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The goals of this work were to compare the relative efficacy of ondansetron, granisetron and tropisetron in a randomised double blind crossover trial, evaluating objective, subjective and pharmacoeconomic parameters. To this end, 136 patients were enrolled, 120 of whom were eligible and evaluable. Each patient received three identical chemotherapy cycles with an antiemetic protocol which consisted in dexamethasone 20 mg i.v. and a tapering dose schedule for 4 days, and a single i.v. dose of an antiserotoninergic drug in each cycle. Arm A patients received tropisetron 5 mg; arm B patients, granisetron 3 mg; and arm C patients, ondansetron 24 mg. Numbers of patients and days with emetic episodes, grade of nausea, patient preference, headaches, need for metoclopramide, nursing or medical consultation, or admission to emergency room or ward were evaluated. There was no difference in the percentage incidence of acute or delayed nausea and vomiting. Twenty-five per cent of patients preferred tropisetron, 30% preferred granisetron, and 45% preferred ondansetron (P<0.01). Toxicity was mild in less than 10% of patients. Direct and indirect costs of treatment varied from 19.74 to 28.53 euros for tropisetron, 31.07-46.51 euros for granisetron and 22.76-62.61 euros for ondansetron. There was no difference in objective activity. In the schedules studied, patients preferred ondansetron. Indirect costs amount to less than 10% of the total antiemetic cost. Direct costs varied widely and should be considered whenever an antiemetic drug is selected.

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e18188 Background: Survival Kaplan-Meier analysis represents the most objective measure of treatment efficacy in oncology, though subjected to potential bias which is worrisome in an era of precision medicine. Independent of the bias inherent to the design of clinical trials, bias may be the result of patient censoring, or incomplete observation. Unlike disease/progression free survival, overall survival is based on a well defined time point and thus avoids interval censoring, but it is our claim that right censoring, due to incomplete follow-up, may still be a source of bias. Methods: The R version 3.5.1 language and the integrated development environment RStudio were used for simulations and survival analysis with the survival package and their available datasets . Survival time was simulated according to a Weibull model with 2 parameters, shape and scale, that determine the event time for every case. Three types of right censoring mechanisms are considered and analyzed independently: 1) case censoring, in which a random number of cases are censored, and the resulting survival time is shortened by a random amount, 2) time censoring, in which a random censoring time variable is applied if and only if it is shorter than the event time, and 3) interim censoring, where a random time variable determines the case inclusion time since the start of trial, and a fixed cutt-off time determines if every case is censored (if the cutt-off time is shorter the the inclusion time plus the event time) or not. For every censoring mechanism, 100 trials was simulated with a 1000 uncensored cases arm and 1000 censored cases arm, in such a way that a censoring Cox hazard ratio (cHR) may be estimated for every trial. An interactive app showing the right censoring effect is presented. Results: A bias index (BI) was buit based on the survival time of event and censored cases. Case censoring was associated with higher BI (mean = 1.75, SD = 0.29) than time censoring (mean = 1.15, SD = 0.19, p = 2.02e-30) and interim censoring (mean = 0.72, SD = 0.21, p = 3.46e-34). It was found an inverse relationship between the censoring proportion and the cHR in case censoring (r = -0.86). Of all the available datasets, the Veterans' Administration Lung Cancer study showed a bias of 1.83, suggesting case censoring bias in both treatment arms. Conclusions: Based in the results of this study it is suggested that: 1) Final results should include all the events in the defined period of interest, 2) a bias index may help in detecting potential bias and correct estimated survival. Censoring bias analysis is planned in recent clinical trials.

The new Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) coronavirus has generated a pandemic, in which there are population groups at higher risk and who are potentially fatal victims of the disease. Cancer patients have been considered a group with special susceptibility, particularly patients with lung tumour involvement and haematological neoplasms. The Spanish Lymphoma Oncology Group (GOTEL) carried out a multicenter study of SARS-CoV-2 seroprevalence in patients with lymphoma. Results: A total of 150 patients were included between 22 May and 11 June 2020. The mean age was 65 years (range 17–89), 70 women (46.5%) and 80 men (53, 5%). At the time of diagnosis of lymphoma, 13 cases were stage I (9%), 27 (18%) stage II, 37 (24.5%) stage III, and 73 (48.5%) stage IV, while 6.6% had a primary extranodal origin. A total of 10 cases with positive serology for SARS-CoV-2 were identified, which is a prevalence of 6% in this population. None of the patients required intensive care unit management and all fully recovered from the infection. Conclusion: IgG antibody seroprevalence in lymphoma patients appears similar to that of the general population and does not show greater aggressiveness.

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e13543 Background: Independent of the bias inherent to the design and execution of clinical trials, bias may be the result of patient censoring. A bias index (BI) was developed to detect right-censoring bias and tested in datasets availabe at Project Data Sphere, a data sharing research platform maintained by the CEO Roundtable on Cancer, Inc.,* a nonprofit corporation to improve outcomes for cancer patients by openly sharing deidentified data. Methods: Project Data Sphere platform was searched for clinical comparative trials with available experimental and comparator survival datasets: overall survival (OS) and event-free survival (EFS: disease-free survival, DFS, or progresssion-free survival, PFS). The R language and the integrated development environment Rstudio were used to import and manage the datasets. BI was defined in the events time domain as the adjusted proportion of censor times below the mean event time. Comparison of BI in different datasets were made with the two-sided Wilcoson unpaired test. A weighted regression model was applied to estimate the influence of bias on survival results as measured by the hazard ratio (HR). Results: Out of 184 trials, 19 trials offered both comparator and experimental arms, 3 of them not based on survival analysis and 4 of them with 2 substudies, providing 72 datasets based on OS and/or EFS, for a total of 16532 patients (90.8% of the 18198 patients in published trials). BI over the theshold was found in 24% of EFS datasets (versus 0% in OS datasets, Wilcoxon p = 0.0007), especially in PFS (35% vs 0% in DFS datasets, p = 0.00004). Nearly two thirds of the variance in the HR of EFS datasets was explained by the HR of OS datasets (adj.R2 = 0.638, p = 1.5e-5), approaching to what was found in the corresponding publications (adj.R2 = 0.751, p = 7.81e-5). Though the trials sample is small, introducing the BI of control and experimental datasets in the model decreases the residual standard error (3.831 vs 3.958) and increases the correlation (adj.R2 = 0.99, p < 2.2e-16), resulting in the model: HR(EFR) = 0.985 HR(OS) + 0.36 BI(exper) – 0.42 BI(control). Conclusions: This study is a proof of concept that right-censoring bias may be detected and estimated in clinical trials, especially in PFS datasets, and opens the possibility for correcting biased estimations in survival and increasing the precision in the prediction of OS from preliminary EFS. (*) This abstract is based on research using information obtained from ProjectDataSphere.org, which is maintained by Project Data Sphere LLC. Neither Project Data Sphere nor the owners of any information from the web site have contributed to, approved or are in any way responsible for the contents of this abstract.