Constructing a database of individual clinical trials for longitudinal analysis.

Published

Journal Article

Individual patient data are often required to evaluate how patient-specific factors modify treatment effects. We describe our experience combining individual patient data from 1946 subjects in 11 randomized controlled trials evaluating the effect of angiotensin-enzyme converting (ACE) inhibitors for treating nondiabetic renal disease. We sought to confirm the results of our meta-analysis of group data on the efficacy of ACE inhibitors in slowing the progression of renal disease, as well as to determine whether any study or patient characteristics modified the beneficial effects of treatment. In particular, we wanted to find out if the mechanism of action of ACE inhibitors could be explained by adjusting for follow-up blood pressure and urine protein. Each trial site sent a database of multiple files and multiple records per patient containing longitudinal data of demographic, clinical, and medication variables to the data coordinating center. The databases were constructed in several different languages using different software packages with unique file formats and variable names. Over 4 years, we converted the data into a standardized database of more than 60,000 records. We overcame a variety of problems including inconsistent protocols for measurement of key variables; varying definitions of the baseline time; varying follow-up times and intervals; differing medication-reporting protocols; missing variables; incomplete, missing, and implausible data values; and concealment of key data in text fields. We discovered that it was easier and more informative to request computerized data files and merge them ourselves than to ask the investigators to abstract partial data from their files. Although combining longitudinal data from different trials based on different protocols in different languages is complex, costly, and time-intensive, analyses based on individual patient data are extremely informative. Funding agencies must be encouraged to provide support to collaborative groups combining databases.

Full Text

Duke Authors

Cited Authors

  • Schmid, CH; Landa, M; Jafar, TH; Giatras, I; Karim, T; Reddy, M; Stark, PC; Levey, AS; Angiotensin-Converting Enzyme Inhibition in Progressive Renal Disease (AIPRD) Study Group,

Published Date

  • June 2003

Published In

Volume / Issue

  • 24 / 3

Start / End Page

  • 324 - 340

PubMed ID

  • 12757997

Pubmed Central ID

  • 12757997

International Standard Serial Number (ISSN)

  • 0197-2456

Digital Object Identifier (DOI)

  • 10.1016/s0197-2456(02)00319-7

Language

  • eng