Clinical Endpoints in. Breast Cancer AN OVERVIEW OF TRIAL DESIGN, ANALYSIS, AND CLINICAL ENDPOINTS

Clinical Endpoints in Breast Cancer AN OVERVIEW OF TRIAL DESIGN, ANALYSIS, AND CLINICAL ENDPOINTS

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Table of Contents A Guide for Oncology Professionals...4 Why We Need Clinical Trials Why We Need Clinical Trials.......................................... 4 Who Conducts Clinical Trials?... 5 Trial Design....6 Endpoints in Breast Cancer Clinical Trials...10 Summary of Common Endpoints in Breast Cancer Trials...17 Interpreting Results...18 References...26 3

Why We Need Clinical Trials An Overview for Oncology Professionals This brochure is designed to provide an overview of clinical trial endpoints from the perspective of an investigator designing a clinical trial. Much of the information applies to clinical trials in general, but the focus wherever possible is on trials in breast cancer. The main topics are as follows: What are the key elements of trial design, and why do they matter? What endpoints (outcomes) are typically measured in breast cancer trials? What are the advantages and limitations of different endpoints? How do investigators interpret and report trial results? Why We Need Clinical Trials Although less than 5% of patients with cancer are enrolled in clinical trials, 1 these trials are an important step between basic cancer research and clinical practice. Clinical trials support evidence-based medicine by answering specific questions that may lead to improvements in patient care. From a drug-development perspective, clinical trials are important for evaluating the efficacy and safety of new therapies. Trials in each phase of development provide critical information (see figure). Clinical Trials Purpose by Phase 2,3 PHASE 1 PURPOSE NUMBER OF PARTICIPANTS Initial trial of a drug in humans for dosing, safety, and early efficacy information fewer than 100* 4 2 3 4 Subsequent trial of a drug s safety and efficacy in a particular disease setting Larger trial comparing a drug with best available therapy to confirm efficacy and safety; often used for US Food and Drug Administration (FDA) drug approval Trial conducted after FDA approval to gain additional information about the drug s risks and benefits * The patient numbers cited here apply to clinical trials in general. Patient numbers in breast cancer trials may be smaller. up to several hundred* hundreds to thousands* up to thousands*

Who Conducts Clinical Trials? Many people working in different organizations and locations can be involved in conducting a clinical trial. Here is a summary of the roles of these companies and individuals in a typical cancer therapy trial. 4,5 Sponsor. A sponsor is a person or organization such as a pharmaceutical company or government agency that decides to start a clinical trial and takes overall responsibility for it. Although sponsors do not actually conduct the trial, they have the following general responsibilities under federal law: Selecting qualified investigators Providing the information investigators need to conduct the trial Ensuring proper monitoring of the trial and compliance with protocols Informing investigators and the FDA when significant drug risks are identified Trial Design Contract research organization. A sponsor may choose to transfer some or all of its responsibilities to a contract research organization, or CRO. Once this transfer occurs, the CRO is subject to the same federal regulations that a sponsor would be. Investigator. This is the person who actually conducts the trial and directs the administration of drugs to trial participants. If a team of individuals conducts the trial, the person leading the team may be called the principal investigator. Investigators are responsible for the following: Ensuring the trial is conducted according to a formal plan and relevant laws Protecting the rights, safety, and welfare of participants Controlling the drugs used in the trial Research coordinator. Research coordinators work under the supervision of an investigator to perform many of the daily activities involved in the trial. Some of their main responsibilities are as follows: Screening, enrolling, and educating participants Obtaining participants informed consent Coordinating study visits and follow-up care Maintaining documents Reporting adverse events 5

Trial Design Well-designed clinical trials generate meaningful results. Following are key decisions investigators must make in designing a trial. Trial Design Patient Population When deciding whom to enroll in a clinical trial, investigators aim to include patients who may benefit from the intervention being tested. 6 They also select a population that will allow results of the trial to be generalized to patients in clinical practice. In general, the more diverse the trial population, the more relevant the results may be to the wider, real-world population. 7 To meet these goals, investigators define inclusion and exclusion criteria that determine whether individual patients are eligible for a trial. Inclusion and exclusion criteria can be demographic characteristics or disease- and treatment-related characteristics. 7 EXAMPLES OF INCLUSION CRITERIA: Gender Age Type and stage of cancer EXAMPLES OF EXCLUSION CRITERIA: Drug intolerance History of certain medical conditions Previous treatment with certain drugs Treatments and Controls In most phase 3 and some phase 2 trials, the treatment being investigated is compared with a control intervention to assess any difference in effect (hence the term controlled trials). The control may be a placebo (if no effective therapies are available for the disease being studied) or a standard treatment one in wide use and considered effective at the time the trial is designed. 8 Oncology trials rarely use placebos as controls, as doing so may be unethical. However, with standard treatment as the control, if a trial takes a long time to complete (some can take years), the standard treatment may no longer be in wide use by the time the trial results are reported, making the results less relevant. 6

If the objective of a trial is to show that the experimental treatment is more effective than the control, then the trial is considered to have a superiority design. If the trial s objective is simply to show that the experimental treatment is about as effective as the control (not substantially worse), then the trial is said to have a noninferiority design. 9 Endpoints Efficacy and safety in clinical trials are assessed by measuring certain outcomes, or endpoints, that investigators specify before the trial starts. 8 These may include clinical endpoints, such as overall survival, as well as surrogate endpoints, which are expected to predict a clinical outcome. 10 The primary endpoint is the key measure by which clinical benefit is assessed, and it influences the number of patients needed for the trial. 11 Secondary endpoints are other outcomes that provide additional, potentially valuable information. 6 Some studies have more than one primary endpoint (coprimary endpoints), and many studies have several secondary endpoints. A third type of endpoint, exploratory endpoints, may be used to analyze results for the purpose of generating hypotheses that can be explored in future trials. Unlike primary and secondary endpoints, exploratory endpoints are often not prespecified. 12 Can the trial be conducted in a reasonable time frame? Some endpoints require longer follow-up than others, lengthening the time required to complete trials and obtain meaningful results. 13 SELECTING THE PRIMARY ENDPOINT: QUESTIONS INVESTIGATORS ASK What is the most clinically meaningful measure of benefit that could guide treatment decisions in this disease state and patient population? What is the current standard of care? Can a sufficient number of patients be recruited to complete the trial? Some endpoints need larger trials in order to demonstrate statistically significant differences between arms, potentially making recruitment difficult. 13 Because endpoints are such an important part of trial design, they are covered in detail in a special section starting on page 10. See that section for descriptions of the most common endpoints used in breast cancer clinical trials. 7

Sample Size Sample size is the number of patients participating in the trial. Determining the sample size is an important step in trial design because sample size influences the power of a trial the probability that the data will demonstrate efficacy or inefficacy. The more patients included in a trial, the greater its power to detect differences between the treatment and the control.14 Randomization Phase 3 trials and some phase 2 trials use randomization to assign patients to treatment groups. This ensures that patients have an equal, random chance of being assigned to any group, and it helps create groups that are comparable at baseline. This way, if the groups have different results, the difference can be attributed to the treatments patients received and not the choice of which group they were assigned to. Randomization is a way to reduce bias.7 Some randomized trials use a process called stratification to presort patients by characteristics that could influence results (eg, extent of disease). This creates groups that are comparable in terms of patients prognosis and allows investigators to examine the effect of the experimental treatment in these subgroups. Random assignment to treatments then occurs within the stratified groups.7 Control group RANDOMIZATION Investigational group STRATIFICATION Control group RANDOMIZATION Investigational group 8

WHAT IS A CROSSOVER DESIGN? In a crossover study design, patients begin in one study arm but switch treatments partway through the trial, allowing for the comparison of treatments within the study group. 2 A washout period is sometimes used between treatments to minimize effects of the first treatment extending into the second treatment period. 15 In oncology clinical trials, patients sometimes cross over to another arm of the trial upon disease progression. Crossover in those trials is usually not intended to compare treatments but to provide patients with an alternative if the first treatment fails. 16 Blinding Blinding means ensuring that patients, investigators, or both are unaware of which patients are receiving the experimental treatment and which are receiving the control. If only the patients are unaware, the trial is called single-blinded. If investigators are also unaware, the trial is called double-blinded. Blinding helps prevent influence caused by knowledge of treatment assignments. 7 Endpoints in Breast Cancer Trials 9

Endpoints in Breast Cancer Clinical Trials Typically, new breast cancer therapies are investigated first in patients with metastatic breast cancer (MBC). 17 Trials in patients with early breast cancer (EBC) often come later. Endpoints in breast cancer clinical trials may be specified as primary or secondary endpoints (see discussion on page 7). Commonly used endpoints are defined in this section, along with advantages and limitations that investigators might consider in choosing the endpoints in a trial. Overall Survival Overall survival (OS) is the time from randomization or study enrollment until death from any cause. 18 Endpoints in Breast Cancer Trials ADVANTAGES FOR CLINICAL TRIAL DESIGN OS is a universally accepted measure of benefit. 18 OS is easily and precisely measured. 18 Of all the clinical endpoints, it is the most reliable and least subject to investigator bias. 18 LIMITATIONS OS is often more difficult to demonstrate in earlier lines of therapy because multiple lines of treatment after the experimental treatment may confound the OS results, making it more difficult to determine how the experimental treatment contributed to patient outcomes. 19-21 Some studies are designed to allow patients in the control arm to cross over and receive the experimental treatment if their disease progresses. This may also confound the overall effect of the drug being studied. 18 Compared with other endpoints, OS often requires larger patient populations and longer follow-up times to show statistically significant differences between groups, depending on the disease state and event rate. 18,22,23 OS events include deaths unrelated to cancer. 18 10

Progression-Free Survival Progression-free survival (PFS) is the time from randomization or trial enrollment until disease progression or death from any cause. 18 It is often used as a surrogate endpoint for OS. 19 ADVANTAGES FOR CLINICAL TRIAL DESIGN PFS is a clinically valid endpoint that reflects tumor growth. 13,18 It is based on fairly objective and quantitative assessments. 18 Depending on the disease state and event rate, PFS typically requires smaller trials and shorter follow-up times than OS, allowing faster completion of trials. 18 PFS is not confounded by crossover or by the administration of subsequent therapies following disease progression; PFS measures only the effect of the treatment being investigated. 18,19 Study designs may include review by an independent review committee (IRC) or an independent review facility (IRF) to ensure the objectivity of PFS results and remove the possibility of investigator bias. PFS results may be reported as being based on IRC or IRF review. 18 LIMITATIONS Validation of PFS as a surrogate for OS can be difficult in some disease settings. 18 Measurement of PFS may be subject to investigator bias. 18 The definition of PFS may vary among clinical trials. 18 Measuring PFS requires frequent tumor assessments and balanced timing of assessment among treatment arms. 18 11

Event-Free Survival Event-free survival (EFS) is the time from study entry to disease progression, local or distant disease recurrence, death, or discontinuation of treatment for any reason (eg, toxicity, patient preference, or initiation of a new treatment in the absence of documented progression). 25 This endpoint is not generally used in breast cancer trials. ADVANTAGES FOR CLINICAL TRIAL DESIGN EFS may be useful in evaluating highly toxic therapies. 25 LIMITATIONS Initiation of subsequent therapy is subjective, and regulatory agencies generally discourage the use of an EFS endpoint because it combines data on efficacy, toxicity, and patient withdrawal. 25 Disease-Free Survival Disease-free survival (DFS) is the time from randomization or trial enrollment until tumor recurrence or death from any cause. The most frequent use of this endpoint is in the adjuvant setting after definitive surgery or radiotherapy. 18 ADVANTAGES FOR CLINICAL TRIAL DESIGN Smaller sample size 18 Shorter follow-up times compared with OS 18 Useful in situations where survival may be prolonged 18 LIMITATIONS Not statistically validated as a surrogate for OS in all settings 18 Not precisely measured and therefore subject to assessment bias, particularly in open-label studies 18 Sometimes complicated to define, particularly when deaths are noted without prior tumorprogression documentation, creating variability in DFS among studies 18,26 12

Response Rate Response rate (RR) represents the objective tumor response to treatment in a clinical study. Parameters for tumor size reduction and minimum response time are clearly outlined in the protocol to ensure consistency across all treatment groups. Measurements are taken before and throughout the study to determine RR. 18 Although RR is not recognized as a valid surrogate endpoint for survival, it does reflect a change in tumor size and is therefore considered a direct measure of treatment efficacy. Because it provides immediate evidence that the treatment is having a positive effect, it may be combined with other surrogate endpoints to measure efficacy. 18 RR actually comprises several distinct endpoints, including complete response, partial response, progressive disease, and stable disease (see table). Response-Related Endpoints 27 ENDPOINT Complete response Partial response Progressive disease Stable disease DEFINITION ACCORDING TO THE RESPONSE EVALUATION CRITERIA IN SOLID TUMORS (RECIST) VERSION 1.1* Disappearance of all target lesions and reduction in lymph node size At least a 30% decrease in the size of all target lesions in relation to the lesion size observed at baseline At least a 20% increase in the size of all target lesions or the appearance of 1 or more new lesions in relation to the lesion size and number observed at baseline Neither sufficient shrinkage to qualify as a partial response nor sufficient increase to qualify as progressive disease * RECIST guidelines were updated to version 1.1 in 2008 for further clarification and to accommodate newer imaging technologies. Clinical trials that began prior to the update used RECIST version 1.0 to measure RR. 13

Response-related endpoints are often used in combination to assess clinical benefit in a given population. For example, clinical benefit rate is the percentage of patients with a complete or partial response or with stable disease for a minimum period of time. 28 The objective RR includes patients with a confirmed complete or partial response (at least 2 consecutive responses). 18 ADVANTAGES FOR CLINICAL TRIAL DESIGN Can be assessed in single-arm trials, which may be used when there is no available treatment for comparison 18 May use a smaller population and can be assessed earlier than survival 18 Attributable directly to the drug, not the natural history of the disease 18 LIMITATIONS Not a comprehensive measure of drug activity 18 Does not always include a time/duration component Duration of Response Duration of response (DoR) is the time from documentation of tumor response to disease progression or death from any cause in patients who have a confirmed response. 27 ADVANTAGES FOR CLINICAL TRIAL DESIGN Because DoR is based on tumor assessments, it is considered a direct measure of treatment duration and efficacy and adds a level of detail to the objective RR. 27 DoR is relevant to clinical practice because it gives an indication of the durability of response in a patient population. It can be assessed in single-arm trials. 18 It can be measured in a smaller patient population and assessed earlier than OS. LIMITATIONS DoR is not a comprehensive measure of drug activity. 14

Two less commonly used endpoints in cancer trials are time to progression (TTP) and time to treatment failure (TTF). Time to Progression TTP is the time from randomization until objective tumor progression. 18 It is similar to PFS but does not include deaths. TTP is an uncommon endpoint. Time to Treatment Failure TTF is the time from randomization until discontinuation of treatment for any reason, including disease progression, treatment toxicity, and death. 18 TTF is an uncommon endpoint. Summary of Endpoints 15

Pathological Complete Response (Surrogate Endpoint, Neoadjuvant Setting) Pathological complete response (pcr) is a surrogate endpoint used in trials of neoadjuvant treatment. pcr indicates tumor response to systemic therapy. It can be thought of as the disappearance of pathology in tissue samples examined after neoadjuvant therapy. It does not, however, mean the cancer has been cured. 17 Definitions of pcr may vary among clinical trials, but most incorporate the standard American Joint Committee on Cancer (AJCC) TNM (tumor, node, metastasis) pathologic staging system. 17 Because definitions vary with regard to nodal status and in situ disease, FDA recognizes 2 pcr definitions for use in neoadjuvant clinical trials 17 : No trace of invasive disease in breast or nodes; remaining in situ disease permitted (ypt0/tis ypn0) No trace of invasive disease in breast or nodes (ypt0 ypn0) Summary of Endpoints ADVANTAGES FOR CLINICAL TRIAL DESIGN pcr can be measured relatively quickly (several months after the start of a trial). 17 pcr may be used as a surrogate endpoint to support FDA accelerated approval in the neoadjuvant setting. 17 LIMITATIONS Differing pcr definitions can make it difficult to interpret studies. 17 pcr may not be able to predict long-term outcomes (survival). 17 For FDA approval of a drug, endpoints such as OS or EFS must also be examined, either in the same trial or in a different trial. 17 16

Summary of Common Endpoints in Breast Cancer Trials COMMON ENDPOINTS IN BREAST CANCER CLINICAL TRIALS Overall survival Progression-free survival Event-free survival Disease-free survival Response rate Duration of response Time to progression Time to treatment failure Pathological complete response (surrogate endpoint used in neoadjuvant trials) Interpreting Results 17

Interpreting Results After statisticians analyze the data collected in a clinical trial, results can be reported. Following are some terms and types of analyses commonly found in reported trial results. Confidence Interval The numerical value of a reported outcome or difference in outcome between treatment groups is only an estimate of the actual value in a broader population. A confidence interval (CI) reported along with the estimate shows the range within which the true value is likely to fall, indicating how precise the estimate is. A narrow interval is more precise. 14,29 A CI comes with a percentage usually 95% that tells how certain investigators are that the true value lies within the interval given. 29 EXAMPLE: If the median OS in a treatment group is reported as 12.4 months (95% CI, 9.9-15.2), it loosely means there is a 95% chance that the population median OS (if the study were repeated many times with different patients) is between 9.9 and 15.2 months. P Value A P value reported with an outcome is the probability that the results occurred due to chance rather than a true difference in the treatments being compared. The smaller the P value, the more likely that the result is statistically significant. 7 Interpreting Results 18 EXAMPLE: A P value of 0.05 means the probability that the results are due to chance is 5%. Common cutoffs for statistical significance are 5% and 1%, meaning results with P values below the cutoff are considered statistically significant, and results with P values above the cutoff are not.

Risk REPORTS OF CLINICAL TRIAL RESULTS MAY MENTION SEVERAL KINDS OF RISK, EACH OF WHICH HAS A DIFFERENT MEANING. ABSOLUTE RISK The numerical chance of something happening, such as a patient s chance of breast cancer recurrence30 RELATIVE RISK The comparison between groups; it is the risk in one group (typically the treatment group) stated as a percentage of the risk in another group (typically the control group)30 ABSOLUTE RISK REDUCTION This refers to the difference between the absolute risk of an event in one group and the absolute risk of that event in another group.30 RELATIVE RISK REDUCTION This refers to how much the relative risk is reduced in the treatment group, calculated as 1 minus the relative risk.30 In the example below, In the example below, In the example below, In the example below, Absolute risk for patients in the treatment group = 40 % Risk in the % treatment group 40 Risk in the control group = 67% 60% 60% 40% = 20% the relative risk for patients in the treatment group is 67% of the control group s risk 1 0.67 = 0.33 0.33 or 33% the absolute risk reduction is 20% Data on risk reduction should be interpreted carefully. EXAMPLE TREATMENT GROUP: PATIENTS 100 PATIENTS WITH RECURRENCE PATIENTS WITHOUT RECURRENCE PATIENTS WITH RECURRENCE PATIENTS WITHOUT RECURRENCE 40 60 CONTROL GROUP PATIENTS 100 60 40 19

Hazard Ratio The hazard ratio (HR) is the relative risk of experiencing the event being measured (eg, disease progression) in one trial arm compared with the other over the entire time period of the trial. An HR of 1 indicates equal risk in both trial arms. An HR of less than 1 indicates a reduced risk in one of the trial arms. An HR of greater than 1 indicates an increased risk. The HR measures the effect over the time of the treatment analysis. Sometimes it is used to assess benefit when medians have not been reached (eg, in interim analyses). 31 HRs can be used to calculate the reduction in risk of an outcome. 14 For example, an HR for OS of 0.53 indicates there is a 1 HR, or 47%, reduction in the risk of death in one arm compared with the other. HRs can also be used to calculate overall improvement in an outcome measure (eg, PFS or OS) by using the formula (1 HR)/HR. For example, if the HR for OS is 0.53, overall improvement in OS would be (1 0.53)/0.53, or 89%. The HR is typically reported with a 95% CI. Median Value This is the true midpoint. It can also be considered the 50th percentile. 14 Median values are often used to describe PFS or OS duration in clinical trials. For example, assuming all patients are included in the analysis, median PFS is the point in time when 50% (half) of patients in a treatment arm have had disease progression or died and half are alive with no disease progression. Only at this point can median PFS be measured. Before then, the HR may be used to describe benefit, as it estimates the risk of an event (in this case, disease progression or death) regardless of whether a median time point has been reached. 20 Intention to Treat Many phase 3 trials use a method of data analysis called intention to treat (ITT). This method includes in the efficacy analysis every person randomized to a treatment arm at the start of the trial, regardless of whether they actually completed treatment or even received treatment. An ITT analysis may cause efficacy to be understated, but it helps prevent biased results. 7

Adverse-Event Grades To standardize the reporting of adverse reactions in clinical trials, the National Cancer Institute (NCI) has developed Common Terminology Criteria for Adverse Events (CTCAE). The criteria were most recently updated in June 2010 (version 4.03). Clinical trials begun earlier than this date may use earlier versions to report adverse reactions. Using the NCI-CTCAE, adverse reactions are reported by grade (level of severity) on a scale of 1 to 5 (see figure). NCI-CTCAE Grades 32 GRADE DEGREE OF SEVERITY 1 Mild, with no or mild symptoms; no intervention required 2 Moderate; minimal intervention indicated; some limitation of activities 3 Severe but not life threatening; hospitalization required; limitation of patient s ability to care for himself/herself 4 Life threatening; urgent intervention required 5 Death related to adverse event 21

Subgroup Analyses After a clinical trial is completed, analyses of patient subgroups within the overall patient population may be performed in order to provide more detailed information, such as 33 How the experimental treatment works in patients who share a particular relevant characteristic, such as age or extent of disease Whether the benefit seen in the patient population as a whole is maintained across diverse patient types or is derived from only a subgroup of patients Subgroup analyses may be specified before any data are examined (prospective analysis) or may occur, unplanned, after a first analysis of the data (retrospective analysis). Because they cannot be influenced by the data, prospective analyses may be considered more reliable. Performing too many subgroup analyses of either type, however, can increase the chance of false-positive findings. 33 It is important to note that most clinical trials are not statistically designed to show significant benefit in each patient subgroup, and there is a chance that the subgroups are not equally balanced and could therefore show results that are confounded by other baseline characteristics. Thus, subgroup analyses are often considered exploratory, whether prespecified or not. A subgroup analysis can be graphically represented as a forest plot, which shows the relative sizes of the subgroups being evaluated, the magnitude of benefit in each subgroup, and the CI for each subgroup. The following figure explains how to read a forest plot. Regression Analysis A regression analysis is a statistical modeling technique that evaluates the relationship between 2 or more variables in a clinical trial. 14 For example, a regression analysis might show how age, sex, and treatment affect PFS in a trial. 22

Reading a Forest Plot 1 2 3 4 0 1.0 FAVORS EXPERIMENTAL ARM 2.0 FAVORS CONTROL ARM Data are for illustrative purposes only. 1 The beginning and end of the purple line indicate the lower and upper limits of the CI, respectively. 2 Placement of the plot indicates the HR point estimate; the size of the plot indicates the relative size of the patient subgroup. 3 The HR falls to the left of 1, indicating a favorable result for the experimental arm relative to the control arm. The large block indicates a relatively large subgroup of patients. The narrow CI indicates a relatively precise HR estimate. 4 Although the HR appears to favor the experimental arm, the small size of the block indicates a relatively small subgroup, and the large CI indicates a relatively low level of precision. 23

Time-to-Event Analyses Many endpoints used in breast cancer trials measure the time until occurrence of an event, such as disease progression, recurrence, or death. For survival endpoints (eg, OS and DFS), data are often reported graphically with a Kaplan-Meier curve, which shows events over time.14 Kaplan-Meier curves may become unreliable when the number of patients available for analysis at particular time points becomes small.34 While the following example illustrates PFS, the same principle can be applied to OS. Reading a Kaplan-Meier Curve 100 1 14-month median follow-up 9 8 PROGRESSION-FREE SURVIVAL, % 80 5 4 60 50 40 7 10 3 P < 0.01 HR = 0.76 (95% CI, 0.64-0.88) 6 11 20 2 Investigational arm Control arm 0 0 10 12 PATIENTS AT RISK 24 20 30 86 59 0 0 TIME, MONTHS 400 399 225 164 Data are for illustrative purposes only.

1 Median follow-up is the duration of time for which 50% of the population has been followed. 2 An HR of < 1 indicates reduced risk in the investigational arm. 3 The 95% CI of the HR. Lower limit = 0.64; upper limit = 0.88. 4 Median PFS in control arm = 15.6 months. 5 Median PFS in experimental arm = 20.3 months. 6 Time difference between median PFS points (20.3 months 15.6 months = 4.7 months). 7 The P value indicates a high level of statistical significance. 8 Shading indicates the differences in PFS between the 2 study groups for the entire time period (reported as the HR). 9 An early separation of the curve shows a rapid response in the treatment arm vs the control arm. 10 A maintained separation of the curve shows duration of benefit over time. 11 The steep drop in the curve could be due to the small number of patients available for analysis. For example, this curve represents only 17 patients over 28 months. 12 Patients at risk: the number of patients who have not had a progression event and whose follow-up extends at least that far into the curve. The number at risk also helps you determine the reliability of the curve at that time point (especially toward the end of the curve). 25

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