A simulation model of colorectal cancer surveillance and recurrence

BACKGROUND: Approximately one-third of those treated curatively for colorectal cancer (CRC) will experience recurrence. No evidence-based consensus exists on how best to follow patients after initial treatment to detect asymptomatic recurrence. Here, a new approach for simulating surveillance and recurrence among CRC survivors is outlined, and development and calibration of a simple model applying this approach is described. The model’s ability to predict outcomes for a group of patients under a specified surveillance strategy is validated. METHODS: We developed an individual-based simulation model consisting of two interacting submodels: a continuous-time disease-progression submodel overlain by a discrete-time Markov submodel of surveillance and re-treatment. In the former, some patients develops recurrent disease which probabilistically progresses from detectability to unresectability, and which may produce early symptoms leading to detection independent of surveillance testing. In the latter submodel, patients undergo user-specified surveillance testing regimens. Parameters describing disease progression were preliminarily estimated through calibration to match five-year disease-free survival, overall survival at years 1–5, and proportion of recurring patients undergoing curative salvage surgery from one arm of a published randomized trial. The calibrated model was validated by examining its ability to predict these same outcomes for patients in a different arm of the same trial undergoing less aggressive surveillance. RESULTS: Calibrated parameter values were consistent with generally observed recurrence patterns. Sensitivity analysis suggested probability of curative salvage surgery was most influenced by sensitivity of carcinoembryonic antigen assay and of clinical interview/examination (i.e. scheduled provider visits). In validation, the model accurately predicted overall survival (59% predicted, 58% observed) and five-year disease-free survival (55% predicted, 53% observed), but was less accurate in predicting curative salvage surgery (10% predicted; 6% observed). CONCLUSIONS: Initial validation suggests the feasibility of this approach to modeling alternative surveillance regimens among CRC survivors. Further calibration to individual-level patient data could yield a model useful for predicting outcomes of specific surveillance strategies for risk-based subgroups or for individuals. This approach could be applied toward developing novel, tailored strategies for further clinical study. It has the potential to produce insights which will promote more effective surveillance—leading to higher cure rates for recurrent CRC

Optimizing patient risk stratification for colonoscopy screening and surveillance of colorectal cancer: The role for linked data

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Individualisation du suivi post-thérapeutique des patients traités du cancer en fonction des facteurs pronostiques et du type de rechute

Les questions de l’organisation de la surveillance des patients ayant reçu un traitement pour un cancer sont toujours ouvertes. Les pratiques courantes sont principalement fondées sur des recommandations d’experts. Peu de preuves scientifiques sont posées pour les valider. Cette thèse propose une méthodologie pour organiser une surveillance post-thérapeutique des patients traités du cancer. Cette surveillance sera individualisée en tenant compte des caractéristiques du patient. Elle sera aussi flexible en s’adaptant aux caractéristiques propres de la maladie, de sa sévérité et des différents types de récidives attendues. Une première partie permet de déterminer la durée optimale de suivi du patient. Les fonctions d’incidences cumulées des différents types de récidives sont modélisées par une approche directe de modélisation de risques compétitifs. La deuxième propose une méthodologie pour fixer les dates de visite de façon optimale. Cette méthode passe par la modélisation des dates d’apparition des événements par une approche multi-états en utilisant une hypothèse de Markov homogène. Enfin, un algorithme est proposé pour évaluer un programme de surveillance post-thérapeutique. Cet algorithme permet de simuler de façon numérique les transitions dynamiques par une technique de simulation des événements discrets. L’ensemble des modèles se basent sur l’histoire naturelle de la maladie.There still are open questions about the organization of the surveillance of patients who received treatment for cancer. Current practices are mainly based on expert recommendations. Little scientific evidence are found to confirm them. This thesis proposes a methodology to organize the post-therapeutic follow-up of patients treated for cancer. This follow-up will be individualized according to the patient’s characteristics. It will also be flexible and adapt to the characteristics of the disease, its severity and the expected types of recurrences. The first part considers the determination of the patient’s follow-up period. The cumulative incidence functions of the different recurrence types are modeled by a direct competing risks modeling approach. The second part proposes a methodology to determine the optimal visit dates. This approach involves modeling the dates of recurrence by a multi-state approach using a homogeneous Markov assumption. Finally, an algorithm is proposed to evaluate a post-therapeutic surveillance program. This algorithm simulates dynamic states transitions by a discrete events simulation approach. All models are based on the natural history of the disease

Individualisation du suivi post-thérapeutique des patients traités du cancer en fonction des facteurs pronostiques et du type de rechute

Les questions de l’organisation de la surveillance des patients ayant reçu un traitement pour un cancer sont toujours ouvertes. Les pratiques courantes sont principalement fondées sur des recommandations d’experts. Peu de preuves scientifiques sont posées pour les valider. Cette thèse propose une méthodologie pour organiser une surveillance post-thérapeutique des patients traités du cancer. Cette surveillance sera individualisée en tenant compte des caractéristiques du patient. Elle sera aussi flexible en s’adaptant aux caractéristiques propres de la maladie, de sa sévérité et des différents types de récidives attendues. Une première partie permet de déterminer la durée optimale de suivi du patient. Les fonctions d’incidences cumulées des différents types de récidives sont modélisées par une approche directe de modélisation de risques compétitifs. La deuxième propose une méthodologie pour fixer les dates de visite de façon optimale. Cette méthode passe par la modélisation des dates d’apparition des événements par une approche multi-états en utilisant une hypothèse de Markov homogène. Enfin, un algorithme est proposé pour évaluer un programme de surveillance post-thérapeutique. Cet algorithme permet de simuler de façon numérique les transitions dynamiques par une technique de simulation des événements discrets. L’ensemble des modèles se basent sur l’histoire naturelle de la maladie.There still are open questions about the organization of the surveillance of patients who received treatment for cancer. Current practices are mainly based on expert recommendations. Little scientific evidence are found to confirm them. This thesis proposes a methodology to organize the post-therapeutic follow-up of patients treated for cancer. This follow-up will be individualized according to the patient’s characteristics. It will also be flexible and adapt to the characteristics of the disease, its severity and the expected types of recurrences. The first part considers the determination of the patient’s follow-up period. The cumulative incidence functions of the different recurrence types are modeled by a direct competing risks modeling approach. The second part proposes a methodology to determine the optimal visit dates. This approach involves modeling the dates of recurrence by a multi-state approach using a homogeneous Markov assumption. Finally, an algorithm is proposed to evaluate a post-therapeutic surveillance program. This algorithm simulates dynamic states transitions by a discrete events simulation approach. All models are based on the natural history of the disease

Shed urinary ALCAM is an independent prognostic biomarker of three-year overall survival after cystectomy in patients with bladder cancer.

Proteins involved in tumor cell migration can potentially serve as markers of invasive disease. Activated Leukocyte Cell Adhesion Molecule (ALCAM) promotes adhesion, while shedding of its extracellular domain is associated with migration. We hypothesized that shed ALCAM in biofluids could be predictive of progressive disease. ALCAM expression in tumor (n = 198) and shedding in biofluids (n = 120) were measured in two separate VUMC bladder cancer cystectomy cohorts by immunofluorescence and enzyme-linked immunosorbent assay, respectively. The primary outcome measure was accuracy of predicting 3-year overall survival (OS) with shed ALCAM compared to standard clinical indicators alone, assessed by multivariable Cox regression and concordance-indices. Validation was performed by internal bootstrap, a cohort from a second institution (n = 64), and treatment of missing data with multiple-imputation. While ALCAM mRNA expression was unchanged, histological detection of ALCAM decreased with increasing stage (P = 0.004). Importantly, urine ALCAM was elevated 17.0-fold (P < 0.0001) above non-cancer controls, correlated positively with tumor stage (P = 0.018), was an independent predictor of OS after adjusting for age, tumor stage, lymph-node status, and hematuria (HR, 1.46; 95% CI, 1.03-2.06; P = 0.002), and improved prediction of OS by 3.3% (concordance-index, 78.5% vs. 75.2%). Urine ALCAM remained an independent predictor of OS after accounting for treatment with Bacillus Calmette-Guerin, carcinoma in situ, lymph-node dissection, lymphovascular invasion, urine creatinine, and adjuvant chemotherapy (HR, 1.10; 95% CI, 1.02-1.19; P = 0.011). In conclusion, shed ALCAM may be a novel prognostic biomarker in bladder cancer, although prospective validation studies are warranted. These findings demonstrate that markers reporting on cell motility can act as prognostic indicators

Evidence-based sizing of non-inferiority trials using decision models

Abstract Background There are significant challenges to the successful conduct of non-inferiority trials because they require large numbers to demonstrate that an alternative intervention is “not too much worse” than the standard. In this paper, we present a novel strategy for designing non-inferiority trials using an approach for determining the appropriate non-inferiority margin (δ), which explicitly balances the benefits of interventions in the two arms of the study (e.g. lower recurrence rate or better survival) with the burden of interventions (e.g. toxicity, pain), and early and late-term morbidity. Methods We use a decision analytic approach to simulate a trial using a fixed value for the trial outcome of interest (e.g. cancer incidence or recurrence) under the standard intervention (pS) and systematically varying the incidence of the outcome in the alternative intervention (pA). The non-inferiority margin, pA – pS = δ, is reached when the lower event rate of the standard therapy counterbalances the higher event rate but improved morbidity burden of the alternative. We consider the appropriate non-inferiority margin as the tipping point at which the quality-adjusted life-years saved in the two arms are equal. Results Using the European Polyp Surveillance non-inferiority trial as an example, our decision analytic approach suggests an appropriate non-inferiority margin, defined here as the difference between the two study arms in the 10-year risk of being diagnosed with colorectal cancer, of 0.42% rather than the 0.50% used to design the trial. The size of the non-inferiority margin was smaller for higher assumed burden of colonoscopies. Conclusions The example demonstrates that applying our proposed method appears feasible in real-world settings and offers the benefits of more explicit and rigorous quantification of the various considerations relevant for determining a non-inferiority margin and associated trial sample size.https://deepblue.lib.umich.edu/bitstream/2027.42/146777/1/12874_2018_Article_643.pd