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Understanding
Cancer Clinical
Trial Data
Updated April 2013
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Understanding Clinical Trial Data
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Clinical Trials in Oncology
•  Clinical trials in oncology are organized slightly differently
than other disease
–  “Normal, Healthy Human Volunteer” Phase I studies are uncommon
in cancer
–  Placebo-controlled trials are usually considered unethical in most
cancers
–  Early-stage trials are usually dose escalation and PK/PD studies
•  Phase I and Phase II studies are more commonly linked to expand the
cohort of appropriately dosed patients to see if there is an efficacy signal
•  Depending on the cancer and indication, Phase II trials may be
considered “pivotal” or “approval directed”
–  Most often utilized to validate appropriate dosing and efficacy
•  Approval in oncology is usually based on a single, wellcontrolled pivotal study
–  Two pivotal trials are routinely required outside of oncology
Understanding Cancer Clinical Trial Data • April 2013
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Safety Requirements
•  Oncology has traditionally accepted a greater degree of
toxicity than other therapeutic areas
–  Metastatic disease is lethal
–  Therapeutic window is often narrow (weeks or months)
–  Oncologists are practiced in managing toxicities
•  However, as outcomes improve, the tolerance for toxicity
drops
•  Reduced toxicity must not come at the expense of survival or
patient outcome
–  Targeted therapies do not necessarily mean less toxicity:
gastrointestinal perforation (Avastin), cardiotoxicity (Herceptin),
myelosuppression (Sutent)
•  Patient selection becomes increasingly important
–  Less unnecessary treatment
–  Toxicity more “acceptable” in the context of enhanced benefit
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Criteria for Efficacy
•  Clinical benefit: “a longer or better life”
– Survival (“gold standard”)
– Improvements in tumor-related symptoms
•  Accepted surrogate for clinical benefit
– Durable response rate (hematologic malignancies)
– Disease-free survival (adjuvant setting)
•  Surrogate likely to predict clinical benefit (accelerated
approval)
– Tumor response rate
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Common Endpoints in Oncology
•  Efficacy endpoints
– Tumor/Hematologic Response and Stable Disease (Clinical
Benefit)
– Time to Progression or Progression-Free Survival
– Overall Survival
– Hazard Ratio
•  Safety endpoints
– Dose-limiting toxicities
– Specific toxicities, such as cardiac events or hemorrhage
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Tumor Response
•  Changes in tumor mass — growth (progression) or shrinkage
(response) — are the major endpoint in Phase II trials
•  The predominant system is RECIST criteria (Response
Evaluation Criteria in Solid Tumors, Feb 2000)
–  Complete Remission (CR)
•  Disappearance of all target lesions
•  No new lesions
•  Sustained at least four weeks
–  Partial Remission (PR)
•  Greater than 30% decrease in the sum of the longest diameters of target
lesions taking the baseline sum as reference
–  Progressive Disease
•  Greater than 20% increase in the sum of the longest diameters of target
lesions taking the smallest sum as reference
•  The appearance of a new lesions
•  Ideally, response should be durable
Source: Therasse et al, JNCI 2000
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Tumor Response Issues
Tumor Response
Stable Disease •  Advantages
•  Not defined by RECIST
•  Essentially “lack of progression”
•  Should be accompanied by a minimal
duration
–  Most experience: historically used in
development of cytotoxics
–  Clear criteria
–  Direct measurement of treatment
effect: “antitumor activity”
–  Flexible •  Can be incorporated into trial design
(Fleming/Simon) to minimize number of
patients exposed to an inactive agent
•  Can be incorporated into novel trials
designs (randomized discontinuation trial)
•  Disadvantages
–  Poor predictor of survival, or other
clinically meaningful measures of
patient benefit
–  Unsuited to cytostatics: agents that
slow or stop growth without causing
tumor regression
Understanding Cancer Clinical Trial Data • April 2013
•  Advantages
–  Most relevant to cytostatics
•  Disadvantages
–  Requires baseline measurement
–  Difficult to interpret: treatment effect
or natural course of disease?
–  Significance varies as a function of
tumor type, number of prior therapies
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Hematological Response
•  Normalization of peripheral blood cell counts is a
common endpoint for hematological malignancies
– Complete Hematologic Response (CHR)
• 
• 
• 
• 
Normalization of counts (disease-specific thresholds)
Elimination of immature cells
Disappearance of signs and symptoms of disease
Sustained at least four weeks
– Partial Hematologic Response (PHR)
•  Reduction to 50% of pretreatment count (disease-specific)
•  Presence of immature cells
•  Persistence of symptoms of disease, but < 50% of pretreatment effect
•  Ideally, responses should be durable
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Time-to-Event Variables: TTP, PFS,TTF
•  Measure the time to pre-specified events (discontinuation,
progression, death)
–  Assessed using Kaplan-Meier method
–  Expressed either as the median value for trial population
•  Time to Tumor Progression — Time from randomiza/on to progression of disease — Censored at date of death without progression •  Advantages — Captures ac/vity of cytosta/c agent — May be more clinically meaningful than tumor response, since progression o?en associated with increase in symptoms — May not be influenced by salvage therapy — Smaller sample size than overall survival •  Progression-­‐Free Survival — Time from randomiza/on to progression of disease or death from any cause •  Disadvantages — May confuse treatment effect with natural course of disease — May not correlate with overall survival •  Time to Treatment Failure — Time from randomiza/on to discon/nua/on of treatment, progression of disease, or death from any cause Understanding Cancer Clinical Trial Data • April 2013
— Sensi/ve to frequency and /ming of assessments — Baseline assessment prior to treatment: indolent disease — Interpreta/on requires randomized, concurrent control or good historical value Slide 9
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TTE Variables: How do they compare?
•  It is important for everyone to understand the differences between Time-to-Event
variables, including specific definitions and censor criteria
•  The following values are from the pemetrexed vs. docetaxel NSCLC study
–  Time-to-treatment failure (discontinuation, progression, or death) was the shortest since it had
the largest number of possible outcomes
–  Progression-free survival (progression or death) was the “middle” endpoint since it allowed for
a fairly broad set of outcomes
–  Time-to-progression (progression only) was the longest due to the need to include only tumor
progression and uninformative censoring assumption (censored patients who died without
progression)
Variable
Pemetrexed Group
Docetaxel Group
Time-to-treatment failure
Median
Patients censored
2.3 mos
1.4%
2.1 mos
1.7%
Progression-free survival
Median
Patients censored
2.9 mos
6.4%
2.9 mos
10.4%
Time-to-progression
Median
Patients censored
3.4 mos
24.7%
3.5 mos
27.8%
Source: Hanna et al, JCO 2004
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Survival Outcomes: OS, DFS
•  Time to event endpoints in which event is death or
recurrence of disease
–  Advantages
•  Easy to measure
•  Unbiased •  Clinically meaningful: “prolongation of life”
–  Disadvantages
•  Function of all therapies administered
•  Can require large and lengthy trials
•  Overall survival
–  Time from randomization to death from any cause
•  Disease-free survival
–  Time from randomization to recurrence of cancer at local/regional/
distant sites, appearance of second cancer, or death without a cancer
–  Appropriate when patient rendered free of cancer by definitive
therapy
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Overall vs. Median Survival
•  When discussing survival endpoints there are two
distinct types of statistics commonly quoted: – Overall Survival is usually quoted as the percent of patients
who are still alive at a specific timeframe (e.g., 5 years) and is
limited by the total of all treatments received
– Median Survival is the time frame at which 50% (half) of
the patients in a group are still alive and is limited by the
follow-up timeframe and data completion levels
•  Median survival is often utilized to highlight the value of a
new intervention (drug or combination) – The value of a single time point may not accurately reflect the
clinical benefit, or lack thereof, over the entire trial duration
– Statistically significant differences in medians may become nonsignificant with longer follow-up and more “patient events”
Source: Zwiener, Dtsch Artztebl Ing, 2011. Understanding Cancer Clinical Trial Data • April 2013
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Issues with Kaplan-Meier Curves
•  Kaplan-Meier curves
represent estimates of
survival over time, allowing
for patients who may be
lost to follow-up or are
studied for different lengths
of time
–  Patients who have not had an
event (death or progression)
may be “censored” from the
data to prevent their future
events from influencing the
clinical trial data
–  It is important to note that
each future “event” will
change the outcome of the
trial reflecting the limitations
of survival statistics
Sources: Natale et al, JCO 2011; Singh, Persp Clin Res, 2011.
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Limitations of Survival Curves
•  The graphs illustrate the
differences when the relative
proportions between arms are
constant (1a) and not constant
(1b)
•  The lower graph can occur
when an intervention delays
the event but does not alter its
long-term probability
–  Over time, the events in the
experimental arm “catch up” and
the curves meet
–  An example of this is interferon
alfa in renal cell carcinoma which
has improved PFS but not OS
Source: Duerden, April 2009
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Key Kaplan-Meier Assumptions
•  The following are the key assumptions required for a
Kaplan-Meier curve – Censored individuals have the same prospect of survival as
those who continue to be followed
•  Unfortunately, it is not possible to test for this hypothesis and this can
lead to a bias in the outcome of the trial (e.g., if a subset of patients
have a different outcome (better survival), whenever one of these
patients is censored, it may skew the data)
– Survival prospects are the same for early as for late recruits
(can usually be verified)
– The event studied (death or progression) happens within the
specified timeframe
•  Events that occur at a later time can result in inaccurate survival
estimates, mainly an artificial inflation of survival
Source: Costello, http://johncostella.com/physics/, 2010.
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Comparing Clinical Endpoints
•  The following table presents a summary of the advantages and
disadvantages for key clinical endpoints
Endpoint
Overall Survival (OS)
Overall Response Rate (ORR)
Time to Tumor Progression (TTP)
Progression-Free Survival (PFS)
Time to Treatment Failure (TTF)
Advantages
Disadvantages
•  Easy to measure
•  Unbiased
•  Clinically meaningful:
“prolongation of life”
•  Function of ALL therapies
administered
•  Can require large and lengthy
trials
•  Most historical experience
•  Clear criteria (RECIST)
•  Direct measurement of “antitumor
activity”
•  Flexible implementation
•  Poor predictor of survival or other
clinically meaningful measures of
patient benefit
•  Unsuited to cytostatics that slow
or stop growth without causing
tumor regression
•  Captures activity of cytostatic
agents
•  May be more clinically meaningful
than tumor response (progression
and/or death and/or failure)
•  May not be influenced by salvage
therapy
•  Smaller sample size than overall
survival
•  May confuse treatment effect with
natural course of disease
•  May not correlate with overall
survival
•  Sensitive to frequency and timing
of assessments
•  Interpretation requires
randomized controls or good
historical values
Source: Pazdur, The Oncologist, 2008.
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Interpreting Clinical Data
•  Overall survival statistics are the most clinically relevant
data in that they provide physicians with clear benefits
that they can discuss with patients instead of “perceived”
benefits (such as PFS without an OS benefit) •  Most importantly, it is critical that users clearly
understand statistical significance compared to clinical
significance – A statistically significant difference between treatments does
not mean that the results are clinically significant
– Studies involving large numbers of subjects can find statistically
significant differences that actually represent very small effects
– A key point of discussion with physicians can revolve around
whether statistically significant endpoints are clinically
worthwhile
Understanding Cancer Clinical Trial Data • April 2013
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