Introduction: I am Moses Ngari from Clinical Trial Facility; Kenya Medical Research Institute/Wellcome Trust Research Programme (KWTRP) in Kilifi, Kenya. I trained in BSc (Applied Statistics) from Maseno University at undergraduate and MSc (Epidemiology) from London School of hygiene and Tropical Medicine. I have been working at KWTRP since March 2009. My initial roles were purely data management of clinical trials but this has since changed to a biostatistician.
My experience: Over these years, I have managed data for 6 clinical trials; 3 small (N<500) single site trials (ClinicalTrials.gov registration number: NCT00890695, NCT01593969 and NCT01841099) in Kenya and 3 large (N~1,000 plus) multisite trials (ClinicalTrials.gov registration number: NCT00934492, NCT01899820 and NCT02246296). I share my experience from one of the large (N=1781) multisite trial in which I was in charge of data management and analysis. The trial was conducted in four hospitals in Kenya from November 2009 to April 2014 (ClinicalTrials.gov registration number: NCT00934492) with the acronym “CTX”. This was a double blind, placebo-controlled randomized trial designed to evaluate the efficacy of daily cotrimoxazole prophylaxis in reducing long-term mortality among HIV-uninfected but hospitalized children with severe acute malnutrition aged 2 to 59 months. We have published the study results: https://www.ncbi.nlm.nih.gov/pubmed/27265353 and its design and execution were described as “outstanding” by Trehan and Manar: http://www.thelancet.com/journals/langlo/article/PIIS2214-109X(16)30110-3/fulltext?rss=yes. I created the study database using OpenClinica, an open source software at our Kilifi servers and made it accessible remotely to the sites via GSM modems. Data entry was done in real time remotely from the sites. To ensure data quality, I had validation checks, automation of data capture of some variables like current date, continuous training of study staff and inbuilt audit trails to track changes to data. Lost to follow up is a nightmare to investigators, I created a STATA script to generate lists of missed visits in previous week and expected participants in coming week. Using the lists, participants were reminded of their upcoming clinic visits and those who had missed their visits were traced at home, this way the study managed complete follow up of 95% of the study participants.
Challenges: The major challenge has been managing the ‘dynamic’ environment of data management. Most trials rarely complete as planned, new technology emerges or updated versions of existing software are released. In addition, protocol amendments are implemented, altering the sample size, adding new data variables or even new sites to expedite trial participant recruitment. In the course of CTX trial, we had to migrate the OpenClinica database from version 2.54 to 3.10. This was a delicate task to ensure no data were lost and the structure of the data variables was unaltered. The trial also amended its protocol to add a new site and modified the trial recruitment criteria. These amendments necessitated implementing similar changes on the trial database. This involved; a) archiving already collected data, b) creating updated versions of electronic clinical report form (eCRF), c) updating the STATA script that manages the data after extraction since the updated database produced data variables names with new prefix and d) mapping the data variables from updated eCRFs with the old eCRFs variable names to yield a consolidated data set. This was a learning experience and very challenging since this had not be done previously at KWTRP. General challenges include interoperability; different software systems in use at KWTRP do not connect seamlessly, adoption of paperless CRFs in clinical trials and customization of the OpenClinica system to meet our specific needs like being able to extract data in STATA format directly and managing study follow ups effectively.
Interest: One major bottleneck of running a clinical trial in developing countries is limited resources especially budgets for data management including buying and paying annual license for database software. I am therefore, interested in exploring use of open source software in data management and statistical analysis. To this end, as a team from the KWTRP clinical Trial Facility, we have successfully experimented and adopted OpenClinica, REDCap and R in our data management and analysis and shared this experience: https://www.ncbi.nlm.nih.gov/pubmed/25424974. I am also keen on innovative statistical techniques to flag data errors in additional to the database validation checks and data monitoring employed at data entry and collection levels.
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