Data Quality In Clinical Trials Research Articles

Points to consider when developing drugs for dry eye syndrome.

Changes in both the social environment (e.g., the increased use of electronic media) and the atmospheric environment (e.g., air pollution and dust) have contributed to an increasing incidence of eye disease and an increased need for eye care. Notably, the signs and symptoms of dry eye syndrome can impact the daily quality of life for various age groups, including the elderly, and usually requires active treatment. The symptoms of dry eye syndrome include tear film instability, hyperosmolarity, ocular surface inflammation and damage, and neurosensory abnormalities. As treatments for dry eye are being developed, a standardized guideline is needed to increase the efficiency of drug development and improve the quality of clinical trial data. In this paper, we present general considerations for the pharmaceutical industry and clinical trial investigators designing clinical trials focused on the development of drugs to treat dry eye syndrome.

AI-Based Adherence Prediction for Patients: Leveraging a Mobile Application to Improve Clinical Trials

AbstractIntroductionMedication nonadherence is a public health concern and can impact clinical trial data quality. Traditional compliance collection (pill counts, diaries) can be unreliable in central nervous system trials. As such, strategies such as adherence technologies may play a key role in trial outcomes. AiCure, a computer vision-assisted dosing mobile application (app), collects dosing data and connects patients to sites for dosing support. Phone-based computer vision algorithms confirm dosing and transfer videos for artificial intelligence and human review. Boehringer Ingelheim is partnering with AiCure on pilot trials using AiCure adherence data to improve patient retention and clinical trial data quality. Here we report initial findings.MethodsThis pilot used data from two Phase II trials on the efficacy and safety of BI 409306 in people with schizophrenia (NCT03351244) or Attenuated Psychosis Syndrome (NCT03230097). The AiCure mobile app alerted participants to dosing protocols. The dose event was visually confirmed, providing sites a real-time view of adherence and allowing for targeted outreach and intervention. Adherence data from the first 2 weeks generated quantitative, machine-learning models to predict the individual adherence over the trial. Predictive modeling explored different monitoring periods (7-, 10-, and 14-day) and adherence cutoff points (0.8, 0.7, 0.6).ResultsInitial AiCure assessment identified 43% of participants in NCT03351244 as ≤80% compliant (definition of compliance >80% compliant). Variance in adherence rates between electronic case report forms (eCRF; 78%) and AiCure (26%) data was also observed in the highly compliant/adherent group in NCT03230097. Using the first 2 weeks of adherence data (both studies combined), a participant’s adherence predicted their average adherence for the remainder of the trial. Observation of a participant’s adherence for the latest 4 weeks predicted the probability of premature dropout from the trial. There were further correlations of lower predicted adherence with actual disposition-based dropouts.The early adherence predictive model (0.6 adherence cutoff) identified 22%, 20%, and 19% of patients for trial NCT03351244 (total n=235) as high-risk patients (low-adherence prediction) across 7-, 10-, and 14-day monitoring periods, respectively. Of those high-risk patients, 81%, 90%, and 96%, respectively, were truly nonadherent based on actual adherence data. The 14-day monitoring period model provided the lowest false omission rate, indicative of a better performing model.ConclusionsAiCure data provided insights into patient behavior and adherence patterns which would not be available via CRF. Predictive models developed with AiCure adherence data can identify and predict future poor adherers. This creates opportunities to plan interventions and mitigation strategies to improve patient adherence during trials, thereby providing test drugs the best opportunity at proving efficacy.FundingBoehringer Ingelheim International GmbH (NCT03351244/1289-0049 and NCT03230097/1289-0032)

Impact of monitoring approaches on data quality in clinical trials.

Source data verification (SDV) has been reported to account for up to 25% of the budget in clinical trials (CT) and cost-benefit of SDV has been questioned. Guidelines for risk-based monitoring (RBM) were published in 2013 by agencies and in 2016, ICH-GCP-E6-(R2) added a requirement for risk-based approaches. This report will perform a comparison of the impact of RBM vs classic monitoring (CM) on data quality (defined as accuracy of data reporting from source data to final trial data) and expected impact on costs of CTs. Data on residual errors from four, large comparable randomised CTs were examined by post-trial SDV. Observed discrepancies were analysed in the categories of "overall" data, "major efficacy" and "major safety". In each category, the residual error rate was calculated as the number of discrepancies divided by the number of data-fields verified. A total of 1 716 087 data points were verified using CM and 323 174 using RBM. The overall error rate was 0.40% for RBM and 0.37% for CM (P < 0.01). For major efficacy, defined by risk assessment, the error rate was 0.15% and 0.28% (P < 0.0001); in major safety, defined by risk assessment, the error rate was 0.49% and 0.67% (P = 0.15), both in favour of the RBM approach. These empirical data, directly comparing RBM with CM, suggest that RBM improves data quality regarding data-points of major importance to trial outcomes, efficacy and major safety. Overall, the RBM approach showed a correlation to reduced amount of data collection errors with major relevance for interpretation of study results and subject safety as well as reducing on-site monitoring and data cleaning resources.

Comparison of Non-Commercial Risk Based Monitoring Tools by Their Application on Clinical Trial Protocols

Clinical trial monitoring involves intensive on-site monitoring visits at clinical trial sites and exhaustive source data verification (monitoring) of clinical trial data [1]. Clinical researchers have questioned the validity and necessity for traditional monitoring methods [2], which have been under investigation due to their ineffectiveness in improving the quality of clinical trial data or in protecting trial participants [3]. Implementing a risk based monitoring (RBM) system is suggested by the ICH’s newly adapted guidelines to improve overall quality management [3]. The RBM involves the identification of any risk that might have an effect on areas routinely subject during monitoring activities. Risks should be identified by a RBM system followed by an evaluation of their likelihood of occurring and the extent to detect these errors and their impact on human subject protection, trial data reliability, and GCP- and protocol compliance [4].

Case studies on the use of central statistical monitoring and interventions to optimize data quality in clinical trials

Purpose: QDSS is an analytics-driven centralized monitoring system that uses a Statistical Process Control (SPC) approach with added clinical surveillance. The objective of this study was to review recent cases that arose in the course of Central Statistical Monitoring and intervention to illustrate the nature of the data quality issues that can be observed and corrected using this approach. Methods: SPC charts were generated with upper and lower limits based on the distribution of the data, which is updated as the study progresses. Red signals indicate an “out of control” data point and green signals represent data points that are within the control limits. Cases with red signals that illustrate the full lifecycle of QDSS were chosen to examine the process of signal detection, root cause analysis, intervention, and follow-up. Results: Three cases with “out of control” data points were selected. Data was visualized using SPC charts such as an X-bar control chart. The out of control data points signaled a root-cause analysis to begin. Further examination into related data discrepancies, discussions with study staff, and review of study protocols resulted in the identification of the root cause of the poor data quality, for example, a lack of training for new study staff that entered part way through the study. Based on the root cause, interventions were suggested to the study staff, such as retraining new staff members. After the implementation of the intervention, the data was re-examined to confirm the resolution of the “out of control” data points. Conclusions: Centralized statistical monitoring is an important tool in clinical trials to detect data outliers and anomalies that may have an effect on study outcomes. In all three cases studies were threatened by a data quality risk in their primary or secondary endpoints that were easily corrected. These case studies illustrate the power of QDSS to effectively identify and resolve data aberrancies to increase assay sensitivity.

Risk-Based Data Monitoring: Quality Control in Central Nervous System (CNS) Clinical Trials.

Monitoring the quality of clinical trial efficacy outcome data has received increased attention in the past decade, with regulatory guidance encouraging it to be conducted proactively, and remotely. However, the methods utilized to develop and implement risk-based data monitoring (RBDM) programs vary, and there is a dearth of published material to guide these processes in the context of central nervous system (CNS) trials. We reviewed regulatory guidance published within the past 6 years, generic white papers, and studies applying RBDM to data from CNS clinical trials. Methodologic considerations and system requirements necessary to establish an effective, real-time risk-based monitoring platform in CNS trials are presented. Key RBDM terms are defined in the context of CNS trial data, such as "critical data," "risk indicators," "noninformative data," and "mitigation of risk." Additionally, potential benefits of, and challenges associated with implementation of data quality monitoring are highlighted. Application of methodological and system requirement considerations to real-time monitoring of clinical ratings in CNS trials has the potential to minimize risk and enhance the quality of clinical trial data.

Assessing data quality and the variability of source data verification auditing methods in clinical research settings

IntroductionData audits within clinical settings are extensively used as a major strategy to identify errors, monitor study operations and ensure high-quality data. However, clinical trial guidelines are non-specific in regards to recommended frequency, timing and nature of data audits. The absence of a well-defined data quality definition and method to measure error undermines the reliability of data quality assessment. This review aimed to assess the variability of source data verification (SDV) auditing methods to monitor data quality in a clinical research setting. Material and methodsThe scientific databases MEDLINE, Scopus and Science Direct were searched for English language publications, with no date limits applied. Studies were considered if they included data from a clinical trial or clinical research setting and measured and/or reported data quality using a SDV auditing method. ResultsIn total 15 publications were included. The nature and extent of SDV audit methods in the articles varied widely, depending upon the complexity of the source document, type of study, variables measured (primary or secondary), data audit proportion (3–100%) and collection frequency (6–24 months). Methods for coding, classifying and calculating error were also inconsistent. Transcription errors and inexperienced personnel were the main source of reported error. Repeated SDV audits using the same dataset demonstrated ∼ 40% improvement in data accuracy and completeness over time. No description was given in regards to what determines poor data quality in clinical trials. ConclusionsA wide range of SDV auditing methods are reported in the published literature though no uniform SDV auditing method could be determined for “best practice” in clinical trials. Published audit methodology articles are warranted for the development of a standardised SDV auditing method to monitor data quality in clinical research settings.

Detection of Rater Errors on Cognitive Instruments in a Clinical Trial Setting.

This study examines errors committed by raters in a clinical trial of a memory enhancement compound. Findings of clinical trials are directly dependent on the quality of the data obtained but there is little literature on rates or nature of rater errors on cognitive instruments in a multi-site setting. Double-blind placebo-controlled study. 21 clinical sites in North America. Two hundred seventy-five participants. MMSE, WMS-R Logical Memory I and II, WMS-R Verbal Paired Associates I, WASi Vocabulary, WASi Matrix Reasoning, GDS and MAC-Q. The WMS-R Logical Memory I and II and WASi Vocabulary tests were found to have the greatest number of scoring errors. Few substantive errors were detected on source document review of the MMSE, GDS, MAC-Q and WMS-R Verbal Paired Associates I. Some additional administration and scoring issues were identified during feedback sessions with the raters. Cognitive measures used in clinical trials are prone to errors which can be detected with proper monitoring. Some instruments are particularly prone to inter-rater variably and should therefore be targets for focused training and ongoing monitoring. Areas in need of further investigation to help inform and optimize quality of clinical trial data are discussed.

Trial Design and Endpoint Evaluation in Clinical Studies Addressing Chronic Back Pain.

A review of the literature evaluating clinical trials of chronic back pain. To assist physicians in assessing the quality of clinical trial data to make the most informed treatment decisions. Chronic pain is a tremendous public health issue, affecting close to 100 million adults in the United States, and costs the American people billions of dollars. One traditional treatment approach, the long-term use of opiate medications, has recently come under intense scrutiny for problems with complications, diversion, abuse, addiction, and lack of efficacy. In addition, the Centers for Disease Control and Prevention has recognized that overprescribing opiates has enabled an overdose crisis, and written guidelines that are intended to limit their use. It is for this reason that physicians must have a comprehensive understanding of the range of drug-free alternative therapies available and have the tools needed to rigorously evaluate the chronic pain literature so they can make appropriate treatment decisions. An evaluation of how clinical trials are designed and ranked, outcome measures, and costs for a variety of therapies is necessary to determine which treatment option is the most efficacious for an individual patient. Clinical trial data demonstrate that spinal cord stimulation (SCS) is a safe and effective treatment option for many types of chronic pain, including back pain. The last 10 years has brought tremendous advances in the field of neuromodulation. Today, several treatment modalities exist for SCS requiring the physician to be able to critically evaluate and interpret the literature and determine which modality has the strongest evidence. When evaluating clinical trial data of patients with chronic back pain, emphasis must be placed on well designed, randomized controlled trials with long-term follow-up producing level I evidence. These data are obtained in a rigorous manner and are likely to have less bias when compared with lower level studies. The level I studies performed to date have provided evidence that treatment with SCS results in sustainable pain reduction and improvements in scores measuring quality of life and patient functioning in those patients with chronic intractable back pain. 5.

Risk Based Monitoring (RBM): A global study focusing on perception and merits among clinical investigational sites.

BackgroundSeveral approaches to clinical trial monitoring, including the Risk Based Monitoring (RBM) are aimed at the protection of the human subjects (safety), improved data quality, and ultimately, reducing the cost of drug development and operations. There exists minimal evidence globally about the perceptions and the level of confidence among the clinical staff on the merits of RBM. The present study assessed the perception among clinical research staff globally (developed and emerging countries) on the applicability and adaptability of RBM.MethodsAn electronic questionnaire survey consisting of twelve items was developed, validated, and then circulated globally via email to three thousand clinical research staff members at various investigational sites. This survey collected information on the use of RBM and factors that relate to clinical trial cost, data quality, subject safety, and the readiness to adopt RBM practices. The survey responses were summarized and analyzed by using the information e.g. responder's age, sex, clinical research role, global location, and experience in clinical research trials.ResultsResponses were received from ten countries, six emerging and four developed. Of the 3000 surveys sent to emerging (1,000) and developed (2,000) countries, a total response of 595 (261 vs 334) participants was received, respectively. The emerging versus developed group had 100 vs 137 participants with complete responses (CR); 34 vs 35 participants with partial responses (PR); and 127 vs 162 participants were disqualified with no exposure (NE) responses. About 67% of the overall responders were investigators, followed by 23%, 10% coordinator and other staff respectively. There was not significant difference in feedback between the researchers in developing versus emerging countries (p = 0.20) with regards to their perception of RBM reducing the overall cost of conducting a clinical research. Responders from emerging countries had a more favorable response than in the developed countries. Similarly, when asked if RBM will be more effective in addressing data quality (p = 0.006), patient safety (p = 0.05) and findings fraud/fabrication (p = 0.01), researchers from emerging countries indicated more confidence than researchers from developed countries. There was also a significant difference in the readiness to adopt RBM between responders of emerging versus developed markets (p < 0.0001).ConclusionThis unique study performed across ten emerging and developed countries strongly supported the need for systematic global training, education, and implementation of RBM regulatory guidance, with an aim for better safety of subjects and improved quality of clinical trial data. Furthermore, studies with larger sample sizes are recommended to provide an evidence-based approach.

The data quality risks assessment by the FMEA method

Globalization, limited resources, increased complexity and amount of clinical trials (CT), and of participating patients / volunteers create the conditions in which to obtain reliable and scientifically valid results about the effectiveness and safety of medicines is a complex, but a priority task. In this regard, actual is the use of advanced management approaches in the planning and organization of CT of drugs, in particular, quality risk management. The article discusses the methodology of risk assessment for the data quality in clinical trials based on FMEA tools, as well as the results of its testing in Clinical and Diagnostic Center of the National University of Pharmacy. During the development of this method the main types of data quality risks in clinical trial were analyzed, and the basic violations and errors which underlie them were determined. As a result of the expert assessment of the causes and consequences of the identified violations, it was determined the value of data quality risks in clinical trials and their categorization into serious, moderate and minor were executed. It was found that the highest risk is "a lack of understanding by the patient / volunteer questions of an investigator, errors in filling in the diary" the value of which exceeds the level of moderate risk. Based on these results the recommendations to reduce the impact of this risk were given. According to the mentioned above, ease of analysis and interpretation of the results obtained, considered method allows to identify and prevent the threat of violations and errors in data management at the trial site, and it can be used in the risk management process at the planning stage of clinical trials, during audits and regulatory inspections.

Statistical monitoring of data quality and consistency in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial.

Data quality may impact the outcome of clinical trials; hence, there is a need to implement quality control strategies for the data collected. Traditional approaches to quality control have primarily used source data verification during on-site monitoring visits, but these approaches are hugely expensive as well as ineffective. There is growing interest in central statistical monitoring (CSM) as an effective way to ensure data quality and consistency in multicenter clinical trials. CSM with SMART™ uses advanced statistical tools that help identify centers with atypical data patterns which might be the sign of an underlying quality issue. This approach was used to assess the quality and consistency of the data collected in the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, involving 1495 patients across 232 centers in Japan. In the Stomach Cancer Adjuvant Multi-institutional Trial Group Trial, very few atypical data patterns were found among the participating centers, and none of these patterns were deemed to be related to a quality issue that could significantly affect the outcome of the trial. CSM can be used to provide a check of the quality of the data from completed multicenter clinical trials before analysis, publication, and submission of the results to regulatory agencies. It can also form the basis of a risk-based monitoring strategy in ongoing multicenter trials. CSM aims at improving data quality in clinical trials while also reducing monitoring costs.

The impact of electronic administration of the adas-cog on clinical trial data quality

The impact of electronic administration of the adas-cog on clinical trial data quality

Impact of source data verification on data quality in clinical trials: an empirical post hoc analysis of three phase 3 randomized clinical trials

The aim of this project was to perform an empirical evaluation of the impact of on site source data verification (SDV) on the data quality in a clinical trial database to guide an informed decision on selection of the monitoring approach. We used data from three randomized phase III trials monitored with a combination of complete SDV or partial SDV. After database lock, individual subject data were extracted from the clinical database and subjected to post hoc complete SDV. Error rates were calculated with focus on the degree of on study monitoring and relevance and analyzed for potential impact on end points. Data from a total of 2566 subjects including more than 3 million data fields were 100% source data verified post hoc. An overall error rate of 0.45% was found. No sites had 0% errors. 100% SDV yielded an error rate of 0.27% as compared with partial SDV having an error rate of 0.53% (P < 0.0001). Comparing partly and fully monitored subjects, minor differences were identified between variables of major importance to efficacy or safety. The findings challenge the notion that a 0% error rate is obtainable with on site monitoring. Data indicate consistently low error rates across the three trials analyzed. The use of complete vs. partial SDV offers a marginal absolute error rate reduction of 0.26%, i.e. a need to perform complete SDV of about 370 data points to avoid one unspecified error and does not support complete SDV as a means of providing meaningful improvements in data accuracy.

Data quality in clinical trials.

e17610 Background: Ensuring correct and complete source data in clinical trials is of outmost importance. Journal audits in the Dept. of Oncology revealed that source data were not always complete ...

Analysis of data query as parameter of quality.

Introduction:Data query (DQ) rate per case record form (CRF) page is an index of quality of clinical trial data, which can be affected by the phase of clinical trial, the therapeutic area, and the country, where investigator site is located.Objective:To compare DQ rate per page by countries, phases, and therapeutic areas.Materials and Methods:Data from 19 paper CRF clinical trials conducted at 352 sites, in 5,610 patients were entered into clinical data management system by double data entry method, and DQs were generated. The DQ rate per page was compared for the phase, therapeutic area, and the country, by parametric analysis of variance (ANOVA) and nonparametric test-Kruskal-Wallis and median test. Multiple comparisons test was conducted for each category using Tukey's Studentized Range Test.Results:The total number of DQs from 108,599 CRF pages was 33,177 (0.306/CRF page). The studies included 12 countries, all phases, and seven therapeutic areas. Comparison of DQ rate per page showed a significant difference between phases, countries, and therapeutic areas. However, multiple comparisons showed that the DQ rate per page was significantly different between Phase I and other phases, and oncology, cardiology, endocrinology, and dermatology. The DQ categories were: Missing 21.8%, confirmatory 50.1%, and clarification 28.2%. The most common DQs by CRF field were: Lab data (23.2%), physical examination (17.4%), study assessment (17.4), and concomitant medication (12%). There was no correlation between the number of CRF pages and DQ rate per page per study.Conclusion:The phase of study and therapeutic area could impact the data quality as measured by DQ rates.

Central site monitoring: Results from a test of accuracy in identifying trials and sites failing Food and Drug Administration inspection

Site monitoring and source document verification account for 15%-30% of clinical trial costs. An alternative is to streamline site monitoring to focus on correcting trial-specific risks identified by central data monitoring. This risk-based approach could preserve or even improve the quality of clinical trial data and human subject protection compared to site monitoring focused primarily on source document verification. To determine whether a central review by statisticians using data submitted to the Food and Drug Administration (FDA) by clinical trial sponsors can identify problem sites and trials that failed FDA site inspections. An independent Analysis Center (AC) analyzed data from four anonymous new drug applications (NDAs) where FDA had performed site inspections overseen by FDA's Office of Scientific Investigations (OSI). FDA team members in the OSI chose the four NDAs from among all NDAs with data in Study Data Tabulation Model (SDTM) format. Two of the NDAs had data that OSI had deemed unreliable in support of the application after FDA site inspections identified serious data integrity problems. The other two NDAs had clinical data that OSI deemed reliable after site inspections. At the outset, the AC knew only that the experimental design specified two NDAs with significant problems. FDA gave the AC no information about which NDAs had problems, how many sites were inspected, or how many were found to have problems until after the AC analysis was complete. The AC evaluated randomization balance, enrollment patterns, study visit scheduling, variability of reported data, and last digit reference. The AC classified sites as 'High Concern', 'Moderate Concern', 'Mild Concern', or 'No Concern'. The AC correctly identified the two NDAs with data deemed unreliable by OSI. In addition, central data analysis correctly identified 5 of 6 (83%) sites for which FDA recommended rejection of data and 13 of 15 sites (87%) for which any regulatory deviations were identified during inspection. Of the six sites for which OSI reviewed inspections and found no deviations, the central process flagged four at the lowest level of concern, one at a moderate level, and one was not flagged. Central data monitoring during the conduct of a trial while data checking was in progress was not evaluated. Systematic central monitoring of clinical trial data can identify problems at the same trials and sites identified during FDA site inspections. Central data monitoring in conjunction with an overall monitoring process that adapts to identify risks as a trial progresses has the potential to reduce the frequency of site visits while increasing data integrity and decreasing trial costs compared to processes that are dependent primarily on source documentation.

Using audit information to adjust parameter estimates for data errors in clinical trials

Audits are often performed to assess the quality of clinical trial data, but beyond detecting fraud or sloppiness, the audit data are generally ignored. In an earlier study, using data from a nonrandomized study, Shepherd and Yu developed statistical methods to incorporate audit results into study estimates and demonstrated that audit data could be used to eliminate bias. In this article, we examine the usefulness of audit-based error-correction methods in clinical trial settings where a continuous outcome is of primary interest. We demonstrate the bias of multiple linear regression estimates in general settings with an outcome that may have errors and a set of covariates for which some may have errors and others, including treatment assignment, are recorded correctly for all subjects. We study this bias under different assumptions, including independence between treatment assignment, covariates, and data errors (conceivable in a double-blinded randomized trial) and independence between treatment assignment and covariates but not data errors (possible in an unblinded randomized trial). We review moment-based estimators to incorporate the audit data and propose new multiple imputation estimators. The performance of estimators is studied in simulations. When treatment is randomized and unrelated to data errors, estimates of the treatment effect using the original error-prone data (i.e., ignoring the audit results) are unbiased. In this setting, both moment and multiple imputation estimators incorporating audit data are more variable than standard analyses using the original data. In contrast, in settings where treatment is randomized but correlated with data errors and in settings where treatment is not randomized, standard treatment-effect estimates will be biased. And in all settings, parameter estimates for the original, error-prone covariates will be biased. The treatment and covariate effect estimates can be corrected by incorporating audit data using either the multiple imputation or moment-based approaches. Bias, precision, and coverage of confidence intervals improve as the audit size increases. The extent of bias and the performance of methods depend on the extent and nature of the error as well as the size of the audit. This study only considers methods for the linear model. Settings much different than those considered here need further study. In randomized trials with continuous outcomes and treatment assignment independent of data errors, standard analyses of treatment effects will be unbiased and are recommended. However, if treatment assignment is correlated with data errors or other covariates, naive analyses may be biased. In these settings, and when covariate effects are of interest, approaches for incorporating audit results should be considered.

A statistical approach to central monitoring of data quality in clinical trials

Classical monitoring approaches rely on extensive on-site visits and source data verification. These activities are associated with high cost and a limited contribution to data quality. Central statistical monitoring is of particular interest to address these shortcomings. This article outlines the principles of central statistical monitoring and the challenges of implementing it in actual trials. A statistical approach to central monitoring is based on a large number of statistical tests performed on all variables collected in the database, in order to identify centers that differ from the others. The tests generate a high-dimensional matrix of p-values, which can be analyzed by statistical methods and bioinformatic tools to identify extreme centers. Results from actual trials are provided to illustrate typical findings that can be expected from a central statistical monitoring approach, which detects abnormal patterns that were not (or could not have been) detected by on-site monitoring. Central statistical monitoring can only address problems present in the data. Important aspects of trial conduct such as a lack of informed consent documentation, for instance, require other approaches. The results provided here are empirical examples from a limited number of studies. Central statistical monitoring can both optimize on-site monitoring and improve data quality and as such provides a cost-effective way of meeting regulatory requirements for clinical trials.

GCP Data Quality for Early Clinical Development

Good Clinical Practice (GCP) provides an internationally accepted standard to ensure subject safety and data quality in clinical trials. Much of GCP parallels ethical considerations that have accumulated in successive versions of the World Medical Association's Declaration of Helsinki. This document advocates for preservation of rights, safety, and well-being of human study participants. By contrast, GCP data quality provisions follow from evolution in the United States drug regulatory system during the 1960s. Evidence of fraudulent or otherwise biased data-gathering ultimately led to U.S. Food and Drug Administration (FDA) data integrity regulations that were subsequently embraced as GCP principles in the Declaration of Helsinki. This manuscript summarizes GCP data quality provisions and describes practices that clinical site investigators can adopt to comply with these principles and to prevent adverse audit findings in the event of a regulatory inspection.