Publications by authors named "Célia Touraine"

10 Publications

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Longitudinal analysis of health-related quality of life in cancer clinical trials: methods and interpretation of results.

Qual Life Res 2021 Jan 18;30(1):91-103. Epub 2020 Aug 18.

National Platform Quality of Life and Cancer, Montpellier, France.

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.
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http://dx.doi.org/10.1007/s11136-020-02605-3DOI Listing
January 2021

A New Score to Predict the Resectability of Pancreatic Adenocarcinoma: The BACAP Score.

Cancers (Basel) 2020 Mar 25;12(4). Epub 2020 Mar 25.

The Digestive Surgery and Liver Transplantation Department, Toulouse University Hospital, 31400 Toulouse, France.

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 ( = 0.017), performance status 1 ( = 0.032) or ≥ 2 ( = 0.010), pain ( = 0.003), weight loss ≥ 8% ( = 0.019), topography of the tumor (body/tail) ( = 0.005), and maximal tumor size 20-33 mm ( < 0.013) or >33 mm ( < 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.
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http://dx.doi.org/10.3390/cancers12040783DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226323PMC
March 2020

Correcting for misclassification and selection effects in estimating net survival in clinical trials.

BMC Med Res Methodol 2019 05 16;19(1):104. Epub 2019 May 16.

Aix Marseille Univ, APHM, INSERM, IRD, SESSTIM (Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale), Hop Timone, BioSTIC (Biostatistique et Technologies de l'Information et de la Communication), Marseille, France.

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.
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http://dx.doi.org/10.1186/s12874-019-0747-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524224PMC
May 2019

Distribution- and anchor-based methods to determine the minimally important difference on patient-reported outcome questionnaires in oncology: a structured review.

Health Qual Life Outcomes 2018 Dec 11;16(1):228. Epub 2018 Dec 11.

Methodology and Quality of Life in Oncology Unit (INSERM UMR 1098), University Hospital of Besançon, Besançon, France.

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.
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http://dx.doi.org/10.1186/s12955-018-1055-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288886PMC
December 2018

Gait Speed and Decline in Gait Speed as Predictors of Incident Dementia.

J Gerontol A Biol Sci Med Sci 2017 May;72(5):655-661

Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, Villejuif, France.

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.
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http://dx.doi.org/10.1093/gerona/glw110DOI Listing
May 2017

ADL disability and death in dementia in a French population-based cohort: New insights with an illness-death model.

Alzheimers Dement 2016 08 19;12(8):909-16. Epub 2016 Apr 19.

Centre INSERM U897, ISPED, Bordeaux, France; Université Bordeaux, Bordeaux, France; CMRR d'Aquitaine, CHU de Bordeaux, Bordeaux, France.

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.
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http://dx.doi.org/10.1016/j.jalz.2016.03.007DOI Listing
August 2016

Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring.

Stat Methods Med Res 2016 12 6;25(6):2750-2766. Epub 2014 May 6.

Université Bordeaux Segalen, ISPED, Centre INSERM U897, Bordeaux, France.

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.
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http://dx.doi.org/10.1177/0962280214531691DOI Listing
December 2016

Interval-censored time-to-event and competing risk with death: is the illness-death model more accurate than the Cox model?

Int J Epidemiol 2013 Aug 30;42(4):1177-86. Epub 2013 Jul 30.

University of Bordeaux, ISPED, Centre INSERM U897-Epidemiology-Biostatistics, Bordeaux, France.

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.
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http://dx.doi.org/10.1093/ije/dyt126DOI Listing
August 2013

Predictions in an illness-death model.

Stat Methods Med Res 2016 08 22;25(4):1452-70. Epub 2013 May 22.

ISPED, University of Bordeaux, INSERM U-897-Epidemiologie-Biostatistique, Bordeaux, France.

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.
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http://dx.doi.org/10.1177/0962280213489234DOI Listing
August 2016

Prevalence projections of chronic diseases and impact of public health intervention.

Biometrics 2013 Mar 4;69(1):109-17. Epub 2013 Feb 4.

University of Bordeaux, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000 Bordeaux, France.

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.
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http://dx.doi.org/10.1111/j.1541-0420.2012.01827.xDOI Listing
March 2013