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Table 1 Hypothetical example illustrating identification biasa when individuals identified using post-randomization data are included in analyses

From: Addressing identification bias in the design and analysis of cluster-randomized pragmatic trials: a case study

Research Question: Does the intervention decrease the number of days of acute care utilization among patients with OUD (patient-level outcome)?
Assumption: Assume the intervention has no effect on reducing acute care utilization
Analytic sample: An open cohort of individuals with an OUD diagnosis documented in the EHR (pre- and/or post-randomization)
Patients identified using pre-randomization data:
• Suppose the number of patients with documented OUD pre-randomization is 100 in each trial arm (control and intervention)
• Assume an average of 9 days of acute care utilization per year at baseline among these patients with OUD in each arm
Patients identified using post-randomization data:
• Control: 25 patients receive a new documented OUD diagnosis post randomization. These patients have an average of 9 days of acute care per year at baseline
• Intervention: 50 patients receive a new documented OUD diagnosis. Of these, 25 are diagnosed as part of the intervention program and the other 25 are diagnosed through other mechanisms as in the control sites
• Suppose patients diagnosed via the intervention program are sicker as compared to those diagnosed through other mechanisms, with an average of 12 days of acute care per year (versus 9) at baseline
Estimated intervention effect:
• Control: among 125 patients with a diagnosis, there is an average of 9 days of acute care per year of follow-upb
• Intervention: among 150 patients with a diagnosis, there is an average of 9.5 days of acute care per year of follow-upb [= 9*125/150 + 12*25/150]
Summary: We would estimate that the intervention results in greater acute care utilization relative to control, even if there is truly no effect. The bias could go in the other direction if patients diagnosed as part of the intervention program are healthier (rather than sicker) than patients diagnosed through other mechanisms.
  1. EHR electronic health record, OUD opioid use disorder
  2. a Identification bias is a form of selection bias that can occur in open-cohort cluster-randomized trials when the randomized intervention group assignment affects who is identified as eligible for a particular analysis
  3. b For simplicity, here we assume no time trend (i.e., that average number of days of acute care per year of follow up is the same as the average per year at baseline)