@article{ref1,
title="Using predictive analytics to improve pragmatic trial design",
journal="Clinical trials",
year="2020",
author="Shortreed, Susan M. and Simon, Gregory E.",
volume="17",
number="4",
pages="394-401",
abstract="Clinical trials embedded in health systems can randomize large populations using automated data sources to determine trial eligibility and assess outcomes. The suicide prevention outreach trial used real-world data for trial design and randomized 18,868 individuals in four health systems using patient-reported thoughts of death or self-harm (Patient Health Questionnaire item 9). This took 3.5 years. We consider if using predictive analytics, that is, suicide risk estimates based on prediction models, could improve trial &quot;efficiency.&quot; We used data on mental health outpatient visits between 1 January 2009 and 30 September 2017 in seven health systems (HealthPartners; Henry Ford Health System; and Colorado, Hawaii, Northwest, Southern California, and Washington Kaiser Permanente regions). We used a suicide risk prediction model developed in these same systems. We compared five trial designs with different eligibility criteria: a response of a 2 or 3 on Patient Health Questionnaire item 9, a response of a 3, suicide risk score above 90th, 95th, or 99th percentile. We compared the sample that met each criterion, 90-day suicide attempt rate following first eligible visit, and necessary sample sizes to detect a 15%, 25%, and 35% relative reduction in the suicide attempt rate, assuming 90% power, for each eligibility criterion. Our sample included 24,355,599 outpatient visits. Despite wide-spread use of Patient Health Questionnaire, 21,026,985 (86.3%) visits did not have a recorded Patient Health Questionnaire. Of the 2,928,927 individuals in our sample, 109,861 had a recorded Patient Health Questionnaire item 9 response of a 2 or 3 over the study years with a 1.40% 90-day suicide attempt rate and 50,047 had a response of a 3 (suicide attempt rate 1.98%). More patients met criteria requiring a certain risk score or higher: 331,273 had a 90th percentile risk score or higher (suicide attempt rate: 1.36%); 182,316 a 95th percentile or higher (suicide attempt rate 2.16%), and 78,655 a 99th percentile or higher (suicide attempt rate: 3.95%). Eligibility criterion of a Patient Health Questionnaire item 9 response of a 2 or 3 would require randomizing 44,081 individuals (40.2% of eligible population in our sample); eligibility criterion of a 3 would require 31,024 individuals (62.0% of eligible population). Eligibility criterion of a suicide risk score of 90th percentile or higher would require 45,675 individuals (13.8% of eligible population), 95th percentile 28,699 individuals (15.7% of eligible population), and 99th percentile 15,509 (19.7% of eligible population). A suicide risk prediction calculator could improve trial &quot;efficiency&quot;; identifying more individuals at increased suicide risk than relying on patient-report. It is an open scientific question if individuals identified using predictive analytics would respond differently to interventions than those identified by more traditional means.<p /><p>Language: en</p>",
language="en",
issn="1740-7745",
doi="10.1177/1740774520910367",
url="http://dx.doi.org/10.1177/1740774520910367"
}