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.

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 cons...

AI Technical Summary

Benefits of technology

[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.

[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.

Problems solved by technology

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.

However, these methods lead to several challenges, since they prevent the clinical trial sponsor from tracking key information related to safety and efficacy.

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.

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.

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.

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.

Another related problem is statistical power.

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.

Although this maintains data collection integrity, there are inherent inefficiencies in the system, regardless of the outcome.

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.

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.

These “extension studies”, however, can only begin after a full closure of the parent study, frequently requiring months to years before starting again.

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 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.

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.

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.

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.

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.

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