both informative and non-informative dropout., Trial Registration: This study is registered with ClinicalTrials.gov, number NCT00861094., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Breast cancer (BC) treatments induce vitamin D (VD) insufficiency and bone metabolism changes, resulting in osteoporosis and skeletal morbidity risk. We report the results of a bicentric phase II trial (ClinicalTrials.gov Identifier: NCT04091178) on the safety and efficacy of high-dose oral VD supplementation for VD deficiency correction in 44 patients with early BC treated with adjuvant chemotherapies. Patients received one dose of 100,000 IU 25-OH VD every 3 weeks from day 1 of cycle 1 to day 1 of cycle 5. The primary endpoint was the percentage of patients achieving serum 25-OH VD concentration normalization on day 1 of cycle 6 (D1C6). Secondary endpoints were safety, VD and calcium parameters at baseline and during chemotherapy, and identification of predictive biomarkers of VD normalization on D1C6. On D1C6, 21 patients (47.7%, 95% CI: 33.0-62.8) achieved VD normalization. No VD-related clinical toxicity was reported. However, 13 patients (29.5%) presented asymptomatic grade 1 hypercalciuria, leading to interruption of the high-dose oral VD supplementation in 10, followed by a rapid reduction in serum VD concentration. No baseline clinical factor was predictive of VD normalization on D1C6. This high-dose VD supplementation appears safe and efficient in patients with early BC receiving adjuvant chemotherapy.

Background: Changes in calcium metabolism and calcium urinary excretion during chemotherapy have not been thoroughly assessed in patients with early breast cancer (EBC), a population who frequently present vitamin D insufficiency. As hypercalciuria is a classical contra-indication to vitamin D (VD) supplementation, this study evaluated changes in VD and calcium metabolism parameters in patients with EBC undergoing adjuvant chemotherapy (CT)., Methods: In patients with EBC who received six cycles of adjuvant CT, VD and calcium parameters were monitored at inclusion, and then every 3 weeks, at each CT cycle initiation. The primary endpoint was the percentage of patients showing hypercalciuria during adjuvant CT (between Day 1, Cycle 1 [D1C1] and Day 1, Cycle 6 [D1C6])., Results: The primary endpoint could be evaluated in 82 patients. Most patients (n = 66, 80.5%) had VD insufficiency (< 30 ng/mL) at baseline. Hypercalciuria was detected in 29 patients (35.4%; 95% CI: 25.6-46.5) between D1C1 and D1C6, but was not clinically significant in any of the affected patients. The percentage of hypercalciuria events was not different between patients with sufficient and insufficient baseline VD levels (34.8% vs. 37.5%), and between patients who received or not VD supplementation (37.5% vs. 34.5%,)., Conclusions: This comprehensive study on VD and calcium parameter changes in patients with EBC during adjuvant chemotherapy shows that hypercalciuria is a frequent abnormality in this setting, although asymptomatic. Therefore, it should not be considered as a limitation for high dose VD supplementation in this population., Trial Registration: EudraCT:2014-A01454-43 . Registered 29 august 2016., (© 2021. The Author(s).)

Purpose: Health-related quality of life (HRQoL) is assessed by self-administered questionnaires throughout the care process. Classically, two longitudinal statistical approaches were mainly used to study HRQoL: linear mixed models (LMM) or time-to-event models for time to deterioration/time until definitive deterioration (TTD/TUDD). Recently, an alternative strategy based on generalized linear mixed models for categorical data has also been proposed: the longitudinal partial credit model (LPCM). The objective of this article is to evaluate these methods and to propose recommendations to standardize longitudinal analysis of HRQoL data in cancer clinical trials., Methods: The three methods are first described and compared through statistical, methodological, and practical arguments, then applied on real HRQoL data from clinical cancer trials or published prospective databases. In total, seven French studies from a collaborating group were selected with longitudinal collection of QLQ-C30. Longitudinal analyses were performed with the three approaches using SAS, Stata and R software., Results: We observed concordant results between LMM and LPCM. However, discordant results were observed when we considered the TTD/TUDD approach compared to the two previous methods. According to methodological and practical arguments discussed, the approaches seem to provide additional information and complementary interpretations. LMM and LPCM are the most powerful methods on simulated data, while the TTD/TUDD approach gives more clinically understandable results. Finally, for single-item scales, LPCM is more appropriate., Conclusion: These results pledge for the recommendation to use of both the LMM and TTD/TUDD longitudinal methods, except for single-item scales, establishing them as the consensual methods for publications reporting HRQoL.

Surgery remains the only curative treatment for pancreatic ductal adenocarcinoma (PDAC). Therefore, a predictive score for resectability on diagnosis is needed. A total of 814 patients were included between 2014 and 2017 from 15 centers included in the BACAP (the national Anatomo-Clinical Database on Pancreatic Adenocarcinoma) prospective cohort. Three groups were defined: resectable (Res), locally advanced (LA), and metastatic (Met). Variables were analyzed and a predictive score was devised. Of the 814 patients included, 703 could be evaluated: 164 Res, 266 LA, and 273 Met. The median ages of the patients were 69, 71, and 69, respectively. The median survival times were 21, 15, and nine months, respectively. Six criteria were significantly associated with a lower probability of resectability in multivariate analysis: venous/arterial thrombosis ( p = 0.017), performance status 1 ( p = 0.032) or ≥ 2 ( p = 0.010), pain ( p = 0.003), weight loss ≥ 8% ( p = 0.019), topography of the tumor (body/tail) ( p = 0.005), and maximal tumor size 20-33 mm ( p < 0.013) or >33 mm ( p < 0.001). The BACAP score was devised using these criteria (http://jdlp.fr/resectability/) with an accuracy of 81.17% and an area under the receive operating characteristic (ROC) curve of 0.82 (95% confidence interval (CI): 0.78; 0.86). The presence of pejorative criteria or a BACAP score < 50% indicates that further investigations and even neoadjuvant treatment might be warranted. Trial registration: NCT02818829.

Background: Net survival, a measure of the survival where the patients would only die from the cancer under study, may be compared between treatment groups using either "cause-specific methods", when the causes of death are known and accurate, or "population-based methods", when the causes are missing or inaccurate. The latter methods rely on the assumption that mortality due to other causes than cancer is the same as the expected mortality in the general population with same demographic characteristics derived from population life tables. This assumption may not hold in clinical trials where patients are likely to be quite different from the general population due to some criteria for patient selection., Methods: In this work, we propose and assess the performance of a new flexible population-based model to estimate long-term net survival in clinical trials and that allows for cause-of-death misclassification and for effects of selection. Comparisons were made with cause-specific and other population-based methods in a simulation study and in an application to prostate cancer clinical trial data., Results: In estimating net survival, cause-specific methods seemed to introduce important biases associated with the degree of misclassification of cancer deaths. The usual population-based method provides also biased estimates, depending on the strength of the selection effect. Compared to these methods, the new model was able to provide more accurate estimates of net survival in long-term clinical trials., Conclusion: Finally, the new model paves the way for new methodological developments in the field of net survival methods in multicenter clinical trials.

Background: Interpretation of differences or changes in patient-reported outcome scores should not only consider statistical significance, but also clinical relevance. Accordingly, accurate determination of the minimally important difference (MID) is crucial to assess the effectiveness of health care interventions, as well as for sample size calculation. Several methods have been proposed to determine the MID. Our aim was to review the statistical methods used to determine MID in patient-reported outcome (PRO) questionnaires in cancer patients, focusing on the distribution- and anchor-based approaches and to present the variability of criteria used as well as possible limitations., Methods: We performed a systematic search using PubMed. We searched for all cancer studies related to MID determination on a PRO questionnaire. Two reviewers independently screened titles and abstracts to identify relevant articles. Data were extracted from eligible articles using a predefined data collection form. Discrepancies were resolved by discussion and the involvement of a third reviewer., Results: Sixty-three articles were identified, of which 46 were retained for final analysis. Both distribution- and anchor-based approaches were used to assess the MID in 37 studies (80.4%). Different time points were used to apply the distribution-based method and the most frequently reported distribution was the 0.5 standard deviation at baseline. A change in a PRO external scale (N = 13, 30.2%) and performance status (N = 15, 34.9%) were the most frequently used anchors. The stability of the MID over time was rarely investigated and only 28.2% of studies used at least 3 assessment timepoints. The robustness of anchor-based MID was questionable in 37.2% of the studies where the minimal number of patients by anchor category was less than 20., Conclusion: Efforts are needed to improve the quality of the methodology used for MID determination in PRO questionnaires used in oncology. In particular, increased attention to the sample size should be paid to guarantee reliable results. This could increase the use of these specific thresholds in future studies.

Background: Slow gait is common in dementia, but it remains unknown whether the slowing happens many years prior to dementia onset. We therefore examined the relationship between slow baseline gait speed (GS), change in GS, and the hazard of incident dementia in a community dwelling of elderly people., Methods: A total of 3,663 participants dementia-free at baseline (mean age, 73.5 years) were followed up for 9 years from a prospective cohort (Three-City study, France) for incident dementia (all-cause, Alzheimer's disease, vascular dementia, and other causes). GS over 6 m was assessed 4 times over the follow-up using two photoelectric cells. We used a multistate model to estimate the hazard ratio (HR) of dementia for baseline GS and tested a washout period of 4 to 7 years. The role of GS change between 65 and 85 years was examined using linear mixed models and joint models for survival and longitudinal data., Results: A total of 296 participants developed dementia during the follow-up. In age/sex-adjusted models, 1-SD (0.204 m/s) lower GS was associated with an increased hazard of dementia (HR = 1.59, 95% confidence interval [CI] = 1.39, 1.81, p < .001), with associations evident when gait assessments were taken from 4 years (HR = 1.46; CI = 1.26, 1.68) and 7 years (HR=1.30; CI = 1.00, 1.70) prior to dementia onset. Independently of baseline GS, those with a steeper decline had a higher hazard of dementia (HR per 1 SD [0.007 m/s/year] decrease = 3.39 [1.37-8.43], p = .009)., Conclusions: Gait is slower up to 7 years prior to clinical onset of dementia. Decline in GS is also more accelerated, suggesting strong links between cognitive and motor function in older adults., (© The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account for these two issues. In recent years, methods for estimating the time-dependent receiver operating characteristic curve and the associated area under the ROC curve have been extended to account for right censored data and competing risks. In this paper, we show how an approximation allows to use the inverse probability of censoring weighting estimator for semicompeting events with interval censored data. Then, using an illness-death model, we propose two model-based estimators allowing to rigorously handle these issues. The first estimator is fully model based whereas the second one only uses the model to impute missing observations due to censoring. A simulation study shows that the bias for inverse probability of censoring weighting remains modest and may be less than the one of the two parametric estimators when the model is misspecified. We finally recommend the nonparametric inverse probability of censoring weighting estimator as main analysis and the imputation estimator based on the illness-death model as sensitivity analysis., (© The Author(s) 2014.)

Introduction: Transition to bathing or dressing disability is a milestone in the evolution of dementia. We examined the transition to disability in these specific activities and considered death to be a competitive event and age and sex to be prognostic factors., Methods: From a large cohort of 570 incident dementia cases screened in two prospective population-based cohorts, the Paquid study, and the Three-City study, we estimated the probabilities of remaining nondisabled, becoming disabled in bathing or dressing, or dying after the diagnosis using an illness-death model., Results: On average, approximately half of the period (3 years) of living with dementia was free of disability. In women, a higher survival rate was associated with an average of 1 additional year with disability., Discussion: The joint prediction of death and disability in dementia by an illness-death model gives original and useful parameters for the prognosis and management of dementia., (Copyright © 2016 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.)

Multi-state models allow subjects to move among a finite number of states during a follow-up period. Most often, the objects of study are the transition intensities. The impact of covariates on them can also be studied by specifying regression models. Thus, estimation in multi-state models is usually focused on the transition intensities (or the cumulative transition intensities) and on the regression parameters. However, from a clinical or epidemiological point of view, other quantities could provide additional information and may be more relevant to answer practical questions. For example, given a set of covariates for a subject, it may be of interest to estimate the probability to experience a future event or the expected time without any event. To address these kinds of issues, we need to estimate quantities such as transition probabilities, cumulative probabilities and life expectancies. The purpose of this paper is to review a large number of these quantities in an illness-death model which is perhaps the most common multi-state model in the medical literature, and to propose a way to estimate them in addition to the transition intensities and the regression parameters. An illustration is given using interval-censored data from a large cohort study on cognitive ageing., (© The Author(s) 2013.)

Background: In survival analyses of longitudinal data, death is often a competing event for the disease of interest, and the time-to-disease onset is interval-censored when the diagnosis is made at intermittent follow-up visits. As a result, the disease status at death is unknown for subjects disease-free at the last visit before death. Standard survival analysis consists in right-censoring the time-to-disease onset at that visit, which may induce an underestimation of the disease incidence. By contrast, an illness-death model for interval-censored data accounts for the probability of developing the disease between that visit and death, and provides a better incidence estimate. However, the two approaches have never been compared for estimating the effect of exposure on disease risk., Methods: This paper compares through simulations the accuracy of the effect estimates from a semi-parametric illness-death model for interval-censored data and the standard Cox model. The approaches are also compared for estimating the effects of selected risk factors on the risk of dementia, using the French elderly PAQUID cohort data., Results: The illness-death model provided a more accurate effect estimate of exposures that also affected mortality. The direction and magnitude of the bias from the Cox model depended on the effects of the exposure on disease and death. The application to the PAQUID cohort confirmed the simulation results., Conclusion: If follow-up intervals are wide and the exposure has an impact on death, then the illness-death model for interval-censored data should be preferred to the standard Cox regression analysis.

The estimation of future prevalences of chronic diseases is essential for public health policy. Using incidence estimates from cohort data and demographic projections for general mortality and population sizes, we propose a method based on a general illness-death model to make prevalence projections for chronic diseases. In contrast to previously published methods, we account for differences between global mortality and mortality of healthy subjects and compare two assumptions regarding the secular trend for mortality of diseased subjects. Then we develop a methodology to estimate changes in future disease prevalences resulting from prevention campaign to reduce the frequency or the excess risk associated with a risk factor. The methods are applied for estimating dementia prevalence in France between 2010 and 2030., (Copyright © 2013, The International Biometric Society.)