U.S. patent application number 12/817458 was filed with the patent office on 2011-06-30 for system and method for continuous data analysis of an ongoing clinical trial.
This patent application is currently assigned to MEDIDATA SOLUTIONS, INC.. Invention is credited to Glen M. DeVries, Edwin Goodman, Edward F. Ikeguchi, Tarek A. Sherif.
Application Number | 20110161101 12/817458 |
Document ID | / |
Family ID | 34393396 |
Filed Date | 2011-06-30 |
United States Patent
Application |
20110161101 |
Kind Code |
A1 |
Ikeguchi; Edward F. ; et
al. |
June 30, 2011 |
SYSTEM AND METHOD FOR CONTINUOUS DATA ANALYSIS OF AN ONGOING
CLINICAL TRIAL
Abstract
System and method of continuously analyzing trial data of an
ongoing clinical trial is provided. A statistical analysis is
performed on a trial database containing subject trial data without
suspending the ongoing clinical trial. If the result of the
statistical analysis does not exceed a predetermined threshold
value, then the statistical analysis is repeated while the clinical
trial is ongoing. In a blinded clinical trial, a grouped database
is generated from the trial database and a blinding database prior
to performing the statistical analysis. The grouped database groups
the subject trial data according to the study groups. The ability
to continuously monitor and analyze the trial data for statistical
significance in tandem with data collection while the trial is
ongoing provides many benefits to the researchers because the trial
database no longer becomes the bottleneck in obtaining useful
results and statistical analysis can be conducted on a near
real-time basis without having to wait until completion of the
trial.
Inventors: |
Ikeguchi; Edward F.;
(Larchmont, NY) ; DeVries; Glen M.; (New York,
NY) ; Sherif; Tarek A.; (New York, NY) ;
Goodman; Edwin; (New York, NY) |
Assignee: |
MEDIDATA SOLUTIONS, INC.
New York
NY
|
Family ID: |
34393396 |
Appl. No.: |
12/817458 |
Filed: |
June 17, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11810483 |
Jun 6, 2007 |
7752057 |
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12817458 |
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10667848 |
Sep 22, 2003 |
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11810483 |
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Current U.S.
Class: |
705/2 |
Current CPC
Class: |
G16H 10/20 20180101;
G06Q 10/10 20130101; G16H 50/70 20180101; G16H 70/20 20180101 |
Class at
Publication: |
705/2 |
International
Class: |
G06Q 50/00 20060101
G06Q050/00; G06Q 10/00 20060101 G06Q010/00 |
Claims
1. A computer readable storage medium comprising computer
executable instructions that, when executed by a processor, cause
the processer to: calculate, during at least one time interval of
an ongoing clinical trial, a statistical term of interest
associated with a parameter in trial data of at least one arm of
the ongoing clinical trial; and determine, in near-real time,
whether the calculated statistical term of interest at the at least
one time interval indicates the occurrence of a statistically
significant event in the ongoing clinical trial.
2. The computer readable storage medium of claim 1, further
comprising instructions that, when executed by the processor cause
the processor to iteratively calculate and determine based on
whether the statistically significant event has occurred.
3. The computer readable storage medium of claim 1, further
comprising instructions that, when executed by the processor, cause
the processor to retrieve the trial data for an ongoing clinical
trial from a plurality of records maintained in at least one data
storage device.
4. The computer readable storage medium of claim 3, further
comprising instructions that, when executed by the processor cause
the processor to retrieve the plurality of records having a
predetermined level of cleanliness that satisfies a user defined
cleanliness criteria.
5. The computer readable storage medium of claim 3, wherein the
plurality of records include at least two of a subject identifier,
an arm identifier, a value associated with said parameter, and a
cleanliness value.
6. The computer readable storage medium of claim 3, further
comprising computer executable instructions that, when executed by
a processor, cause the processer to unblind the retrieved trial
data; and group the retrieved trial data into a plurality of
arms.
7. The computer readable storage medium of claim 6, wherein said
determined statistical term of interest is determined by comparing
a first parameter of a first arm to a second parameter of a second
arm of the ongoing clinical trial.
8. The computer readable storage medium of claim 1, wherein said
statistically significant event represents one of efficacy of the
ongoing clinical trial, safety of the ongoing clinical trial, and
level of participation in said at least one arm of the ongoing
clinical trial.
9. The computer readable storage medium of claim 1, wherein
instructions to determine further cause the processor to determine
a rate of change in said determined statistical term of interest
between at least two time intervals during the ongoing clinical
trial.
10. The computer readable storage medium of claim 1, further
comprising instructions that, when executed by the processor cause
the processor to generate an alert based on the determination that
said statistically significant event has occurred during the
ongoing clinical trial.
11. A system to analyze trial data of a multi-arm study in an
ongoing clinical trial, comprising: a processing device configured
to: calculate, during at least one time interval of the ongoing
clinical trial, a statistical term of interest associated with a
parameter in trial data of at least one arm of the ongoing clinical
trial; and determine, in near-real time, whether the calculated
statistical term of interest at the at least one time interval
indicates the occurrence of a statistically significant event in
the ongoing clinical trial.
12. The system of claim 11, wherein the system is further
configured to iteratively calculate and determine based on whether
the statistically significant event has occurred.
13. The system of claim 11, wherein the processor is further
configured to retrieve the trial data for an ongoing clinical trial
from a plurality of records maintained in at least one storage
device.
14. The system of claim 13, wherein the processor is further
configured to retrieve the plurality of records having a
predetermined level of cleanliness that satisfies a user defined
cleanliness criteria.
15. The system of claim 13, wherein the plurality of records
include at least two of a subject identifier, an arm identifier, a
value associated with said parameter, and a cleanliness value.
16. The system of claim 13, wherein the processer is further
configured to unblind the retrieved trial data; and group the
retrieved trial data into a plurality of arms.
17. The system of claim 16, wherein said determined statistical
term of interest is determined by comparing a first parameter of a
first arm to a second parameter of a second arm of the ongoing
clinical trial.
18. The system of claim 11, wherein said statistically significant
event represents one of efficacy of the ongoing clinical trial,
safety of the ongoing clinical trial, and level of participation in
said at least one arm of the ongoing clinical trial.
19. The system of claim 11, wherein the processor is further
configured to determine a rate of change in said determined
statistical term of interest between at least two time intervals
during the ongoing clinical trial.
20. The system of claim 11, wherein further the processor is
further configured to generate an alert based on the determination
that said statistically significant event has occurred during the
ongoing clinical trial.
21. A method comprising: calculating in a computing device, during
at least one time interval of the ongoing clinical trial, a
statistical term of interest associated with a parameter in trial
data of at least one arm of the ongoing clinical trial; and
determining in the computing device, in near-real time, whether the
calculated statistical term of interest during the at least one
time interval indicates the occurrence of a statistically
significant event in the ongoing clinical trial.
22. The method of claim 21, further comprising iteratively
calculating and determining based on whether the statistically
significant event has occurred.
23. The method of claim 21, further comprising retrieving the trial
data of the ongoing clinical trial from trial records maintained in
at least one data storage device.
24. The method of claim 23, wherein retrieving the trial data
includes retrieving a portion of the trial records having a level
of cleanliness that satisfies a predetermined criterion.
25. The method of claim 23, wherein a trial record includes at
least two of a subject identifier, an arm identifier, a parameter
value, and a cleanliness value.
26. The method of claim 21, further comprising: unblinding the
trial data; and grouping the unblinded trial data into a plurality
of arms.
27. The method of claim 26, wherein said determining the statically
term of interest includes comparing a first parameter of a first
arm to a second parameter of a second arm of the ongoing clinical
trial.
28. The method of claim 21, wherein said statistically significant
event represents one of efficacy of the ongoing clinical trial,
safety of the ongoing clinical trial, and level of participation in
said at least one arm of the ongoing clinical trial.
29. The method of claim 21, further comprising determining a rate
of change in said determined statistical term of interest between
at least two time intervals during the ongoing clinical trial.
30. The method of claim 21, further comprising generating an alert
based on the determination that said statistically significant
event has occurred during the ongoing clinical trial.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation application of U.S.
application Ser. No. 11/810,483 filed on Jun. 6, 2007, which is a
continuation-in-part of U.S. application Ser. No. 10/667,848 filed
Sep. 22, 2003, now abandoned, which is incorporated herein by
reference in its entirety.
TECHNICAL FIELD OF THE INVENTION
[0002] This application relates to data processing of clinical
trial data and more specifically a system and method for
statistically analyzing the clinical trial data.
BACKGROUND OF THE INVENTION
[0003] In the United States, the Food and Drug Administration (FDA)
oversees the protection of consumers exposed to health-related
products ranging from food, cosmetics, drugs, gene therapies, and
medical devices. Under the FDA guidance, clinical trials are
performed to test the safety and efficacy of new drugs, medical
devices or other treatments to ultimately ascertain whether or not
a new medical therapy is appropriate for widespread human
consumption.
[0004] More specifically, once a new drug or medical device has
undergone studies in animals, and results appear favorable, it can
be studied in humans. Before human testing is begun, findings of
animal studies are reported to the FDA to obtain approval to do so.
This report to the FDA is called an application for an
Investigational New Drug (IND).
[0005] The process of experimentation is referred to as a clinical
trial, which involves four phases. In Phase I, a few research
participants, referred to as subjects, (approximately 5 to 10) are
used to determine toxicity of a new treatment. In Phase II, more
subjects (10-20) are used to determine efficacy and further
ascertain safety. Doses are stratified to try to gain information
about the optimal portion. A treatment may be compared to either a
placebo or another existing therapy. In Phase III, efficacy is
determined. For this phase, more subjects on the order of hundreds
to thousands of patients are needed to perform a meaningful
statistical analysis. A treatment may be compared to either a
placebo or another existing therapy. In Phase IV (post-approval
study), the treatment has already been approved by the FDA, but
more testing is performed to evaluate long-term effects and to
evaluate other indications.
[0006] During clinical trials, patients are seen at medical clinics
and asked to participate in a clinical research project by their
doctor, known as an investigator. After the patients sign an
informed consent form, they are considered enrolled in the study,
and are subsequently referred to as study subjects. A study
sponsor, generally considered to be the company developing a new
medical treatment and supporting the research, develops a study
protocol. The study protocol is a document describing the reason
for the experiment, the rationale for the number of subjects
required, the methods used to study the subjects, and any other
guidelines or rules for how the study is to be conducted. Prior to
usage, the study protocol is reviewed and approved by an
Institutional Review Board (IRB). An IRB serves as a peer review
group, which evaluates a protocol to determine its scientific
soundness and ethics for the protection of the subjects and
investigator.
[0007] Subjects enrolled in a clinical study are stratified into
groups that allow data to be assessed in a comparative fashion. In
a common example, one study arm, known as a control group (or
"control"), will use a placebo, whereby a pill containing no active
chemical ingredient is administered. In doing so, comparisons can
be made between subjects receiving actual medication versus
placebo.
[0008] Subjects enrolled into a clinical study are assigned to a
study arm in a random fashion, which is done to avoid biases that
may occur in the selection of subjects for a trial. For example, a
subject who is a particularly good candidate to respond to a new
medication might be intentionally entered into the study arm to
receive real medication and not a placebo. This could skew the data
and outcome of the clinical trial to favor the medication under
study, by the selection of subjects who are most likely to perform
well with the medication. In instances where only one study group
is present, randomization is not performed.
[0009] Blinding is a process by which the study arm assignment for
subjects in a clinical trial is not revealed to the subject (single
blind) or to both the subject and the investigator (double blind).
This minimizes the risk of data bias. Virtually all randomized
trials are blinded by definition. In instances where only one study
group is present, blinding is not performed.
[0010] Generally, at the end of the trial, the database containing
the completed trial data is shipped to a statistician for analysis.
If particular occurrences, such as adverse events, are seen with an
incidence that is greater in one group over another such that it
exceeds the likelihood of pure chance alone, then it can be stated
that statistical significance has been reached. Using statistical
calculations, the comparative incidence of any given occurrence
between groups can be described by a numeric value, referred to as
a "p-value". A p-value of 1.0 indicates that there is a 100%
likelihood that an incident occurred as the result of chance alone.
Conversely, a p-value of 0.0 indicates that there is a 0%
likelihood that an incident occurred as a result of chance alone.
Generally, values of p<0.05 are considered to be "statistically
significant", and values of p<0.01 are considered "highly
statistically significant".
[0011] In some clinical trials, multiple study arms, or even a
control group, may not be utilized. In such cases, only a single
study group exists with all subjects receiving the same treatment.
This is typically performed when historical data about the medical
treatment, or a competing treatment is already known from prior
clinical trials, and may be utilized for the purpose of making
comparisons.
[0012] The creation of study arms, randomization, and blinding are
techniques that are used in most clinical trials where scientific
rigor is of high importance. However, these methods lead to several
challenges, since they prevent the clinical trial sponsor from
tracking key information related to safety and efficacy.
[0013] Regarding safety, the objective of any clinical trial is to
document the safety of a new treatment. However, in clinical trials
where randomization is conducted between two or more study arms,
this can be determined only as a result of analyzing and comparing
the safety parameters of one study group to another. Unfortunately,
because the study arm assignments are blinded, there is no way to
separate out subjects and their data into corresponding groups for
purposes of performing comparisons while the trial is being
conducted. Since many clinical trials may last for time periods
extending for years, it is conceivable to have a treatment toxicity
go unnoticed for prolonged periods without intervention.
[0014] Regarding efficacy, any clinical trial seeking to document
efficacy will incorporate key variables that are followed during
the course of the trial to draw the desired conclusion. In
addition, studies will define certain outcomes, or endpoints, at
which point a study subject is considered to have completed the
protocol. These parameters, including both key variables and study
endpoints, cannot be analyzed by comparison between study arms
while the subjects are randomized and blinded. This poses potential
problems in ethics and statistical analysis.
[0015] When new medications or other health-related treatments are
of superior efficacy to anything else, it is ethical to allow usage
of the treatment for those in imminent need, even prior to final
government approval. Conversely, when available, it is considered
unethical to withhold such treatments. For example, if a medication
were to be identified that eradicated the Human Immunodeficiency
Virus (HIV), it would be unethical to allow diseased patients to
continue suffering and even die of the illness, while the
medication was being clinically tested for purposes of government
approval. Ideally, in such situations, identification of effective
treatments should occur early in the project. Under these
circumstances, non-treatment arms (i.e., those taking placebos)
could be construed as unethical and should be eliminated. At
present, when clinical trials are randomized and blinded,
identification of a particularly effective treatment may not be
realized until the entire clinical trial is completed.
[0016] Another related problem is statistical power. By definition,
statistical power refers to the probability of a test appropriately
rejecting the null hypothesis, or the chance of an experiment's
outcome being the result of chance alone. Clinical research
protocols are engineered to prove a certain hypothesis about a
medical treatment's safety and efficacy, and disprove the null
hypothesis. To do so, statistical power is required, which can be
achieved by obtaining a large enough sample size of subjects in
each study arm. When too few subjects are enrolled into the study
arms, there is the risk of the study not accruing enough subjects
to enable the null hypothesis to be rejected, and thus not reaching
statistical significance. Because clinical trials that are
randomized are blinded, the actual number of subjects distributed
throughout study arms is not defined until the end of the project.
Although this maintains data collection integrity, there are
inherent inefficiencies in the system, regardless of the
outcome.
[0017] In a case where the study data reaches statistical
significance, as accrual of subjects continues, and data is
received, an optimal time to close a clinical study would be at the
very moment when statistical significance is achieved. While that
moment may arrive earlier in the course of a clinical trial, there
is no way of knowing this, and therefore time and money are lost.
Moreover, study subjects are enrolled above and beyond what is
needed to reach the goals of the study, thus placing human subjects
under experimentation unnecessarily.
[0018] In a case where the study data nearly reaches statistical
significance, while the study data falls short of statistical
significance, there is reason to believe that this is due to a
shortage of enrollment in the study. Frequently, to develop more
supportive data, clinical trials will be extended. These "extension
studies", however, can only begin after a full closure of the
parent study, frequently requiring months to years before starting
again.
[0019] In a case where the study data does not reach statistical
significance, there is no trend toward significance, and there is
little chance of reaching the desired conclusion. In that case, an
optimal time to close a study is as early as possible once the
conclusion can be established that the treatment under
investigation does not work, and study data has little chance of
reaching statistical significance (i.e., it is futile). In
randomized and blinded clinical trials, this conclusion is
difficult to arrive at until data analysis can be conducted. In
these situations, time and money are lost. Moreover, an excess of
human subjects are placed under study unnecessarily.
[0020] To mitigate some of the risks related to the conduct of
randomized and blinded clinical trials, a Data Safety Monitoring
Board (DSMB) may be formed at the beginning of each protocol. In
general, a DSMB is recommended for clinical trials that involve a
potentially serious outcome (e.g., death, heart attack, etc.), are
randomized and blinded, and extend for prolonged periods of time.
In addition, a DSMB is required for trials that are sponsored by
the United States government, namely, the National Institute of
Health (NIH).
[0021] A DSMB generally consists of members who are domain experts
in the field of study, such as physicians, as well as
bio-statisticians. It is important that DSMB members be separate
from personnel of the sponsor organization, and financial
disclosure for all members is performed to minimize conflicts of
interest. Prior to start of a clinical trial, standard operating
procedures are established for the DSMB, including the frequency of
meetings, initiation of interim analyses, conduct during interim
analyses and criteria for discontinuation of the clinical trial. As
it relates to the safety of study subjects, DSMB functions to
examine trends of adverse occurrences rather than investigate
specific reports, which are generally left to each IRB responsible
for the activities of any given investigator. That is, DSMB
receives only a snapshot data of a clinical trial and not a
continuous analysis of trial data as with the present invention.
Additionally, if dangerous conditions/events (e.g., deaths of study
patients) are detected then the clinical trial must be
suspended/interrupted to perform data analysis of the clinical
trial. Further, DSMB cannot determine whether such dangerous
conditions exist with the control group taking the placebo or the
study group taking the drug under study without suspending the
clinical trial. That is, the snapshot data is not sufficient for
DSMB to determine the cause of the dangerous condition.
Accordingly, DSMB's specificity and sensitivity of detecting
dangerous condition is very low because it cannot determine whether
the dangerous condition is related to the drug under study.
Therefore the present invention proceeds upon the desirability of
resolving this problem by increasing the sensitivity to such
dangerous conditions by performing continuous data analysis without
interrupting the clinical trial.
[0022] A typical method of collecting and analyzing patient data is
illustrated in the flow chart shown in FIG. 1. Patient data or
charts 10 from the clinical trial are collected manually in paper
forms. Using a technology called Electronic Data Capture (EDC) or
Remote Data Entry (RDE), a computer (not shown) displays a Case
Report Form (CRF) to a clinical research coordinator (CRC) 12,
typically a nurse or doctor. The CRC 12 then enters the patient
data 10 through the computer display which is received in block 14
by an EDC system which executes all of the steps included in a box
11. The received data is stored in a clinical trial database 38
through a link 20 which can be an electronic link such as a
telephone line or Internet link. In block 18, it is determined
whether the data inputted by the CRC 12 is clean using one or more
rules. The rules may be implemented by simple range checking
scripts, or by an inference rule engine or deterministic rule
engine in order to identify potential problems with the data.
[0023] In addition to the software programs, block 18 may also
involve research personnel known as monitors or Clinical Research
Associates (CRA) who travels to the various research sites to
perform source document verification (SDV) whereby the data in the
database 38 is reconciled against individual patient charts to the
degree required in the protocol.
[0024] If it is determined that the data entered is not clean, then
block 22 generates a query which is then sent over the link 20 to
the CRC 12. The blocks 14, 18 and 22 are repeated until all of the
subject data 10 are entered. This is an iterative process that
continues until resolution of all queries in the database 38.
[0025] Once all data 10 are entered, block 24 determines whether
the clinical trial is over. If no, then the EDC system continues to
receive the patient trial data 10 through block 14 as the trial
continues. If the trial is over, control passes to block 26 where
the entire database is locked from any changes, deletions or
insertions of the data in the database 38. In one embodiment,
locking involves turning the database 38 into a "read-only"
state.
[0026] In block 28, a blinding data from a blinding database is
retrieved. A simplified example blinding database 40 is shown in
FIG. 4. The blinding database 40 is a database table having two
columns. The first column contains a patient subject ID (subject
identifier) and the second column contains an associated study arm
or group the patient belongs to. In the table 40, 13 subjects
belong to Study Arm "A" and 12 subjects belong to Study Arm "B".
Because the database 40 is not associated with actual trial data,
the table 40 by itself is relatively uninformative.
[0027] A simplified example trial database 38 is shown in FIG. 5.
The embodiment shown is a database table containing two columns.
The first column contains a patient subject ID and the second
column is a database field called "Heart Attack" which specifies
whether the subject had a heart attack. An entry of 0 means NO and
entry of 1 means YES. As can be seen from the trial database 38,
due to blinding of the subjects in the study groups, there is no
way of knowing whether or not any discrepancy exists in the number
of heart attacks seen in Group A versus B. Because the trial is
randomized, without the blinding data 40, the table 38 by itself is
relatively uninformative.
[0028] In block 28, an unblinded database is produced from the
trial database 38 and the retrieved blinding database 40 in which
the subject ID is used as a common key. The result of the
unblinding process of block 28 is shown in FIG. 6 as the unblinded
database 41. In the embodiment shown, one database table is
produced. The table 41 contains subject identifiers, Study Arm of
the subjects, and Heart Attack data of those subjects. As can be
appreciated by a person of ordinary skill in the art, there is a
direct traceability from study data and subject ID to Study
Arm.
[0029] In block 30, statistical analysis is performed on the
unblinded data 42 to find out the efficacy and safety of the
completed clinical trial.
[0030] During the course of any given randomized and blinded
clinical trial, an interim analysis may be conducted. An interim
analysis may result from urging of the DSMB for cause, or be a
pre-planned event as described in the study protocol.
[0031] Conducting an interim analysis involves a process where the
available data is verified and cleaned. The clinical trial is
typically interrupted or suspended to enable the available data to
be verified and cleaned. The verification process generally
involves a process by which trained personnel travel to the various
research sites to reconcile submitted data against source
documents, which generally implies the patient's chart, laboratory
reports, radiographic readings, and others. The data cleaning
process may involve a series of documented communications between
the research site and a central data coordinating personnel to
resolve inconsistencies or other conflicting data.
[0032] The refined database must then be sent to an impartial third
party for statistical analysis. To conduct the analysis, the
statistician must un-blind the clinical trial database by combining
both the study data with the blinding key of which subjects are
assigned to particular study arms. Since the clinical study is
expected to continue beyond the interim analysis, the process of
un-blinding must be conducted with great caution, so as not to
reveal the blind status of subjects to any personnel involved in
the execution of the clinical trial. Once a statistician has
completed the interim analysis, a report is issued to the trial
sponsor and DSMB.
[0033] Inclusive of the data cleaning, verification, un-blinding
and statistical analysis processes, as well as the administrative
resources for coordinating several groups of personnel for the
un-blinding process, an interim analysis is often arduous,
time-consuming and expensive.
[0034] In spite of the latest technological advancements made in
the area of data collection through electronic systems, there is
still a disadvantage in that it is very difficult to draw
conclusions about a medical treatment while the data is being
collected during the trial. This limitation stems primarily from
the fact that statistical analysis cannot begin until the trial
data has been fully cleaned and processed. At present, statistical
analysis can only be conducted upon data in an "en bloc" fashion.
This creates a situation where the ability to draw conclusions
about a medical therapy inevitably lags behind the process of
simply obtaining data in a database.
[0035] Regardless of how efficient the data collection process may
be made through automation, the ability to acquire the information
needed for critical decision-making is still suspended by the
requirement to obtain a locked database in order for statistical
work to advance.
[0036] Therefore, it is desirable to provide a method and system
for conducting data analysis, i.e., statistical analysis, on the
clinical data collected while the clinical trial is ongoing. This
advantageously permits the present invention to identify positive
or negative conditions/events/trends much more rapidly than
possible with currently available systems and methods.
[0037] In the case of a randomized clinical trial where maintaining
confidentiality is important, it is also desirable to provide a
secure system in which the blinding information is integrated in
such a way that the clinical trial data and blinding data are
stored securely to prevent users from accessing the data and yet
allow the execution of programs for performing statistical
comparisons between study arms while the clinical trial is
ongoing.
SUMMARY OF THE INVENTION
[0038] It is an object of the present invention to provide a system
and method for continuously analyzing trial data of an ongoing
clinical trial.
[0039] Another object of the present invention is to provide the
system and method as aforesaid which analyzes the trial data
without interrupting or suspending the ongoing clinical trial.
[0040] A further object of the present invention is to provide the
system and method as aforesaid which performs statistical analysis
on the trial data and repeatedly performs such statistical analysis
until the result of such statistical analysis or the rate of change
in a predetermined statistical parameter exceeds a predetermined
threshold.
[0041] In accordance with an embodiment of the present invention,
the system and method continuously analyzes trial data of an
ongoing clinical trial. The system and method accesses the subject
trial data from a trial database and performs statistical analysis
on the accessed trial data. The system and method repeatedly
performs the statistical analysis while the clinical trial is on
going if it determines that result of the statistical analysis or
the rate of change of the statistical parameter does not exceed a
predetermined threshold value.
[0042] In accordance with an exemplary embodiment of the present
invention, a method of continuously analyzing trial data of an
ongoing clinical trial, comprises the steps of: accessing a trial
database comprising trial data of subjects in the ongoing clinical
trial; performing statistical analysis on the trial data to
determine a parameter of statistical significance without
suspending the ongoing clinical trial, determining whether the
result of the statistical analysis exceeds a threshold value, and
repeating the steps of accessing, performing and determining during
the ongoing clinical trial if it is determined that the results of
the statistical analysis does not exceed the threshold value.
[0043] In accordance with an exemplary embodiment of the present
invention, a computer readable media comprising a code for
continuously analyzing trial data of an ongoing clinical trial. The
code comprises instructions for accessing a trial database
comprising trial data of subjects in the ongoing clinical trial,
performing statistical analysis on the trial data to determine a
parameter of statistical significance without suspending the
ongoing clinical trial, determining whether the result of the
statistical analysis exceeds a threshold value, and repeating the
steps of accessing, performing and determining during the ongoing
clinical trial if it is determined that the result of the
statistical analysis does not exceed the threshold value.
[0044] In accordance with an exemplary embodiment of the present
invention, a system a for continuously analyzing trial data of an
ongoing clinical trial comprises a trial database comprising trial
data of subjects in the ongoing clinical trial and a processor. The
processor performs statistical analysis on the trial data to
determine a parameter of statistical significance without
suspending the ongoing clinical trial and determines whether the
result of the statistical analysis exceeds a threshold value. The
processor is operable to repeatedly access, perform and determine
during the ongoing clinical trial if it is determined that the
result of the statistical analysis does not exceed the threshold
value.
[0045] In accordance with an embodiment of the present invention,
the system and method uses a user definable criteria that defines
the level of cleanliness of subject data for statistical analysis.
In that case, only those subject data that meet the user defined
criteria are selected from the trial database for statistical
analysis.
[0046] In accordance with an embodiment of the present invention,
the system and method uses a rate of change for a given statistical
parameter. The rate of change may be a user definable criteria
including the parameter to be measured and the time interval
through which the parameter has changed.
[0047] In accordance with an embodiment of the present invention,
the system and method uses a value for the degree of disparity in
any given statistical evaluation between two groups of a multi-arm
clinical study. The value for the degree of disparity may be a user
definable criteria including the parameter to be measured.
[0048] In accordance with an embodiment of the present invention,
the system and method waits for a predetermined time period before
repeating the statistical analysis if the result of the statistical
analysis does not exceed the threshold value. This is done so
additional subject data can be collected and added to the trial
database.
[0049] In accordance with an embodiment of the present invention,
the clinical trial is blinded. Accordingly, the system and method
of the present invention accesses a blinding database in addition
to the trial database to obtain additional information, such as
subject identifiers and associated study group identifiers. Each
study group identifier identifies which study group a particular
subject belongs to. The system and method of the present invention
generates a grouped database from the clinical database and the
blinding database for statistical analysis in which the trial data
is grouped according to the subject's study group. Preferably, the
system and method generates a data table for each study group and
contains trial data associated with all of the subjects that belong
to that study group.
[0050] In accordance with an embodiment of the invention, the
system and method stores the unblinded database in a memory device
that is inaccessible by any user in order to preserve the blindness
of the clinical trial. The unblinded database is physically part of
the trial database and/or electronic data collection (EDC) system
of the present invention, but logically separated by user
access-permission. Alternatively, the unblended database can be
physically separate from the trial database.
[0051] In accordance with an embodiment of the invention, the
system and method performs the statistical analysis without locking
the trial database.
[0052] In accordance with an embodiment of the invention, if the
result of the statistical analysis or the rate of change of a
statistical parameter exceeds the threshold value, a user is
alerted. The predetermined threshold value may include a
predetermined statistical significance value or a rate of
change.
[0053] In accordance with an embodiment of the invention, the
system and method offers many statistical models to users to choose
from. The system and method retrieves and runs a user selected
statistical model on the clinical trial database.
[0054] In accordance with an embodiment of the invention, the
system and method graphically presents the statistical parameters
and their trends over time to end-users with the correct permission
level.
[0055] In accordance with an embodiment of the present invention,
the system and method enables the user to adjust the distribution
of the subjects within the blinding table for future enrollees to
be grouped in a particular manner.
[0056] Various other objects, advantages, and features of the
present invention will become readily apparent from the ensuing
detailed description, and the novel features will be particularly
pointed out in the appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0057] The following detailed description, given by way of example,
and not intended to limit the present invention solely thereto,
will best be understood in conjunction with the accompanying
drawings in which:
[0058] FIG. 1 is a flow diagram of a method of collecting and
analyzing clinical trial data using an EDC system;
[0059] FIG. 2 is a functional block diagram of a clinical trial
management system in accordance with an exemplary embodiment of the
present invention;
[0060] FIG. 3 is a flow diagram of a software routine that
continuously analyzes the trial data while the clinical trial is
ongoing in accordance with an exemplary embodiment of the present
invention;
[0061] FIG. 4 is an example of a blinding database;
[0062] FIG. 5 is an example of a trial database containing subject
trial data;
[0063] FIG. 6 is an example of an unblinded database derived from
the blinding database of FIG. 4 and the trial database of FIG. 5 in
accordance with an exemplary embodiment of the present
invention;
[0064] FIG. 7A is an example of a trial database containing a
status field that represents the levels of cleanliness of the
subject data records in accordance with an exemplary embodiment of
the present invention;
[0065] FIG. 7B is a filtered trial database containing a subset of
the trial database of FIG. 7A which have been selected as a
function of a user specified status in accordance with an exemplary
embodiment of the present invention; and
[0066] FIG. 8 is an example of a grouped database derived from the
blinding database of FIG. 4 and the filtered trial database of FIG.
7B in accordance with an exemplary embodiment of the present
invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0067] The present application is applicable to any clinical
studies utilizing electronic data collection, including but not
limited to collecting clinical data over a network from a plurality
of trial participants. Clinical studies can involve multiple groups
to enable comparisons to be made between subjects receiving the
actual medication versus placebo. Also, clinical studies can
involve a single study group, wherein data collected from the
clinical trial can be compared to data from other similar clinical
trials or studies, or to historical data. Although randomization or
randomized studies lend themselves to clinical trials with multiple
study groups, it is not necessary for clinical trials with single
study group. It is appreciated that randomization is not necessary
required for clinical trials with multiple study groups, the study
subjects can request assignment to a particular study group or arm,
or can be assigned to a particular study group at the discretion of
the investigator.
[0068] Most clinical trials utilize multiple study arms,
randomization, and blinding so they can maintain scientific rigor
and conform to requirements of FDA approval for a new drug and/or
new treatment. However, these rigorous clinical trials can lead to
several challenges, since they prevent the clinical trial sponsor
from tracking key information related to safety and efficacy while
the clinical trial is ongoing. Such analysis and comparisons are
not possible with the prior art systems and methods until the
clinical trials have ended or unless the clinical trials are
interrupted/suspended. That is, it is conceivable that a potential
positive or negative effects of a drug under study will go
unnoticed until the clinical trial is completed. The present
invention proceeds upon desirability of making such positive or
negative information available while the clinical trial is ongoing
and without interrupting the clinical trial.
[0069] Even in non-randomized, single arm, and unblinded clinical
studies, little if any statistical conclusion can be drawn until
after the trial database comprising the trial data can be cleaned
of errors and locked. Current operational methods call for paper
forms that are filled out by investigators and later keyed into a
database. This latter activity may lag behind the paper forms for
many days to weeks, ultimately compounding the delays seen in a
clinical trial that may involve hundreds of thousands of forms. The
delay in transforming data from a paper form to an electronic
format further delays the analysis of clinical study data because
data cleaning operation cannot start until the data is in
electronic format. The present invention resolves these issues by
performing these data analysis on trial data while the clinical
trial is ongoing and without interrupting the clinical trial.
[0070] Turning now to FIG. 2, there is shown a clinical trial data
management system 100 in accordance with an exemplary embodiment of
the present invention which is an Internet-enabled application
solution framework that automates data collection, data cleaning,
grouping if needed (as will be explained more fully later herein)
and statistical data analysis while the trial is ongoing. The
system 100 is connected to a computer network such as the Internet
120 through, for example, an I/O interface 102, which receives
information from and sends information to Internet users over a
communication link 20 and to one or more operators using a work
station 117. The Internet users are typically clinical research
coordinators (CRC's) located at various trial sites who transcribe
the subjects' charts to the system 100. The system 100 comprises,
for example, memory 104 which is volatile, processor (CPU) 106,
program storage 108, and data storage device 118, all commonly
connected to each other through a bus 112. The program storage 108
stores, among others, a clinical trial analysis program or module
114 and one or more mathematical models 116 that are used to
analyze the subject data and obtain the p-value for statistical
significance. The data storage device 118 stores a clinical trial
database 38 and blinding database 40. An example of a clinical
trial database 38 is shown in FIG. 5 and an example of a blinding
database 40 is shown in FIG. 4. The clinical trial database 38 and
the blinding database 40 can be separated either physically or
logically through user access-permission. The blinding database 40
can be accessed manually by permitted users or by the system 100 to
establish grouping assignments for upcoming or future enrollment
into the clinical trial. It is appreciated that some clinical
studies are performed without the use of blinding. In such
instances, the system does not utilize the blinding database 40
because the study patients and or their investigators are informed
of what therapy is being received and or administered respectively.
Any of the software program modules in the program storage 108 and
data from the data storage 110 are transferred to the memory 104 as
needed and is executed by the processor 106.
[0071] The system 100 can be any computer such as a WINDOWS-based
or UNIX-based personal computer, server, workstation, minicomputer
or a mainframe, or a combination thereof. While the system 100 is
illustrated as a single computer unit for purposes of clarity,
persons of ordinary skill in the art will appreciate that the
system may comprise a group of computers which can be scaled
depending on the processing load and database size.
[0072] FIG. 3 illustrates a flow diagram of a software routine 50
that continuously analyzes the trial data while the trial is
ongoing in accordance with an exemplary embodiment of the present
invention. The routine 50 is stored in the storage device 108 and
works with the EDC system 11 of FIG. 1 while the system 11
continuously collects and cleans the trial data in accordance with
an exemplary embodiment of the present invention.
[0073] The routine 50 connects to a trial database 56 through a
log-in procedure at block or step 52. A simplified exemplary trial
database 56 is shown in FIG. 7A. The trial database 56 contains
three columns comprising a patient subject ID field, a data status
field, which specifies the level of cleanliness, and a "Heart
Attack" field similar to FIG. 5.
[0074] FIG. 7A illustrates simplified trial data records that are
at different levels of cleanliness. In the example shown in FIG.
7A, there are five levels of status. Level 1 indicates that there
is an outstanding query that needs to be answered by the CRC 12
(see step 22 in FIG. 1). Level 2 indicates that the record is
pending a review by another reviewer such as the sponsor of the
trial. Level 3 indicates that it is pending a review by a clinical
research associate (CRA) to travel to a research site to perform
what is known as a source document verification (SDV). This
typically involves a verification of the trial record with an
actual patient chart. Level 4 indicates that it is pending a lock
barring any intervention by any reviewer. Finally, Level 5
indicates that the record is locked which represents the highest
level of clean data.
[0075] In the "Heart Attack" field, an entry of 0 means NO and
entry of 1 means YES. The "Heart Attack" field also includes some
erroneous data such as "don't know" for subject 118 or "Y" for
subject 107. Accordingly, the status for those records indicates a
"1" in which queries are outstanding.
[0076] Once connected, the routine 50 retrieves a user specified
criteria 54 stored in the storage device 108 which specifies the
status or level of cleanliness of the trial database at step 60 and
retrieves the trial database 56 which is filtered for those
database records that satisfy the retrieved criteria at step 61.
For an example, if the retrieved user specified criteria is 3, the
routine 50 selects only those records that have a status of 3 or
better at step 61 Such a filtered database 58 is shown in FIG. 7B.
While the database 58 has a relatively higher level of cleanliness,
it does have a fewer number of records. This is useful since, at
any given point in time during the data collection process, the
clinical trial database 56 may have data that has any combination
of data pending SDV, containing outstanding queries, completed SDV
but awaiting lock, and so on. Depending upon the operating
procedures defined for any such clinical trial, only certain
subsets of data may be suitable for inclusion in an analysis.
[0077] Once the trial database 58 is filtered according to the user
specified criteria at step 61, the routine 50 retrieves the
blinding data, such as those exemplary blinding data shown in FIG.
4, from the blinding database 40 at step 62. The routine 50
utilizes the filtered trial database 58 and the blinding database
40 to produce a grouped database 42, such as exemplary shown in
FIG. 8, at step 64. In accordance with an exemplary embodiment of
the present invention, two database tables 66, 68, one for each
study group without identifying subjects, are produced for example,
as shown in FIG. 8. One table 66 groups the Heart Attack data of
subjects that belong to a control group (Study Arm A) while the
other table 68 groups the Heart Attack data of subjects that belong
to a non-control group (Study Arm B). As can be appreciated by
person of ordinary skill in the art, there is no way to trace the
origins of any given data point in either table 66 or table 68, to
its original subject, and therefore either table, by itself, is
relatively uninformative. Taken together, however, note that there
seems to be a lot more heart attacks occurring in Study Arm B.
[0078] Turning now to FIG. 3, there is illustrated a process of
continuously analyzing trial data of an ongoing randomized clinical
trial in accordance with an exemplary embodiment of the present
invention. The system 100 maintains the clinical trial database 38
and the blinding database 40 as separate physical and digital
entities, in order to maintain their distinct nature.
Alternatively, the distinction between the two databases could be
logical rather than physical, based upon user access-permission. In
other words, the trial data and blind data remain as two separate
data tables and no table is created containing all of the following
information: the subject identifier, study group and heart attack
status. Furthermore, system communication with the blinding
database table occurs only by virtue of the machine programs of the
present invention executing specified actions to sort the clinical
trial data in accordance with an exemplary embodiment of the
present invention. The clinical trial data is preferably segregated
into generic pools of data and remains de-identified or unlinked to
both the subject and the study arm, and thus indecipherable from
the standpoint of the ability to trace a particular data item back
to a specific subject.
[0079] The routine 50 retrieves a user defined analysis method 72
stored in the storage device 108 and retrieves the method from the
mathematical models 116 stored in the storage device. The routine
50 of the present invention runs the model to analyze the grouped
database 42 at step 70. Preferably, the routine 50 of the present
system and method obtains a parameter of statistical significance,
e.g., a p-value (a statistical significance of the safety and
efficacy of the unblinded database 41) at step 76. An exemplary
unblinded database 41 in accordance exemplary embodiment of the
present invention is shown in FIG. 6. It is appreciated that the
mathematical model can include one or more formulas, representing
mathematical calculations, whereby one or more variables in the
clinical trial database are identified, and numeric result can be
obtained. Such formulas can include calculations of: mean, median,
mode, range, average deviation, standard deviation, and variance.
In addition, an administrator can enter mathematical formulas to
further analyze the data to make comparisons between groups of
data, as defined by the study arms, to determine statistical
metrics and significance by methods including Chi-square analysis,
t-test, f-test, one-tailed test, two-tailed test, and Analysis of
Variance (ANOVA). In accordance with an aspect of the present
invention, the system and method stores these calculated
statistical values and associated time points, thereby allowing the
present system and method to track trends, such as a rate of
change.
[0080] Once the mathematical analysis is completed, the routine
retrieves a user-defined p-value 74 stored in the storage device
108 at step 76. The routine 50 then determines whether the derived
p-value exceeds the retrieved user defined p-value at step 78. As
discussed in detail herein, a typical user defined p-value can be
0.05 indicating that the difference between the control group and
non-control group is statistically significant. Thus, if the
derived value is less than 0.05, then the inquiry at step 78 is
answered in the affirmative and the routine 50 sends an alert to
the user or operator without displaying the actual output value(s)
at step 80. The alert can be in the form of a flashing display,
alarm, a change in the system output display to the user by virtue
of color-coding, fonts, icons or text, or an automated system
generated message to the user by way of email, facsimile, telephone
or pager.
[0081] Alternatively, in accordance with an exemplary embodiment of
the present invention, the routine 50 can retrieve a user-defined
rate of change value for a given statistical parameter at step 76.
The value of the rate of change can be positive or negative number,
or any indication of positivity or negativity in the rate of
change. A negative rate of change in the p-value can indicate a
lack of efficacy in a particular study arm, and the routine 50 can
establish this negative rate of change in the p-value as a trigger
for alerting the user or operator at step 78. Thus a negative rate
of change in the p-value would result in the inquiry at step 78
being answered in the affirmative resulting in the routine 50
sending an alert to the user or operator at step 80.
[0082] In accordance with an exemplary embodiment of the present
invention, the routine 50 retrieves a user-defined difference in a
statistical value between two study arms of the clinical study at
step 76. Such difference can signify a divergence in statistical
trends between the study arms of a clinical study and the routine
50 can establish this difference in the statistical value as a
trigger for alerting the user or operator at step 78. Thus if the
degree of disparity between the two study arms of a clinical study
exceeds a user-defined value, then the inquiry at step 78 is
answered in the affirmative and the routine 50 sends an alert to
the user or operator at step 80.
[0083] In accordance with an exemplary embodiment of the present
invention, at step 82, the routine 50 can generate and display
output in accordance with the user defined output mode 84, as the
generic data tables 66, 68 generated at step 64. The output data
can take various formats including plain text, American Standard
Code for Information Interchange (ASCII), and SAS. Where
appropriate, this allows the user to perform customized statistical
analysis using the present invention to be performed. It is
appreciated that these outputs can also be integrated with other
software packages to generate customized graphical reports.
[0084] In accordance with an exemplary embodiment of the present
invention, if the trial is a randomized clinical trial, then the
routine 50 stops at step 80 and provides a Boolean output as to
whether or not a particular study parameter has reached the desired
level of statistical significance or not at step 80. The routine 50
skips step 82 or makes it available only to a select group of users
based upon access-permission. It is appreciated that this
functionality or accessibility can be determined by an
administrative user as a configurable aspect of the present system
and software. This advantageously maintains the blinding
information as secure as possible, thereby minimizing any inference
that can be made about the study arm of any given subject. In
monitoring the exact numeric determination of statistical
significance for any given clinical trial variable, it is
conceivable that the accession of new data could cause statistical
metrics for a particular study arm to change in such a manner that
inference can be made regarding the blinding status of the subject
whose data was most recently added, thus compromising statistical
veil.
[0085] It is appreciated that even in non-randomized clinical
trials, the display of specific numeric value corresponding to a
parameter of statistical significance by the routine 50 at step 80
is useful and beneficial. Since there is no blinding information to
protect in non-randomized clinical trials, the display of such
parameter of statistical significance can be offered as a second
mode of operation by the present invention. Alternatively, the
present invention can provide a third mode of operation, whereby
numeric ranges of statistical significance can be defined into
groups that can be displayed to the user of the present
invention.
[0086] However, if the derived p-value is higher than the
user-defined p-value, the rate of change is positive or the degree
of disparity between the study arms does not exceed the user
defined threshold value, then the inquiry at step 78 is answered in
the negative and the routine 50 proceeds to step 86. The routine 50
waits a predetermined time so additional clinical data is collected
and stored in the clinical trial database 58 at step 86 and
proceeds to step 52. The routine 50 then repeats the process of
analyzing the trial data of an ongoing clinical trial. In other
words, the system 100 is active throughout the data collection
phase of the clinical trial, sending alerts when key parameters
reach the pre-set or predetermined level of statistical measure or
significance.
[0087] As can be appreciated by persons of ordinary skill in the
art, the ability of the present clinical trial system 100 to
continuously and confidentially monitor and analyze the trial data
for statistical significance in tandem with data collection while
the trial is ongoing is a tremendous benefit to the researchers.
The trial database no longer becomes the bottleneck in obtaining
useful results and statistical analysis can be conducted on a near
real-time basis.
[0088] This continuous near real-time statistical analysis feature
in turn has far reaching implications. Specifically, by providing
researchers with an early indication of the clinical trial, the
present invention shortens the time frame required to reach
critical decisions about a new medical therapy. Still another
advantage is that the present system improves patient safety by
setting thresholds for triggering alerts for adverse events. A
related advantage is that a futile trial can be ended early,
thereby saving the substantial cost of conducting the trial.
Conversely, for a successful medical treatment, a trial can be
ended early or the placebo arm can be eliminated. Based upon
statistical trends, the distribution of enrollees can be altered
while the clinical trial is ongoing in order to adjust or better
test the objectives of the clinical trial or scientific hypothesis.
The present invention also provides the ability to more accurately
identify the need to perform a full-scale interim analysis.
[0089] In accordance with an exemplary embodiment of the present
invention, a computer readable media comprising a code for
continuously analyzing trial data of an ongoing clinical trial. The
code comprises instructions for accessing a trial database
comprising trial data of subjects in the ongoing clinical trial,
performing statistical analysis on a trial data of statistical
significance without suspending the ongoing clinical trial,
determining whether the result of the statistical analysis exceeds
a threshold value, and repeating the steps of accessing, performing
and determining during the ongoing clinical trial if it is
determined that the result of the statistical analysis does not
exceed the threshold value. Further the code comprises instructions
for selecting only those subject data that meets the user defined
criteria from the trial database for statistical analysis. The user
defined criteria defining the level of cleanliness of the subject
data for statistical analysis.
[0090] In accordance with an exemplary embodiment of the present
invention, a system a for continuously analyzing trial data of an
ongoing clinical trial comprises a trial database comprising trial
data of subjects in said ongoing clinical trial and a processor.
The processor performs statistical analysis on the trial data to
determine a parameter of statistical significance without
suspending the ongoing clinical trial and determines whether the
result of the statistical analysis exceeds a threshold value. The
processor is operable to repeatedly access, perform and determine
during the ongoing clinical trial if it is determined that the
result of the statistical analysis does not exceed the threshold
value.
[0091] Various omissions, modifications, substitutions and changes
in the forms and details of the device illustrated and in its
operation can be made by those skilled in the art without departing
in any way from the spirit of the present invention. Accordingly,
the scope of the invention is not limited to the foregoing
specification, but instead is given by the appended claims along
with their full range of equivalents.
* * * * *