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Endpoints (also Outcomes, Major Response Variables)

Endpoints (also Outcomes, Major Response Variables). Outline. General endpoint considerations Surrogate endpoints Composite endpoints Safety outcomes (adverse events). Major Problems that Limit Interpretation of Randomized Trials . Inappropriate controls

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Endpoints (also Outcomes, Major Response Variables)

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  1. Endpoints(also Outcomes, Major Response Variables)

  2. Outline • General endpoint considerations • Surrogate endpoints • Composite endpoints • Safety outcomes (adverse events)

  3. Major Problems that Limit Interpretation of Randomized Trials • Inappropriate controls • Endpoints which are not clinically relevant • Inadequately powered studies • Poor follow-up • Improper interim analyses

  4. Protocol and Trial Report - 1 • Endpoints should be pre-specified: • Written down in the protocol before trial begins • Defines the trial objectives (in part) • Basis for sample size • Example: Herpes Zoster Vaccine Protocol • Hypotheses and Objectives: “The primary objective of this study is to determine whether immunization with live-attenuated varicella-zoster vaccine (OKA/Merck Strain) can reduce the incidence and/or severity of herpes zoster (HZ) and its complications, primarily postherpetic neuralgia (PHN) in persons 60 years of age and older. This will be accomplished by comparing a measure of the burden of illness due to HZ and PHN in vaccine and placebo recipients.

  5. Protocol and Trial Report - 2 • Example: Herpes Zoster Vaccine Trial Report (N Engl J Med 2005) • “The Shingles and Prevention Study (Department of Veterans Affairs [VA] Cooperative Study No. 403) was conducted to determine whether vaccination with a live attenuated VZV vaccine would decrease the incidence, severity, or both of herpes zoster and post-herpetic neuralgia in adults 60 years of age or older.” • “The primary endpoint was the burden of illness due to herpes zoster, a severity-by-duration measure of the total pain and discomfort associated with herpes zoster in the population of study subjects.”

  6. Primary Endpoint • Usually one outcome is specified as most important (primary endpoint) • Key variable in design (follows from objective) • Basis for sample size • A focus of interim monitoring and QA • Response variable given major attention in trial report • Usually, but not always, e.g., Cox-2 trials on GI bleeding, relates to efficacy. In some studies the primary endpoint encompasses efficacy and safety, e.g., mortality in CHF study, lipid study, HIV treatment study • “A clinical endpoint that provides evidence sufficient to fully characterize the effect of a treatment in a manner that would support a regulatory claim for treatment” (O’Neill RT, Cont Clinical Trials, 1997;18:550-556)

  7. Secondary Endpoints • There are usually several efficacy endpoints and these are commonly referred to as secondary endpoints or secondary efficacy endpoints. • Safety endpoints must also be specified and are usually consider secondary • Discontinuation of study treatment • Side effects/adverse events • Serious adverse events • O’Neill (FDA) defines a secondary endpoint as one that “provides additional characterization of treatment effect but that is not sufficient to characterize fully the benefit or to support a claim for a treatment effect”.

  8. Characteristics Desired for Endpoint • Relevant; easy to interpret • Easy to diagnose • Can be ascertained and classified in an unbiased manner • Sensitive to treatment differences • Measurable within a reasonable period of time

  9. General Considerations - 1 • More commonly occurring endpoints (high incidence) will result in smaller sample sizes than less frequent events (low incidence) as long as expected relative difference between treatment groups is similar • CHD + non-fatal MI vs. CHD death for lipid-lowering trial • Progression-free survival vs survival for cancer trial • Continuous response variables usually result in smaller sample sizes than binary or time to event • BP change vs. % with normal BP • HIV RNA change vs. % < 50 copies/mL • Weight change vs. % who lose > 5% of baseline weight

  10. General Considerations - 2 • More serious events should be considered along with less serious ones • Count CHD deaths along with non-fatal MIs • Related to this, some events may have to be included to avoid misinterpretation due to informative censoring (this could result in a loss of power) • Non-arrhythmic deaths along with arrhythmic deaths (DEFINITE, NEJM 2004) • Death and missing data along with change in exercise duration (PICO, Heart 1996) • Progression to AIDS or death from any cause (SMART, NEJM 2006)

  11. Endpoint Examples - 1 MRFIT (JAMA 1982 and Amer J Cardiol 1986) Primary: CHD Death Secondary: CHD Death or non-fatal MI CVD mortality All-cause mortality NuCombo HIV Study (N Engl J Med 1996) Primary: Progression to AIDS or death from any cause Secondary: Death AMIS (JAMA 1982) Primary: All deaths Secondary: CHD death or non-fatal MI

  12. Endpoint Examples - 2 • New BP-lowering drug – Systolic BP change • CHF device – change in NYHA class; 6-minute walk • New antiretroviral drug – HIV RNA suppression at 24 and 48 weeks Special considerations: longitudinal measurements; informative missing data; left censoring

  13. Choice of Endpoint in TOMHS[Antihypertensive Drugs] BP, Side Effects, Quality of Life Echocardiographic and Electrocardiographic Changes (Asymptomatic CVD) Non-fatal MI, Stroke, Angina, Peripheral Artery Disease (Symptomatic CVD) CVD Death • CHD • Stroke Total Mortality

  14. Choice of Endpoint in Antiretroviral Trials CD4+ count; viral load Genotypic/phenotypic resistance; Loss of drug options Clinical disease progression (AIDS) Serious AIDS and non-AIDS events Survival

  15. Choice of Endpoint in HIV Vaccine Trials HIV Infection Durable control of viremia post-infection (viral load set point) CD4+ decline/ART AIDS or death

  16. Endpoints Used to Approve Cancer Drugs and Biologics • Survival • Symptom endpoints (patient reported outcomes) • Disease-free survival (e.g., time to tumor recurrence or death) • Objective response rate (e.g., proportion of patients with tumor size reduction of a pre-defined amount for a minimum time period) Guidance for Industry. Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics. May 2007.

  17. Requirements for FDA Approval Vary • Antiretroviral drugs for HIV – viral load (regimen failure) • Antihypertensive drug – BP • CHF drug – morbidity and mortality • Device for CHF – functional status • Osteoporosis drugs – bone density

  18. For Other Areas There is Uncertainty • Treatments for community acquired bacterial pneumonia (CABP) • Should focus be on clinical endpoints that capture how a patient feels or should outcomes incorporate clinical signs (e.g., fever) and laboratory tests (e.g., WBC count)? • FDA position in November 2011: “improvement in at least 2 symptoms attributable to CABP… at a minimum cough, sputum production, chest pain, and shortness of breath at an early time point (i.e., day 3 or 5 after enrolment). Anti-Infective Drugs Advisory Committee Briefing Document November 2011

  19. Choice of Endpoint: General Hierarchical Categorization • Clinical outcome (morbidity and mortality) • Surrogate for clinical outcome (may be hard to establish) • Intermediate outcome that is likely to predict clinical benefit (non-validated surrogate) • Biomarker which measures biologic activity

  20. Types of Endpoints from an Analytic Point of View • Binary • Ordered categorical • Continuous (single point in time, repeated measures, slope) • Counts • Time to event and rates

  21. Other Endpoint Considerations 1. Training of evaluators (e.g., BP measurement) • Ongoing quality assurance (e.g., laboratory QC) • Endpoint classification committee 4. Blinding of endpoint determination (as noted previously this can be done even in open- label studies) 5. Methods for reducing missing data • Informed consent • Training • Quality assurance procedures • Collect identifying information at entry • National Death Index

  22. Outline • General endpoint considerations • Surrogate endpoints • Composite endpoints • Safety outcomes (adverse events)

  23. Surrogate Endpoint(Definition) • Failure of treatment (relative to control) to influence the surrogate implies failure to influence “true” endpoint (Prentice, Stat Med, 1989) • Surrogate must be able to capture “full” dependence of “true” endpoint rate on randomization group, i.e., no pathways whereby randomization assignments affect “true” endpoint that bypass the surrogate response • Occurs with greater frequency than “true” endpoint • Occurs sooner after treatment than “true” endpoint

  24. Other Definitions of Surrogate Endpoints • “a laboratory measurement or clinical sign used as a substitute for a clinically meaningful endpoint that measures directly how a patient feels functions or survives” (Temple). • “ A surrogate endpoint is expected to expected to predict clinical benefit (or harm or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence” (Biomarker Definitions Working Group; also, IOM report). • “an outcome measure that substitutes for a clinical event of true importance” (Grimes and Schulz).

  25. Institute of Medicine Report: Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease • Biomarker evaluation should consist of 3 steps: • Analytical validation (e.g., performance of an assay) • Qualification (assessment of association of biomarker with disease and the effect of interventions on biomarkers) • Utilization (contextual analysis of available evidence on the specific use proposed). A biomarker may be used as a surrogate for some disease states and not in others, e.g., HIV viral load in setting where suppression is complete versus partial IOM 2010, National Academies Press

  26. Treatment Other Mechanisms of Action Surrogate Marker Relationship Between Treatment (A or B), a Surrogate Marker (S), and the Clinical Outcome (T) A or B Treatment has many mechanisms of action S T Clinical Outcomes Fleming and DeMets, Ann Int Med, 1996

  27. Surrogate Endpoint: Not in Causal Pathway of Disease Process SurrogateTrue Clinical EndpointEndpoint Causal Pathway Disease Fleming and DeMets, Ann Int Med, 1996

  28. A correlate does not a surrogate make! Fleming TR and DeMets DL, Ann Int Med, 1996.

  29. Systolic Blood Pressure (mmHg) 9 0 8 0 7 0 6 0 Age-adjusted CHD Mortality (per 10,000 person-years) 5 0 4 0 3 0 2 0 1 0 0 < 1 2 0 1 2 0 - 1 3 0 - 1 4 0 - 1 6 0 - 1 8 0 - 2 1 0 + 1 2 9 1 3 9 1 5 9 1 7 9 2 0 9 Data from the MRFIT Study

  30. Blood Pressure is Considered An Acceptable Surrogate Endpoint by the FDA • Substantial epidemiological and clinical trial data. • Demonstration that diverse BP-lowering agents provide benefit. • Considered to be the principal causal pathway.

  31. ALLHAT Results on Doxazosina: Cumulative 4-Year Rate (%) DoxazosinChlorthalidone No. Patients 9,067 15,268 Fatal/Non-Fatal CHD 6.3% 6.3% Fatal/Non-Fatal Stroke 4.2% 3.6% Congestive Heart Failure 8.1% 4.5% All Cause Mortality 9.6% 9.1% a JAMA; 283:1967-1975, 2000

  32. Multiple Pathways of the Disease Process Intervention Surrogate True Clinical Endpoint Endpoint Disease Intervention True Clinical Endpoint Disease Surrogate Endpoint

  33. Best Situation for Assessing Surrogacy Intervention Surrogate True Clinical Endpoint Endpoint Disease

  34. Interleukin -2 Trials for HIV IL-2 CD4+ AIDS or Count Death Disease • IL-2: known to increase CD4+ cell count • IL-2: known to be associated with toxicities • Unknown whether IL-2 is increasing functional CD4+ cells

  35. Deaths and Serious AIDS Event Rates by Latest CD4+ Count Following Initiation of ART All-Cause Mortality Serious AIDS CD4+ Level PY Events Rate PY Events Rate < 200 1327 29 4.29 907 74 8.16 200-350 2624 18 0.84 2284 38 1.66 350-499 3532 21 0.42 3228 18 0.56 500+ 8425 22 0.30 7964 21 0.26 CASCADE Collaboration http://www.cascade-collaboration.org

  36. ESPRIT Study Design Patients taking ART with CD4+ counts ≥ 300/μL N = 2040 N = 2071 • IL-2 • ART plus: • 3 cycles of IL-2 (7.5 MIU twice daily for 5 days, 8 wks apart) • additional cycles to maintain goal (2x baseline or ≥ 1000 CD4+ cells) Control ART without IL-2 Plan: 320 primary events Closure date 15 Nov 2008 323 primary events observed Median follow-up = 7 years N Engl J Med 2009 361:1548-1559.

  37. Median CD4+ During Follow-up Avg Difference: 160 cells, p<.001 No. pts

  38. Primary EndpointOpportunistic Disease or Death

  39. Interventions having Mechanisms of Action Independent of the Disease Process Intervention Surrogate True Clinical Endpoint Endpoint Disease

  40. Concorde Study Results(Lancet 341:889-90, 1993) Deaths 95 76 AIDS or Death 175 171 ARC, AIDS or Death 263 284 Immediate ZDV Deferred ZDV CD4 difference over 3 years of follow-up (immediate - deferred) = 30 cells (p < 0.0001)

  41. CD4+ Count and Mortality: Pre-HAART Era Baseline CD4+ Lymphocyte Count(cells/mm3) 4 0 3 5 3 0 2 5 12-month Cumulative Mortality (%) 2 0 1 5 1 0 5 0 < 2 5 2 5 - 5 0 - 1 0 0 - 2 0 0 - 4 9 9 9 1 9 9 4 9 9 CID 1996; 22:513-520

  42. Overview of Trialsof ZDV vs. Placebo (Immediate vs. Deferred) Total 4431 3291 No. deaths 734 617 No. AIDS/deaths 1026 882 ImmediateZDV DeferredZDV (placebo) Risk ratio = 1.04 Risk ratio = 0.96 Lancet 353:2014-2025, 1999.

  43. Operational Criteriafor Valid Surrogates • The surrogate (S) must predict the clinical event (T) • Treatment must effect surrogate • The surrogate (S) must fully capture the effect of treatment on the clinical event (T) Prentice R, Stat Med, 1989.

  44. Evaluation of Surrogacy Determine relative risk (treatment versus control) of long-term clinical outcome Show that relative risk when adjusted for marker in each treatment group is one Not an optimal approach. Ideally, results from several studies with surrogate and clinical outcomes would be compared.

  45. Statistical Analysis for Single Study • Fit logistic regression models: • 1) log odds (long-term clinical outcome) = • a + b (trt) • 2) log odds (long-term clinical outcome) = • a + ba (trt) + c (marker change) • If marker change fully explains treatment effect, then ba would be zero • (b-ba)/b measures proportion of effect on long-term clinical outcome explained by effect on marker

  46. Validation of Surrogate Endpoints Statistical – want more than one study · Meta-analyses of clinical trials data Clinical · Comprehensive understanding of the causal pathways and intended and unintended mechanisms of action

  47. Overview of 16 Antiretroviral Trials State-of-the-Art Conference 1993 Clinical Disease Progression Sig. Diff. No Diff. 7 6 13 Sig. Diff. CD4+ Change 2 3 1 No Diff. 8 8

  48. Colon Cancer Example • Traditional endpoint is overall survival (OS) • Hypothesis: Disease free survival (DFS), assessed after 3 yrs, is an appropriate endpoint to replace overall survival (OS) in adjuvant colon trials • Allow more rapid completion, reporting of trials • Allow promising agents to benefit patients more quickly • Approach: Compare difference between treatment and control (hazard ratio) within each trial for DFS and OS Sargent DJ et al, J Clin Oncol, 2005;23:8664-8670.

  49. Overall and Disease-Free Survival for Adjuvant Treatment for Colon Cancer Sargent DJ et al, J Clin Oncol, 2005;23:8664-8670.

  50. “The validity of a surrogate endpoint should be judged by the probability that the trial results based on the surrogate endpoint alone are ‘concordant’ with the trial results that would be obtained if the true endpoint were observed and used for the analysis” Begg and Leung, J R Statis Soc A 2000; 163:15-28

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