Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5195_Библиотеки_им_академика_М_И_Перельмана.pdf
Study goals will depend upon the question being asked, the expectations for
the size of the effect, the available resources, and the feasibility of
implementation of the study design. Studies are often categorized by type
based on the goals.
Pharmacokinetic studies have a series of predefined parameters that
describe the fate of a drug and its metabolites at different doses in different
patient populations. Exposure response studies examine the relationship
between exposures to a product and physiologic or clinical events (both
beneficial and adverse) associated with its use. If pharmacodynamics are also
measured, then the study may be considered a
pharmacokinetic/pharmacodynamic study. Pharmacokinetic studies and
exposure response studies are generally considered exploratory. A case
where they may not be considered exploratory is when extrapolation of
efficacy is feasible between two populations and pharmacokinetic and
exposure response studies are used to extend the use of the product to the new
population.
Efficacy studies are by design adequate with regard to power and planned
analysis to demonstrate patient benefit and to assess risks. The results of
efficacy studies are usually expressed as a calculated number, often called the
point estimate, with associated confidence intervals. Confidence intervals are
by convention based on the 95% probability that the true result is within a
range between an upper and a lower limit.
Efficacy studies may be designed to demonstrate superiority to available
therapies or no inferiority. To demonstrate superiority, the 95% confidence
intervals of a therapy should not overlap with a comparator; that is, the lower
limit of one result must be greater than the upper limit of the comparator. All
studies have by implication a historical comparator, although historical
comparators can be difficult to determine and not appropriate for direct
comparison due to differences in study populations and standards of medical
care. The most persuasive and credible comparator is one that is measured
concurrently with the study regimen. Several design strategies exist to
minimize bias in assigning patients to comparative treatments.
To demonstrate equivalence would usually require large numbers of
patients and precise measurements. To conserve resources and maintain a
level of confidence in being able to substitute one product for another, a
noninferiority approach is employed. Noninferiority implies that the
difference in benefit and outcome between a standard therapy and the new
therapy is within a predefined and acceptable margin of effect. It requires that
the standard have an effect that is measurable, clinically meaningful, and
reproducible.
There are several approaches to setting the margin of acceptable
difference and analyzing the results of a study. As an example, it may be
considered acceptable to preserve at least 80% of the effect of the standard
therapy or have a margin of 20%. A caution is that if serial studies use
different standards, the efficacy effect could drift down. To be specific, if a
new product preserves 80% of the effect of a standard and the next new
product preserves 80% of the effect of the first new product, the result is a
reservation of about 60% of the original standard. Safety studies are intended
to demonstrate the relationship between exposure to a product and adverse
events associated with its use. All clinical studies are in one form or another
safety studies.
Vaccine studies enlarged in scope in the late 1990s and early 2000s due to
the realization that the study population should have sufficient exposures to the
candidate vaccine to detect rare but medically significant events.
In general, the sensitivity of signal detection is based on and expressed in
logarithmic or semi-logarithmic scales. For example, if the typical range is
based on logarithmic 10, then semi-logarithmic factors would be based on the
square root of 10. To an approximation, that number is 3, so a semilogarithmic scale would have sample sizes that correspond to 1, 3, 10, 30,
100, 300, 1,000, 3,000,10,000, 30,000, 100,000, etc.
To provide the public assurance that a vaccine is safe, with the
assumption that millions of people will be administered the product, the
initial studies are sized to detect rare but medically significant events. Thus, a
vaccine safety study may be on the order of magnitude of 30,000 to 60,000+
participants in order to detect such events with a frequency of <0.1%.
Vaccine efficacy can be based on three general types of outcomes. They
are:
use of a biomarker or surrogate such as immune response like generation
of specific antibodies above a specific level
use of randomized controlled trials with a precise case definition of the
condition of interest and comparison of the rates that meet the definition
TABLE 1.3
use of animals to demonstrate efficacy for a rare or life-threatening
condition and then applying extrapolation of a relevant biomarker to the
human population
Since the 1950s clinical studies have been classified into phases based on
this study. Initial drug dose finding and safety studies have been termed Phase
1. Exploratory studies to determine biologic or clinical activity of a drug have
been termed Phase 2. Confirmatory studies to compare an investigational
regimen with an established regimen that are powered to establish efficacy
have been termed Phase 3. Studies that have been requested by the FDA to
comply with postmarketing commitments following approval of a claim for
marketing exclusivity have been termed Phase 4.
Alternative nomenclature such as learning phase and confirming study
94,103
or initial exposure phase, development phase, and validation phase may also
be acceptable as shown in Table 1.3.
Types of Clinical Trial Goals
Type Comment
Superiority The test treatment is better than a comparator. The confidence
intervals around the measurement for the test treatment and for the
comparator should not overlap. For example, if the standard treatment
shows that median survival for a population is 22 mo and the
confidence intervals are ±3 mo, then the test treatment must have a
lower confidence interval that is greater than 25 mo (22 + 3) to be
considered superior. Results of 29 ± 3 mo or 28 ± 2 mo would qualify.
Noninferiority The test treatment is not worse than a comparator. Exact equivalence
is difficult to prove requiring large study populations and precise
measures. The usual approach is to consider that a treatment is not
worse than an accepted treatment by direct comparison with the
understanding that:
1. The effect of the accepted treatment is measurable, reproducible,
and meaningful. An acceptable difference between the accepted
treatment and the new treatment is defined prior to beginning the
study and is smaller than the total effect of the accepted
treatment. For example, if the accepted treatment increases
median survival by 6 mo and the acceptable difference is 1 mo,
then the new treatment in direct comparison to the accepted
treatment must not differ by more than 1 mo and the accepted
treatment must have a median survival that is consistent with
previous results.
Exploratory A study to examine biologic or clinical activity but not designed to
establish efficacy.
STUDY POPULATION SELECTION
As noted previously, the FDA held public hearings and issued guidance in the
early 2000s to clarify that children that are considered healthy should not be
enrolled in clinical trials that exceed minimal risk; however, children that
have a disease or condition that compromises their health or are at risk for the
development of such a disease or condition may be enrolled in clinical trials
that offer no direct benefit and are a minor increase over minimal risk. The
perspective both clarifies what populations are appropriate for interventional
studies and provides an ethical and regulatory framework for preventive or
prophylactic studies such as vaccine inoculation.
Study population selection is based on the Belmont Report principles of
beneficence, justice, and respect for persons. All three principles apply, and
implementation decisions are rarely limited to a single criterion. For example,
integration of the principles in a balanced manner supports enrollment of
children on the basis of justice and respect for persons, yet limits enrollment
to children with or at high risk of a disease or condition on the basis of
beneficence and respect for persons.
While the specific target population may not be the only population
enrolled, both justification for including others and the formal mechanism to
apply the clinical trial results to the relevant populations are part of the
planning and implementation process.
The principle of justice is nuanced using the examples of enrollment and
subsequent analysis. For enrollment, the demographics of the enrolled
population should reflect the demographics of the future population to which
the analytic inferences will apply. For enrollment purposes, not only should a
broad and diverse population that meet the health-related phenotypic
characteristics of the target populations be included, but it may be necessary
to actively initiate measures to remove barriers to enrollment. Selection based
on race typically utilizes the U.S. Census Bureau classification as declared by
the study participant or, most often in the case of children, declaration of the
mother of the child. However, analyses based on race can introduce
complexities related to the variability of biologic and demographic factors
within the standard classifications of race, which show as much or greater
differences within a race as between races. Thus, analyses should reflect the
biologic and demographic characteristics of the individual. If the selection
process for a study included a broad and diverse population, then the highlevel aggregate inferences from the study results should also apply to a broad
and diverse population.
Age and, perhaps more critically, developmental stage are additional
factors to include in eligibility criteria if an intervention is likely to be widely
used in a defined population, such as neonates or premature infants. Although
efforts to include children in studies of regulated products have resulted in
enhanced activity and label changes, the youngest and least mature children
have received disproportionately fewer benefits. Consequently, recent
legislation, regulatory initiatives, and international cooperation generated
additional focus and resources to include neonates and premature infants.
OUTCOME MEASURES—GENERAL
CONSIDERATIONS
Outcome assessments, sometimes called endpoints, are the measures taken to
determine benefit or risk in a clinical trial. If the goal of a clinical trial is to
evaluate an intervention, then the evaluation is expected to be based on
reproducible objective results. It is the reproducibility and objectivity that
make the results of a clinical trial generalizable. Therefore, the selection of
outcome assessments should be based on the characteristics of objectivity,
reproducibility, and, whenever feasible, a quantity. In some cases, the quantity
can be continuous over a known range while in other cases the quantity may
place the outcome into a category such as mild, moderate, or substantial.
The general concept of benefit is that people with a particular phenotype,
more commonly referred to as a condition or a status, are through an
intervention able to alter the natural history of the condition of interest and
either live longer or live better. Sometimes living better is further subdivided
into functioning better or feeling better, but for the topic of outcome
assessments, the principles and practice are the same.
Living longer is objective and quantifiable, but unless the condition under
study is rapidly progressive and fatal, survival often is not practical to
measure in the time frame and context of most clinical trials.
The objective, quantifiable assessment of living better can be challenging
and is often performed using indirect measures such as structured
questionnaires, functional tests, images, or other modalities. Thus, benefit, in
general, is inferred from indirect measures and both the objective and
subjective perception of living better can be relatively imprecise, but still
acceptable. For example, hearing function is often assessed through an
audiogram using pure tones, but the results of the audiogram may not be
informative regarding the ability to understand table conversation in a
crowded restaurant or enjoy a piece of music.
All outcome assessments have characteristics or properties that provide
some information about the precision of the measure. Laboratory or functional
assessments usually have a range over which the measurements are
considered accurate and predictable. In other words, changes in values over
the informative range will reflect improvement or deterioration for some
physiologic function or anatomic structure. The properties usually linked to an
assessment are the sensitivity, meaning the ability to detect true positives, the
specificity, meaning the ability to reject false positives, and the receiver
operating characteristics, meaning how much input produces how much signal
across the range. The properties for survey questions and other testing relying
on communication skills regarding feelings or perceptions have other
measures such as interclass correlation, reliability, and validity for the
specific populations that were tested. Generally, survey instruments are
validated as a whole, and taking sections out of the whole or changing the
sequence will require new validation studies in the population of interest.
The characteristics of individual assessments can be a guide to the type
and size of clinical trial. An outcome assessment with high precision that has
been validated specifically in the population of interest will provide the best
results for constructing analytic datasets and interpreting the overall trial
outcome. In general, the larger the effect size coupled with high
reproducibility, the smaller the clinical trial needs to be to provide the
requisite data. The smaller the effect size and the lower the reproducibility or
consistency, the larger the clinical trial needs to be to have confidence in the
results.
If a clinical trial is at a preliminary phase of the evaluation of the
intervention, that is, there is little prior knowledge and the intervention is new
or innovative, then it is necessary to establish the preliminary effect size. The
effect size can have at least two components—one for benefit and one for
safety or tolerability. Additional measures can be taken to evaluate various
types of benefit measures and various types of safety or tolerability measures.
An early phase study will typically vary the exposure of an individual to the
intervention to determine if there are changes in the benefit or safety with
different exposures and identify, if possible, an optimal exposure that
provides the greatest benefit with acceptable risk. To achieve that goal may
require more than one study.
If a clinical trial is at a more advanced phase of the evaluation of an
intervention, that is, there is prior experience using the intervention of interest
in the population with the phenotype of interest, then the goals will shift from
preliminary characterization of the effects of the intervention to capturing
enough data to reliably predict the benefit and risks using some of the same
outcome assessments as used previously. In addition, the introduction of new
supplemental outcome assessments may be of value for purposes of either
Initial characterization of the supplemental outcome assessment in the
population of interest exposed to the intervention of interest or
Establishing consistency in the benefit and safety measures using
different outcome assessments to broaden understanding of the overall
effects and to different dimensions of those effects
Later phase clinical trials that are intended to formally and quantitatively
establish benefit and risk, sometimes known as efficacy studies, must be
designed to provide greater precision and reliability on the initial estimated
effect size of the outcome assessment of primary interest. Introducing a new
outcome assessment that has not been at least initially validated in the
population of interest with the intervention of interest will complicate the
design and interpretation because there are no prior data to guide either the
design or the analysis.
Evaluating an intervention is a systematic objective process where all the
clinical trials exploring the use of the intervention will contribute to a general
TABLE 1.4
body of knowledge about both the intervention and the population of interest.
Thus, components of prior studies must be included in any new study to
properly calibrate and interpret the results.
A trial may have more than one outcome measure, but as the number of
outcome measures increases, the complexity of the study and the analysis
increases. Clinical outcomes that directly demonstrate patient benefit such as
improvement in survival, improvement of functioning, improvement of
symptoms, or delay of disease progression are generally preferable because
interpretation is simplest.
Tables 1.4 and 1.5 list endpoints based on their relationship to time. If
time is variable (Table 1.4), then the outcome that is being measured is
usually expressed in units of time and duration. In general, the longer the
therapeutic effect or benefit, the more favorable is the outcome. Another way
to express the concept is time to event, where the event is either the duration
of benefit or the appearance of an unfavorable outcome.
Types of Time-De pendent Endpoints
Time-Dependent or
Variable Time Expressed
as Units of Time (Usually
the Median Time to an
Event for a Population)
Population Comment
Pharmacokinetics All patients A series of parameters that describe the absorption,
distribution, metabolism, and elimination of a drug as a
function of time and exposure.
Overall survival All patients Typically time between study entry and death.
Measurement is usually unambiguous. Cause of death
may be difficult to determine. Effective therapies can
result in long follow-up times for completing studies.
Progression-free
survival/time to progression
All patients
that
progress
Typically, time between study entry and first date of
disease progression with death being considered as
progression. The parameters for progression must be
reliably defined and the assay validated—may be
symptom based, imaging study, biomarker, or patientreported outcome.
Disease-free survival Only Parameter for progression must be reliably defined
complete
responders
and assay validated—may be symptom based, imaging
study, biomarker, or patient-reported outcome.
Time to treatment failure All patients
that change
treatment
Treatment failure must be reliably defined and may
include disease progression or unacceptable toxicity.
Unacceptable toxicity can be a highly individual
assessment and treatment failure can be due to
multiple factors, not all of which are objective. Time to
treatment failure is particularly difficult to interpret.
Duration of response Only
responders
Typically, time between first date of response and first
date of disease progression. Response is usually
defined as having a minimum duration (typically 4 wk)
to be considered a response.
Time to response Only
responders
Typically, time between date of study entry and first
date of response. Response is usually defined as
having a minimum duration (typically 4 wk) to be
considered a response.
Examples include overall survival, time to disease progression, and
duration of favorable response (Figs. 1.5 and 1.6).
Figure 1.5 Typical definitions of some time-dependent endpoints.