Some data quality issues at ClinicalTrials.gov

Linking Cancer Clinical Trials to their Result Publications

The results of clinical trials are a valuable source of evidence for researchers, policy makers, and healthcare professionals. However, online trial registries do not always contain links to the publications that report on their results, instead requiring a time-consuming manual search. Here, we explored the application of pre-trained transformer-based language models to automatically identify result-reporting publications of cancer clinical trials by computing dense vectors and performing semantic search. Models were fine-tuned on text data from trial registry fields and article metadata using a contrastive learning approach. The best performing model was PubMedBERT, which achieved a mean average precision of 0.592 and ranked 70.3% of a trial's publications in the top 5 results when tested on the holdout test trials. Our results suggest that semantic search using embeddings from transformer models may be an effective approach to the task of linking trials to their publications.

A review of clinical trials registered in India from 2008 to 2022 to describe the first-in-human trials

Aim

This analysis was conducted to review the number, and describe the characteristics of first-in-human (FIH) Phase 1 clinical trials registered in India from 2008 to 2022.

Materials and Methods

The data were extracted from the Clinical Trials Registry - India database for all FIH Phase 1 clinical trials registered between 2008 and 2022. Early-phase trials that were not FIH trials (e.g., pharmacokinetic studies and drug-drug interaction studies) were excluded from the study.

Results

A total of 1891 trials were retrieved and 220 were included in the analysis. Most of the investigational products were drugs (55%) followed by vaccines (38.2%). The most common therapeutic class of drugs was cancer chemotherapy (19.8%), followed by antimicrobial chemotherapy and endocrinology (18.2% each). The most common vaccine was the influenza vaccine (21.4%), followed by the measles-mumps-rubella vaccine (15.5%). The pharmaceutical industry was the predominant sponsor for most (91%) of the Phase 1 trials. Of the top five sites where most of the Phase 1 trials were conducted, three were private nonacademic centers (cumulatively 31%) and two were tertiary care medical colleges (cumulatively 9%).

Conclusion

Phase 1 clinical trials seem to be conducted in India predominantly with industry sponsorship. There is a need to have an alternate ecosystem to take forward molecules that do not receive adequate attention from the industry and molecules that are of national health priority other than areas such as chemotherapy, antimicrobials, and endocrinology. The Indian Council of Medical Research is setting up Phase 1 clinical trial capacity for molecules that predominantly may arise from nonindustry channels.

Data monitoring committees in pediatric randomized controlled trials registered in ClinicalTrials.gov

BACKGROUND

Data monitoring committees advise on clinical trial conduct through appraisal of emerging data to ensure participant safety and scientific integrity. While consideration of their use is recommended for trials performed with vulnerable populations, previous research has shown that data monitoring committees are reported infrequently in publications of pediatric randomized controlled trials. We aimed to assess the frequency of reported data monitoring committee adoption in ClinicalTrials.gov registry records and to examine the influence of key trial characteristics.

METHODS

We conducted a cross-sectional data analysis of all randomized controlled trials performed exclusively in a pediatric population and registered in ClinicalTrials.gov between 2008 and 2021. We used the Access to Aggregate Content of ClinicalTrials.gov database to retrieve publicly available information on trial characteristics and data on safety results. Abstracted data included reported trial design and conduct parameters, population and intervention characteristics, reasons for prematurely halting, serious adverse events, and mortality outcomes. We performed descriptive analyses on the collected data and explored the influence of clinical, methodological, and operational trial characteristics on the reported adoption of data monitoring committees.

RESULTS

We identified 13,928 pediatric randomized controlled trial records, of which 39.7% reported adopting a data monitoring committee, 49.0% reported not adopting a data monitoring committee, and 11.3% did not answer on this item. While the number of registered pediatric trials has been increasing since 2008, we found no clear time trend in the reported adoption of data monitoring committees. Data monitoring committees were more common in multicenter trials (50.6% vs 36.9% for single-center), multinational trials (60.2% vs 38.7% for single-country), National Institutes of Health-funded (60.3% vs 40.1% for industry-funded or 37.5% for other funders), and placebo-controlled (47.6% vs 37.5% for other types of control groups). Data monitoring committees were also more common among trials enrolling younger participants, trials employing blinding techniques, and larger trials. Data monitoring committees were more common in trials with at least one serious adverse event (52.6% vs 38.4% for those without) as well as for trials with reported deaths (70.3% vs 38.9% for trials without reported deaths). In all, 4.9% were listed as halted prematurely, most often due to low accrual rates. Trials with a data monitoring committee were more often halted for reasons related to scientific data than trials without a data monitoring committee (15.7% vs 7.3%).

CONCLUSION

According to registry records, the use of data monitoring committees in pediatric randomized controlled trials was more frequent than previously reported in reviews of published trial reports. The use of data monitoring committees varied across key clinical and trial characteristics based on which their use is recommended. Data monitoring committees may still be underutilized in pediatric trials, and reporting of this item could be improved.

Representation from India in multinational, interventional, phase 2 or 3 trials registered in Clinical Trials Registry-India: A cross-sectional study

In multinational trials that have run in India, we wished to determine whether there was too much (60% or higher) recruitment from India. We downloaded all trial records from Clinical Trials Registry-India, CTRI, and stored them in a local SQLite database. We queried records registered in a recent 8-year period, ie 2013-2020 and evaluated the fraction of local participants in interventional Phase 2 or Phase 3 studies. 62 trials were completed, with completion dates available. Five trials (8%) had 60% or more planned recruitment from India. Four of the five (7% of 62) had a foreign sponsor, and therefore there was an unfair burden-benefit ratio on the Indian population. Seven trials (11%), of which six (10% of 62) had foreign sponsors, had 60% or more (of the total) actual recruitment from India, and for two trials (both with foreign sponsors), the data were meaningless. There were 362 studies that were listed as not completed, although, given their start date and estimated duration, some of them ought to have been. Twenty five cases (7% of 362) had 60% or more planned recruitment from India. Of these, 18 (5% of 362) had foreign sponsors and were potentially problematic. Even allowing for some delays in completion, 128 (35% of 362) studies ought to have been completed by the time of our study. As such, we identified several problematic trials for which the planned recruitment from India in multinational studies was 60% or more. We also identified trials in which the actual recruitment was significantly higher than the planned recruitment. Further, the records of several studies that were probably completed were not updated in CTRI in a timely manner. The Indian drug regulator needs to be particularly alert to the planned, or actual, over-recruitment of participants from India. Further, CTRI, alone or in collaboration with the regulator, needs to ensure that multinational trial records for the enrollment fields in particular are updated, in a timely manner.

Improving clinical trial design using interpretable machine learning based prediction of early trial termination

This study proposes using a machine learning pipeline to optimise clinical trial design. The goal is to predict early termination probability of clinical trials using machine learning modelling, and to understand feature contributions driving early termination. This will inform further suggestions to the study protocol to reduce the risk of wasted resources. A dataset containing 420,268 clinical trial records and 24 fields was extracted from the ct.gov registry. In addition to study characteristics features, 12,864 eligibility criteria search features are used, generated using a public annotated eligibility criteria dataset, CHIA. Furthermore, disease categorization features are used allowing a study to belong more than one category specified by clinicaltrials.gov. Ensemble models including random forest and extreme gradient boosting classifiers were used to train and evaluate predictive performance. We achieved a Receiver Operator Characteristic Area under the Curve score of 0.80, and balanced accuracy of 0.70 on the test set using gradient boosting classification. We used Shapley Additive Explanations to interpret the termination predictions to flag feature contributions. The proposed pipeline will lead to an optimised clinical trial design and consequently help potentially life-saving treatments reach patients faster.

Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study

BACKGROUND

Publication of registered clinical trials is a critical step in the timely dissemination of trial findings. However, a significant proportion of completed clinical trials are never published, motivating the need to analyze the factors behind success or failure to publish. This could inform study design, help regulatory decision-making, and improve resource allocation. It could also enhance our understanding of bias in the publication of trials and publication trends based on the research direction or strength of the findings. Although the publication of clinical trials has been addressed in several descriptive studies at an aggregate level, there is a lack of research on the predictive analysis of a trial's publishability given an individual (planned) clinical trial description.

OBJECTIVE

We aimed to conduct a study that combined structured and unstructured features relevant to publication status in a single predictive approach. Established natural language processing techniques as well as recent pretrained language models enabled us to incorporate information from the textual descriptions of clinical trials into a machine learning approach. We were particularly interested in whether and which textual features could improve the classification accuracy for publication outcomes.

METHODS

In this study, we used metadata from ClinicalTrials.gov (a registry of clinical trials) and MEDLINE (a database of academic journal articles) to build a data set of clinical trials (N=76,950) that contained the description of a registered trial and its publication outcome (27,702/76,950, 36% published and 49,248/76,950, 64% unpublished). This is the largest data set of its kind, which we released as part of this work. The publication outcome in the data set was identified from MEDLINE based on clinical trial identifiers. We carried out a descriptive analysis and predicted the publication outcome using 2 approaches: a neural network with a large domain-specific language model and a random forest classifier using a weighted bag-of-words representation of text.

RESULTS

First, our analysis of the newly created data set corroborates several findings from the existing literature regarding attributes associated with a higher publication rate. Second, a crucial observation from our predictive modeling was that the addition of textual features (eg, eligibility criteria) offers consistent improvements over using only structured data (F₁-score=0.62-0.64 vs F₁-score=0.61 without textual features). Both pretrained language models and more basic word-based representations provide high-utility text representations, with no significant empirical difference between the two.

CONCLUSIONS

Different factors affect the publication of a registered clinical trial. Our approach to predictive modeling combines heterogeneous features, both structured and unstructured. We show that methods from natural language processing can provide effective textual features to enable more accurate prediction of publication success, which has not been explored for this task previously.

Characteristics of clinical trials related to hip fractures and factors associated with completion

BACKGROUND

This study aimed at investigating the characteristics of clinical trials related to hip fractures that were registered at ClinicalTrials.gov. It also aimed to identify potential risk factors associated with completion.

MAIN BODY

We obtained 733 clinical studies related to hip fractures from the ClinicalTrials.gov database and included 470 studies in the analysis. These clinical trials were divided into behavioral, drug/biological, device, procedure, and other categories based on intervention types. Clinical trials investigating drugs or biologics were categorized based on the specific agents administered in each trial. Multiple logistic and Cox regression models were used to test the ability of 24 potential risk factors in predicting recruitment status and completion time, respectively. Among the included clinical trials, 44.89% (211/470) had complete recruitment status. The overall median completion time was 931.00 days (95% confidence interval [CI]: 822.56-1039.44 days). The results of only 8.94% (42/470) of clinical trials were presented on the ClinicalTrials.gov website. Bupivacaine (a local anesthetic) was most commonly investigated (in 25 clinical trials); this was followed by ropivacaine (another local anesthetic, 23 clinical trials) and tranexamic acid (a hemostatic, 21 clinical trials). Multivariate analysis showed that trials including children (P = 0.03) and having National Institutes of Health funds (P < 0.01) had significantly higher rates of complete recruitment. Higher enrollment (P < 0.01), National Institutes of Health funding (P < 0.01), location in the United States (P = 0.04), and location in Europe (P = 0.03) predisposed to longer completion time, while studies involving drugs/biologics (P < 0.01) had shorter completion times.

CONCLUSIONS

A considerable proportion of clinical trials related to hip fractures were completed, but the results of only a small fraction were presented on the ClinicalTrials.gov website. The commonly investigated drugs focused on anesthesia, pain relief, and hemostasis. Several independent risk factors that affect recruitment status and completion time were identified. These factors may guide the design of clinical trials relating to hip fractures.

Drugs and convalescent plasma therapy for COVID-19: a survey of the interventional clinical studies in Italy after 1 year of pandemic

Background

The 2019 novel coronavirus disease (COVID-19) pandemic has highlighted the importance of health research and fostered clinical research as never before. A huge number of clinical trials for potential COVID-19 interventions have been launched worldwide. Therefore, the effort of monitoring and characterizing the ongoing research portfolio of COVID-19 clinical trials has become crucial in order to fill evidence gaps that can arise, define research priorities and methodological issues, and eventually, formulate valuable recommendations for investigators and sponsors. The main purpose of the present work was to analyze the landscape of COVID-19 clinical research in Italy, by mapping and describing the characteristics of planned clinical trials investigating the role of drugs and convalescent plasma for treatment or prevention of COVID-19 disease.

Methods

During an 11-month period between May 2020 and April 2021, we performed a survey of the Italian COVID-19 clinical trials on therapeutic and prophylactic drugs and convalescent plasma. Clinical trials registered in the Italian Medicines Agency (AIFA) and ClinicalTrials.gov websites were regularly monitored. In the present paper, we report an analysis of study design characteristics and other trial features at 6 April 2021.

Results

Ninety-four clinical trials planned to be carried out in Italy were identified. Almost all of them (91%) had a therapeutic purpose; as for the study design, the majority of them adopted a parallel group (74%) and randomized (76%) design. Few of them were blinded (33%). Eight multiarm studies were identified, and two of them were multinational platform trials. Many therapeutic strategies were investigated, mostly following a drug repositioning therapeutic approach.

Conclusions

Our study describes the characteristics of COVID-19 clinical trials planned to be carried out in Italy over about 1 year of pandemic emergency.

High level quality clinical trials were identified, although some weaknesses in study design and replications of experimental interventions were observed, particularly in the early phase of the pandemic.

Our findings provide a critical view of the clinical research strategies adopted for COVID-19 in Italy during the early phase of the pandemic. Further actions could include monitoring and follow-up of trial results and publications and focus on non-pharmacological research areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06474-8.

Data management in diabetes clinical trials: a qualitative study

Background

Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in clinical trials. Lack of appropriate planning for data management in clinical trials may negatively influence achieving the desired results. The aim of this study was to explore data management processes in diabetes clinical trials in three research institutes in Iran.

Method

This was a qualitative study conducted in 2019. In this study, data were collected through in-depth semi-structured interviews with 16 researchers in three endocrinology and metabolism research institutes. To analyze data, the method of thematic analysis was used.

Results

The five themes that emerged from data analysis included (1) clinical trial data collection, (2) technologies used in data management, (3) data security and confidentiality management, (4) data quality management, and (5) data management standards. In general, the findings indicated that no clear and standard process was used for data management in diabetes clinical trials, and each research center executed its own methods and processes.

Conclusion

According to the results, the common methods of data management in diabetes clinical trials included a set of paper-based processes. It seems that using information technology can help facilitate data management processes in a variety of clinical trials, including diabetes clinical trials.

A web-based tool for automatically linking clinical trials to their publications

OBJECTIVE

Evidence synthesis teams, physicians, policy makers, and patients and their families all have an interest in following the outcomes of clinical trials and would benefit from being able to evaluate both the results posted in trial registries and in the publications that arise from them. Manual searching for publications arising from a given trial is a laborious and uncertain process. We sought to create a statistical model to automatically identify PubMed articles likely to report clinical outcome results from each registered trial in ClinicalTrials.gov.

MATERIALS AND METHODS

A machine learning-based model was trained on pairs (publications known to be linked to specific registered trials). Multiple features were constructed based on the degree of matching between the PubMed article metadata and specific fields of the trial registry, as well as matching with the set of publications already known to be linked to that trial.

RESULTS

Evaluation of the model using known linked articles as gold standard showed that they tend to be top ranked (median best rank = 1.0), and 91% of them are ranked in the top 10.

DISCUSSION

Based on this model, we have created a free, public web-based tool that, given any registered trial in ClinicalTrials.gov, presents a ranked list of the PubMed articles in order of estimated probability that they report clinical outcome data from that trial. The tool should greatly facilitate studies of trial outcome results and their relation to the original trial designs.

An analysis of deficiencies in the ethics committee data of certain interventional trials registered with the Clinical Trials Registry-India

There is widespread agreement that clinical trials should be registered in a public registry, preferably before the trial commences. It is also important that details of each trial in the public record are complete and accurate. In this study, we examined the trial sites and ethics committee (EC) data for 1359 recent Phase 2 or Phase 3 interventional trials registered with Clinical Trials Registry-India (CTRI), to identify categories of problems that prevent the clear identification of which EC approved a given site. We created an SQLite database that hosted the relevant CTRI records, and queried this database, as needed. We identified two broad categories of problems: those pertaining to the understanding of an individual trial and those to adopting a data analytics approach for a large number of trials. Overall, about 30 problems were identified, such as an EC not being listed; an uninformative name of the EC that precluded its clear identification; ambiguity in which EC supervised a particular site; repetition of a site or an EC; the use of a given acronym for different organizations; site name not clearly listed, etc. The large number of problems with the data in the EC or site field creates a challenge to link particular sites with particular ECs, especially if a programme is used to find the matches. We make a few suggestions on how the situation could be improved. Most importantly, list the EC registration number for each EC, merge the site and EC tables so that it is clear which EC is linked to which site; and implement logic rules that would prevent a trial from being registered unless certain conditions were met. This will raise user confidence in CTRI EC data, and enable data based public policy and inferences. This will also contribute to increased transparency, and trust, in clinical trials, and their oversight, in India.

Discrepancies between FDA documents and ClinicalTrials.gov for Orphan Drug-related clinical trial data

Clinical trial registries such as ClinicalTrials.gov (CTG) hold large amounts of data regarding trials. Drugs for rare diseases are known as orphan drugs (ODs), and it is particularly important that trials for ODs are registered, and the data in the trial record are accurate. However, there may be discrepancies between trial-related data that were the basis for the approval of a drug, as available from Food and Drug Administration (FDA) documents such as the Medical Review, and the data in CTG. We performed an audit of FDA-approved ODs, comparing trial-related data on phase, enrollment, and enrollment attribute (anticipated or actual) in such FDA documents and in CTG. The Medical Reviews of 63 ODs listed 422 trials. We used study identifiers in the Medical Reviews to find matches with the trial ID number, 'Other ID' or 'Acronyms' in CTG, and identified 202 trials that were registered with CTG. In comparing the phase data from the 'Table of Clinical Studies' of the Medical Review, with the data in CTG, there were exact matches in only 75% of the cases. The enrollment matched only in 70% of the cases, and the enrollment attribute in 91% of the cases. A similar trend was found for the sub-set of pivotal trials. Going forward, for all trials listed in a registry, it is important to provide the trial ID in the Medical Review. This will ensure that all trials that are the basis of a drug approval can be swiftly and unambiguously identified in CTG. Also, there continue to be discrepancies in trial data between FDA documents and CTG. Data in the trial records in CTG need to be updated when relevant.

Clinical trials and tribulations: lessons from spinal cord injury studies registered on ClinicalTrials.gov

STUDY DESIGN

Article.

OBJECTIVE

ClinicalTrials.gov is an online trial registry that provides public access to information on past, present, and future clinical trials. While increasing transparency in research, the quality of the information provided in trial registrations is highly variable. The objective of this study is to assess key areas of information on ClinicalTrials.gov in interventional trials involving people with spinal cord injuries.

SETTING

Interventional trials on ClinicalTrials.gov involving people with spinal cord injuries.

METHODS

A subset of data on interventional spinal cord injury trials was downloaded from ClinicalTrials.gov. Reviewers extracted information pertaining to study type, injury etiology, spinal cord injury characteristics, timing, study status, and results.

RESULTS

Of the interventional trial registrations reviewed, 62.5%, 58.6%, and 24.3% reported injury level, severity, and etiology, respectively. The timing of intervention relative to injury was reported in 72.8% of registrations. Most trials identified a valid study status (89.2%), but only 23.5% of those completed studies had posted results.

CONCLUSIONS

Our review provides a snapshot of interventional clinical trials conducted in the field of spinal cord injury and registered in ClinicalTrials.gov. Areas for improvement were identified with regards to reporting injury characteristics, as well as posting results.

A comparative analysis of important public clinical trial registries, and a proposal for an interim ideal one

Background

It is an ethical and scientific obligation to register each clinical trial, and report its results, accurately, comprehensively and on time. The WHO recognizes 17 public registries as Primary Registries, and has also introduced a set of minimal standards in the International Standards for Clinical Trial Registries (ISCTR) that primary registries need to implement. These standards are categorized into nine sections—Content, Quality and Validity, Accessibility, Unambiguous Identification, Technical Capacity, Administration and Governance, the Trial Registration Data Set (TRDS), Partner registries and Data Interchange Standards. This study compared the WHO’s primary registries, and the US’s ClinicalTrials.gov, to examine the implementation of ISCTR, with the aim of defining features of an interim ideal registry.

Methods and findings

The websites of the 18 registries were evaluated for 14 features that map to one or more of the nine sections of ISCTR, and assigned scores for their variations of these features. The assessed features include the nature of the content; the number and nature of fields to conduct a search; data download formats; the nature of the audit trail; the health condition category; the documentation available on a registry website; etc. The registries received scores for their particular variation of a given feature based on a scoring rationale devised for each individual feature analysed. Overall, the registries received between 27% and 80% of the maximum score of 94. The results from our analysis were used to define a set of features of an interim ideal registry.

Conclusions

To the best of our knowledge, this is the first study to quantify the widely divergent quality of the primary registries’ compliance with the ISCTR. Even with this limited assessment, it is clear that some of the registries have much work to do, although even a few improvements would significantly improve them.

Obstacles to the reuse of study metadata in ClinicalTrials.gov

Metadata that are structured using principled schemas and that use terms from ontologies are essential to making biomedical data findable and reusable for downstream analyses. The largest source of metadata that describes the experimental protocol, funding, and scientific leadership of clinical studies is ClinicalTrials.gov. We evaluated whether values in 302,091 trial records adhere to expected data types and use terms from biomedical ontologies, whether records contain fields required by government regulations, and whether structured elements could replace free-text elements. Contact information, outcome measures, and study design are frequently missing or underspecified. Important fields for search, such as condition and intervention, are not restricted to ontologies, and almost half of the conditions are not denoted by MeSH terms, as recommended. Eligibility criteria are stored as semi-structured free text. Enforcing the presence of all required elements, requiring values for certain fields to be drawn from ontologies, and creating a structured eligibility criteria element would improve the reusability of data from ClinicalTrials.gov in systematic reviews, metanalyses, and matching of eligible patients to trials.

Letter on: “An analysis of deficiencies in the data of interventional drug trials registered with Clinical Trials Registry – India”

An article published in this journal analyses the deficiencies in the data of interventional drug trials registered with Clinical Trials Registry - India. We wish to rebut some of the inferences and highlight the pitfalls of a purely automated analysis of registry data as posited by the authors.

An algorithm provided as initial guidance for reporting registry records and published protocols in systematic reviews

OBJECTIVE

We aim to synthesize the available guidance with existing practices by Cochrane reviewers to generate an algorithm as a starting point in assisting reviewers reporting of registry records and published protocols (TRRs/PPs) use in systematic reviews of interventions.

STUDY DESIGN

We used existing guidance from major review bodies, assessed the current reporting of TRRs/PPs use in a sample of Cochrane reviews, and engaged in critical analysis. Independent reviewers identified and extracted textual excerpts reporting the use of trial registry records and published protocols and codes following a systematic review framework. Based on these elements, and our initial research, we created an algorithm/graphical aid to visualize initial direction.

RESULTS

We included 166 Cochrane systematic reviews published between August 2015 and 2016 from 48 review groups. Review authors' terminology (e.g., ongoing, terminated) varied between and within reviews. Reporting practices were diverse and inconsistent.

CONCLUSIONS

This is a timely investigation in an era where evidence synthesis informs health and health care decisions. Our proposed algorithm provides initial direction to systematize the reporting of TRR/PP use. We hope that the algorithm generates further discussion to enhance the transparency of TRR/PP reporting and methodological research into the complexities of using protocols in systematic reviews of interventions.

An analysis of deficiencies in the data of interventional drug trials registered with Clinical Trials Registry - India

Background

Clinical Trials Registry - India (CTRI) was established in July 2007 and today hosts thousands of trials, a significant fraction of them registered in the last couple of years. We wished to undertake an up-to-date analysis of specific fields of the registered trials. In doing so we discovered problems with the quality of the data, which we describe in this paper.

Methods

We downloaded CTRI records and reformatted the data into an SQLite database, which we then queried. We also accessed ClinicalTrials.gov records as needed.

Results

We discovered various categories of problems with the data in the CTRI database, including (1) a lack of clarity in the classification of Types of Study, (2) internal inconsistencies, (3) incomplete or non-standard information, (4) missing data, (5) variations in names or classification, and (6) incomplete or incorrect details of ethics committees. For most of these problems, error rates have been calculated, over time. Most were found to be in single digits, although others were significantly higher. We suggest how data quality in future editions of CTRI could be improved, including (1) a more elaborate and structured way of classifying the Type of Study, (2) the use of logic rules to prevent internal inconsistencies, (3) less use of free text fields and greater use of drop-down menus, (4) more fields to be made compulsory, (5) the pre-registration of individuals’ and organizations’ names and their subsequent selection from drop-down menus while registering a trial, and (6) more information about each ethics committee, including (a) its address and (b) linking the name of the trial site to the relevant ethics committee. As we discuss problems with the data of specific fields, we also examine — where possible — the quality of the data in the corresponding fields in ClinicalTrials.gov, the largest clinical trial registry in the world.

Conclusions

It is a scientific and ethical obligation to correctly record all information pertaining to each trial run in India. CTRI is a valuable database that has proved its worth in terms of improving the record of trials in the country. The suggestions made herein would improve it further.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3592-0) contains supplementary material, which is available to authorized users.