Endpoints in clinical studies

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Endpoints in clinical studies

• Mark Conaway
• Div of Biostatistics and Epidemiology

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Outline

• Definitions
• Classification of endpoints
• Multiple endpoints
• Surrogate endpoints

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Endpoints

• Quantitative measurements required by or implied
  by the objectives of the study/trial
• Desirable features of endpoints
• Relevant to disease process, easy to interpret
• Free from measurement or assessment error
• Sensitive to treatment differences
• Measurable within a reasonable period of time

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Prefer hard endpoints

• hard endpoints clinical landmarks that are
• well-defined in study protocol
• definitive with respect to disease process
• not subjective
• Examples
• death,
• time to disease progression/relapse,
• some laboratory measurements

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Soft endpoints

• Soft endpoints
• not directly related to disease process or
• require subjective assessment by
  patient/physician
• Examples
• Quality of life questionnaires
• Symptom questionnaires

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Not all endpoints can be classified

• Some endpoints are useful and reliable but
  require some subjectivity
• Examples
• pathology
• Key issue is not the classification as hard or
  soft, but how prone to error is the endpoint ?

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Multiple endpoints and type I error

• Often a discussion with regard to clinical trials
  but applies to observational studies as well
• Randomly assign n patients to group A and n
  patients to group B.
• Or have well-defined cohorts of patients treated

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In principle

• Have chosen an endpoint
• Clinical relevant
• Have an appropriate sample size
• to have a type I error rate of 5
• sufficient power for a clinically meaningful
  difference

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In practice

• Rare that trials use a single endpoint
• Endpoints
• cover clinical events
• symptoms
• physiologic measures
• side effects
• quality of life

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Example

• Example CALGB 9182
• Survival time
• Time to disease progression
• Response
• PSA (at 8 week intervals)
• Quality of life (5 instruments at 8 week
  intervals)
• Toxicity (90 item checklist)

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Example Michalowicz et al (NEJM, Nov 2, 2006)

• Study of periodontal therapy and birth outcome
• Several outcomes of interest
• preterm birth (before 37 weeks),
• birth weight,
• proportion of infants who are small for
  gestational age,
• Apgar scores,
• admissions to NICU
• .

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Whats the problem?

• If test each endpoint at the 5 level
• overall chance of finding at least one endpoint
  where there is a significant difference is larger
  than 5, even if the treatments are identical
• Prone to distorted reporting (i.e. pick most
  significant)
• Good reference Pocock (1997) Controlled Clinical
  Trials, p 530-545

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Dealing with problems with multiple endpoints

• Have a pre-defined strategy
• Some advocate
• all results pre-written, with results filled in
  as trial concludes
• Alternative view
• need to be flexible
• need to allow for unexpected findings
• but recognize potential for problems type I
  error rate is not 5

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Delineate primary and secondary outcomes

• Many advocate having a single primary endpoint
• drives sample size calculations
• test based on this endpoint has a 5 type I error
  rate
• All other endpoints are secondary

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Example Michalowicz et al (NEJM, Nov 2, 2006)

• Primary
• Gestational age at end of pregnancy
• Secondary
• birth weight, proportion of infants who are small
  for gestational age, Apgar scores, admissions to
  NICU

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Delineate primary and secondary outcomes

• Can be hard to adhere to in practice
• For example, what if primary outcome is not
  different among groups, but all secondary
  outcomes are?

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What to do with primary and secondary endpoints?

• ONeill, R. (1997) Secondary endpoints cannot be
  validly analyzed if the primary endpoint does not
  demonstrate clear statistical significance
  Controlled Clinical Trials, 550 556
• Davis, C.E. (1997) Secondary endpoints can be
  validly analyzed, even if the primary endpoint
  does not provide clear statistical significance
  Controlled Clinical Trials, 557 - 560

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ONeill (1997)

• Primary endpoint definition
• clinical endpoint that provides evidence
  sufficient to fully categorize clinically the
  effect of a treatment that would support a
  regulatory claim for the treatment
• Secondary endpoint
• additional clinical characterization of a
  treatment but could not, by itself, be convincing
  of a clinically significant treatment effect

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ONeill (1997)

• Argues that primary and secondary outcomes are
  generally related
• Analysis of secondary should be conditional on
  the primary outcome analysis result
• especially true when secondary outcomes depend
  directly on primary (survival)
• Cant quantify the uncertainty in analyses done
  after looking at results

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Davis (1997)

• Strict adherence could miss important and
  unexpected results
• Argues that the major problem is multiple
  comparison issue
• its a statistical problem, so use a statistical
  solution
• One such solution is the Bonferroni adjustment

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Bonferroni procedure

• If you have k endpoints
• Multiply observed p-value by number of endpoints
• For example, with k 8, convert an observed
  p-value of 0.01 to 0.08
• Ensures that if Ho is true for all endpoints,
  probability of rejecting Ho for at least one
  endpoint is less than or equal to ?

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Limitations forBonferroni procedure

• Endpoints tends to be correlated, so this is
  conservative
• probability of type I error is much smaller than
  ?
• Treats all outcomes as equal in importance
• Can lead to illogical results
• Trial with p-values 0.01, 0.75,0.75,0.75,0.75
  significant
• Trial with p-values 0.02, 0.02, 0.02, 0.02, 0.02
  is not significant

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Limitations forBonferroni procedure

• Procedure reduces power to detect real
  differences in specific outcomes, if they exist
• Protect type I error at expense of power
• Difficult to apply strictly in many cases

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So whats the answer?One persons opinion...

• Selection of a primary outcome is important
• Need to allow for surprises
• Full disclosure of endpoints, instead of selected
  endpoints, can alleviate a lot of the problems
• Adjust p-values?
• Whats the goal of the study?

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Surrogate endpoints

• Hesitate to use the term
• Has a specific technical definition
• Issue
• Quicker, less expensive, less clinically relevant
  endpoint or
• More expensive, clinically definitive endpoint?

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Example

• Treatment for osteoporosis
• Endpoint
• Bone density via DEXA?
• Fracture?
• If fracture, how would this be ascertained?

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Example

• Choice is, for same amount of resources
• more patients with less clinically relevant
  outcome (bone density)
• Fewer patients with more clinically relevant
  outcome (fracture)
• Frequently see the quick endpoint in earlier
  stage trials.

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Summary

• Choice of endpoints is crucial to the success of
  the study either RCT or observational
• Issues about
• Which one?
• How many?
• Primary vs secondary?