Patient-centered simulations to assess the usefulness of the 70-gene signature for adjuvant chemotherapy administration in early-stage breast cancer.

PURPOSE: From the MINDACT trial, Cardoso et al. did not demonstrate a significant efficacy for adjuvant chemotherapy (CT) for women with early-stage breast cancer presenting high clinical and low genomic risks. Our objective was to assess the usefulness of the 70-gene signature in this population by using an alternative endpoint: the number of Quality-Adjusted Life-Years (QALYs), i.e., a synthetic measure of quantity and quality of life. METHODS: Based on the results of the MINDACT trial, we simulated a randomized clinical trial consisting of 1497 women with early-stage breast cancer presenting high clinical and low genomic risks. The individual preferences for the different health states and corresponding decrements were obtained from the literature. RESULTS: The gain in terms of 5-year disease-free survival was 2.8% (95% CI from - 0.1 to 5.7%, from 90.4% for women without CT to 93.3% for women with CT). In contrast, due to the associated side effects, CT significantly reduced the number of QALYs by 62 days (95% CI from 55 to 70 days, from 4.13 years for women without CT to 3.96 years for women with CT). CONCLUSION: Our results support the conclusions published by Cardoso et al. by providing additional evidence that the 70-gene signature can be used to avoid overtreatment by CT for women with high clinical risk but low genomic risk.

Dynamic predictions of long-term kidney graft failure: an information tool promoting patient-centred care.

BACKGROUND: Informing kidney transplant recipients of their prognosis and disease progression is of primary importance in a patient-centred vision of care. By participating in decisions from the outset, transplant recipients may be more adherent to complex medical regimens due to their enhanced understanding. METHODS: We proposed to include repeated measurements of serum creatinine (SCr), in addition to baseline characteristics, in order to obtain dynamic predictions of the graft failure risk that could be updated continuously during patient follow-up. Adult recipients from the French Données Informatisées et VAlidées en Transplantation (DIVAT) cohort transplanted for the first or second time from a heart-beating or living donor and alive with a functioning graft at 1 year post-transplantation were included. RESULTS: The model was composed of six baseline parameters, in addition to the SCr evolution. We validated the dynamic predictions by evaluating both discrimination and calibration accuracy. The area under the receiver operating characteristic curve varied from 0.72 to 0.76 for prediction times at 1 and 6 years post-transplantation, respectively, while calibration plots showed correct accuracy. We also provided an online application tool (https://shiny.idbc.fr/DynPG). CONCLUSION: We have created a tool that, for the first time in kidney transplantation, predicts graft failure risk both at an individual patient level and dynamically. We believe that this tool would encourage willing patients into participative medicine.

Comparison of graft and patient survival according to the transplantation centre policy for 1-year screening biopsy among stable kidney recipients: a propensity score-based study.

BACKGROUND: The clinical utility of screening biopsies (SBs) at 1 year post-transplantation is still debated, especially for stable kidney graft recipients. Given the heterogeneity in practices between transplantation centres, the objective of this study was to compare graft and patient survival of stable patients according to whether they were followed up in a transplantation centre with or without a policy for having an SB at 1 year post-transplantation. MATERIALS: From a French multicentre cohort, we studied 1573 kidney recipients who were alive with stable graft function at 1 year post-transplantation, with no acute rejection in their first year post-transplantation. RESULTS: Using propensity score-based analyses, we did not observe any significant difference in the relative risk for graft failure between patients from centres with a 1-year SB policy and those from other centres [hazard ratio = 1.15, 95% confidence interval (CI) 0.86-1.53]. The corresponding adjusted survival probability at 8 years post-transplantation was 69% (95% CI 61-74%) for patients from centres with a 1-year SB policy versus 74% (95% CI 67-79%) for those from other centres. CONCLUSION: A 1-year SB policy for stable patients may not lead to therapeutical benefits for improved graft and patient survival. Further studies examining the benefits versus the risks of a 1-year SB policy are warranted to demonstrate the long-term utility of this intervention.

Meta-analysis of predictive models to assess the clinical validity and utility for patient-centered medical decision making: application to the CAncer of the Prostate Risk Assessment (CAPRA).

BACKGROUND: The Cancer of the Prostate Risk Assessment (CAPRA) score was designed and validated several times to predict the biochemical recurrence-free survival after a radical prostatectomy. Our objectives were, first, to study the clinical validity of the CAPRA score, and, second, to assess its clinical utility for stratified medicine from an original patient-centered approach. METHODS: We proposed a meta-analysis based on a literature search using MEDLINE. Observed and predicted biochemical-recurrence-free survivals were compared to assess the calibration of the CAPRA score. Discriminative capacities were evaluated by estimating the summary time-dependent ROC curve. The clinical utility of the CAPRA score was evaluated according to the following stratified decisions: active monitoring for low-risk patients, prostatectomy for intermediate-risk patients, or radio-hormonal therapy for high risk patients. For this purpose, we assessed CAPRA's clinical utility in terms of its ability to maximize time-dependent utility functions (i.e. Quality-Adjusted Life-Years - QALYs). RESULTS: We identified 683 manuscripts and finally retained 9 studies. We reported good discriminative capacities with an area under the SROCt curve at 0.73 [95%CI from 0.67 to 0.79], while graphical calibration seemed acceptable. Nevertheless, we also described that the CAPRA score was unable to discriminate between the three medical alternatives, i.e. it did not allow an increase in the number of life years in perfect health (QALYs) of patients with prostate cancer. CONCLUSIONS: We confirmed the prognostic capacities of the CAPRA score. In contrast, we were not able to demonstrate its clinical usefulness for stratified medicine from a patient-centered perspective. Our results also highlighted the confusion between clinical validity and utility. This distinction should be better considered in order to develop predictive tools useful in practice.

An R2 -curve for evaluating the accuracy of dynamic predictions.

In the context of chronic diseases, patient's health evolution is often evaluated through the study of longitudinal markers and major clinical events such as relapses or death. Dynamic predictions of such types of events may be useful to improve patients management all along their follow-up. Dynamic predictions consist of predictions that are based on information repeatedly collected over time, such as measurements of a biomarker, and that can be updated as soon as new information becomes available. Several techniques to derive dynamic predictions have already been suggested, and computation of dynamic predictions is becoming increasingly popular. In this work, we focus on assessing predictive accuracy of dynamic predictions and suggest that using an R2 -curve may help. It facilitates the evaluation of the predictive accuracy gain obtained when accumulating information on a patient's health profile over time. A nonparametric inverse probability of censoring weighted estimator is suggested to deal with censoring. Large sample results are provided, and methods to compute confidence intervals and bands are derived. A simulation study assesses the finite sample size behavior of the inference procedures and illustrates the shape of some R2 -curves which can be expected in common settings. A detailed application to kidney transplant data is also presented.

Inverse probability weighting to control confounding in an illness-death model for interval-censored data.

Multistate models with interval-censored data, such as the illness-death model, are still not used to any considerable extent in medical research regardless of the significant literature demonstrating their advantages compared to usual survival models. Possible explanations are their uncommon availability in classical statistical software or, when they are available, by the limitations related to multivariable modelling to take confounding into consideration. In this paper, we propose a strategy based on propensity scores that allows population causal effects to be estimated: the inverse probability weighting in the illness semi-Markov model with interval-censored data. Using simulated data, we validated the performances of the proposed approach. We also illustrated the usefulness of the method by an application aiming to evaluate the relationship between the inadequate size of an aortic bioprosthesis and its degeneration or/and patient death. We have updated the R package multistate to facilitate the future use of this method.

The 1-year Renal Biopsy Index: a scoring system to drive biopsy indication at 1-year post-kidney transplantation.

Surveillance biopsies after renal transplantation remain debatable. To drive the decision of such intervention, we propose a predictive score of abnormal histology at 1-year post-transplantation, named 1-year Renal Biopsy Index (1-RBI). We studied 466 kidney recipients from the DIVAT cohort alive with a functioning graft and a surveillance biopsy at 1-year post-transplantation. Patients displaying abnormal histology (49%) (borderline, acute rejection, interstitial fibrosis and tubular atrophy [IFTA] grade 2 or 3, glomerulonephritis) were compared to the normal or subnormal (IFTA grade 1) histology group. Obtained from a lasso penalized logistic regression, the 1-RBI was composed of recipient gender, serum creatinine at 3, 6, and 12 month post-transplantation and anticlass II immunization at transplantation (internal validation: AUC = 0.71, 95% CI [0.53-0.83]; external validation: AUC = 0.62, 95% CI [0.58-0.66]). While we could not determinate a threshold able to identify patients at high chance of normal or subnormal histology, we estimated and validated a discriminating threshold capable of identifying a subgroup of 15% of the patients with a risk of abnormal histology higher than 80%. The 1-RBI is computable online at www.divat.fr. The 1-RBI could be a useful tool to standardize 1-year biopsy proposal and may for instance help to indicate one in case of high risk of abnormal histology.

A mini-review of quality of life as an outcome in prostate cancer trials: patient-centered approaches are needed to propose appropriate treatments on behalf of patients.

BACKGROUND: Patients with prostate cancer (PC) may be ready to make trade-offs between their quantity and their quality of life. For instance, elderly patients may prefer the absence of treatment if it is associated with a low-risk of disease progression, compared to treatments aiming at preventing disease progression but with a substantial deterioration of their Health-Related Quality of Life (HRQoL). Therefore, it seems relevant to compare the treatments by considering both survival and HRQoL. In this mini-review, the aim was to question whether the potential trade-offs between survival and HRQoL are considered in high impact factor journals. METHODS: The study was conducted from the PubMed database for recent papers published between May 01, 2013, and May 01, 2015. We also restricted our search to nine medical journals with 2013 impact factor > 15. RESULTS: Among the 30 selected studies, only six collected individual HRQoL as a secondary endpoint by using the Functional Assessment of Cancer Therapy-Prostate (FACT-P) questionnaire. In four studies, the time to HRQoL change was analyzed, but its definitions varied. In two studies, the mean changes in HRQoL between the baseline and the 12- or 16-week follow-up were analyzed. None of the six studies reported in a single endpoint both the quantity and the quality of life. CONCLUSIONS: Our mini-review, which only focused on recent publications in journals with high-impact, suggests moving PC clinical research towards patient-centered outcomes-based studies. This may help physicians to propose the most appropriate treatment on behalf of patients. We recommend the use of indicators such as Quality-Adjusted Life-Years (QALYs) as principal endpoint in future clinical trials.

Comparison of survival outcomes between Expanded Criteria Donor and Standard Criteria Donor kidney transplant recipients: a systematic review and meta-analysis.

In 2002, the United Network for Organ Sharing proposed increasing the pool of donor kidneys to include Expanded Criteria Donor (ECD). Outside the USA, the ECD definition remains the one used without questioning whether such a graft allocation criterion is valid worldwide. We performed a meta-analysis to quantify the differences between ECD and Standard Criteria Donor (SCD) transplants. We paid particular attention to select studies in which the methodology was appropriate and we took into consideration the geographical area. Thirty-two publications were included. Only five studies, all from the USA, reported confounder-adjusted hazard ratios comparing the survival outcomes between ECD and SCD kidney transplant recipients. These five studies confirmed that ECD recipients seemed to have poorer prognosis. From 29 studies reporting appropriate survival curves, we estimated the 5-year pooled nonadjusted survivals for ECD and SCD recipients. The relative differences between the two groups were lower in Europe than in North America, particularly for death-censored graft failure. It is of primary importance to propose appropriate studies for external validation of the ECD criteria in non-US kidney transplant recipients.

A joint model for longitudinal and time-to-event data to better assess the specific role of donor and recipient factors on long-term kidney transplantation outcomes.

In renal transplantation, serum creatinine (SCr) is the main biomarker routinely measured to assess patient's health, with chronic increases being strongly associated with long-term graft failure risk (death with a functioning graft or return to dialysis). Joint modeling may be useful to identify the specific role of risk factors on chronic evolution of kidney transplant recipients: some can be related to the SCr evolution, finally leading to graft failure, whereas others can be associated with graft failure without any modification of SCr. Sample data for 2749 patients transplanted between 2000 and 2013 with a functioning kidney at 1-year post-transplantation were obtained from the DIVAT cohort. A shared random effect joint model for longitudinal SCr values and time to graft failure was performed. We show that graft failure risk depended on both the current value and slope of the SCr. Deceased donor graft patient seemed to have a higher SCr increase, similar to patient with diabetes history, while no significant association of these two features with graft failure risk was found. Patient with a second graft was at higher risk of graft failure, independent of changes in SCr values. Anti-HLA immunization was associated with both processes simultaneously. Joint models for repeated and time-to-event data bring new opportunities to improve the epidemiological knowledge of chronic diseases. For instance in renal transplantation, several features should receive additional attention as we demonstrated their correlation with graft failure risk was independent of the SCr evolution.

Each additional hour of cold ischemia time significantly increases the risk of graft failure and mortality following renal transplantation.

Although cold ischemia time has been widely studied in renal transplantation area, there is no consensus on its precise relationship with the transplantation outcomes. To study this, we sampled data from 3839 adult recipients of a first heart-beating deceased donor kidney transplanted between 2000 and 2011 within the French observational multicentric prospective DIVAT cohort. A Cox model was used to assess the relationship between cold ischemia time and death-censored graft survival or patient survival by using piecewise log-linear function. There was a significant proportional increase in the risk of graft failure for each additional hour of cold ischemia time (hazard ratio, 1.013). As an example, a patient who received a kidney with a cold ischemia time of 30 h presented a risk of graft failure near 40% higher than a patient with a cold ischemia time of 6 h. Moreover, we found that the risk of death also proportionally increased for each additional hour of cold ischemia time (hazard ratio, 1.018). Thus, every additional hour of cold ischemia time must be taken into account in order to increase graft and patient survival. These findings are of practical clinical interest, as cold ischemia time is among one of the main modifiable pre-transplantation risk factors that can be minimized by improved management of the peri-transplantation period.

Restricted mean survival time to estimate an intervention effect in a cluster randomized trial.

For time-to-event outcomes, the difference in restricted mean survival time is a measure of the intervention effect, an alternative to the hazard ratio, corresponding to the expected survival duration gain due to the intervention up to a predefined time t*. We extended two existing approaches of restricted mean survival time estimation for independent data to clustered data in the framework of cluster randomized trials: one based on the direct integration of Kaplan-Meier curves and the other based on pseudo-values regression. Then, we conducted a simulation study to assess and compare the statistical performance of the proposed methods, varying the number and size of clusters, the degree of clustering, and the magnitude of the intervention effect under proportional and non-proportional hazards assumption. We found that the extended methods well estimated the variance and controlled the type I error if there was a sufficient number of clusters (≥ 50) under both proportional and non-proportional hazards assumption. For cluster randomized trials with a limited number of clusters (< 50), a permutation test for pseudo-values regression was implemented and corrected the type I error. We also provided a procedure to estimate permutation-based confidence intervals which produced adequate coverage. All the extended methods performed similarly, but the pseudo-values regression offered the possibility to adjust for covariates. Finally, we illustrated each considered method with a cluster randomized trial evaluating the effectiveness of an asthma-control education program.

Renal function decline and heart failure hospitalisation in patients with type 2 diabetes: Dynamic predictions from the prospective SURDIAGENE cohort.

AIMS: For type 2 diabetes persons, we assessed the association between renal function decline and heart failure hospitalisation (HFH) and validated dynamic HFH predictions (DynHFH) based on repeated estimated Glomerular Filtration Rate (eGFR) values. METHODS: We studied 1413 patients from the SURDIAGENE cohort. From a joint model for longitudinal CKD-EPI measures and HFH risk, we calculated the probability of being HFH-free in the next five years. RESULTS: The mean eGFR decline was estimated at 1.48 ml/min/1.73 m2 per year (95 % CI from 1.23 to 1.74). We observed that eGFR decline was significantly associated with the HFH risk (adjHR = 1.15 for an increase in yearly decline of 1 ml/min/1.73 m2, 95 % CI from 1.03 to 1.26) independently of 7 baseline variables (from clinical, biological and ECG domains). Discrimination was good along the prediction times: AUC at 0.87 (95 %CI from 0.84 to 0.91) at patient inclusion and 0.77 (95 %CI from 0.67 to 0.87) at seven years' follow-up. CONCLUSIONS: Renal function decline was significantly associated with the HFH risk. In the era of computer-assisted medical decisions, the DynHFH, a tool that dynamically predicts HFH in type 2 diabetes persons (https://shiny.idbc.fr/DynHFH), might be helpful for precision medicine and the implementation of stratified medical decision-making.

The role of antibody responses against glycans in bioprosthetic heart valve calcification and deterioration.

Bioprosthetic heart valves (BHVs) are commonly used to replace severely diseased heart valves but their susceptibility to structural valve degeneration (SVD) limits their use in young patients. We hypothesized that antibodies against immunogenic glycans present on BHVs, particularly antibodies against the xenoantigens galactose-α1,3-galactose (αGal) and N-glycolylneuraminic acid (Neu5Gc), could mediate their deterioration through calcification. We established a large longitudinal prospective international cohort of patients (n = 1668, 34 ± 43 months of follow-up (0.1-182); 4,998 blood samples) to investigate the hemodynamics and immune responses associated with BHVs up to 15 years after aortic valve replacement. Early signs of SVD appeared in <5% of BHV recipients within 2 years. The levels of both anti-αGal and anti-Neu5Gc IgGs significantly increased one month after BHV implantation. The levels of these IgGs declined thereafter but anti-αGal IgG levels declined significantly faster in control patients compared to BHV recipients. Neu5Gc, anti-Neu5Gc IgG and complement deposition were found in calcified BHVs at much higher levels than in calcified native aortic valves. Moreover, in mice, anti-Neu5Gc antibodies were unable to promote calcium deposition on subcutaneously implanted BHV tissue engineered to lack αGal and Neu5Gc antigens. These results indicate that BHVs manufactured using donor tissues deficient in αGal and Neu5Gc could be less prone to immune-mediated deterioration and have improved durability.

Dynamic predictions of kidney graft survival in the presence of longitudinal outliers.

In kidney transplantation, dynamic predictions of graft survival may be obtained from joint modelling of longitudinal and survival data for which a common assumption is that random-effects and error terms in the longitudinal sub-model are Gaussian. However, this assumption may be too restrictive, e.g. in the presence of outliers, and more flexible distributions would be required. In this study, we relax the Gaussian assumption by defining a robust joint modelling framework with t-distributed random-effects and error terms to obtain dynamic predictions of graft survival for kidney transplant patients. We take a Bayesian paradigm for inference and dynamic predictions and sample from the joint posterior densities. While previous research reported improved performances of robust joint models compared to the Gaussian version in terms of parameter estimation, dynamic prediction accuracy obtained from such approach has not been yet evaluated. Our results based on a training sample from the French DIVAT kidney transplantation cohort illustrate that estimates for the slope parameters in the longitudinal and survival sub-models are sensitive to the distributional assumptions. From both an internal validation sample from the DIVAT cohort and an external validation sample from the Lille (France) and Leuven (Belgium) transplantation centers, calibration and discrimination performances appeared to be better under the robust joint models compared to the Gaussian version, illustrating the need to accommodate outliers in the dynamic prediction context. Simulation results support the findings of the validation studies.

Covariates adjustment questioned conclusions of predictive analyses: an illustration with the Kidney Donor Risk Index.

OBJECTIVES: We aimed to illustrate that considering covariates can lead to meaningful interpretation of the discriminative capacities of a prognostic marker. For this, we evaluated the ability of the Kidney Donor Risk Index (KDRI) to discriminate kidney graft failure risk. STUDY DESIGN AND SETTING: From 4114 French patients, we estimated the adjusted area under the time-dependent ROC curve by standardizing the marker and weighting the observations. By weighting the contributions, we also studied the impact of KDRI-based transplantations on the patient and graft survival. RESULTS: The covariate-adjusted AUC varied from 55% (95% confidence interval [CI]: 51-60%) for a prognostic up to 1 year post-transplantation to 56% (95% CI: 52-59%) up to 7 years. The Restricted Mean Survival Time (RMST) was 6.44 years for high-quality graft recipients (95% CI: 6.30-6.56) and would have been 6.31 years (95% CI: 6.13-6.46) if they had medium-quality transplants. The RMST was 5.10 years for low-quality graft recipients (95% CI: 4.90-5.31) and would have been 5.52 years (95% CI: 5.17-5.83) if they had medium-quality transplants. CONCLUSION: We demonstrated that the KDRI discriminative capacities were mainly explained by the recipient characteristics. We also showed that counterfactual estimations, often used in causal studies, are also interesting in predictive studies, especially regarding the new available methods.

External Validation of the DynPG for Kidney Transplant Recipients.

BACKGROUND: In kidney transplantation, dynamic prediction of patient and kidney graft survival (DynPG) may help to promote therapeutic alliance by delivering personalized evidence-based information about long-term graft survival for kidney transplant recipients. The objective of the current study is to externally validate the DynPG. METHODS: Based on 6 baseline variables, the DynPG can be updated with any new serum creatinine measure available during the follow-up. From an external validation sample of 1637 kidney recipients with a functioning graft at 1-year posttransplantation from 2 European transplantation centers, we assessed the prognostic performance of the DynPG. RESULTS: As one can expect from an external validation sample, differences in several recipient, donor, and transplantation characteristics compared with the learning sample were observed. Patients were mainly transplanted from deceased donors (91.6% versus 84.8%; P < 0.01), were less immunized against HLA class I (18.4% versus 32.7%; P < 0.01) and presented less comorbidities (62.2% for hypertension versus 82.7%, P < 0.01; 25.1% for cardiovascular disease versus 33.9%, P < 0.01). Despite these noteworthy differences, the area under the ROC curve varied from 0.70 (95% confidence interval [CI], 0.64-0.76) to 0.76 (95% CI, 0.64-0.88) for prediction times at 1 and 6 years posttransplantation respectively, and calibration plots revealed reasonably accurate predictions. CONCLUSIONS: We validated the prognostic capacities of the DynPG in terms of both discrimination and calibration. Our study showed the robustness of the DynPG for informing both the patient and the physician, and its transportability for a cohort presenting different features than the one used for the DynPG development.

Methodological quality of multivariate prognostic models for intracranial haemorrhages in intensive care units: a systematic review.

OBJECTIVES: Patients with severe spontaneous intracranial haemorrhages, managed in intensive care units, face ethical issues regarding the difficulty of anticipating their recovery. Prognostic tools help clinicians in counselling patients and relatives and guide therapeutic decisions. We aimed to methodologically assess prognostic tools for functional outcomes in severe spontaneous intracranial haemorrhages. DATA SOURCES: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations, we conducted a systematic review querying Medline, Embase, Web of Science, and the Cochrane in January 2020. STUDY SELECTION: We included development or validation of multivariate prognostic models for severe intracerebral or subarachnoid haemorrhage. DATA EXTRACTION: We evaluated the articles following the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies and Transparent Reporting of multivariable prediction model for Individual Prognosis Or Diagnosis statements to assess the tools' methodological reporting. RESULTS: Of the 6149 references retrieved, we identified 85 articles eligible. We discarded 43 articles due to the absence of prognostic performance or predictor selection. Among the 42 articles included, 22 did not validate models, 6 developed and validated models and 14 only externally validated models. When adding 11 articles comparing developed models to existing ones, 25 articles externally validated models. We identified methodological pitfalls, notably the lack of adequate validations or insufficient performance levels. We finally retained three scores predicting mortality and unfavourable outcomes: the IntraCerebral Haemorrhages (ICH) score and the max-ICH score for intracerebral haemorrhages, the SubArachnoid Haemorrhage International Trialists score for subarachnoid haemorrhages. CONCLUSIONS: Although prognostic studies on intracranial haemorrhages abound in the literature, they lack methodological robustness or show incomplete reporting. Rather than developing new scores, future authors should focus on externally validating and updating existing scores with large and recent cohorts.

Optimal threshold estimator of a prognostic marker by maximizing a time-dependent expected utility function for a patient-centered stratified medicine.

Defining thresholds of prognostic markers is essential for stratified medicine. Such thresholds are mostly estimated from purely statistical measures regardless of patient preferences potentially leading to unacceptable medical decisions. Quality-Adjusted Life-Years are a widely used preferences-based measure of health outcomes. We develop a time-dependent Quality-Adjusted Life-Years-based expected utility function for censored data that should be maximized to estimate an optimal threshold. We performed a simulation study to compare estimated thresholds when using the proposed expected utility approach and purely statistical estimators. Two applications illustrate the usefulness of the proposed methodology which was implemented in the R package ROCt ( www.divat.fr ). First, by reanalysing data of a randomized clinical trial comparing the efficacy of prednisone vs. placebo in patients with chronic liver cirrhosis, we demonstrate the utility of treating patients with a prothrombin level higher than 89%. Second, we reanalyze the data of an observational cohort of kidney transplant recipients: we conclude to the uselessness of the Kidney Transplant Failure Score to adapt the frequency of clinical visits. Applying such a patient-centered methodology may improve future transfer of novel prognostic scoring systems or markers in clinical practice.

A multistate additive relative survival semi-Markov model.

Medical researchers are often interested to investigate the relationship between explicative variables and times-to-events such as disease progression or death. Such multiple times-to-events can be studied using multistate models. For chronic diseases, it may be relevant to consider semi-Markov multistate models because the transition intensities between two clinical states more likely depend on the time already spent in the current state than on the chronological time. When the cause of death for a patient is unavailable or not totally attributable to the disease, it is not possible to specifically study the associations with the excess mortality related to the disease. Relative survival analysis allows an estimate of the net survival in the hypothetical situation where the disease would be the only possible cause of death. In this paper, we propose a semi-Markov additive relative survival (SMRS) model that combines the multistate and the relative survival approaches. The usefulness of the SMRS model is illustrated by two applications with data from a French cohort of kidney transplant recipients. Using simulated data, we also highlight the effectiveness of the SMRS model: the results tend to those obtained if the different causes of death are known.