Contribution of Clinical Trial Event Data by Data Source A Prespecified Analysis of the ADAPTABLE Randomized Clinical Trial

被引:0
|
作者
Rymer, Jennifer A. [1 ]
Mulder, Hillary [1 ]
Wruck, Lisa M. [1 ]
Munoz, Daniel [2 ]
Kripalani, Sunil [2 ]
Effron, Mark B. [3 ]
Gupta, Kamal [4 ]
Handberg, Eileen [5 ]
Jain, Sandeep [6 ]
Girotra, Saket [7 ]
Whittle, Jeffrey [8 ]
Hess, Rachel [9 ]
Benziger, Catherine P. [10 ]
Knowlton, Kirk U. [11 ]
Curtis, Lesley H. [1 ]
Roe, Matthew T. [1 ]
Hammill, Bradley G. [1 ]
Rothman, Russell L. [2 ]
Harrington, Robert [12 ]
Hernandez, Adrian [1 ]
Jones, W. Schuyler [1 ]
机构
[1] Duke Clin Res Inst, 300 W Morgan St, Durham, NC 27701 USA
[2] Vanderbilt Univ, Med Ctr, Nashville, TN USA
[3] Univ Queensland, John Ochsner Heart & Vasc Inst, Ochsner Clin Sch, New Orleans, LA USA
[4] Univ Kansas, Sch Med, Kansas City, MO USA
[5] Univ Florida, Gainesville, FL USA
[6] Univ Pittsburgh, Pittsburgh, PA USA
[7] Univ Iowa, Iowa City, IA USA
[8] Med Coll Wisconsin, Milwaukee, WI USA
[9] Univ Utah, Salt Lake City, UT USA
[10] Essentia Hlth, Duluth, MN USA
[11] Intermt Hlth, Salt Lake City, UT USA
[12] Stanford Univ, Stanford, CA USA
关键词
D O I
10.1001/jamacardio.2024.2019
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Importance Pragmatic randomized clinical trials (RCTs) often use multiple data sources to examine clinical events, but the relative contribution of data sources to clinical end-point rates is understudied. Objective To assess the contribution of data sources (electronic health records [EHRs], public/private insurance claims, and/or participant-reported data) to clinical end points among ADAPTABLE participants who had available data. Design, Setting, and Participants The ADAPTABLE study was an open-label, pragmatic RCT from April 2016 through June 2019 conducted in research networks within clinical practice. Participants had existing atherosclerotic cardiovascular disease and available data to analyze. The characteristics of patients by combinations of data source availability were compared to examine the contribution of each of the data sources to end-point ascertainment. Data for this prespecified analysis were examined from January 2022 to June 2023. Exposures Randomized exposure to 81 mg or 325 mg of aspirin daily. Main Outcomes and Measures Number of events for the primary end point (composite of death, hospitalization for myocardial infarction, and hospitalization for stroke) that were contributed by EHR or claims data and then number of events contributed by each additional data source. Results Of 15 006 participants randomized with at least 1 other source of data available beyond participant-reported data, there were 8756 (58.3%) with participant-reported and EHR data; 4291 (28.6%) with participant-reported, EHR, and claims data; 1412 (9.4%) with EHR-only data; 262 (1.7%) with participant-reported and claims data; 202 (1.3%) with EHR and claims data; and 83 (0.6%) with claims-only data. Participants with EHR-only data were younger (median age, 63.7 years; IQR, 55.8-71.4) compared with the other groups (range, 65.6-71.9 years). Among participants with both EHR and claims data, with or without participant-reported data (n = 4493), for each outcome, most events (92%-100%) were identified in the EHR or in claims data. For all clinical end points, participant-reported data contributed less than 10% of events not otherwise available from claims or EHR data. Conclusions and Relevance In this analysis of a pragmatic RCT, claims and EHR data provided the most clinical end-point data when compared with participant-reported events. These findings provide a framework for collecting end points in pragmatic clinical trials. Further work is needed to understand the data source combinations that most effectively provide clinical end-point data in RCTs.
引用
收藏
页码:852 / 857
页数:6
相关论文
共 50 条
  • [1] Contribution of Clinical Trial Event Data by Data Source: Insights From the ADAPTABLE Trial
    Rymer, Jennifer A.
    Mulder, Hillary
    Wruck, Lisa M.
    Munoz, Daniel
    Kripalani, Sunil
    Effron, Mark B.
    Gupta, Kamal
    Handberg, Eileen M.
    Jain, Sandeep
    Girotra, Saket
    Whittle, Jeffrey
    Hess, Rachel
    Benziger, Catherine
    Knowlton, Kirk U.
    Curtis, Lesley
    Roe, Matthew T.
    Hammill, Bradley G.
    Rothman, Russell
    Harrington, Josephine
    Hernandez, Adrian F.
    Jones, William
    CIRCULATION, 2022, 146
  • [2] Reanalyses of Randomized Clinical Trial Data
    Ebrahim, Shanil
    Sohani, Zahra N.
    Montoya, Luis
    Agarwal, Arnav
    Thorlund, Kristian
    Mills, Edward J.
    Ioannidis, John P. A.
    JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2014, 312 (10): : 1024 - 1032
  • [3] Analysis of messy longitudinal data from a randomized clinical trial
    Qian, W
    Parmar, MKB
    Sambrook, RJ
    Fayers, PM
    Girling, DJ
    Stephens, RJ
    STATISTICS IN MEDICINE, 2000, 19 (19) : 2657 - 2674
  • [4] FACTOR ANALYSIS OF CLINICAL TRIAL DATA
    FREE, SM
    BIOMETRICS, 1968, 24 (02) : 454 - &
  • [5] Analysis of randomized comparative clinical trial data for personalized treatment selections
    Cai, Tianxi
    Tian, Lu
    Wong, Peggy H.
    Wei, L. J.
    BIOSTATISTICS, 2011, 12 (02) : 270 - 282
  • [6] The effect of dietary protein source on serum lipids: Secondary data analysis from a randomized clinical trial
    Gonciulea, Anda R.
    Sellmeyer, Deborah E.
    JOURNAL OF CLINICAL LIPIDOLOGY, 2017, 11 (01) : 46 - 54
  • [7] The Value of Source Data Verification in a Cancer Clinical Trial
    Smith, Catrin Tudur
    Stocken, Deborah D.
    Dunn, Janet
    Cox, Trevor
    Ghaneh, Paula
    Cunningham, David
    Neoptolemos, John P.
    PLOS ONE, 2012, 7 (12):
  • [8] Applying single-trial analysis to clinical event related potential data
    Westerfield, M
    Townsend, J
    Courchesne, E
    Jung, TP
    PSYCHOPHYSIOLOGY, 2000, 37 : S20 - S20
  • [9] Subgroup identification from randomized clinical trial data
    Foster, Jared C.
    Taylor, Jeremy M. G.
    Ruberg, Stephen J.
    STATISTICS IN MEDICINE, 2011, 30 (24) : 2867 - 2880
  • [10] Clinical trial identifiers in publications and accessibility of clinical trial data
    Giel, Jennifer L.
    Kaup, Susan M.
    CURRENT MEDICAL RESEARCH AND OPINION, 2010, 26 : S8 - S8