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Oncology endpoints: An unexpected journey

Waseem Jugon & Mijanur Rahman

Disclaimer

The views expressed in this paper are those of the authors and do not necessarily reflect those of Roche or any

• ther organization that may be cited for

reference in this presentation

Introduction

• What is oncology?
• What is an endpoint?
• What statistical analyses are done?
• How is an endpoint interpreted?
• The evolution of a programmer
• Summary

What is oncology?

• The term oncology means a branch of science that deals with tumours

and cancers.

• Cells grow out of control forming abnormal tissue growth, they may

become cancerous. This is known as cell mutation and can form tumours in the body.

Image Reference: http://eschooltoday.com/cancer/what-is-cancer.html

Types of cancer

• Benign tumours

– Non-cancerous cells that can be removed.

• Malignant tumours

– Cancerous tumor cells that can spread.

• Solid and non-solid tumours

– Non-solid tumour resulting from defective blood cells (e.g. Leukemia). – Solid tumours are excess tissues made up of cancer cells. (E.g. Breast, Lung)

• Over 200 different types of cancer:

– Breast, lung, bowel (colorectal) and prostate - make up over half of all new cases (approx 54%). – The most common cancer type for men is prostate cancer. – The most common cancer type for women is breast cancer.

Evaluation of cancer therapies

• Medical professionals who specialise in cancer are referred to as
• ncologists and can help diagnose and recommend and evaluate

therapies.

• RECIST - Response Evaluation Criteria in Solid Tumours

– A set of rules first published in February 2000 but revised in 2008.

• The evaluations of tumours are often collected as lesions.
• These can be Target or Non-Target; however the presence of New

Lesions may occur at subsequent tumour assessments.

Respond Stable Progress

Complete Response (CR) Partial Response (PR) Stable Disease (SD) Progressive Disease (PD)

Oncology Endpoints

• Defined as a measure of evaluating cancer therapies.
• Primary endpoint of a study must be able to provide a valid and

reproducible measure of clinical and statistical benefit.

• Specified in the protocol of the clinical trial and the methods of

interpreting the data of how to calculate these are contained within a statistical analysis plan.

• Some endpoints may be referred to as a surrogate endpoint.

– Indication to predict clinical benefit. – Accelerated approval, less expensive, Novel treatments to patients faster. “As payers seek to keep pace with groundbreaking changes in the oncology arena, it is critical that they have a solid understanding of how oncology clinical trial endpoints can or should be used to guide decisions about the care that patients receive.”

Kogan, A. J, & Haren, M - Translating Cancer Trial Endpoints Into the Language of Managed Care.

Types of Oncology Endpoints

Statistical Endpoint Definition Limitation Overall survival (OS) Time from randomization until death from any cause; most commonly used endpoint in phase 3 trials and trials for regulatory approval and is considered the gold standard used to determine patient benefit. The reason this endpoint is preferred is that it is not subject to any investigator bias.

• Requires randomized trial with lengthy

follow-up.

• Can be affected by subsequent therapies

Progression-free survival (PFS) A surrogate endpoint for OS. Time from randomization to

• bjective tumour progression or death. Unlike time to

progression (TTP), PFS includes death from any cause as well as progression. Like TTP, it is unaffected by subsequent therapies. FDA prefers PFS rather than TTP as regulatory endpoint.

• Not statistically validated as surrogate for

survival in all treatment settings

• Not precisely measured subject to

assessment bias particularly in open- label studies

• Definitions vary among studies
• Frequent radiological or other

assessments

• Involves balanced timing of assessments

among treatment arms Time to progression (TTP) A surrogate endpoint for OS. Defined as time from randomization until objective tumour progression. Unlike PFS, it does not include deaths, but if most deaths are not cancer-related TTP can be acceptable endpoint. Like PFS, it is unaffected by subsequent therapies. Objective response rate (ORR) A surrogate endpoint for OS. A proportion of patients with reduction in tumour size by a predefined amount (using standardized criteria, such as RECIST). Directly attributable to drug effect.

• Not a direct measure of benefit
• Not a comprehensive measure of drug

activity

• Only a subset of patients who benefit

Patient Reported Outcomes (PRO) Patient-reported outcomes, such as quality of life (QOL), complement information from traditional endpoints, generating the patient’s global assessment of the direct clinical benefit of a drug

• Blinding often is difficult
• Data frequently are missing or

incomplete

• Clinical significance of small changes is

unknown

• Multiple analyses
• Lack of validated instruments

Definitions are as per the FDA guidelines for Clinical Oncology endpoints

Novel Endpoints and The Future

• OS is still considered the gold standard in terms of determining

treatment benefit. Why do we need different endpoints?

• OS can take many years to prove any clinical benefit.
• If a surrogate endpoint can be used to gain accelerated regulatory

approval, more novel therapies can reach patients faster. – PFS has been an example of a surrogate that has been used as the primary endpoint for a novel targeted therapy treatment in breast cancer.

• Continuing to learn about the effect of cancer treatments

A Novel Endpoint: Pathological Complete Response (pCR)

• Pathological complete response (pCR) — The Food and Drug

Administration (FDA) defines pCR as no evidence of disease in the breast or lymph nodes as examined by a pathologist.

• The FDA recently published a draft guidance document for the use of

Pathological Complete Response (pCR) in Neoadjuvant Treatment (before surgery) of High Risk Early-Stage Breast Cancer.

• FDA approval granted - major breakthrough for treatment in early

breast cancer and achieve a common goal of bringing treatment to patients quicker and safer.

Understand primary endpoint data

Investigator assessed PFS

• The tumour does increase from baseline, but not by 20%.
• Tumour increase is only 9.8%
• Incorrectly assessed as PD

No New Lesions

Statistical Analysis

• Survival analysis - In clinical trials, the effect of an intervention is

evaluated by measuring the number of patients survived after that intervention over a period of time.

• Key terms in Survival Analysis

– Response Rate – Median Survival – P-value – Censoring – Hazard Ratio – Odds Ratio

Kaplan - Meier Curve

• Definition: The Kaplan – Meier (KM) curve is a graphical

representation of a time to event analysis showing when a patient reaches a trials survival endpoint.

• Can be summarised by observing the median survival.
• Median survival is the measure of how long patients will live on average

with a certain disease or treatment, and corresponds to the point on the KM curve where the survival probability equals 0.5.

• The KM estimate describes the probability of surviving in a given length
• f time while considering time in many small intervals depicted as

steps, occurring at the time of each new event.

• Censoring - Certain situations can occur in a study such as patients

refuse to remain in the study, or loss of contact with the patient etc. These are labelled as censored observations.

Kaplan - Meier Curve

Cox Regression

• Definition: The Cox regression model provides us with estimates of the effect

that different factors event (e.g. age, weight, sex etc.) have on the time until the end.

• The hazard ratio (HR) is the relevant risk of experiencing an event being

measured (e.g. death) between two groups. – HR = 1: indicates no difference – HR <1 : indicates there was a reduced risk in one of the treatment arms. – HR > 1: indicates an increased risk in one of the treatment arms.

Log Rank Test

• Definition: The Log rank test is a hypothesis test and provides no

direct information on how different the treatment groups are but can be used to compare the KM survival distributions between 2 groups.

• This test is commonly used in clinical trials to give an indication of the

efficacy of a new treatment to standard of care.

• The P-value can tell us if the difference between the survival

distributions is statistically significant.

Odds Ratio

• Definition: This is the ratio of an event happening compared to an

event not happening in the sampled population.

Cox Regression – Time to event

• 1. Responders

Nearly 60% of patients in the control arm were deemed to have had an event compared to approximately 48% in the experimental arm.

• 2. Time to Event

The experimental arm shows an improvement of PFS by approximately 6.1 months. A P-Value calculated using a log rank test of less than 0.0001 indicates that this is statistically significant.

• 3. Hazard Ratio

A Hazard ratio of 0.63 indicates that there is 37% lower risk of a PFS event occurring in the experimental arm compared to the control arm.

• 4. Truncated Analyses

More patients have not experienced an event in the experimental arm.

Objective Response Rates

• 1. Responders

Approximately 77% of the patients respond to treatment in the experimental arm in comparison to approximately 68% in the control arm.

• 2. Treatment Group

comparison The difference in the response rates between the 2 treatment arms is approximately 9.2 %. P-value shows there is a statistically significant difference between the 2 treatment groups.

• 3. Odds Ratio

An Odds ratio of 1.60 indicates that the likelihood of achieving a response is 1.6 times higher in the experimental arm compared to the control arm.

• 4. Response Rate

The number of responses for each observed tumour response is calculated as a percentage out of the population N. The associated 95%

Forest Plots

• Definition: Shows the corresponding magnitude of benefit and

confidence limits of each subgroup analyzed.

• Forest plots are extremely useful when it comes to displaying or trying

to identify the treatment effect in different subgroups of patients.

• Forest plots are usually created for exploratory analyses as many

clinical trials are not always designed to show treatment benefits in many different subgroups.

Forest Plots

Evolution of Programmers

Summary

• Oncology is the most significant therapeutic area in terms of R&D
• Knowledge of the clinical endpoint helps with:

– Understanding the science to challenge analyses – Reviewing the key data points to ensure accuracy – Interpreting the statistical analysis to ensure the correct message is being delivered

• Programmers need to up-skill in order to add value

The role of a programmer is constantly

EVOLVING

Reading Recommendations

Oncology Endpoints

• Genentech USA, I. (2011). The Ongoing Evolution Of Endpoints in Oncology. Retrieved

from NAMCP Medical Directors: http://www.namcp.org/institutes/cancer/Oncology %20Endpoints.pdf

• Oncology, A. (2009). CLINICAL DRUG TRIALS IN CANCER: STUDY ENDPOINTS –

WHAT DO THEY REALLY MEAN? Retrieved 2013, from PR Newswire: http:// multivu.prnewswire.com/mnr/astrazeneca/38570/docs/38570- ClinicalTrialsEndpointFactsheet.pdf RECIST

• Eisenhauera, E., Therasseb, P., Bogaertsc, J., Schwartzd, L., Sargente, D., Fordf, R., et
• al. (2008, October 28). New response evaluation criteria in solid tumours: Revised

RECIST guideline (version 1.1). Retrieved 2013, from EUROPEAN JOURNAL OF CANCER: http://www.eortc.be/Recist/documents/RECISTGuidelines.pdf Statistics

• Harris, M., & Taylor, G. (2008). Medical Statistics Made Easy. Scion Publishing Limited.
• Machin, D., Campbell, M. J., & Walters, S. J. (2007). Medical Statistics: A Textbook for

The Health Sciences. John Wiley & Sons Ltd.