SYSTEM AND METHOD FOR EVALUATING RISKS OF CLINICAL TRIAL CONDUCTING SITES

A computer implemented method for evaluating risks of clinical trial conducting sites is provided. The method includes steps of (i) obtaining a first data that corresponds to a first duration from the clinical trial conducting sites; (ii) performing a regression analysis on the first data to obtain a number of monitoring visit findings at a site in accordance with an equation (Y)=B1X1+B2X2+ . . . BnXn+ error; (iii) obtaining regression coefficients by applying the equation on the first data; (iv) obtaining a second data that corresponds to a second duration from the clinical trial conducting sites; (v) applying the regression coefficients on the second data to predict potential risks associated with the clinical trial conducting sites; (vi) computing an overall risks associated with the clinical trial conducting sites; and (vii) classifying a risk level associated with the site based on the overall risk associated with the clinical trial conducting site.

BACKGROUND

Technical Field

The embodiments herein generally relate to a system and method for evaluating risks of clinical trial conducting sites, and more particularly, to a system and method for evaluating risks of clinical trial conducting sites using regression analysis.

Description of the Related Art

Generally clinical trials are conducted on subjects to analyse performance data of drugs, diagnostics, devices, therapy protocols, and other health or disease management related aspects. Typically, centers, clinics and hospitals at which clinical trials are conducted are known as clinical trial conducting sites. Issues in data quality and site compliance with the study protocol and regulatory requirements can increase time required to complete a study. Unforeseen developments in the field can increase both data and procedural errors. Correction of both data errors and procedural errors consumes sponsor resources. The ability to correct errors is dependent on access to timely information about what is happening in the field and the ability to respond dynamically to unforeseen developments. Therefore, there is a need for a system and method for forecasting the performance of clinical trial conducting sites and take proactive actions to improve the conduct of clinical trials. The existing approaches attempt to evaluate risks associated with clinical trial conducting sites by analysing past and present performance of the sites. However, such approaches have failed in forecasting performances of the clinical trial conducting sites. Accordingly, there remains a need for a system and method that forecasts performance of clinical trial conducting sites and facilitates proactive actions to improve the conduct of the clinical trials.

SUMMARY

In view of the foregoing, an embodiment herein provides a system for evaluating risks of clinical trial conducting sites is provided. The system includes a memory and a processor. The memory stores (i) a set of modules, and (ii) a database. The database stores a risk level associated with each of the clinical trial conducting sites. The processor executes the set of modules. The set of modules includes a first duration data obtaining module, a regression analysis module, a regression coefficients computing module, a second duration data obtaining module, a regression coefficients applying module, an overall site risks computing module and a site risk classifying module. The first duration data obtaining module, implemented by the processor, obtains a first data corresponding to a first duration. The first data includes parameters associated with (i) protocol risks, (ii) site performance risks, and (iii) site process risks of the clinical trial conducting sites. The regression analysis module, implemented by the processor, performs a regression analysis on the first data to obtain a number of monitoring visit findings at a clinical trial conducting site in accordance with an equation (Y)=B1X1+B2X2+ . . . BnXn+ error, where X1, X2, . . . Xn are parameters and Y is the number of monitoring visit findings at the clinical trial conducting site. The regression coefficients computing module, implemented by the processor, applies the equation on the first data to obtain regression coefficients. The second duration data obtaining module, implemented by the processor, obtains a second data corresponding to a second duration. The second data includes parameters associated with (i) the protocol risks, (ii) the site performance risks, and (iii) the site process risks of the clinical trial conducting sites. The regression coefficients applying module, implemented by the processor, applies the regression coefficients on the second data to predict potential risks associated with the clinical trial conducting sites. The overall site risks computing module, implemented by the processor, computes an overall risks associated with the clinical trial conducting sites. The site risk classifying module, implemented by the processor, classifies a risk level as high, medium, or low that associated with the clinical trial conducting site based on the overall risk associated with the clinical trial conducting site.

In one embodiment, the overall site risks computing module includes a protocol compliance risk score computing module, a site performance risk score computing module and a site process risk score computing module. The protocol compliance risk score computing module, implemented by the processor, computes a protocol compliance score for each clinical trial conducting sites by applying the regression coefficients on the second data. The site performance risk score computing module, implemented by the processor, computes a site performance score for the each clinical trial conducting sites by applying the regression coefficients on the second data. The site process risk score computing module, implemented by the processor, computes a site process compliance score for the each clinical trial conducting sites by applying the regression coefficients on the second data.

In another embodiment, the system is implemented using at least one (i) statistical models under generalized linear model (GLM), or (ii) a statistical models selected from (a) Linear Regression, (b) Logistic Regression, (c) Polynomial Regression, (d) Stepwise Regression, (e) Ridge Regression, (f) Lasso Regression, and (g) ElasticNet Regression to evaluate the overall risks associated with the clinical trial conducting sites. In yet another embodiment, the overall risks associated with the clinical trial conducting sites is computed based on (i) the corresponding protocol compliance risk scores, (ii) the corresponding site performance scores, and (ii) the corresponding site process compliance scores. In yet another embodiment, the first duration associated with the first data is calculated from a start of clinical trials until most recent interventions with the clinical trial conducting sites. In yet another embodiment, the second duration associated with the first data is calculated from recent interventions of the clinical trial conducting sites, in yet another embodiment, the second duration is calculated from (i) operational, medical, and study management, (ii) remote monitoring visit, (iii) telephonic follow-up, (iv) on-site visits, or (v) tele-presence till a current date. In yet another embodiment, the regression analysis module calculates a number of monitoring issues per the clinical trial conducting site by a mathematical form of a regression model

Y=B1X1+B2X2+ . . . BnXn[PR]+B1X1+B2X2+ . . . BnXn[Sp]+B1X1+B2X2+ . . . BnXn[Spe], where PR is related to a protocol risk, Sp is related to a site process, and Spe is related to a site performance. X1 X2 . . . Xn are independent variables.

In yet another embodiment, the at least one parameter is selected by a clinical trial administrator to monitor the clinical trial conducting site. In yet another embodiment, the system allows to add one or more new risk categories and associated parameters to compute the overall risks associated with the clinical trial conducting sites.

In another aspect, a computer implemented method for evaluating risks of clinical trial conducting sites is provided. The method includes the following steps: (i) obtaining a first data that corresponds to a first duration from the clinical trial conducting sites; (ii) performing a regression analysis on the first data to obtain a number of monitoring visit findings at a clinical trial conducting site in accordance with an equation

where X1, X2, . . . Xn are parameters and Y is the number of monitoring visit findings at the clinical trial conducting site; (iii) obtaining regression coefficients by applying the equation on the first data; (iv) obtaining a second data that corresponds to a second duration from the clinical trial conducting sites; (v) applying the regression coefficients on the second data to predict potential risks associated with the clinical trial conducting sites; (vi) computing an overall risks associated with the clinical trial conducting sites; and (vii) classifying a risk level associated with the clinical trial conducting site based on the overall risk associated with the clinical trial conducting site.

In one embodiment, the first data includes parameters associated with (i) protocol risks, (ii) site performance risks, and (iii) site process risks of the clinical trial conducting sites. In another embodiment, the second data includes parameters associated with (i) the protocol risks, (ii) the site performance risks, and (iii) the site process risks of the clinical trial conducting sites. In yet another embodiment, the method further includes the step of recommending the clinical trial conducting site visit when the risk level associated with the clinical trial conducting site is higher than a predefined threshold value. In yet another embodiment, the method further includes step to compute the overall risks associated with the clinical trial conducting sites using the overall site risks computing module that performing steps of: (i) computing a protocol compliance score for each clinical trial conducting sites by applying the regression coefficients on the second data; (ii) computing a site performance score for the each clinical trial conducting sites by applying the regression coefficients on the second data; and (iii) computing a site process compliance score for the each clinical trial conducting sites by applying the regression coefficients on the second data.

In yet another embodiment, the overall risks associated with the clinical trial conducting sites is computed based on (i) the corresponding protocol compliance risk scores, (ii) the corresponding site performance scores, and (ii) the corresponding site process compliance scores. In yet another embodiment, the first duration associated with the first data is calculated from a start of clinical trials until most recent interventions with the clinical trial conducting sites. In yet another embodiment, the second duration associated with the first data is calculated from recent interventions of the clinical trial conducting sites, in yet another embodiment, the second duration is calculated from (i) operational, medical, and study management, (ii) remote monitoring visit, (iii) telephonic follow-up, (iv) on-site visits, or (v) tele-presence till a current date. In yet another embodiment, the method further includes step of calculating a number of monitoring issues per the clinical trial conducting site by a mathematical form of a regression model

where PR is related to a protocol risk, Sp is related to a site process, and Spe is related to a site performance. X1 X2 . . . Xn are independent variables.

In yet another aspect, an one or more non-transitory computer readable storage mediums storing one or more sequences of instructions, which when executed by one or more processors, causes evaluating risks of clinical trial conducting sites, by performing the steps of: (i) obtaining a first data that corresponds to a first duration from the clinical trial conducting sites; (ii) performing a regression analysis on the first data to obtain a number of monitoring visit findings at a clinical trial conducting site in accordance with an equation

where X1, X2, . . . Xn are parameters and Y is the number of monitoring visit findings at the clinical trial conducting site; (iii) obtaining regression coefficients by applying the equation on the first data; (iv) obtaining a second data that corresponds to a second duration from the clinical trial conducting sites; (v) applying the regression coefficients on the second data to predict potential risks associated with the clinical trial conducting sites; (vi) computing a protocol compliance score for each clinical trial conducting sites by applying the regression coefficients on the second data; (vii) computing a site performance score for the each clinical trial conducting sites by applying the regression coefficients on the second data; (viii) computing a site process compliance score for the each clinical trial conducting sites by applying the regression coefficients on the second data; and (ix) classifying a risk level associated with the clinical trial conducting site based on the overall risk associated with the clinical trial conducting site.

In one embodiment, the first data includes parameters associated with (i) protocol risks, (ii) site performance risks, and (iii) site process risks of the clinical trial conducting sites. In another embodiment, the second data includes parameters associated with (i) the protocol risks, (ii) the site performance risks, and (iii) the site process risks of the clinical trial conducting sites. In yet another embodiment, overall risks associated with the clinical trial conducting sites computed based on (i) the corresponding protocol compliance risk scores, (ii) the corresponding site performance scores, and (ii) the corresponding site process compliance scores.

In yet another embodiment, the method further includes step of recommending the clinical trial conducting site visit when the risk level associated with the clinical trial conducting site is higher than a predefined threshold value. In yet another embodiment, the method further includes step of calculating a number of monitoring issues per the clinical trial conducting site by a mathematical form of a regression model

where PR is related to a protocol risk, Sp is related to a site process, and Spe is related to a site performance. X1 X2 . . . Xn are independent variables.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

As mentioned, there remains a need for a system and method that enables foreseeing performances of clinical trial conducting sites and taking proactive actions to improve the conduct of the clinical trials. Referring now to the drawings and more particularly toFIGS. 1 through 9, where similar reference characters denote corresponding features consistently throughout the figures, preferred embodiments are shown.

FIG. 1is a system view100illustrating a computing device102that includes a risk analyser104for evaluating risks associated with a plurality of sites according to an embodiment herein. In one embodiment, the plurality of sites is clinical trial conducting sites. Examples of categories of risks to be evaluated for clinical trial conducting sites include, but not limited to, protocol risks, site performance risks, and/or site process risks. Each of the protocol risks, site performance risks, and/or site process risks includes various parameters. The risk analyser104obtains values of parameters associated with the protocol risks, the site performance risks, and/or the site process risks, statistically analyses, and evaluates risks associated with the clinical trial conducting sites.

Examples of parameters associated with the protocol risks, the site performance risks, and/or the site process risks include, but not limited to, patients with protocol deviations, patients with adverse events, patients with early termination, patients failed screening, patients with serious adverse effects, patients with medical history, patients with concomitant medication, number of sites assigned to contract research associates, total monitoring issues, missing informed consent count, and/or missing drug accountability count.

The system view100further includes a user interface106, a configuration table108, an input database110, and an output database112. Each clinical trial has a clinical trial administrator who defines risks parameters to be monitored while conducting clinical trials. The configuration table108stores user inputs including risks parameters to be monitored during a clinical study and corresponding threshold values for the plurality of sites. The input database110inputs the user inputs to the risk analyser104for evaluating risks of the sites. In one embodiment, the computing device102is a laptop, a desktop, a tablet, a mini, a smartphone, etc.

The risk analyser104obtains a first data associated with clinical trial conducting sites including values of parameters associated with the protocol risks, the site performance risks, and/or the site process risks. The first data is specific to a first duration (i.e., from a start of a clinical trial until a most recent intervention with a site). The risk analyser104applies a regression model on the first data, and computes regression coefficients for each of the parameters. The risk analyser104applies the regression coefficients on a second data associated with the clinical trial conducting sites, and forecasts risks of the clinical trial conducting sites. The second data is specific to a second duration (i.e., from the most recent intervention with sites including, but not limited to, from operational, medical, and study management, remote monitoring visit, telephonic follow-up, on-site visits, and tele-presence to a current date). The output database112stores a risk level (e.g., high, medium, or low) associated with each of the clinical trial conducting sites. In one embodiment, the risk analyser104alerts a user for an immediate action when a risk level associated with a clinical trial conducting site is high.

FIG. 2is an exploded view of the risk analyser104of the computing device102ofFIG. 1according to an embodiment herein. The risk analyser104includes a database202, a first duration data obtaining module204, a regression analysis module206, a regression coefficients computing module208, a second duration data obtaining module210, a regression coefficients applying module212, an overall score computing module/overall site risks computing module214, a sites risk classifying module216, and a recommending module218. The first duration data obtaining module204obtains a first data including parameters associated with the protocol risks, the site performance risks, and/or the site process risks of clinical trial conducting sites, and corresponding values for a first duration. The first duration is calculated from a start of clinical trials until most recent interventions with the sites. The regression analysis module206performs an analysis on the first data in accordance with an equation

where X1, X2, Xn are parameters or key risk indicators or predictor variables that are also classified into site performance parameters, site process parameters and protocol compliance parameters to support in risk identification and mitigation, and Y is a number of monitoring visit findings at a site. A mathematical form of a regression model is given below.

Where PR is related to a protocol risk, Sp is related to a site process, and Spe is related to a site performance. X1 X2 . . . Xn are independent variables for the respective categories, and Y is a number of monitoring issues per site.

The regression coefficients computing module208applies the above equation 1 on the first data to obtain regression coefficients. The second duration data obtaining module210obtains a second data including parameters associated with the protocol risks, the site performance risks, and/or the site process risks of the clinical trial conducting sites, and corresponding values for a second duration. The second duration is from the most recent interventions of clinical trial conducting sites including, but not limited to, from operational, medical, and study management, remote monitoring visit, telephonic follow-up, on-site visits, and tele-presence till a current date. The regression coefficients applying module212applies the regression coefficients on the second data to predict potential risks associated with the clinical trial conducting sites.

The overall site risks computing module214includes a protocol compliance risk score computing module220, a site performance risk score computing module222, and a site process risk score computing module224. The protocol compliance risk score computing module220computes a protocol compliance score for each site by applying the regression coefficients on the second data. The site performance risk score computing module222computes a site performance score for each site by applying the regression coefficients on the second data. Similarly, the site process risk score computing module224computes a site process compliance score for each site by applying the regression coefficients on the second data. The overall site risks computing module214computes overall risks associated with sites based on corresponding protocol compliance scores, corresponding site performance scores, and corresponding site process compliance scores. The sites risk classifying module216classifies a risk level (e.g., high, medium, or low) associated with a site based on an overall risk associated with the site. The recommending module218recommends a site visit when a risk level associated with the site is higher than a predefined threshold value. The database202stores the risk level associated with each of the clinical trial conducting sites.

FIG. 3is a user interface view300illustrating a risk category field302, a parameter field304and an enabling or disabling field306according to an embodiment herein. The risk category field302displays a plurality of risks categories associated with sites (e.g., protocol risks, site performance risks, and/or site process risks). The parameter field304displays a plurality of parameters associated with the risk categories (e.g., protocol risks, site performance risks, and/or site process risks). A clinical trial administrator may select one or more parameters to be monitored for a site by enabling the one or more parameters using the enabling or disabling field306. A clinical trial administrator adds one or more new risk categories and associated parameters using an add category field308and an add parameter field310respectively.

FIG. 4is a table view illustrating a first data400that is obtained from clinical trial conducting sites, and is specific to a first duration according to an embodiment herein. The first data400includes site ID's402associated with a plurality of sites (i.e., sites1to N), actual enrolments404, investigator active trial counts406, parameters of protocol risks408, parameters of site performance risks410, and parameters of site process risks412for each site, and corresponding values. Example parameters of the protocol risks408include number of patients with IE protocol deviation and number of patients with non IE protocol deviation. Example parameters of the site performance risks410include number of patients failed in screening and number of patients entered screening. Example parameters of the site process risks412include average days between patient's monitoring visits and number of patients in assigned sites. A person having ordinary skill in the art will appreciate examples of parameters is not limited only to those listed in theFIG. 4.

With reference toFIG. 4,FIG. 5is an exemplary view500illustrating performing a regression analysis on the first data400ofFIG. 4to obtain regression coefficients502for parameters associated with different risk categories including, but not limited to, the protocol risks408, the site performance risks410, and the site process risks412according to an embodiment herein.

FIG. 6is a table view illustrating a second data600that is obtained from clinical trial conducting sites, and is specific to a second duration according to an embodiment herein. The second data600includes site ID's602associated with a plurality of sites (i.e., sites10to14), actual enrolments604, investigator active trial counts606, parameters of protocol risks608, parameters of site performance risks610, and parameters of site process risks612for each site, and corresponding values. Example parameters of the protocol risks608include number of patients with IE protocol deviation and number of patients with non IE protocol deviation. Example parameters of the site performance risks610include number of patients failed in screening and number of patients entered screening. Example parameters of the site process risks612include average days between patient's monitoring visits and number of patients in assigned sites. A person having ordinary skill in the art will appreciate examples of parameters is not limited only to those listed in theFIG. 6.

The risk analyser104applies the regression coefficients502on the second data600to predict potential issues that can be expected between most recent monitoring visits of sites to a current date. The risk analyser104computes a protocol compliance score, a site performance score, and a site process score for each site by applying the regression coefficients502on the second data. The risk analyser104also predicts number of issues on each site by performing regression analysis.

With reference toFIG. 6,FIG. 7is a table view700illustrating a risk level associated with sites according to an embodiment herein. For example, when a number of issues predicted for a site ‘10’ are 5, the risk analyser104categorizes the site ‘10’ as a low risk site. In another example, when a number of issues predicted for a site ‘13’ are 9, the risk analyser104categorizes the site ‘13’ as a highly risk site. In this example, the risk analyser104alerts a user for an immediate action to mitigate risks associated the site ‘13’.

FIG. 8is a flow diagram800illustrating a method for evaluating risks associated with a plurality of sites according to an embodiment herein. In step802, a first data that is specific to a first duration is obtained from clinical trial conducting sites. The first duration is from start of clinical trials until most recent interventions with sites. In step804, a regression analysis is performed on the first data to obtain regression coefficients. In step806, a second data that is specific to a second duration is obtained from the clinical trial conducting sites. The second duration is from the most recent intervention of sites including, but not limited to, from operational, medical, and study management, remote monitoring visit, telephonic follow-up, on-site visits, and tele-presence to a current date. In step808, the regression coefficients are applied on the second data. In step810, risks scores associated with the clinical trial conducting sites are computed for the second duration. In step812, risks levels associated with the clinical trial conducting sites are classified based on the risk scores. The system100and methods of various embodiments discussed above can also be implemented using statistical models under generalized linear model (GLM) and other statistical models (i.e., apart from a regression model) which are responsive or dynamic in nature to evaluate risks of sites. In one embodiment, the statistical models selected from (a) Linear Regression, (b) Logistic Regression, (c) Polynomial Regression, (d) Stepwise Regression, (e) Ridge Regression, (f) Lasso Regression, and (g) ElasticNet Regression to evaluate said overall risks associated with said clinical trial conducting sites.

The techniques provided by the embodiments herein may be implemented on an integrated circuit chip (not shown). The chip design is created in a graphical computer programming language, and stored in a computer storage medium (such as a disk, tape, physical hard drive, or virtual hard drive such as in a storage access network). If the designer does not fabricate chips or the photolithographic masks used to fabricate chips, the designer transmits the resulting design by physical means (e.g., by providing a copy of the storage medium storing the design) or electronically (e.g., through the Internet) to such entities, directly or indirectly.

The stored design is then converted into the appropriate format (e.g., GDSII) for the fabrication of photolithographic masks, which typically include multiple copies of the chip design in question that are to be formed on a wafer. The photolithographic masks are utilized to define areas of the wafer (and/or the layers thereon) to be etched or otherwise processed.

A representative hardware environment for practicing the embodiments herein is depicted inFIG. 9. This schematic drawing illustrates a hardware configuration of an information handling/computer system in accordance with the embodiments herein. The system comprises at least one processor or central processing unit (CPU)10. The CPUs10are interconnected via system bus12to various devices such as a random access memory (RAM)14, read-only memory (ROM)16, and an input/output (I/O) adapter18. The I/O adapter18can connect to peripheral devices, such as disk units11and tape drives13, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.

The system further includes a user interface adapter19that connects a keyboard15, mouse17, speaker24, microphone22, and/or other user interface devices such as a touch screen device (not shown) or a remote control to the bus12to gather user input. Additionally, a communication adapter20connects the bus12to a data processing network25, and a display adapter21connects the bus12to a display device23which may be embodied as an output device such as a monitor, printer, or transmitter, for example.