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L. De Petris

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    YI01a - Clinical Trials & Scientific Mentoring (ID 414)

    • Event: WCLC 2016
    • Type: Young Investigator Session
    • Track: WCLC 2016
    • Presentations: 5
    • Now Available
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      YI01a.01 - How to Implement an Idea/Hypothesis into a Clinical Trial (Now Available) (ID 6735)

      C.A. Silva

      • Abstract
      • Presentation
      • Slides

      Abstract:
      Last decades have shown an impressive advance in terms of biological knowledge in cancer. Traditional way to bring new ideas/hypotesis into clinical trials was overcoming by this fact. New agents directed against specific molecular targets have important impact in terms of response rate (RR), response duration (RD), progression free survival and eventually overall survival (OS) as well as quality of life (QoL). If you have an interesting idea/hypotesis, today you have to take on account several points that can exclude it. Select population becomes a very important issue. How to do this? Selecting a target, a tumor, both, other conditions? Following the tradition of research phases, Phase I refers to measure safety and pharmacokinetics assesing maximum tolerated dose (MTD) but a number of new agents have a non reachable MTD because they have a low toxicity. On the other hand, phase II refers to the assesing of efficacy in a certain tumor as well as safety, but, in the case of new agents you may select a tumor (as ussual), a specific target no matter wath tumor carry it (basket), or other conditions. In this phase measurement of response is important as a precedent of next phase trials and the challenge is the method you will use to do it. New inmunotherapeutic agents probably need a different way to do this. Also, to have predictive biomarkers for most of these agent will help to select the potential population that will achieve the more benefit and avoid futile toxicity and a waste of time and resources. We have to remember that biological effects not always means clinical benefit. Breaking barriers, for phase III comparator selection, primary and secondary end points as well as inclusion and exclusion criteria become a very important point and are different in the traditional way and in a proposed new way. OS is the gold standard end point but there are many more very important like PFS, RR, DoR, QoL. Again, measurement methods are very important and may be different related with biological mechanism and length of response for different agents than chemotherapy. As phase III trials select (include and exclude) patients troughout very strict criteria and there are some late toxicities that can be as important as the acute and subacute toxicities, phase IV trials are very important because they represent better the daily patient we see at office practice and is a powerfull pharmacovigilance mechanism. Sanctuaries have to be consider as far as the prevalent tumors have a very frequent involvement of Central Nervous System and these patient are mostly excluded from clinical trials at the beggining. Ethics is a fundamental point as far as the most important objective is the patient safety and treatment accesibility. If we went troughout these restriction points and our idea/hypotesis has survive, we can follow the development of trials around wasting less time and resources.

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      YI01a.02 - Basic Statistical Considerations (Now Available) (ID 6736)

      L.R. Pilz

      • Abstract
      • Presentation
      • Slides

      Abstract:
      Introduction: Published and officially approved medical research is based on evidence and subsequently, statistical methods are an essential part in proving the usefulness of results. The translation in statistical terms in most cases is to build hypotheses and their alternatives to be tested. Clearly, medical researchers need some sound understanding of statistical principles which can be taken, however, not as a matter of course. The aim of the contribution is to communicate among readers of medical journals and reports statistical matters focusing on basic statistical considerations to enable a better understanding. [1] Essentials of statistical analysis and reporting: (i) Making the information content of the research results visible in summarizing and prescinding them in tables, graphs, and figures. (ii) Assessing and quantifying any associations of reported measures like possible differences in the outcome of treatment actions etc., and using confidence intervals to express the uncertainty of those associations. (iii) Building hypotheses and their alternatives to prove that these associations have a real biomedical basis which is performed by statistical testing under a given level of significance (p-values). Important is the design of the research project: In randomized trials comparisons are an inherent part of those associations whereas in nonrandomized studies no direct conclusion can be driven that any association not due to chance indicates a causal relationship. Methods: Randomization is a process in which each of the patients has the same but not necessarily the equal chance to be assigned to predefined treatment arms ensuring that the treatment arms are comparable with respect to known or unknown risk factors. Hence, it is a method to remove selection and accidental bias and to guarantee the validity of statistical tests. Main design issues of studies are the formulation of the primary aim, the question of blinding, and the boundary conditions of sample size calculations. [2] Tables of baseline data and outcome events are part of most medical journal papers concerning treatments. Generally the first table displays the patients’ characteristics including some demographic variables and variables related to the primary aim. The main outcome events are forming the key table of every paper stratified by treatment groups. Categorical variables are shown as number and percent by group. Continuous variables can either be presented by mean and the standard deviation or by median and the interquartile range. Latter is preferred if the data are scattered and far from normal distribution with the implication that in the sequel non-parametric tests should be favored. For composite events like severe toxicities, progression of disease, and death the number of patients experiencing any of them plus the number in each component should be given, since we have the effect of multiple events. In focus are often variables displaying the time to the first event (e.g. progression of disease which can happen more than once during treatment history). For time driven events in the sequel analysis of general survival times are applied leading to special statistics and graphs. The Kaplan-Meier plot is the most used graph to show time-to-event outcomes as death, time to progression, disease free interval etc. In general the graph displays the steadily increasing difference in incidence rates of the outcome for two or more treatment arms. To make the process clearer, the numbers at risk in each group should be shown at regular time intervals in the time axis. Individuals who did not reach the endpoint are censored (e.g. still alive, lost to follow-up) and should be marked in the plot. The conditional probabilities of Kaplan-Meier statistics indicate the probability of experiencing the endpoint under consideration beyond a certain length of follow-up. Estimation of treatment effects is to measure the magnitude of the difference between treatments on patient outcomes. Normally this is done by a point estimate showing the actual difference observed. Inherent in this kind of statistics is that the bigger the trial, the more precise the point estimate will be. Such uncertainty is usually expressed by a 95% confidence interval in which this percentage of the sample will be found. The primary aim of the study determines the type of estimate required. Namely, there are three main types of outcomes: (a) Binary (dichotomous) response, e.g. dead or alive, progressive or non-progressive, success or failure, respectively. (b) Time to event outcome most measured in intervals, e.g. time from randomization to death, time of inclusion in the study to treatment failure. (c) Quantitative outcome as the reduction of a certain percentage of tumor loads at a given time point (e.g. a seen reduction of 30% after exactly 6 months). Estimates based in percentage are indicated if a binary outcome has to be judged in terms of absence or presence. Then a confidence interval of the proportion of interest can be given. Relative risks are the ratio of two percentages and can be converted to relative risk reduction. Alternatively relative odds can be applied which is a cross-product relationship and shows the relation of chance. Relative risk and relative odds are sometimes called risk ratio and odds ratio instead. The absolute difference in percentage is taken as a measure of absolute risk reduction. Estimates for time-to-event outcomes are used in all survival statistics as time to death, time to progression etc. The Kaplan-Meier plot depicts the first time of the occurrence of the event but does not in itself provide a simple estimate summarizing the treatment difference. The Kaplan-Meier estimate at the end of plotted time or at any other time between can be taken as cumulative rate of the leading event. That is only a time point estimate. Instead, the most common approach is to use a Cox proportional hazards model to obtain a hazard ratio and its 95% confidence interval. The hazard ratio can be thought of as the hazard rate in one group divided by the hazard rate in the other group averaged over the whole follow-up period. Examples from medical trials will be used to explain the statistical principles shown here. References [1] Pocock SJ, McMurray JJV, and Collier TJ: Making sense of statistics in clinical trial reports. J Am Coll Cardiol 2015; 66(23):2648-2662. [2] Pilz LR, Manegold C: Endpoints in lung cancer trials: Today's challenges for clinical statistics. MEMO 2013; 6(2): 92-97.

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      YI01a.03 - How to Effectively Publish your Results: Suggestions from the JTO Editor (Now Available) (ID 6737)

      A. Adjei

      • Abstract
      • Presentation
      • Slides

      Abstract not provided

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      YI01a.04 - Critical Eye on Practice Changing Literature (Now Available) (ID 6738)

      J.S. Lee

      • Abstract
      • Presentation
      • Slides

      Abstract:
      Clinical trials in cancer have typically investigated agents or regimens in selected groups of patients based primarily on histology and clinical characteristics (e.g., tumor stage, performance status, prior treatment, etc). The major goal of those trials was to demonstrate statistically significant improvement in outcome with minimum p-value of 0.05, as compared with the control arm. In the majority of cases, this approach resulted in only small incremental improvements in overall survival. In some cases, even without any improvement in survival, a certain regimen became the foundation for adding novel targeted agents only based on the favorable toxicity profile and has been widely used in practice over the last two decades. More recently, targeted therapies administered to patients with biologically relevant biomarkers, such as activating EGFR mutations and ALK alternation, have produced substantial improvements in outcomes and rapidly changed the treatment paradigm of lung cancer. In addition, newer treatment modalities such as immune check-point inhibitors and antibody-drug conjugates are emerging as highly effective therapies that are providing improvements in patient outcome. In fact, between 2004 and 2015, 14 new drugs were approved by the FDA for NSCLC. However, the relevance of statistical significance has increasingly been challenged when the treatment effect is small. [1,2] To resolve this issue, there has been growing consensus to raise the bar of efficacy for approving new cancer drugs.[3,4] The critical question is what is clinically meaningful and how can this outcome be measured. The FDA considered OS to be the standard clinical benefit endpoint that should be used to establish efficacy of a treatment in patients with locally advanced or metastatic NSCLC.[5] The FDA also has recognized that PFS may be appropriate as the primary endpoint to establish efficacy for drug approval if the trial is designed to demonstrate a large magnitude for the treatment effect as measured by both the hazard ratio and absolute difference in median PFS and an acceptable risk-benefit profile of the drug is demonstrated. The remaining question is, “What is clinically meaningful?” Modest benefits could be considered worthwhile if associated with moderate costs and toxicity, whereas a new drug with a very high cost and/or substantial toxicity is worthwhile only if it produces sizeable clinical benefits. To address this issue, the ASCO Cancer Research Committee convened four disease-specific working groups, including the lung cancer working group. The Committee generally agreed that relative improvements in median OS of at least 20% are necessary to define a clinically meaningful improvement in outcome.[3] For lung cancer, it was recommended that one experimental agent in non-squamous NSCLC should be considered practice changing if it increases PFS by at least 4 months and OS by 3.5-4 months with a corresponding death risk reduction of 20-24%. Due to less favorable prognosis, the desired benefit in squamous NSCLC was 3 months increase in PFS and 2.5-3 months increase in OS with a death risk reduction of 20-23%.[3] Obviously, if a new treatment is to be introduced into clinical practice, it is not sufficient to demonstrate that it is "better than” or “non- interior to” the standard therapy. As cancer care costs continue to increase at an unsustainable rate, oncology professionals need to focus more on delivering value-based patient care rather than simply practicing evidence-based patient care. In addition, it has become increasingly clear that the traditional fee-for-service model will no longer serve the interest of all the parties involved, including the pharmaceutical company.[6] It seems to be a matter of time that the fee-for-service system will be replaced with the value-based reimbursement system. Reference 1. Sobrero A, Bruzzi P. Incremental advance or seismic shift? the need to raise the bar of efficacy for drug approval. J Clin Oncol 2009;27:5868–73. 2. Ocana A, Tannock IF. When are "positive" clinical trials in oncology truly positive? J Natl Cancer Inst 2011;103:16–20. 3. Ellis LM, Bernstein DS, Voest EE, Berlin JD, Sargent DJ, Cortazar P, et al. American Society of Clinical Oncology perspective: raising the bar for clinical trials by defining clinically meaningful outcomes. J Clin Oncol 2014;32:1277–80. 4. Sobrero AF, Pastorino A, Sargent DJ, Bruzzi P. Raising the bar for antineoplastic agents: How to choose threshold values for superiority trials in advanced solid tumors. Clin Cancer Res. 2015;21:1036-43. 5. United States, Department of Health and Human Services, Food and Drug Administration (FDA). Clinical Trial Endpoints for the Approval of Non-Small Cell Lung Cancer Drugs and Biologics Guidance for Industry (published April 2015) : Available online: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM259421.pdf, 2015. 6. Eaton KD, Jagels B, Martins RG. Value-based care in lung cancer. Oncologist. 2016;21:903-6.

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      YI01a.05 - Coffee Break (ID 6823)

      • Abstract

      Abstract not provided