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TABLE 1.5
Figure 1.6 Relative proportions of population size of some time-dependent endpoints.
If time is fixed (Table 1.5), then the outcome that is being measured is a
rate of events, where, in general, the more events that occur in a population
during the study period or some other predefined time interval, the more
favorable is the outcome. Examples include percentage of responders,
survival at 5 years, and percentage of patients not progressing at 2 years (Fig.
1.7).
Types of Time-Independent Endpoints
Time-Independent or Time Is Fixed
Expressed as a Rate of Events Per
Unit Time (Usually Length of the
Study) Such As Percent of the
Population with An Event During
the Fixed Time Period
Population Comment
Pharmacokinetic/pharmacodynamic
relationships
All patients
where
measurements
are taken
Description of relationship between
drug exposure and a clinical or
biochemical effect
Response % of all patients Criteria are extremely variable; may
or a continuous
variable such as
a drug level
be drug levels, symptom based,
imaging study, biomarker, or patientreported outcome. Response is often
subdivided into categories that may be
ordered (e.g., complete response,
partial response, stable disease,
progression). Ordered categories
require additional analyses and in
some cases are combined
Adverse events Usually % of all
patients
Standard reporting criteria are
available from several sources
Landmark % of all patients Highly variable—paradigm—an
example would be % of patients alive
at 2 y for a life-threatening illness, but
must be meaningful with regard to
disease and patient population
Figure 1.7 Relative proportions of population size of some time-independent endpoints: (A) rate
endpoints and (B) landmark endpoints.
BIOMARKERS AND SURROGATE ENDPOINTS
The NIH Definition Working Group defined the terms clinical endpoint,
biomarker, and surrogate endpoint in 2001 as:
1. A clinical endpoint is a characteristic or variable that reflects how a
patient feels, functions, or survives.
2. A biomarker is a characteristic that is objectively measured and
evaluated as an indicator of normal biologic processes, pathogenic
processes, or pharmacologic responses to a therapeutic intervention.
3. A surrogate endpoint is a biomarker intended to substitute for a clinical
endpoint that should predict clinical benefit or harm or lack of both.
104
The Biomarkers Consortium, a public–private partnership dedicated to
developing biomarkers for general use, defines biomarkers as “characteristics
that are objectively measured and evaluated as indicators of normal
biological processes, pathogenic processes, or pharmacologic responses to
therapeutic intervention.”
105–107
Biomarkers may be submitted for formal FDA qualification, which helps
ensure that the assessment results are reproducible and consistent and
independent of who is performing the assessment or where the assessment is
done. Qualification usually involves the establishment of standard operating
procedures, calibration of the outcome measures, a training procedure, and, if
applicable, specifications for reagents and equipment. The Food and Drug
Administration Center for Drug Evaluation and Research Biomarker
Qualification Program (https://www.fda.gov/drugs/drug-development-toolqualification-programs/cder-biomarker-qualification-program) will provide
guidance on the qualification of candidate biomarkers. The process of
establishing the properties, utility, and validity of a biomarker is a structured
orderly one.
106
Once a biomarker is qualified, it may be a candidate for a surrogate
clinical trial endpoint. Validation of a surrogate endpoint requires specific
clinical studies where the direct measure of the clinical outcome is
statistically compared to values of the candidate biomarker. Changes in both a
positive and negative direction are correlated between the candidate
biomarker and the clinical outcome measure and interpreted in the context of
plausible biologic mechanisms and what is known about the causal pathway
of the intended clinical outcome. The validation process may not apply to all
populations, so should be accepted only for the population in which the
surrogate was studied and validated. This caveat is particularly relevant for
pediatric populations.
Operationally, a surrogate endpoint substitutes for another outcome
variable. The ideal surrogate endpoint is a disease marker that directly
reflects what is happening, both positively and negatively, with the underlying
disease. A surrogate endpoint, to be credible, must predict the benefit based
on scientific evidence. Usually a surrogate endpoint is a laboratory
measurement or an observation or event that serves as a substitute for direct
measure of a clinically meaningful endpoint. Some examples of surrogates are
blood glucose or hemoglobin A1c for diabetes, intraocular pressure for
glaucoma, and blood pressure for hypertension.
Surrogates are often employed as substitutes for efficacy variables but
may also serve as substitutes for safety variables. Among the reasons to use
surrogate endpoints in a study are that a clinical event may be difficult to
measure, a clinical event may have a low event rate, and it may be faster or
cheaper to measure a surrogate. The use of surrogates in an overall
development plan can accelerate the determination of benefit and provide
patients earlier access to therapy than waiting for a direct demonstration of
clinical benefit.
Presumptive surrogate markers can be misleading. Patients who have a
positive outcome based on the surrogate may not have true clinical benefit.
This can arise in several circumstances when the association of a surrogate
endpoint with clinical outcome may not be causal but is based on a statistical
correlation. Possibilities include alternate mechanisms or multiple pathways
for the pathophysiology and alternate or multiple pathways for the action of a
drug. A misleading surrogate assumes patient benefit yet exposes patients to
risk.
108
In addition, the safety of long-term exposure may not be adequately
assessed. Unexpected results relying on surrogates can occur in almost any
clinical setting, such as cardiology (flosequinan for the treatment of heart
failure [PROFILE study]; encainide, flecainide, and moricizine for the
treatment of arrhythmias in patients after a myocardial infarction [CAST
study]; milrinone for the treatment of heart failure [PROMISE Study]);
infectious diseases (interferon gamma for chronic granulomatous disease),
and metabolism (sodium fluoride for osteoporosis).
108
In the heart failure study, the surrogate endpoints were cardiac output and
ejection fraction; whereas the clinical endpoint was survival. The lack of
correlation between changes in the surrogates and survival could be due to
actions of the drug that are independent of the disease process, such as
postulated for flosequinan on survival in chronic heart failure.
109–112
A similar scenario may exist for arrhythmia studies, where the surrogate
endpoints were electrocardiographic readings, whereas the clinical endpoint
was survival.
113–117
For chronic granulomatous disease, the endpoint was
superoxide production and in vitro bacterial killing, whereas the clinical
endpoint was incidence of serious infection. The lack of correlation may be
due to the disease process having an effect on clinical outcome that is
independent of the pathway that the drug acts on and which contains the
surrogate.
118–120
The metabolism study with sodium fluoride for osteoporosis
used bone mineral density as a surrogate for the clinical outcome of fractures.
The lack of correlation could be due to the surrogate not being in the causal
pathway of the disease process.
121,122
The importance of having consistency and alignment is illustrated by
several studies supported by the U.S. NIH to evaluate the impact of inhaled
nitric oxide (iNO) on survival and pulmonary morbidities associated with
preterm birth. There was no effect of iNO on the composite primary endpoint,
death or chronic lung disease (CLD) of prematurity, in two of the large trials,
and a marginal improvement was seen in one trial.
123,124
Each trial had different enrollment criteria, including gestational age at
birth and postnatal age at randomization, dose, and duration of treatment.
International multicenter trials were also underway at the time of the NIHsupported studies, adding to the body of available data on this therapy.
Subsequent meta-analyses have examined the impact of iNO on death or CLD,
and the heterogeneity in trial designs is apparent.
125,126
An NIH consensus
panel considered all the available evidence and determined that the data did
not support routine use of iNO in premature infants to prevent or treat
pulmonary morbidities, although additional studies to define subgroups that
appeared to benefit were supported.
127
The experience of these trials and analyses emphasizes the need for wellcharacterized, precise, and accepted standards to minimize heterogeneity,
potential bias, and uncertainty. Without agreement on key protocol and data
elements, the return on the substantial investment of time, resources, and risk
is unlikely to enhance our knowledge or advance the field.
83
To summarize, understanding the characteristics of the outcome
assessments with regard to sensitivity, specificity, receiver-operator
characteristics, interclass correlation, validity, reproducibility, or any
additional relevant parameters is necessary to describe in detail the study
protocol and the study statistical analytic plan. These characteristics in turn
will inform the design, including the target enrollment, of the clinical trial.
Clinical trial protocols and statistical analytic plans should acknowledge
the phase of therapeutic development, whether it is early with a goal to better
understand the characteristics and effects of an intervention or whether it is a
later phase with a goal to refine the precision of prior knowledge to formally
establish benefit and risk in a consistent and generalizable manner for a
population of interest.
There are multiple statistical approaches to clinical trial analysis, with the
distinctions dependent on whether a normal, or parametric, distribution of
results is expected and whether prior information is incorporated.
All measurements have associated confidence intervals, which are
calculated from statistical tables. The smaller or narrower the confidence
intervals, the greater is the certainty of the result. This can be achieved
through either a large study population size or a large therapeutic effect. The
most difficult results to interpret are from a small population size with a small
effect. It is unlikely that the most informative high-quality design will be the
least resource intensive. A truism in clinical research, as it is for software
development, is “good, fast, cheap-pick any two.”
128
ADDITIONAL CONSIDERATIONS FOR
PEDIATRIC STUDY OUTCOMES
Pediatric clinical trials are easiest to implement and interpret when the
outcome measures are objective and do not require active patient
participation. Outcome measures that are physical signs may be sufficiently
precise and reproducible to substitute for symptom evaluation in some cases
(e.g., respiratory rate and presence, and extent of retractions for shortness of
breath). However, when objective outcomes are not available, trials with
subjective outcome measures or outcome measures that require active patient
participation may be the most feasible option.
Patient-reported outcomes can be direct or indirect, particularly in
younger children. Indirect or proxy reporting can be complex to design,
analyze, and interpret. Rigorous statistical analyses apply, although the
methods to analyze pediatric patient-reported outcomes may differ from the
analyses for other types of clinical trial endpoints.
Several variables can affect patient-reported outcome. Using pain as a
paradigm can be instructive. Pain is a combination of perception plus
sensation. Therapy can usually be effective, and there are multiple scales
available for assessment by the patient or an observer. A literature review
shows that age,
129–136
gender,
137–140
and type of instrument
130,131,141
are variables
that affect outcome in published studies. These reports collectively
demonstrate that particular variables can affect patient-reported outcome and
must be accounted for in the design and analysis of studies. Validation is
context and treatment specific.
EXTRAPOLATION OF EFFICACY
The goal in pediatric clinical studies is to follow Einstein’s dictum of making
things as simple as possible, but not simpler. In 1994 the FDA published a
Pediatric Rule that allowed extrapolation of adult efficacy to a pediatric
population if the course of the disease and the beneficial and adverse effects
of the drug are “sufficiently similar” in the pediatric and adult populations.
64
The goal was to reduce the barrier to pediatric labeling of products by
encouraging the use of borrowed data under appropriate circumstances to
eliminate the need for separate adequate and well-controlled studies in
children.
A 1996 FDA guidance document on the subject notes that the
determination of “sufficiently similar” will depend on numerous factors
including pathophysiology, natural history, drug action, and metabolism and
would be easier to conclude for brief or acute disorders than for chronic
disorders or those with a lengthy and variable history.65 More recent
explorations into factors that may provide a basis for extrapolation have
identified four domains that could provide supporting evidence:
nonclinical evidence
pathophysiology
natural history
response to therapy
Establishing a consistent framework for extrapolation is an ongoing area
of development and, if done effectively, could contribute to the sharing of data
among study populations that would diminish the resource burden for
conducting clinical trials.
142–148
Extrapolation is a subset of the more general
case of borrowing data from one or more sources and applying those data to a
new context or target population. The selection of the source data must align
with biologic plausibility and the quality and stringency of the collected data.
Some important principles related to extrapolation are:
the datasets for both the source population and the target population must
be of high quality and stringency
the outcome measures need to be validated in the populations of interest
as extrapolating data brings additional risks of imprecision and error
extrapolation of efficacy or benefit data is the usual paradigm as safety
data are likely to vary based on physiologic function and developmental
stage
The Center for Drug Evaluation and Research of the FDA published a
flowchart for pediatric extrapolation to guide the types of studies that could
be acceptable for drugs and the Center for Devices and Radiological Health
published a separate flowchart for devices.
142,144
The European Medicines
Agency published a reflection on extrapolation with its own flowchart.
146
The flowcharts all acknowledge adherence to the principle of linking the
biologic basis for extrapolation along with the age-appropriate outcome
measures to use. What is not included in any of the documents are technical
criteria to assess the data quality, limitations, and robustness of the source
dataset and the target dataset in a manner that is used in other contexts such as
meta-analyses or Cochrane evaluations.
149
PATIENT-REPORTED OUTCOMES
Patient-reported outcomes are descriptions of what happens to the patient
based on his or her own direct assessment, usually through answering a list of
standard questions or indicating a perception on some type of a scale. Patient-
reported outcomes can also be captured indirectly through a trained observer
completing the questions or scale on behalf of a study participant that lacks
capacity to do so directly. Examples include young children or people with
impairment of communication or understanding. Patient-reported outcomes
generally address the goals of how an individual feels or improvement in
function or improvement in symptoms.
From a scientific perspective, desirable properties of patient-reported
outcomes are that they be disease related, specifically validated for the
disease and population including improvement and worsening of clinically
meaningful changes, have real-time assessments (not based on recall), can
have confirmation by other assessments, and can be measured in controlled
studies.
Some examples are changes in pain or changes in symptoms that are
disease related and limit activity or function. Valid reproducible
measurements are still required to interpret the results. General advice about
the systematic collection of patient-reported outcomes is available from the
FDA in the form of a guidance document
(https://www.regulations.gov/docket?D=FDA-2006-D-0362).
The general principles are that a series of questions are structured during
an interview or administration of a questionnaire and organized according to
topic. Pediatric-specific aspects of patient-reported outcomes are discussed
later.
PROTOCOL CONSTRUCTION
The mechanics of implementing a study begins with writing a study protocol
consistent with the International Conference of Harmonization guidelines and
relevant regulations. A summary of applicable FDA regulations may be found
at https://www.fda.gov/science-research/clinical-trials-and-human-subjectprotection/preambles-gcp-regulations.
The general features of a study protocol are that it poses a question,
identifies a study population for which the question is relevant, proposes an
intervention, has safety monitoring and escape rules for individual patients,
assesses outcome based on meaningful and validated endpoints, utilizes
systematic and validated measurement techniques to assess the endpoints, and