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Choice of Endpoints for Salvage Studies. Choice of Endpoints. Clinical Endpoints AIDS-defining events Survival QOL Marker-based Endpoints for Efficacy HIV-1 RNA CD4. Choice of Endpoints (Cont.). Endpoints for Toxicity Time to treatment discontinuation

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slide2

Choice of Endpoints

Clinical Endpoints

AIDS-defining events

Survival

QOL

Marker-based Endpoints for Efficacy

HIV-1 RNA

CD4

choice of endpoints cont

Choice of Endpoints (Cont.)

Endpoints for Toxicity

Time to treatment discontinuation

Targeted adverse events (e.g. lipodystrophy)

Composite Endpoint

Combine information across different endpoint categories.

Time to treatment discontinuation for virological failure or intolerance.

hiv rna endpoints

HIV RNA Endpoints

Quantitative (change from baseline to Week x)

Time to Virological Failure

Binary

Cross-sectional; e.g. Above/below threshold at week x

Failed by Week x

cross sectional vs failure over time

Cross-Sectional vs. Failure Over Time

Above/Below threshold at week x

Snapshot; not affected by transitional changes in HIV levels.

Frequent monitoring not required (batch assaying).

Missing data at timepoint especially problematic.

Failure Endpoints

Assessment of response over time; may be affected by transitional changes in HIV levels.

Frequent monitoring required (real time assaying).

Missing data strategies need to be defined/evaluated.

time to failure vs cumulative proportion

Time to Failure vs. Cumulative Proportion

Time to Failure

Patterns of failure depend on failure time (assumptions).

Can be evaluated within an interim analysis (accommodates differential follow-up).

Cumulative Proportion

Time to failure not considered in analysis.

Evaluation with interim analysis may be complicated.

slide7

Power Advanatges of Time to Event

  • If the pooled failure rate is > 50%, a time-to-event endpoint has appreciable sample size advantages
  • Example: 6 months accrual, 1 year additional follow-up, 2 arm trial:
    • e.g., 6 months accrual, 1 year additional follow-up, 2 arm trial:
      • 50% pooled failure rate, 5% sample size savings
      • 70% pooled failure rate, 15% sample size savings
    • e.g., 1 year accrual, 6 months additional follow-up, 2 arm trial:
      • 50% pooled failure rate, 12% sample size savings
      • 70% pooled failure rate, 25% sample size savings
slide8

Analysis Issues

  • With moderate study withdrawal, the sample size savings of the time-to-event endpoint increases further.
  • The sample size savings are larger at interim analyses than at final analyses, in proportion to the fraction of subjects who have less follow-up time than the specified interim analysis time.
  • Time-to-event endpoints also have advantages for evaluating covariate effects and for flexibility in extending the study by prolonging the follow-up period.
purely virologic vs composite

Purely Virologic vs. Composite

Purely Virologic

Focuses on virologic response only

tolerability and safety can be assessed separately

Follow-up for viral load is essential after treatment discontinuation.

Composite

Combines virologic efficacy, tolerability and safety; overall picture.

May differ substantially from purely virologic if toxicity rate is high.

Purely virologic should be done as secondary endpoint.

issues in definition of

Issues in Definition of:

Virologic Failure

Early failure (rise above nadir/baseline, insufficient decline)

Amount of time allowed to go below suppression threshold

Choice of threshold for suppression and for loss of suppression

Fluctuations due to treatment holds, intercurrent illness, etc.

Regimen Completion

Virologic failure definition (see above)

Number of drugs added/changed before declare treatment failure

Subjectivity of treatment discontinuation reasons

clinical beliefs underlying the appropriate use of each endpoint

Clinical Beliefs Underlying the Appropriate Use of Each Endpoint

Purely Virologic Endpoint:

The effect of the investigated therapies on plasma HIV-1 RNA levels captures the essential information needed to define the role of the therapies in clinical practice for the target population.

Regimen completion endpoint:

The necessity to change regimens more closely measures tangible benefit to a patient than does virological failure alone, and, assessing the virologic effect of treatment is unnecessary.

types of study endpoint in hiv disease studies

Types of Study Endpoint in HIV Disease Studies

Time to Failure

Regimen Completion (384, 372A, A5025)

Virologic Failure  Week x (388, A5076, A5095)

Binary

Below Threshold at Week x (359, 364, 370, 373, A5086)

Not Fail by Week x: failure is defined as:

Rise Above Threshold (A5073)

Rise Above Threshold, Early Failure (347, 368, 398, A5080)

Rise Above Threshold, Early Failure, Off Treatment (372B, 400, A5064)

Cumulative Virologic Failure (343)

composite endpoints

Composite Endpoints

Combine efficacy and toxicity information (e.g. time to Rx discontinuation)

Will be more numerous than pure virologic endpoints, but may dilute the effect of treatment.

Especially a concern if Rx discontinuation may be unrelated to Rx (pregnancy, imprisonment, moving).

example

Example

Suppose effect of Rx A (compared to B) reduces percentage reaching event from 35% to 17.5%. We need 100 patients per arm to have 80% power.

Assume Rx discontinuation rate is 10%/yr for both treatments, and is included in endpoint definition.

We have more endpoints but only 60% power to detect the treatment difference.

We need 50 additional patients per arm for 80% power.

example continued

Example Continued

“Pure” Failure

100 Evaluable Patients

Failure including Rx Discontinuation

100 Evaluable Patients

actg 359 proportion vs change

ACTG 359:Proportion vs. Change

ACTG 359 is a randomized, partially double-blinded, multicenter factorial study of six oral combination antiretroviral regimens:

DLV - RD

RTV ADV - RA

DLV + ADV - RDA

SQV

DLV - ND

NFV ADV - NA

DLV + ADV - NDA

Subjects received randomized study treatment for 24 weeks

data completeness

Data Completeness

Data Descriptions

Above 90% of subjects had week 16 virologic and immunologic data.

# of subjects with missing RNA data at week 16.

  Treatment RD RA RDA ND NA NDA

n 5 3 6 5 1 3

Data were assumed to be missing at random.

primary efficacy comparison

Primary Efficacy Comparison

Proportions of HIV-RNA below 500 at week 16

RTV NFV

28% (35/125) 33% (42/127)

P = 0.513, Fisher’s exact test

DLV ADV DLV + ADV

40% (34/85) 18% (16/88) 33% (17/79)

P = 0.006, Chi-square test

secondary efficacy analysis rna change

Secondary Efficacy Analysis: RNA Change

HIV RNA week 16 median change from baseline

 Treatment RD RA RDA ND NA NDA

in log10 -0.41 -0.16 -0.21 -0.61 -0.08 -0.05

RTV vs. NFV: p = 0.834 (Logrank), p = 0.586

(Prentice-Wilcoxon)

DLV vs. ADV: p = 0.003, p = 0.011;

DLV vs. DLV + ADV: p = 0.262, p = 0.231;

ADV vs. DLV + ADV: p = 0.104, p = 0.258.

loss to follow up

Loss to Follow-Up

Need a policy for handling loss to follow-up

Drop-out as censored/failure may be biased

Sensitivity analyses with various levels of association between drop-out and failure events

actg 398

ACTG 398

Subjects were stratified for prior PI (protease inhibitor) exposure, by selective randomization to one of four treatment arms:

SQV Arm: Amprenavir (APV) + Saquinavir (SQVsgc) + Abacavir (ABC) + Efavirenz (EFV) + Adefovir (ADV)

IDV Arm: APV + Indinavir (IDV) + ABC + EFV + ADV

NFV Arm: APV + Nelnavir (NFV) + ABC + EFV + ADV

Placebo Arm: APV + Placebo (matched to SQVsgc, IDV or NFV)+ ABC + EFV + ADV

actg 398 continued

ACTG 398 Continued

Design and Ideal Enrollment

Arms

Prior PI Exposure SQV IDV NFV Placebo Total

SQV only X 25 25 15 65

IDV/RTV only 25 X 25 15 65

NFV only 25 25 X 15 65

NFV and IDV/RTV 33 X X 22 55

NFV and SQV X 33 X 22 55

SQV and IDV/RTV X X 33 22 55

NFV, SQV and IDV/RTV 17 17 17 17 68

Total 100 100 100 128 428

actg 398 continued1

ACTG 398 Continued

Estimated Virologic Failure at Week 24 for MAR and M=F

(Kaplan-Meier)

Treatment NNRTI M=F MAR

Arm Experienced? Failure (95%CI) Failure (95%CI)

SQV Yes 0.85 (0.74, 0.95) 0.76 (0.62, 0.90)

No 0.54 (0.43, 0.66) 0.41 (0.29, 0.53)

IDV Yes 0.87 (0.75, 0.99) 0.80 (0.66, 0.94)

No 0.53 (0.37, 0.69) 0.42 (0.26, 0.59)

NFV Yes 0.73 (0.62, 0.83) 0.66 (0.54, 0.77)

No 0.55 (0.43, 0.67) 0.48 (0.36, 0.60)

Placebo Yes 0.91 (0.83, 0.98) 0.82 (0.73, 0.92)

No 0.63 (0.54, 0.73) 0.52 (0.42, 0.63)

actg 398 continued2

ACTG 398 Continued

Primary Comparison of Treatment Arms vs. Placebo

P-values for RNA < 200 copies/ml at Week 24

SQV vs Placebo IDV vs Placebo NFV vs Placebo SQV/IDV/NFV vs Placebo

0.25 0.29 0.004 0.002

Results based on the exact test with stratification by prior PI and NNRTI experience.

actg 398 continued3

P-values for Confirmed Virologic Failure at/before Week 24

SQV vs Placebo IDV vs Placebo NFV vs Placebo SQV/IDV/NFV vs Placebo

M=F 0.62 1.00 0.005 0.026

MAR 0.74 0.83 0.005 0.038

Notes: Results based on the exact test with stratification by prior PI and NNRTI experience.

MAR = Missing-at-random (missing RNA samples ignored)

P-values for Time to Confirmed Virologic Failure

SQV vs Placebo IDV vs Placebo NFV vs Placebo SQV/IDV/NFV vs Placebo

M=F 0.78 0.83 0.006 0.040

MAR 0.98 0.70 0.003 0.038

Notes: Results based on the stratified log-rank test with stratification by prior PI and NNRTI

experience.

MAR = Missing-at-random (missing RNA samples ignored)

ACTG 398 Continued

analysis of quantitative endpoints

Analysis of Quantitative Endpoints

Censored data methods required (log-rank, Prentice log-rank

Bias results from excluding missing data

Lost observations carried forward can be very biased

Consider last rank carried forward for rank-based analysis

slide38

Discussion Points

  • Count study withdrawal as failure or as censored?
    • each analysis is likely biased
    • recommend carrying out both analyses as well as more sophisticated sensitivity analyses
slide39

Discussion Points

  • What are the criteria for selecting a primary endpoint?
    • Optimally addresses the primary objective, taking into account the patient population and the study drugs
    • Within the pool of possible surrogate markers, it is maximally accurate as a replacement for true clinical endpoints
slide40

Analysis Points

  • If the primary endpoint is binary, the Chi-squared test and Fisher’s exact test for a treatment difference are biased if there are censored data
  • A Z-test based on the difference in Kaplan-Meier estimates of the proportion failed is unbiased and efficient
    • use this test routinely