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Multiple Endpoint Testing in Clinical Trials – Some Issues & ConsiderationsPowerPoint Presentation

Multiple Endpoint Testing in Clinical Trials – Some Issues & Considerations

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Multiple Endpoint Testing in Clinical Trials – Some Issues & Considerations

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Multiple Endpoint Testing in Clinical Trials – Some Issues & Considerations

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Multiple Endpoint Testing in Clinical Trials – Some Issues & Considerations

Mohammad Huque, Ph.D.

Division of Biometrics III/Office of Biostatistics/OPaSS/CDER/FDA

2005 Industry/FDA Workshop, Washington. DC

- Views expressed here is that of the presenter and not necessarily of the FDA

- Multiple endpoints
- Multiple comparisons
- Interim analysis
- Subgroup analysis
- Selection of covariates in an analysis model
- Others

- Type I error concept and type I error control when testing for multiple endpoints. Complexities?
- Multiple endpoints are often triaged into primary, secondary and other types of endpoints. Reasons for doing so and how these endpoints are tested?
- Sequential testing of endpoints - no alpha adjustment is needed. Issues and fixes?
- Some trials require that 2 or more endpoints must show effects for clinical evidence. Reasons for doing so and consequences?
- Composite endpoints. Underlying concepts and complexities?

- Conduct a test for claiming that a new treatment is beneficial
- α = Probability of the Type I error
- β = Probability of the Type II error (power = 1- β )

- Consider a two arm superiority trial, a test treatment versus a control
Endpoints: y1, y2, …, yK

Multiple Null Hypotheses: F = {H01, H02, …, H0K}

H0j: δj = 0, Haj δj ≠ 0, j =1, …, K

- Two scenarios:
(A) In the family F all are true null hypotheses

(B) Some may be true null hypotheses, and some may be false null hypotheses, but their true state are unknown.

- Scenario (A) and the trial has 3 endpoints y1, y2, and y3
- A test procedure can give type I error in multiple ways: (-, -, +), (-, +, -), (+, -, -), (-, +, +), (+, -, +), (+, +, -), (+, +, +). These are chance events because of multiplicity of tests when in fact there is no treatment benefit for any of the endpoint.
- α0 = Pr {of at least one of these chance events | test procedure, H0}, H0= ∩H0j

- α0is called global alpha (or overall alpha). Also, called the familywise type I error rate (FWER) under H0, where
- H0= ∩H0j is the global null hypothesis.
- A test procedure for testing H0 is called a global test procedure

- Useful for non-specific global claims. Difficulty in interpreting the result. Type I error rate can remain inflated for specific claims.
- Examples: Simes test, O’Brien’s OLS/GLS tests, Hotelling’s T2 test (Sankoh et al, DIA Jr.,1999)

- Some of the null hypotheses F = {H01, H02, …, H0K} may be true null hypotheses and some be false, but its not known which ones are which.
- Question: Is there a treatment effect specifically for the endpoint y1?
- For answering this question, the null hypothesis is not a single null hypothesis like a global null hypothesis, rather it is a class of null hypothesis configurations in which there is no treatment effect for y1, and all possible scenarios for treatment effects for the remaining endpoints y2, …, yK

- Consider 3 endpoints y1, y2, and y3.
- Question: Is there a treatment effect specifically for the endpoint y1?
- Null hypothesis configurations F1for testing for treatment effect specifically for the endpoint y1:
F1 = { (δ1 = 0, δ2 = 0, δ3 = 0),

(δ1 = 0, δ2 = 0, δ3 ≠ 0),

(δ1 = 0, δ2 ≠ 0, δ3 = 0),

(δ1 = 0, δ2 ≠ 0, δ3 ≠ 0)}.

- Weak control
- Control FWER only under the global null configuration

- Strong control
- Control FWER under all null configurations
- Specificity property -- useful for making specific claims.
- Examples of methods: Bonferroni, Holm, Hochberg*, closed statistical tests, and other methods
*with some caveats

1) Prospectively defined

2) FWE controlled

- Two important families

Primary endpoints

Secondary endpoints

Exploratoryendpoints

(usually not prospectively defined)

- Primary endpoints are primary focus of the trial. Their results determine
- main benefits of he clinical trial’s intervention.

- Secondary endpoints by themselves generally not sufficient for characterizing
- treatment benefit. Generally, tested for statistical significance for extended
- indication and labeling after the primary objectives of the trial are met.

- Prospective alpha allocation schemes (PAAS) – Moyé (2000)
- Spend alpha1 for the primary endpoints and the remaining alpha for the secondary endpoints - FWER is controlled

- Parallel gatekeeping strategies for clinical trials –
- Dmitrienko-Offen-Westfall (SM 2003)
- Chen-Luo-Capizzi (SM 2005)

- Allows testing of secondary endpoints when at least one of the primary endpoints exhibits a statistically significant result
- These methods controls FWER for both the primary and secondary endpoints in the strong sense.

- A fixed sequence approach allows testing of each of the k null hypotheses at the same significance level of α without any adjustment, as long as the null hypotheses to be tested are hierarchically ordered and are tested in a pre-defined sequential order.
- Hierarchical ordering of null hypotheses can be achieved, for example, by their clinical relevance.

For this fixed-sequence approach, however,

there are two caveats:

- Pre-specification of the testing sequence
- No further testing once the sequence breaks
- Problem: when the sequence breaks and the next p-value is extreme (e.g., p1= 0.50, p2= 0.001)

Test H(02) at

Level α

H(01) is rejected

Test H(01) at

Level α1

Test H(02) at

Level γ

H(01) is rejected

e.g., α1 = 0.04, α = 0.05, γ = 0.0104, ρ = 0

(γ = 0.0214, ρ = 0.8)

Examples:

- Alzheimer trial
- (win on ADAS-Cognitive Sub-scale) and (win on Clinician’s Interview Based Impression of Change)

- Many other examples (PhRMA draft paper)
Main Reason:

- Clinical expectations of the desired clinical benefit
(concept beyond statistics)

Adjustment by Sidak’s method on accounting for correlation

Note: Which method to use depends on on the clinical decision rule set in advance

Alpha = 0.025 (1-sided), Power = 0.90

Correlation K = 2K=3K=4

0.0 22.8% 35.9% 45.0%

0.3 21.1 33.1 41.2

0.4 20.2 31.7 39.7

0.5 19.1 29.8 37.3

0.6 17.7 27.5 34.4

0.7 15.9 24.6 30.7

0.8 13.5 20.8 25.8

0.9 10.0 15.3 18.9

(1)Calculations using mutivariate normal distribution of the test statistics comparing active treatment versus placebo for a 2-arm trial, assuming same delta/sigma for all K endpoints

Two types -

- Total score or index based on a rating scale, e.g., HAMD totals in depression trials, ACR20/ACR70 in rheumatoid arthritis trials
Issues: validity and reliability

Another Type

- Composite endpoint is defined in terms of the time to the first “event”, where event is one of several possible event types
LIFE study: Composite of cardiovascular death, stroke and myocardial infraction events.

Life Study

The Composite endpoint was significantly positive. However, analysis of the first events by individual components and sub-composite endpoints indicate overall composite result mainly due to reduction in fatal and non-fatal stroke.

Issue:

How to interpret composite endpoint results? How to characterize benefits in terms of the component endpoints?

correlation

high

Practically no adjustments

Small

adjustments

Good case

for combining

endpoints

Large

adjustments

low

high

low

Homogeneity of treatment effects across endpoints

- For endpoint specific claims – strong control of the type I error is needed
- Parallel gate-keeping strategies can be used for the primary and secondary endpoint claims
- Flexible sequential test procedure can be used to gain power of the test
- There is a scientific basis when a reasonable clinical decision rule asks for statistically significant efficacy results in more than 1 endpoint – issue of loss of power?
- When 4 or more endpoints included as primary (e.g., arthritis trials), and homogeneity of treatment effects acress endpoints is expected - a composite or responder endpoint approach will be effective.