Comparative Effectiveness of Zolpidem/Trazodone and Cognitive Behavioral Therapy for Insomnia in Rural Adults
Phase: PHASE4
Conditions: Chronic Insomnia
Sponsor: California Pacific Medical Center Research Institute

Comparative Effectiveness of Zolpidem/Trazodone and Cognitive Behavioral 
Therapy for Insomnia in Rural Adults (COZI) 
  
Study Protocol 
2023.04.20 
 
Funder:  Patient-Centered Outcomes Research Institute (PCORI) 
1828 L St NW, Washington, DC 20036 
Contract Number: CER-2018C2-13262 
 
Principal Investigators: 
Katie L. Stone, PhD: California Pacific Medical Center Research Institute 
Daniel J. Buysse, MD: University of Pittsburgh 
 
 
PROJECT SUMMARY 
 
COZI is a randomized, 3-arm, parallel-group, multi-site comparative effectiveness trial of three 
treatment strategies for chronic insomnia. Participants are adults living in rural communities, recruited 
from their primary care provider’s practice.  Participants will be randomized to one of 3 treatment arms 
to compare effectiveness and safety: 1) medication (zolpidem or trazodone); 2) CBT-I (Cognitive-
Behavioral Therapy for Insomnia) using the online SHUTi (Sleep Healthy Using the Internet) program; or 
3) combination treatment with medication + CBT-I . Outcomes will be assessed immediately post-
treatment (9 weeks), and at 6 and 12-month follow-up. 
 
COZI will recruit and study 1200 patients across 8 hub sites (150 per site), each of which will collaborate 
with affiliated primary care practices.  Recruitment methods may vary by hub and practice, according to 
site-specific resources.  Recruitment methods will include traditional methods (posters, brochures, etc.) 
as well as electronic health record (EHR)-based recruitment where supported; all participants must be 
referred by a primary care practitioner. Referrals will be transmitted to the research team at the 
affiliated hub, who will complete telephone or online screening and voluntary informed consent. Eligible 
patients will complete baseline evaluations online, then be randomized to one of three interventions: 
Medication, Medication + CBT-I , or CBT-I , in a 1:1:1 ratio. Pre-treatment baseline and follow-up 
research assessments and other research procedures will be completed online in the participant’s home.  
For participants randomized to a medication arm, the medication will be prescribed and monitored by 
their primary care provider, following published treatment guideline, as would be done in “usual 
practice.” Participants randomized to CBT-I  will be instructed on how to complete the online self-guided  
CBT-I  intervention. All participants will complete follow-up assessments at 9 weeks, 6, and 12 months.  
 
 
 

PROJECT MANAGEMENT 
 
 
 
COZI is guided by a Steering Committee, with input from PCORI, the DSMB, and the Research Cores and 
Centers. The Steering Committee comprises the Dual PIs, Core Directors, two patient-stakeholder 
representatives, and two clinical hub site PIs.  
 
The project team is led by Dual PIs Stone (Contact PI) and Buysse.  Research Cores and Centers include 
the Data Coordinating Center at CPMC (Stone); the Clinical Coordinating Center (CCC) at Pitt (Buysse); 
the Digital Therapeutics Core at University of Virginia (Ritterband); and the Patient-Stakeholder 
Engagement Core (PSEC) at the University of Arizona (Parthasarathy). The PSEC comprises a Study 
Advisory Committee (SAC) and the Recruitment and Retention Center (RRC) at the University of Illinois 
Chicago (Krishnan, Musick, Sculley). The eight clinical hub sites are each led by a PI with expertise in 
insomnia clinical trials, and a Co‐I from primary care practice.  Two site leads serve on the Steering 
Committee to provide input into site-level operations and recruitment/retention plans.  
 
Overview of Leadership and Organizational Structure 
The Steering Committee (SC) is the main decision‐making body of the study. It includes members of 
the EC, 8 site leads, and the study statistician. The SC convenes monthly via teleconference to review 
progress on major milestones, guide decision‐making for execution by the EC, and approve the 
protocol (initially, and subsequent modifications). In addition, the SC will meet once per year in 
person (or virtual depending on status of pandemic) in tandem with SAC meetings in Chicago, Illinois 
or Tucson, Arizona. 

The Executive Committee (EC) is responsible for executing the decisions made by the Study Advisory 
Committee (SAC), and communicating with outside entities including the Data and Safety Monitoring 
Board (DSMB) and PCORI. The EC includes the Lead PIs (Stone and Buysse), the Lead Patients 
(Brothman, Rockwell), Patient Advocacy Lead (Flygare), each of the core/sub‐core leads and Co‐Is 
(Parthasarathy, Krishnan, Erwin and Ritterband), the primary care Co‐I (Massart) and the Data 
Coordinating Center Project Director. The EC will meet weekly-biweekly in Year 1 to review progress 
towards milestones, review preliminary data, troubleshoot challenges, and coordinate operations. 
Thereafter (Years 2‐4) the EC will continue to meet twice per month. We will increase the frequency 
of meetings as needed. 
Study Advisory Committee (SAC). The SAC is the main source of study input from 8 key stakeholder 
groups: patients, patient advocacy, providers, purchasers, payors, product makers, policymakers and 
investigator/ scientists. The SAC will communicate with the EC on issues pertaining to study design 
and feasibility, recruitment methods and procedures, finalization of study outcomes, procedures for 
study implementation, monitoring study progress, and dissemination of findings to communities of 
interest. The SAC will meet two times per year, once via teleconference, and once in person.  
Hub Advisory Committee (HAC). The HAC will convene biweekly in Year 1 and biweekly-monthly in 
Years 2-4. The HAC is led by the RRC PIs. Its mission is to develop, modify, and disseminate best 
practices for recruitment and retention of primary care practices and study participants. The HAC 
serves as a vehicle for hub sites to share best practices and seek input on recruitment challenges. 
In‐Person Project Meetings. We held a kick‐off meeting in year 1 that convened the Steering 
Committee and Study Advisory Committee. At the end of year 4, we will reconvene this group for a 
dissemination meeting to discuss preliminary study findings and decide on plans for publication and 
other dissemination strategies such as press releases and media. 
 
Katie L. Stone, PhD (Prime PI, Director of the Data Coordinating Center [DCC])   
Daniel J. Buysse, MD (Dual PI, Director of the Clinical Coordinating Center [CCC])  
Sairam Parthasarathy, MD (Co‐I, Director of the Patient and Stakeholder Engagement Core[PSEC]),   
Lee Ritterband, PhD (Co‐I, Director of the Digital Therapeutics Core)   
Jerry A. Krishnan, MD, PhD (Co‐I, Director of the Recruitment and Retention Center [RRC], a component 
of the PSEC)   
Arthur Brothman, PhD (patient investigator and consultant to the PSEC)   
Heather Rockwell, MPW (patient member of SAC and SC)   
Julie Flygare, JD (patient advocate and member of the PSEC)   
Eric Vittinghoff, PhD (Statistician), is Professor of Biostatistics at UCSF  
Mylynda Massart, MD, PhD (Co‐I at Pitt/UPMC) is a practicing family physician   
 
Enrollment site 
Principal Investigator 
University of Pittsburgh/ UPMC 
Center for Sleep and Circadian Science 
3811 O’Hara Street, E-1123 
Pittsburgh, PA  15213 
Daniel J. Buysse, MD 
buyssedj@upmc.edu 
 
Penn State University, Hershey 
Penn State College of Medicine 
Psychiatry, Sleep Research & Treatment Center 
500 University Dr. H073 C5644A 
Julio Fernandez‐Mendoza, PhD 
jfernandezmendoza@pennstatehealth.psu.edu 
 

Hershey, PA 17033 
Oregon Health and Science University 
Mark O Hatfield Research Center  
3250 SW Sam Jackson Park Road 
Portland, OR 97239 
Asha Singh, MD 
singas@ohsu.edu 
 
 
Banner Healthcare System 
1501 N Campbell Ave., AHSC Rm 2342D 
Tucson, AZ 85724 
Sairam Parthasarathy, MD 
sparthasarathy@deptofmed.arizona.edu 
University of Virginia 
PO Box 800546 
Division of Pulmonary and Critical Care Medicine 
Charlottesville, Virginia 22908 
Eric Davis, MD 
emd9b@hscmail.mcc.virginia.edu 
University of Alabama Birmingham 
UAB Center for Psychiatric Medical 
1713 6TH Ave South 
Birmingham, AL, 35294 
S. Justin Thomas, PhD 
sjthoma@uabmc.edu 
Mizzou Sleep Research Lab 
University of Missouri, Dept. of Psychiatry 
909 Hitt Street, #C100 
Columbia, MO  65201 
Richelle Koopman, MD 
koopmanr@health.missouri.edu 
 
RATIONALE AND BACKGROUND INFORMATION 
Insomnia is a prevalent health condition with important consequences. By its very definition, chronic 
insomnia disorder (CID) is a patient‐centered problem: Dissatisfaction with the quality or quantity of 
nighttime sleep, marked by difficulty falling asleep, maintaining sleep, or early awakening. Additional 
criteria include distress or impaired function, adequate sleep opportunity, frequency >3 nights/week, 
and duration >3 months. The prevalence of insomnia symptoms in adults is ~20‐30%, and the prevalence 
of CID ~5‐10%. Symptoms persist for >3 years in 40% of patients. Insomnia occurs in 10‐26% of primary 
care patients. It is comorbid with, and associated with increased risk for, depression, obesity, 
hypertension, falls, cognitive decline and mortality. Chronic insomnia leads to reduced quality of life, lost 
work productivity through both presenteeism and absenteeism, and increased health care costs. 
Insomnia disproportionally affects the elderly, women, socio‐economically disadvantaged, and 
racial/ethnic minorities. Despite its prevalence and serious consequences, individuals with insomnia 
often feel isolated and misunderstood by family members, co‐workers, and providers. Patients are 
specifically dissatisfied with health care professionals’ lack of treatment knowledge beyond “sleep 
hygiene” and medications. CID is a common and consequential health condition, but patients’ distress 
and treatment needs are poorly addressed. 
FDA‐approved medications include benzodiazepines (e.g., temazepam, triazolam), 
benzodiazepine receptor agonists (BzRA, e.g., zolpidem, eszopiclone) orexin receptor 
antagonists (e.g., suvorexant), melatonin receptor agonists (e.g., ramelteon), and doxepin. Data 
from the National Health and Nutrition Epidemiological Survey 1999‐2010 showed that 19.2% of 
adults took at least one sleep aid in the prior month. Zolpidem is the most commonly prescribed 
FDA-approved drug for insomnia, accounting for 62% of the 18.5 million prescriptions for 
hypnotics in 2011. Prescriptions for BzRAs have increased since 2004, driven by long‐term 
prescriptions in primary care, and underscoring the need for long‐term studies of their benefits 

and harms. The efficacy of hypnotic medications is supported almost exclusively by industry‐
sponsored studies, most of which compare a single medication to placebo, and none of which 
have compared zolpidem to CBT‐I. Although individual studies for specific hypnotics have large 
sample sizes, the number of trials per agent is small, the magnitude of effects modest, the 
outcomes focused on sleep measured using polysomnography rather than patient‐reported 
outcomes (PROs), and the duration of treatment short (4‐12 weeks). Meta‐analyses support the 
short‐term efficacy of benzodiazepines and BzRA. A recent Clinical Practice Guideline found 
weak evidence for the use of FDA‐approved hypnotics, and weak evidence against other agents, 
including trazodone, diphenhydramine, and melatonin. In particular, zolpidem is efficacious for 
self‐report and objective measures, in both adults and older adults. One longer‐term study has 
demonstrated the efficacy of nightly zolpidem over 6 months. This evidence supports our choice 
of zolpidem as one of the medication treatments. Nevertheless, data from a survey of providers we 
collected suggest that many providers are reluctant to prescribe zolpidem because of its DEA Schedule 
IV designation, the risk for physiological dependence, the risk for other side effects such as complex 
sleep-related behaviors, and concerns about co-prescription with other controlled drugs. Trazodone is a 
sedating drug that is FDA-approved for treatment of depression, but is increasingly used at low doses 
off-label for the treatment of insomnia.  Our survey results suggest that many providers perceive it to be 
safer and less likely to produce physiological dependence or other side effects than zolpidem. Moreover, 
a recently published article in JAMA (on which Dual PI Buysse was an author) utilized an analysis of IBM 
Marketscan Research Databases to examine prescribing trends for zolpidem and low-dose trazodone 
(typical doses prescribed for insomnia) in the United States over the period 2011 through 2018. 
Prescriptions for trazodone increased significantly and prescriptions for zolpidem decreased significantly 
over this time interval. In particular, the percentage of adults prescribed trazodone (< 150mg/day) 
increased from 1.25% to 1.82%, whereas the percentage prescribed zolpidem decreased from 4.56% to 
2.50%. Similar trends were observed for patients with listed insomnia diagnoses. Of note, while 
zolpidem remains somewhat more commonly prescribed than trazodone, the gap is clearly closing.  
Despite being used commonly for the treatment of chronic insomnia, there is limited evidence 
of the effectiveness of trazodone for this purpose. The only randomized, placebo-controlled trial in 
adults with primary insomnia compared 1-week and 2-week changes in self-reported sleep latency and 
duration, and sleep quality among 278 patients randomized to zolpidem (10mg), trazodone (50mg) or 
placebo. At the two-week follow-up, neither medication performed significantly better than controls for 
sleep quality; inspection of the data suggest stable effects of the two active medications, and increasing 
effect of placebo over this interval. A 2018 meta-analysis identified 7 randomized placebo-controlled 
trials of trazodone for treatment of insomnia with a total sample size of 4295. Overall findings of the 
meta-analysis indicated no improvements in sleep efficiency, sleep latency, or sleep duration, but 
patients randomized to trazodone did perceive significantly better subjective sleep quality based on 3 of 
the studies that collected this outcome (standard mean difference= -0.41; 95% confidence interval -0.82 
- -0.0; p=.05). Of note, only one of these 3 studies with sleep quality outcomes was conducted in adults 
with primary insomnia, and this study found no significant effects.  
Thus, data supporting the efficacy and safety of trazodone for insomnia is much more limited 
than is the case for zolpidem. There has never been a large, placebo-controlled trial to assess the 
efficacy and safety of trazodone for chronic insomnia. Extensive discussion of these two medication 
options within the COZI Steering, Executive and Hub Advisory Committees leaves us in equipoise 
about which would be the better medication option for a comparative effectiveness trial with CBT-I, 
which professional organizations recommend as the first line treatment for insomnia. This state of 

equipoise had led us to a stakeholder-guided modification to the COZI study: using patient-provider 
medication choice of zolpidem or trazodone for patients in the two medication conditions. 
 
CBT‐I is a multicomponent therapy including education, stimulus control instruction, sleep restriction, and 
cognitive restructuring. CBT‐I is typically administered over 6‐8 individual sessions by a therapist, with 
self‐monitoring and behavioral/cognitive homework between sessions. Meta‐analyses and standards of 
practice papers support the efficacy of CBT‐I vs. active and inactive comparators. Although most trials 
have included small numbers of participants, effects on self‐report measures are robust, with smaller 
improvements on actigraphy and polysomnography. Efficacy is maintained over 6‐12 months. CBT‐I is 
also efficacious among individuals with comorbid conditions. CBT‐I dissemination is limited by the 
number of therapists and inconsistent insurance coverage. Consequently, CBT‐I has been adapted to 
include briefer treatments and self‐guided Internet versions, including Sleep Healthy Using the Internet 
(SHUTi) and Sleepio. Internet CBT‐I has broad accessibility, consistent treatment fidelity, comparable 
efficacy to standard CBT‐I, and sustained effects over 1 year. SHUTi is supported by multiple RCTs, with 
robust data for acceptance, efficacy, and adherence. This evidence supports our choice of CBT-I (SHUTi) as 
the treatment comparator. 
Critical evidence gaps remain. The extant intervention evidence fails to address the most important 
questions to patients and clinicians: Which treatment is best in terms of benefits and harms? Is combined 
treatment better than medication or CBT‐I alone? Which treatment is best for me? Published clinical 
guidelines for the treatment of CID support CBT‐I, hypnotics, or combination treatment, but they also call 
for long‐term studies comparing effectiveness, adherence, and harms of these efficacious treatments. 
Our study directly addresses these critical evidence gaps. 
Previous studies comparing medication, CBT‐I, and combination treatment have included small samples 
(<80) of highly‐ selected participants at single research sites, inadequately powered for examining 
differences in treatment response . No “real‐world” pragmatic clinical effectiveness trials have been 
conducted. Previous studies suggest faster response to medications, but better short and long‐term (12‐
24 months) outcomes with CBT‐I. The evidence base on combination treatment is even more limited, but 
suggests more enduring effects than medication alone, better response and remission rates with initial 
combined treatment(78), and lower zolpidem use. While instructive, these studies offer no definitive 
conclusions regarding comparative effectiveness of treatments for CID. 
Patient preferences favor behavioral over pharmacologic treatments on the basis of acceptability, 
expectation of overall benefits, effects on daytime function, and side effects. However, the treatments 
are expected to be equally efficacious, and treatment acceptability does not correlate with outcomes. 
Obstacles to CBT‐I include limited awareness among providers, few service providers, and higher initial 
cost due to a lack of insurance coverage(81). Primary care providers believe their patients rely too heavily 
on medications, but lack resources to direct patients to CBT‐I. Our own preliminary data (see below) 
further highlight the equipoise experienced by patients and providers. In their own ways, patients and 
providers identify the same knowledge gaps revealed by currently‐available evidence. 
These evidence gaps are uniquely relevant to rural healthcare settings. There is no single definition for 
“rural”. One example is the US Department of Agriculture, which bases its definition on county‐level 
data. “Nonmetro” counties include some combination of open countryside, rural towns (<2500 people) 
and urban areas with <50,000 people. We propose a composite scale for degree of rurality, based on 
individual scores using five established definitions (for further details see Study Design, Section 4 below). 
Limited rural access to medical care across four dimensions—people, place, providers, and payment—is 
well‐documented, and access to specialists is even more limited. The disproportionate impact of 
substance use in rural populations, particularly opiates, raises additional concerns regarding treatment 

with other controlled drugs, such as zolpidem. On the other hand, the generally wide safety profile and 
straightforward prescribing guidelines for zolpidem make it potentially attractive for rural settings. 
Although more rural than urban Americans lack access to in‐home broadband internet (39% vs. 4%), a 
similar proportion use the Internet (69% vs. 75%), and overall Internet use is increasing at the same rate, 
making Internet CBT‐I feasible. Our study addresses specific needs and knowledge gaps in rural settings. 
We can examine HTE with an adequately‐powered comparative effectiveness study. Previous studies 
suggest that characteristics such as sex, marital status, education, and occupation are generally not 
related to CBT‐I outcomes, but these studies have not been powered to detect such differences. Greater 
pre‐treatment severity and older age are associated with larger changes in quantitative sleep 
characteristics, but smaller changes in categorical outcomes, whereas concurrent sleep medication does 
not appear to affect CBT‐I outcomes. Short objective sleep duration and medical/psychiatric 
comorbidities are associated with smaller treatment effects. Most medication trials select insomnia 
patients on the basis of specific symptoms that match the medication’s pharmacokinetics. 
However, multiple sleep symptoms are the most common phenotype, and symptoms change over time. 
Thus, we do not know if specific insomnia symptoms affect HTE. Our study will address which treatment is 
most effective for which patient. 
DECISIONAL DILEMMA: Patients, clinicians, and other stakeholders are concerned about the health 
consequences of inadequate sleep, but they face a quandary in choosing a treatment for CID. Whereas 
CBT‐I is recommended by current guidelines and often preferred by patients, many providers do not 
know how to access it. BzRA are more familiar to patients and providers, but can have significant side 
effects. Existing evidence does not provide adequate guidance for choosing between these efficacious 
treatments. A long‐term, real‐world comparative effectiveness study will provide evidence about the 
relative benefits and harms of CBT‐I, BzRAs, and combination treatment, and about who benefits most 
from which treatment—evidence that will help providers and patients make informed and individualized 
treatment choices. 
 
SPECIFIC AIMS 
To address current evidence gaps we aim to test the following specific aims: 
Specific Aim 1A: To compare the effectiveness of medication preference (zolpidem or trazodone), 
Internet cognitive behavioral therapy for insomnia (CBT‐I), and combination treatment (medication 
preference + CBT‐I) for insomnia symptoms over 12 months. Hypothesis 1a: Internet CBT‐I alone and 
combination treatment will be superior to medication preference alone in improving insomnia symptoms 
over 12 months.  Hypothesis 1b: Individuals receiving combination treatment will use less medication at 
12 months compared to individuals receiving medication alone.   
 
1B: To compare the effects of treatment group assignment on insomnia symptoms, stratified by 
medication preference. Those randomized to the medication alone and combination treatment 
arms will be prescribed the preferred medication. These subgroups will be compared to those in the 
CBT-I alone arm whose provider indicated preference for that medication prior to randomization. 
 
1C: Compare the effects of prescribed zolpidem versus trazodone on insomnia symptoms, within 
the medication and combination treatment arms of the study.  
Specific Aim 2A: To compare the effectiveness of medication preference, Internet CBT‐I, and 
combination treatment for key patient‐ centered outcomes (PCOs), including health‐related quality of 
life, mood, cognition, fatigue, pain and other health outcomes (based on self‐report) over 12 months. 
Hypothesis 2: CBT‐I alone will be superior to both combination treatment and medication alone for 
improving PCOs over 12 months. 

 
2B: To compare the effects of treatment group assignment on PCOs, stratified by medication 
preference. Those randomized to the medication alone and combination treatment arms will be 
prescribed the preferred medication. These subgroups will be compared to those in the CBT-I alone 
arm whose provider indicated preference for that medication prior to randomization. 
 
2C: Compare the effects of prescribed zolpidem versus trazodone on PCOs, within the medication 
and combination treatment arms of the study.   
 
Specific Aim 3A: To compare the adverse effects of medication preference, CBT‐I, and combination 
treatment. Hypothesis 3: Adverse effects (e.g., falls) will be greater in the medication and combination 
treatment groups vs. CBT‐I alone. 
3B: To compare the effects of treatment group assignment on adverse events, stratified by 
medication preference. Those randomized to the medication alone and combination treatment 
arms will be prescribed the preferred medication. These subgroups will be compared to those in the 
CBT-I alone arm whose provider indicated preference for that medication prior to randomization. 
 
3C: Compare the effects of prescribed zolpidem versus trazodone on adverse events, within the 
medication and combination treatment arms of the study. 
 
Specific Aim 4: To conduct an exploratory analysis that will assess the potential for heterogeneity of 
treatment effects with medication preference, CBT‐I, and combination treatment. We will assess 
whether treatment response varies by degree of rurality, as well as sociodemographic (age, sex, race, 
socio‐economic status), health‐related (insurance, comorbidities) and sleep‐related factors (short sleep, 
insomnia treatment preferences). 
 
COZI will address important knowledge gaps related to the treatment of a highly prevalent chronic 
condition with significant impact on PCOs. Our findings will lead to answers for patients, providers and 
other stakeholders addressing insomnia, improved health and function for millions of Americans, and 
sustainable changes in how insomnia is treated. 
COZI will address important knowledge gaps related to the treatment of a highly prevalent chronic 
condition with significant impact on PCOs. Our findings will lead to answers for patients, providers and 
other stakeholders addressing insomnia, improved health and function for millions of Americans, and 
sustainable changes in how insomnia is treated. 
 
STUDY DESIGN 
COZI is a 3‐arm, randomized comparative effectiveness study. Masking is not possible given two very 
different treatment types. 1200 participants (400 per arm) will be randomized to 1) medication (zolpidem 
or trazodone); 2) SHUT); or 3) combination treatment (medication +CBT-I). Sleep and other PCOs, will be 
assessed via Internet questionnaires at post treatment (9 weeks), 6 and 12 months. Self‐reported 
medication use and adverse events will be assessed at all scheduled follow‐ups as well as at some 
additional time points (See assessment grid). 
 
 
 

 
 
 

METHODOLOGY 
 
Study Population and Recruitment 
 
Recruitment will occur in primary care practices in 8 large healthcare systems, each of which has 
significant coverage of patients in rural locations.  The sites represent a diverse sample based on 
geography, socioeconomic status and race/ ethnicity. Study eligibility criteria were designed to be 
inclusive, to maximize the generalizability of study findings, and to ensure patient safety (see 
inclusion/exclusion criteria).  
 
The development of recruitment methods involves collaboration between the RRC, hub research sites, 
and practices, and will be responsive to ongoing experience. Recruitment efforts will be aided by 
educational programs for physicians and clinic staff prior to study start-up, and by the development of 
branded recruitment materials in clinics (e.g., cards, brochures, posters).To facilitate a productive 
working relationship between the sites and practices around recruitment, we will engage in regular 
communication with the use of newsletters, relevant insomnia literature, and CME programs.  
 
We will enroll patients with CID according to International Classification of Sleep Disorders (ICSD-3) 
terminology, or Insomnia Disorder in Diagnostic and Statistical Manual (DSM-5) terminology. We will 
confirm “caseness” using the Insomnia Severity Index (ISI) and a CID diagnostic checklist, administered 
during online screening. An ISI threshold of >10  has the optimal sensitivity and specificity for identifying 
insomnia “cases” in the general population (86.1% sensitivity and 87.7% specificity and thus constitutes 
our inclusion criteria. 
 
To protect the safety of potential patients in COZI, Primary Care Providers (PCPs) who have a potentially 
eligible patient with CID will review the medical history and medication list to determine whether there 
are any obvious contributors that require further treatment or would increase risk of participation. Such 
conditions include depression and anxiety, and other sleep disorders, particularly sleep apnea and 
restless legs syndrome (RLS). We will also use the STOP-BANG questionnaire to assess risk of obstructive 
sleep apnea during the baseline visit, and return results to PCPs and patients. Specifically, we will 
identify whether patients are in the “green (0-2 points),” “yellow (3-4 points),” or “red (5-8 points)” 
zones for STOP-BANG scores, and provide appropriate recommendations for evaluation and treatment 
to PCPs for each level. We will not exclude any potential participant solely on the basis of STOP-BANG 
scores. When neck circumference (the “N” in STOP-BANG) are unavailable for participants, we will use 
modified (STOP-BA[N]G)scores. We will exclude patients who are older than 80 years, in order to 
minimize incidents of adverse cognitive events and falls. PCPs may order appropriate tests, such as 
home or laboratory sleep apnea testing, and change or add treatments for comorbid conditions.  When 
patients present with clinically significant symptoms of other comorbid conditions (e.g., depression, 
anxiety, sleep apnea) the research team will provide results to the PCP so that they may plan for 
appropriate follow-up. 
   
The RRC will assist in the implantation of ongoing participant retention strategies (see Table 1.). 
Table 1. Participant Retention Strategies 
Strategy 
Details 
Minimize 
participant burden 
The online platform centralizes all interactions with COZI into a single point of contact and includes 
mobile notifications and individualized task lists to facilitate participant reporting 
Provide desired 
benefits 
For patients who join the trial in hopes of receiving CBT‐I but are randomized into the  medication  
only arm, we will offer a year of free CBT‐I starting after the 12‐month data collection period 

 
COZI will employ a variety of options for recruitment. This flexibility will allow clinics to use methods that 
fit best into their current workflow while minimizing burden for both clinic staff/physicians and patients.  
These methods include (but are not necessarily limited to): 
 
• 
EHR searches to identify potential study patients, followed by an invitation letter from the 
patient’s PCP 
• 
Self-referral by the patient via their PCP (e.g., in response to posters or brochures) 
• 
Identification of potentially eligible patients by the PCP at the time of visit 
• 
Review of medical records for scheduled patients to identify potential participants 
 
After identifying a potentially eligible patient, the PCP will provide the patient with instructions for 
contacting the local hub research team by telephone or e-mail. In some instances, PCPs may also refer 
assenting patients to COZI hub research staff verbally or electronically (telephone or secure email). If the 
referral includes an email address, hub research staff will send participants an introductory email 
directing them to the study website for additional information and online screening. Alternatively, study 
staff may contact participants by telephone to complete initial screening. 
 
Finally, at hub sites with the capacity to do so, we will program alerts in the EHR, designed to trigger for 
patients who meet inclusion/exclusion criteria. Upon obtaining the patients' initial assent, physicians will 
refer eligible patients to the study by making the appropriate selection in the EHR. PCPs may also refer 
appropriate patients who do not trigger an alert (e.g., a patient with new-onset insomnia) by placing a 
consult, order, or message to COZI in the EHR.  In either scenario, the patient's information will be 
electronically transmitted to the study team for further assessment.      
 
Regardless of the referral route, potentially eligible patients will meet the inclusion/exclusion criteria 
listed below. 
 
Screening Procedures 
 
Initial screening in conducted to ensure that every participant meets the following Inclusion/Exclusion 
criteria: 
 
Inclusion Criteria: 
• 
Age 18-80 
• 
Meets DSM-5/ICSD-3 criteria for chronic insomnia disorder 
• 
Insomnia Severity Index score > 10 
• 
Regular internet and computer access 
• 
Non-metropolitan/rural residence (Rurality Score > 3) 
 
Build in self‐ 
learning 
COZI will support provide feedback to participants and PCPs on selected research assessments (ones 
that do not threaten the validity of testing study aims). Participants will also be a key audience for 
dissemination of study findings. 
Build pride in 
joining 
Study participants often express pride in joining in an important national study that will potentially 
help others. Regular newsletters and study updates will be delivered through the RICE app. 
Express 
appreciation 
Participants feel bonded to a study if they sense appreciation and a relationship. We will nurture 
relationships with texts and email follow‐up reminders; ‘thank yous’ for data surveys; and quarterly 
newsletters. 

Exclusion Criteria: 
• 
Use of prescription hypnotic medication >2 times in the past week. Patients currently taking OTC 
medications (e.g., diphenhydramine, doxylamine) and naturopathic sleep aids (melatonin, 
valerian, chamomile, etc.) are permitted, at the discretion of the prescribing physician.   
• 
Current cognitive or cognitive behavioral treatment for insomnia. 
• 
Psychotic disorder 
• 
Bipolar disorder 
• 
Current substance use disorder 
• 
Severe pulmonary disease that raises concerns regarding respiratory depression with sedative-
hypnotic agents, as judged by the COZI Investigators. 
• 
Cognitive impairment or dementia 
• 
History of spontaneous or hypnotic-induced complex sleep behavior 
• 
Delayed sleep phase disorder (DSPD) 
• 
Shift work that includes working the night shift (between the hours of 12:00 a.m. - 6:00 a.m.) 
• 
History of fall resulting in a fracture or other injury, in the past 12 months.  Currently pregnant, 
planning to become pregnant, or breastfeeding 
• 
Other severe or uncontrolled mental or physical disorders that would make participation 
difficult or unsafe 
• 
Current use of opiate medication 
 
Inclusion/exclusion criteria will be communicated to PCPs and provided in research recruitment 
materials (cards, flyers, etc.). We will verify inclusion/exclusion criteria during the online or telephone 
screening visit. We will confirm the presence of CID using a diagnostic checklist, and insomnia severity 
using the Insomnia Severity Index (ISI) during screening.  An ISI threshold of >10 has the optimal 
sensitivity and specificity for identifying insomnia “cases” in the general population. Exclusion criteria 
are also addressed in the online/phone screen:  hypnotic use; psychotic disorder; bipolar disorder; 
current substance use disorder; COPD or other severe pulmonary disease; cognitive impairment; history 
of complex sleep behavior; history of fracture or injurious fall within past 12 months; and current 
pregnancy, planning to become pregnant or breastfeeding. 
Rurality of practices and participants will be determined by the COZI Rurality Score. The COZI 
Rurality Score was created by reviewing the existing national definitions of rurality and integrating 
those that do not overlap into a combined score. The COZI Rurality score includes % rural 
population, rural urban commuting area (RUCA) and urban influence code (UIC) scores, Centers for 
Medicaid and Medicare Services (CMS), and frontier and remote area (FAR) scores. (CMS scores in 
turn reflect Census Bureau-defined non-urbanized areas and health professional shortage area 
(HPSA) or medically underserved area/population MUA/P eligibility.) Utilization of the COZI Rurality 
Score allows for characterization of rurality across a continuum. Scores range from 0 (not at all 
rural) to 40 (highly rural); a score >3 is required for clinic/ patient participation. 
 
The initial screening involves self-report information regarding demographics and general health 
information. Participant assent for screening will be obtained, per a written screening script. Screening 
may be conducted either online or by telephone; prospective participants may select their preferred 
method. For increased security, the online screening will be done using the Research Infrastructure 
Containing E-interventions (RICE) system developed at the University of Virginia under the leadership of 
Lee Ritterband, PhD, Digital Therapeutics Core Director. 
 

For screenings conducted by telephone, the screener will begin with a brief description of the research 
study, interventions, and assessment procedures. If the patient expresses interest and gives verbal 
assent, staff will ask screening questions to determine eligibility. Staff will enter patient responses 
directly into the data base via web interface. If the patient is eligible to proceed and expresses interest, 
the screener will schedule a time for the patient to review the study consent form with the study 
coordinator. In states where required by law (Pennsylvania), a physician or licensed nurse practitioner 
will participate in the informed consent process.  
Online web-based screening includes the same sequence of events except that the participant reads the 
material and selects their answer(s) Participants will be informed following the online screen if they are 
not eligible to proceed in the study. Eligible participants will be instructed to contact study staff to 
schedule their consent call.  Study staff will also monitor online screening completions and will reach out 
to eligible participants to ensure that all eligible participants have the opportunity to schedule the 
consent call. If eligibility is questionable (as determined by decision rules to responses) participants will 
be notified that a staff member will call them to discuss the next step. During this call, clarification will 
be sought to determine eligibility. 
 
Voluntary Informed Consent 
 
After screening, hub research staff will send eligible participants an email including a study summary, 
consent form, and the URL for the study website. The website will also include the study summary and 
consent form. Participants will be encouraged to review both documents prior to the consent visit. 
During the consent visit, which will take place via telephone call or videoconference, the patient will 
have the opportunity to ask questions and request clarifications. After a discussion of study procedures, 
risks, benefits, and responsibilities, interested participants will electronically sign the consent form. 
Upon signing the consent, the participant will receive a signed, pdf copy of their consent via e-mail.   
 
After informed consent has been obtained, the EHR will be used to verify the absence of exclusionary 
factors (i.e. fall risk, etc.).  For those participants whose EHR is not accessible, hub research staff will 
contact the PCP to clarify any outstanding questions related to safety, eligibility and participation.    
 
Baseline 
 
Participants who are eligible after the initial telephone/online screening and consent visit will complete 
a set of self-report assessments via web interface (see Table below). Except as otherwise indicated, the 
same assessments will be used at the 9-week, 6-month, and 12-month evaluations. These assessments 
will allow us to address each of the specific aims of the study. We estimate the total time for completion 
of self-report assessments is 20-90 minutes (allowing 2.5 – 8 questions completed per minute). 
 
Randomization 
 
Following completion of the baseline assessment, eligible patients will be randomized to one of three 
interventions: Medication, Medication + CBT-I , or CBT-I, in a 1:1:1 ratio.  The randomization will be 
stratified by enrollment hub and medication preference. The DCC will oversee randomization 
procedures. In brief, the study statistician at the DCC will provide each enrollment hub with a REDCap 
randomization form that will allow the study coordinator to perform randomization by enrollment hub, 
and stratified by medication preference (using a protected listing of random treatment assignment. 

These listings will be generated using a Stata program that generates randomly permuted blocks of size 
3 and 6, stratified by enrollment hub and medication preference. 
 
Intervention 
 
Medication 
 
  Medication will be prescribed by the participant’s primary care provider (PCP) or, in the event that the 
patient does not have a PCP, by a COZI study physician. Following randomization to either of the two 
medication arms (i.e., medication alone or medication + CBT-I), the participant and prescribing provider 
(PCP or COZI physician) will be notified by the study team. The patient’s prescribing provider (PCP or 
COZI physician) will then prescribe zolpidem or trazodone and monitor treatment throughout the one‐
year follow‐up period as per usual care in this pragmatic trial.  There is no difference in risk depending on 
whether the PCP or COZI physician prescribes.  Providers will use their discretion in prescribing, guided by 
their patient’s progress and standard clinical guidelines.  For zolpidem, this will include a starting dosage 
of 5 mg, with an increase to 10 mg, if needed and based on response, for patients <65 years old. 
Trazodone, when prescribed for insomnia, is usually administered in doses of 25 – 100 mg, which is below the 
therapeutic range for depression treatment. In the COZI study, we will recommend a starting dose of 25 mg, 
with dose adjustments to a maximum of 100 mg at the discretion of the provider based on patient response. A 
standard order set will be provided to physicians, as well as a brief set of written instructions, with tips 
for optimal use of the medication (e.g., when the medication should be taken, frequency, dosage, etc.).  
This information will also be covered in the CME program provided prior to the start of recruitment. 
Frequency of medication use will be tracked as an outcome over the one‐year follow‐up period, based 
on self‐report ascertained at regularly scheduled follow‐ups. Note that, in contrast to medications for 
other health conditions, intermittent use several times per week, rather than every night, is often 
recommended for hypnotics (including zolpidem) in order to reduce dependence and tolerance.  
Intermittent dosing is efficacious for zolpidem  and conforms to actual patterns of medication use. The 
effectiveness of intermittent dosing for trazodone is not well documented.  Therefore, traditional measures of 
“adherence,” based on the assumption of daily medication use, are not necessarily appropriate for 
medication in COZI, and might better be termed “pattern of use.”  

COZI Study Assessments 
 
Category 
Instrument 
Primary/ 
Secondary/ 
Descriptive, 
Aim #1 
Survey Total 
Score (18 
max) 
# Items 
Assessment Points 
Screen 
Base 
1, 9 Mos 
9 Wks, 6 Mos, 
12 Mos 
Demographics 
Demographics Questionnaire 
D 
N/A 
14 
 
X 
 
 
Degree of Rurality 
COZI Rurality Score 
D 
N/A 
1 
X 
 
 
 
Insomnia 
Insomnia Severity Index (ISI) 
P1 
18 
7 
X 
X 
 
X 
Insomnia 
DSM-5/ICSD-3 Insomnia Diagnosis 
S1 
N/A 
7 
X 
 
 
X 
Insomnia 
Insomnia treatments 
D 
N/A 
3 
 
X 
 
X 
Sleep 
SASS-Y (retrospective diary analogue) 
S1; D4 
N/A 
16 
 
X 
 
X 
Sleep 
Pittsburgh Sleep Quality Index (PSQI) 
S1 
13 
18 
 
X 
 
X 
Sleep 
Epworth Sleepiness Scale (ESS) 
S1 
13 
8 
 
X 
 
X 
Sleep apnea 
STOP BANG2 
D 
N/A 
9 
 
X 
 
 
Circadian 
rhythms 
Munich Chronotype Questionnaire 
(MCTQ)3 
S1, D 
10 
63 
 
X 
 
X 
Sleep Moderators- 
Mediators 
Dysfunctional Beliefs and Attitudes 
about Sleep 
S4 
12 
104 
 
X 
 
X 
Moderators- 
Mediators 
Treatment Credibility/ Expectancy 
S4 
11 
5 
 
X 
 
 
Moderators- 
Mediators 
Client Satisfaction Questionnaire 
S4 
N/A 
8 
 
X 
 
X 
Mental Health: 
Depression 
PHQ-8 
S2 
11 
8 
 
X 
 
X 
Mental Health: 
Anxiety 
GAD-7 
S2 
12 
7 
 
X 
 
X 
Cognitive Health: 
Cognitive function 
PROMIS Cognitive Function 2.0 8a 
S2 
16 
8 
 
X 
 
X 
Health: Fatigue 
PROMIS Fatigue SF 
S2 
8 
6 
 
X 
 
X 
Health: Pain 
PROMIS Pain Intensity SF 3a 
S2 
7 
3 
 
X 
 
X 
Health: Pain 
PROMIS Pain Intensity SF 6a 
S2 
7 
6 
 
X 
 
X 
Health: Nocturia 
Nocturia questionnaire 
S2 
N/A 
3 
 
X 
 
X 
Health: General 
Medications, tobacco, alcohol, 
caffeine5 
D 
14 
10 
(estimated) 
 
X 
X 
X 

Health: General 
Medical History Checklist 
D 
14 
25 
 
X 
 
 
Health: COVID 
related 
COVID questionnaire 
D, S4 
N/A 
10 
 
X 
 
X? 
Health: Quality of 
life 
Medical Outcome Survey SF-12 
S2 
10 
12 
 
X 
 
X 
Health: Quality of 
life 
PROMIS Global health 
S2 
10 
10 
 
X 
 
X 
Adverse effects 
Insomnia Treatment Side Effects 
S3 
15 
3 
 
 
X 
X 
Adverse effects 
Falls 
S3 
10 
10 
 
X 
X 
X 
Process measures 
Exit interview if withdrawn 
D 
15 
 1 
 
 
X 
X 
Global severity 
Patient Global Impressions Severity 
S1 
13 
1 
 
X 
 
X 
Global 
improvement 
Patient Global Impressions 
Improvement 
S1 
13 
1 
 
 
 
X 
Total questions 
per assessment 
 
 
 
228 
7 
223 
24 
175 
Completion time, 
minutes6 
 
 
 
 
1-3 
27-87 
3-8 
22-70 
Primary 
Outcomes 
 
 
 
106 
 
 
 
 
Secondary 
Outcomes 
 
 
 
78 
 
 
 
 
Sample 
descriptives 
 
 
 
44 
 
 
 
 
Primary + 
Descriptive 
 
 
 
150 
 
146 
 
122 
Completion time, 
minutes6 
 
 
 
 
 
19-59 
 
16-50 
1P = Primary Outcome. S = Secondary Outcome. D = Sample descriptive and/or HTE factor. Numbers indicate Specific Aim #.  
2May need to exclude N = Neck Circumference 
3SASS-Y and MCTQ instruments contain mostly overlapping questions. 6 = number of questions unique to MCTQ 
4DBAS has short forms with either 10 or 16 items 
5No set number of items; counted as 5 items for TOTAL number of items 
6Estimated time for completion based on 2.5 – 8 questions/minute 
 
 
 
 

CBT-I (via SHUTi program) Intervention 
 
SHUTi is a self-guided, automated, interactive, and tailored web-based program modeled on the primary 
components of CBT-I: sleep restriction, stimulus control, cognitive restructuring, sleep hygiene, and 
relapse prevention. Intervention content is metered out over time through 6 “Cores.” Users obtain 
access to a new Core based on a time and event-based schedule (e.g., 7 days after completion of 
previous Core). This schedule is consistent with recommendations from the American Academy of Sleep 
Medicine deeming 6–8 sessions an “adequate treatment exposure.” SHUTi uses online sleep diaries to 
track progress and to tailor treatment (e.g., assign a “sleep restriction” window). Each Core acts as an 
online analog for the weekly sessions of traditional CBT-I, and follows the same structure: 1) Core 
objectives (what will be learned and why it is important), 2) Review of previous week’s homework and 
sleep diary data, 3) New intervention material, 4) Assignment of homework (treatment strategies for the 
coming week), and 5) Summary of the Core’s main points. Intervention content is enhanced through 
interactive features including personalized goal-setting, graphical feedback based on participant-specific 
symptoms, animations/ illustrations, quizzes to test user knowledge, patient vignettes, and video-based 
expert explanation. Automated emails encourage program adherence. After completing the post-
assessment battery and sleep diaries, individuals will have continued access to the online program for 1 
year. Data will be collected through the 6 cores/diaries only.  
 
CBT-I + Medication 
 
Participants in the combined CBT-I + medication arm will complete procedures for each of the respective 
arms as described above. 
 
Adverse Events Reporting 
Patients in the medication conditions will receive printed material describing pharmacotherapy treatment, 
including instructions to discontinue use if serious side effects or other concerns arise.  Adverse events will be 
monitored in two ways: 
• 
Spontaneous Reporting: Participants and PCPs will be encouraged to report any side effects or 
adverse events during the intervention. The randomization assignment e‐mail will encourage 
participants to contact their physician and the study team if they experience any AEs during the 
course of their intervention. Patients and physicians will be instructed on how to spontaneously 
report adverse events on-line or by telephone, 24 hours per day, 7 days per week, for the 
duration of the study. Serious adverse events will be followed up by telephone contact from the 
site coordinator within 24 hours, including assessments with the instruments described in the 
following paragraph. The intervention section of the study website will also include instructions 
for reporting AEs. 
o If a participant spontaneously indicates suicidal ideation during any contact with the 
study team, we will initiate a Suicide Risk Management Protocol (SRMP).  This protocol 
includes procedures adapted from those previously utilized in online treatment studies 
of depression, including the PCORI funded Optimum study.  The SRMP begins with an 
interview of the participant by the site Project Coordinator, following an online 
structured interview format. The online interview automatically generates a risk rating.  
These ratings are tied to specific recommended actions, ranging from the provision of 
information to contacting emergency authorities for a safety check.  Each study site will 
generate a list of local mental health resources for the counties serving each primary 
care practice, in the event that such resources are needed. 

• 
Questionnaire‐Based Reporting: Side effects will be monitored with the Insomnia Side Effects 
and Insomnia Side Effects/Falls questionnaires, administered at each assessment battery. 
Patients will complete adverse effect checklists at 1 month, 9-week, 6-month, and 12-month follow-up 
evaluations. The checklist is adapted for insomnia treatment from the Frequency/Intensity/Burden 
(FIBSER) instrument. Scores >3 on any item will be reported to patients’ physicians, and will be followed 
up with a telephone evaluation by study staff, at which point the Adverse Events checklist  will be 
completed by the site coordinator to determine severity and assessment of causality. Results of this 
evaluation will be reported to the patient’s physician, the Study Advisory Committee (SAC) and Data 
Safety Monitoring Board.  
 
 
FOLLOW-UP 
 
Participants in each of the three intervention conditions will complete follow-up assessments at 9 
weeks, 6, and 12 months following the start of the intervention. These assessments will be conducted by 
Internet-based questionnaires. Refer to the Table above for specific assessments. 
  
In addition to these time points, participants will be asked about falls and other adverse events at 1 
month, 9 weeks, 6 months, 9 months, and 12 months following the start of intervention.  The falls and 
adverse event questionnaires are estimated to take about 1-3 minutes. 
 
Participants will be contacted by secure e-mail via University of Virginia’s "Secure" email setting in order 
to prompt them to complete their follow-up assessments. 
 
If we are unable to reach the participant by secure e-mail, we will attempt to contact him/her by 
telephone and/or email to encourage them to complete them. 
 
 
DATA MANAGEMENT AND STUDY COORDINATION 
 
Data Management and Study Coordination. The Data Coordinating Center (DCC) at the SFCC (CPMC) has 
provided data management and study coordination services to large-scale multi-center studies for decades. 
Their services typically include provision of a standard Research Data System for data collection, editing, 
querying and cleaning and a study web site to facilitate data management and administration. The DCC 
also distributes and maintains the numbered memo system to archive all study-wide communication. While the 
COZI trial will utilize RICE (hosted by the University of Virginia) as the data collection platform, the data 
managers and statisticians at the DCC will be responsible for final data cleaning and we will utilize DCC 
servers for long‐term storage of all data files and access to network services and SAS statistical software. 
DCC data managers will access and download data directly from the RICE platform using a secure 
connection. The DCC will oversee the DSMB and will perform the interim and final statistical analyses to 
address the specific aims of the COZI study. Finally, the DCC will track and manage all study publications 
and ancillary studies and ensure adherence to established publications and ancillary study guidelines. 
 
STATISTICAL ANALYSIS PLAN 
 
1. 
Outcomes 
 

Definition of Insomnia Severity Index outcomes. Insomnia Severity Index (ISI): The ISI is a 7‐item patient-
reported outcome that is well‐validated as an insomnia outcome measure. The ISI has acceptable 
internal consistency (Cronbach’s ɑ=.76‐.78); excellent face and content validity; correlates with sleep 
diaries, polysomnography, and interviews; and has 86.1% sensitivity and 87.7% specificity for detecting 
insomnia cases in the community. The ISI takes <5 minutes to complete, making it useful for clinical 
settings, and has been validated in Spanish. Scores range from 0 to 28, with the following interpretations: 
0‐7 (no clinically significant insomnia), 8‐14 (subthreshold insomnia), 15‐21 (clinical insomnia, moderate 
severity), and 22‐28 (clinical insomnia, severe). The primary endpoint will be change in ISI score from 
baseline to 12 months. Secondary outcomes will be treatment response, using the established definition 
of ≥ 6 point reduction in ISI score from baseline to follow‐up, and remission of insomnia, defined as ISI <8 
at follow-up. 
 
Definition of other patient‐centered outcomes. The COZI Assessments Table summarizes other key 
patient-centered outcomes (PCOs) to be used in secondary analyses (classified as “S2” under variable 
type/aim). We will assess the comparative effectiveness of therapy on PCOs that have not been 
extensively studied in prior insomnia studies. Our outcomes were informed by patient survey results.  
 
Safety outcomes. Safety outcomes, including self-reported falls and other insomnia treatment side effects, are 
indicated in the COZI Assessments Table under the variable type/aim category “S3”. 
 
2. 
Data Analysis 
 
Assessment of balance between treatment arms. To assess balance of the treatment and control 
samples, we will compare participant characteristics by treatment arm, overall and stratified by 
recruitment hub.  These checks will use linear, logistic, and other generalized linear models as 
appropriate to the distribution of each of the baseline covariates.  Baseline variables to be considered 
will include participant age, gender, race, body mass index, education, lifestyle factors, medical history, 
and degree of rurality.   
 
Primary Analysis. Our causal model posits that the 3 treatments will have differential effects on 
insomnia severity, measured by ISI, and on other PCOs.  Primary analyses will be conducted by 
treatment assignment, without regard to treatment adherence; sensitivity analyses with multiple 
imputation of missing outcomes are described below. The primary analysis will estimate between‐group 
differences using linear mixed models (LMMs) for repeated ISI scores at 9-wks, 6-mos, and 12-mos, 
adjusting for baseline score, with normalizing transformation as needed. The primary analysis will model 
treatment differences as constant across outcome visits. Within‐site and within‐participant correlation 
will be accounted for using nested random effects. This approach makes efficient use of partial data for 
participants with incomplete follow‐up data. LMMs will also be used for continuous secondary PCO 
outcomes, after normalization if needed Adverse events will be compared using generalized linear 
mixed models (GLMMs) or exact methods, depending on frequency; a negative binomial GLMM will be 
used for falls.  
 
Sensitivity Analyses. Extensive baseline data will be collected on baseline factors known to affect sleep 
quality, which will be reported by treatment assignment. In addition, sensitivity analyses will be 
conducted adjusting for any imbalanced baseline factors that can be shown to affect outcomes. 
Additional sensitivity analyses will check for variation in between‐group differences across the three 
follow‐up visits. 
 

Heterogeneity of Treatment Effects. Powerful significant effect modifiers may help identify subgroups in 
which one treatment is clearly preferable or contraindicated. For example, although many patients with 
CID will have had exposure to hypnotics and other insomnia medications, we do anticipate that some 
will be naïve to medication treatment. We can ascertain prior medication treatment by self-report and 
EHR review. We expect nearly all patients will be naïve to structured behavioral treatment. We can 
address prior treatment experience in subgroup analyses as part of the broader heterogeneity of 
treatment effect analyses. To assess HTE, we will add interactions between treatment group and the 
hypothesized effect modifier to LMMs for the primary analysis. Within‐subgroup treatment effects will 
only be reported if the test for effect modification is statistically significant at P<0.05; all pre‐specified 
subgroup analyses will be reported. In exploratory analysis, we will use the Least Absolute Shrinkage and 
Selection Operator (LASSO) to develop a multivariate model predicting differential treatment response.  
Finally, we will assess between‐site differences in treatment effects. 
 
Treatment Effects among candidates for Zolpidem or Trazodone. The methods proposed for the primary 
analyses will also be implemented within the two subgroups of participants identified by their provider 
as candidates for zolpidem or trazodone before randomization. This preference will be a stratification 
factor in the randomization, thereby ensuring balance in medication preference across all treatment 
arms of the study. Using this strategy, zolpidem/trazodone users in the medication only and combined 
treatment groups can be compared to those in the CBT-I arm with the same medication preference, 
which avoids confounding of the primary between-group comparisons.  Likewise, overall between-arm 
comparisons will be unconfounded by this factor for the same reasons. Nevertheless, we will include an 
indicator for trazodone/zolpidem preference in models that test overall between-group comparisons, to 
account for the stratification of the randomization.  
 
Head-to-head comparisons of Zolpidem vs Trazodone. We will also compare the associations of zolpidem 
vs trazodone (in the medication alone and combined therapy arms) with study outcomes using LMMs 
and GLMMs as appropriate. For these head-to-head comparisons of zolpidem to trazodone, which will 
be confounded by indication, we will develop propensity scores using multivariate logistic models with 
provider preference for zolpidem as the outcome, and predictors including factors that we hypothesize 
influence medication preference, informed by the literature and our own provider surveys. These will 
include patient factors such as age and gender, health status (e.g. frailty) and comorbidities, history of 
substance abuse or concurrent opioid use, and depression. Other factors that drive medication 
preference may occur at the provider or practice level. Such factors, e.g., concern that zolpidem is a 
controlled substance, will be considered for inclusion as nested random effects in the propensity score 
model.  We will be careful to accommodate non-linearities and between-factor interactions in 
developing the propensity score model.  Because propensity score model development will be 
conducted without regard to ISI, inflation of type-I error by model selection is not a concern.  In 
addition, we will consider inclusion of nested random effects for center and provider in the logistic 
propensity score model, which has been shown to reduce bias in some circumstances. Propensity scores 
will then be estimated by the fitted probability of the observed provider choice, based on the final 
model.  After checking for the overlap of the propensity score distributions between the zolpidem and 
trazodone groups, we will incorporate the propensity scores in outcome models comparing ISI scores as 
inverse probability weights.  
 
Missing Data. Although extensive efforts will be made to maximize retention and minimize missing data, 
some missing data is inevitable. We will record and report reasons for dropout and missing data. The 
proposed LMMs will provide consistent estimates as long as the data are covariate‐dependent missing 
at random (CD‐MAR), the model includes the covariates justifying CD‐MAR, and both its fixed and 

random components are correctly specified. Using sensitivity analyses, we will multiply impute missing 
outcomes, first under the standard CD‐ MAR assumption, and then under plausible missing‐not‐at‐
random scenarios—for example, under the hypothesis that   ISI scores are systematically lower than 
expected under CD‐MAR after dropout in the CBT‐I arm. Multiply imputed data will be analyzed using 
standard methods that account for uncertainty due to missingness. 
 
Multiple comparisons. For the primary outcome, ISI scores, hypothesis testing for between‐arm 
treatment   differences will be conducted using the Hochberg procedure, a more powerful alternative to 
Bonferroni correction, to achieve a familywise error rate (FWER) of 5%. The Hochberg procedure will 
also be used for the additional secondary outcomes. Because power in the HTE analyses will be 
relatively limited, as shown below, a false discovery rate (FDR) of 20% will be targeted. To preserve 
power, no corrections will be made for multiplicity in comparing adverse event rates.  
 
3. 
Frequency of Analysis and Stopping Rules 
A statistical analyst at the DCC will analyze the primary and secondary outcomes after all participants 
have completed the 12-month follow-up and the final dataset is cleaned and released for analysis. 
Interim reports including limited analyses will be prepared for presentation to the DSMB during bi-
annual meetings over the course of data collection. Interim reports will be derived from the database as 
it exists on pre-specified dates, and full copies of the database at the time of each interim analysis will 
be archived. These reports will generally include progress in recruitment and participant status, 
descriptives and safety outcomes (overall, by recruitment hub, and by masked treatment assignment). 
All tables shown by masked treatment assignment will be shared with the DSMB during the closed 
session of each meeting.  
 
We will not apply any stopping rules for effectiveness, but may implement stopping rules for safety if 
suggested by the DSMB. 
 
4. 
Adherence.  
Conceptually, adherence to behavioral and pharmacologic interventions is quite different: We aim for 
patients to follow behavioral recommendations every day, whereas optimal adherence to hypnotic 
medication (for efficacy and safety) may involve less‐than‐nightly use. Adherence to CBT-I  will be 
measured using automatically collected records of access to the system (e.g., core completions, diaries). 
Adherence to medication will be assessed using retrospective self‐ report of frequency of use. In both 
cases, these data may not reflect true adherence. We will use GLMMs to identify independent baseline 
correlates of adherence within each arm. We will also assess the association of adherence with ISI and 
other PROs within each arm. Because no common, valid adherence measure is available for all 3 
treatment arms, correlates of adherence may differ. We will therefore exercise caution in interpreting 
mediation of treatment assignment by adherence and will not estimate between‐group differences in 
treatment efficacy by adherence. 
 
5. 
Blinding 
The patient and treating physician will be notified of the treatment assignment following randomization 
and completion of the baseline questionnaire. Blinding of patients, treating physicians and COZI project staff 
who will be interacting with patients or working with data is not possible given two very different treatment 
types. However, the COZI study principal investigators (Drs. Stone and Buysse) will be blinded to any study 
results until after all data collection is complete and a final dataset is ready for analysis. 
 
6. 
Sample Size and Power (Minimum Detectable Effects [MDEs]) 

Assuming 25% attrition by 12 months, and within‐subject correlations between follow‐up ISI scores of 
0.67 and between baseline and follow‐up scores of 0.4 [L. Ritterband, personal communication], 400 
patients per group will provide 80% power in 2‐sided tests to detect standardized between‐group 
differences of .20 standard deviations (SDs) in the primary outcome. These estimates are obtained using 
a conservative type‐I error rate of 0.05/3, as would obtain under the Hochberg procedure if only one of 
the three pairwise null hypotheses can be rejected, and would shrink if > 1 can be rejected. Based on 
results of EHR queries of COZI practices, we expect approximately 25% of COZI patients will be referred by 
practitioners who indicated preference for zolpidem, whereas 75% may favor trazodone. These proportions would 
be expected yield 100 zolpidem users and 300 trazodone users in the medication alone and combined arms of the 
study, and similar numbers of patients in the CBT-I only arm whose provider indicated preference for zolpidem or 
trazodone, respectively. However, we have calculated power for a wider possible range of preference proportions 
(50, 75, and 90% trazodone preference; and corresponding proportions of 50, 25 and 10% zolpidem preference). 
MDE estimates for subgroup comparisons (Aims 1B and 1C) across this range of scenarios based on the primary 
outcome (change in ISI) are shown in Table 9.  
 
Table 9. MDEs (SD units) for Subgroup Comparisons involving Trazodone vs Zolpidem 
Trazodone Preference, % (N per group) 
50 (200) 
75 (300)  
90 (360) 
Zolpidem Preference, % (N per group) 
50 (200) 
25 (100) 
10 (40) 
Aim 1B MDEs: Trazodone to CBT-I* 
0.28 
0.23 
0.21 
Aim 1B MDEs: Zolpidem to CBT-I* 
0.28 
0.40 
0.63 
Aim 1C MDEs: Trazodone to Zolpidem** 
0.26 
0.30 
0.43 
*Comparison to CBT-I group in the corresponding medication preference stratum  
**Comparison of prescribed medications within the medication alone arm, or combined therapy arm, after penalization for the 
adjustment for propensity score 
 
MDEs for overall between‐group differences in other PCOs will be 0.16 SDs before penalization for multiple 
comparisons, and 0.20-0.21 SDs, using a conservative type‐I error rate of .05/9 (Aim 2A).  MDEs for Aim 2B 
will range from 0.21 to 0.63 SDs, depending on the proportions in the strata indicating preference for 
trazodone and zolpidem. For head-to-head comparisons of zolpidem 
to trazodone in the medication or combined therapy arms (Aim 2C), 
MDEs will be 0.26-0.43 SDs, again depending on stratum sizes.  As 
noted above, epidemiologic evidence suggests that use of 
zolpidem and other hypnotics may have negative impacts on 
some PCOs such as cognition, as well as safety outcomes such as 
falls. In contrast, trazodone may have less harmful effects, or 
even beneficial effects for some outcomes such as cognition. 
Finally, for HTE analyses (Aim 4), MDEs for effect modification are 
shown in Table 10.  
 
We will ensure that there is sufficient balance by medication preference (trazodone vs zolpidem) across 
all study arms through stratified randomization. In order to ensure that we will have at least 10% of 
study participants randomized in the zolpidem preference stratum, we will specify a priori time-points 
during the study timeline to assess and report distribution of medication preference to our DSMB; we 
will make adjustments to our recruitment plans as needed by extending enrollment or targeting 
practices that prefer a particular medication.  
 
 
DISSEMINATION OF RESULTS AND PUBLICATION POLICY 
Table 10. Minimum Detectable Effect 
Modification (SDs) 
ICC of 
Outcome 
Proportion in Subgroup 
(%) 
30 
40 
50 
0.1 
0.32 
0.30 
0.29 
0.5 
0.35 
0.33 
0.32 
0.7 
0.32 
0.30 
0.29 
0.9 
0.21 
0.20 
0.19 

 
Results will be posted on clinicaltrials.gov.  
 
We will disseminate our findings to multiple stakeholder constituencies using methods tailored to each. 
We will work with our SAC and PSEC to develop strategies for dissemination to patient constituencies. 
Strategies will include publications in print and electronic media, web content, and podcasts. 
Dissemination to patients will also benefit from our partnerships with multiple patient advocacy 
groups, most notably Project Sleep and American Alliance for Healthy Sleep. We have previously 
partnered with these groups on a PCORI‐funded engagement award and have published with them. The 
investigators have a strong history of dissemination to scientific constituencies via peer‐reviewed 
publications, conference presentations, and invited talks. The investigators serve as Deputy, Associate, 
and Editorial Board members on all of the major sleep‐focused journal, as outlined in investigator 
biosketches. Similar strategies can be used to disseminate information to providers, health care 
systems, payors, and product manufacturers. The investigators will publish and present not only in 
sleep specialty venues, but also in primary care and health care‐focused journals and meetings. 
Publications and study reports will also include detailed information regarding study premise, design, 
implementation, measures, and findings. These details will permit reviewers, readers, and stakeholders 
to assess the study’s internal and external validity (IR5). As leaders in the field of sleep medicine, the 
study investigators are in frequent contact with professional sleep medicine organizations such as the 
AASM, SRS, SRN, and SBSM, and serve in multiple volunteer roles. For example, Drs. Parthasarathy and 
Buysse presented a session on PCOR for the trainee program at the SLEEP 2018 meeting; Dr. Buysse and 
other investigators and consultants have served on numerous AASM consensus conference and 
guideline panels. Each of these organizations will disseminate study findings via existing communication 
tools. There is significant potential for the study findings to be implemented in clinical practice and 
improve delivery of care. We will de‐brief patients and clinicians regarding their treatment experiences 
in COZI, and use their feedback to develop a treatment manual for deploying zolpidem and/or CBT‐I in 
clinical practice. Our patient‐ stakeholder panel includes payors, purchasers, product manufacturers, 
and policymakers. Our professional sleep medicine organization partners advocate for legislation and 
regulations that promote patient access to high quality sleep care which end‐users and decision‐makers 
will use to make policy and treatment decisions. 
 
We will adhere with all policies pertaining to PCORI‐funded research in order to make the results 
available to study participants. We will make results available to study participants through multiple 
mechanisms: (A) Our co‐investigator and stakeholder, Ms. Flygare of Project Sleep, will encourage 
enrollment of study participants in Project Sleep social media accounts in Twitter and Facebook. Upon 
study completion, we will post study results on these outlets and disseminate the study findings to the 
participants and wider membership of Project Sleep. (B) Study participants will have the opportunity to 
meet with study staff face‐to‐face for a debriefing at the end of their participation of their individual 
results that can be shared without compromising the integrity of the study. (C) We will populate a study 
website with peer‐reviewed manuscripts from the study. Lay narratives and abstracts will also be posted 
on this website. 
 
 
DURATION OF THE PROJECT 
 
COZI will be 4-years in duration.  This includes start-up preparations, 2 years of recruitment, follow-up 
assessments and study close-out. 
 

COZI Study Abbreviations 
 
BzRA 
 
Benzodiazepine receptor agonists 
CBSA 
 
Core Based statistical area 
CBT-I 
 
Cognitive behavioral treatment of insomnia 
CCC 
 
Clinical Coordinating Center 
CD-MAR 
Covariate‐dependent missing at random 
CID 
 
Chronic insomnia disorder 
COZI 
 
Comparative Effectiveness of Zolpidem and Cognitive Behavioral Therapy for Insomnia  
in Rural Adults 
 
CMS 
 
Centers for Medicaid and Medicare Services 
CPMC  
California Pacific Medical Center 
DCC 
 
Data Coordinating Center 
DSMB  
Data Safety Monitoring Board 
EC 
 
Executive Committee 
EHR 
 
Electronic health record 
FAR 
 
Frontier and remote area 
FDR 
 
False discovery rate 
FWER  
Familywise error rate 
  
GLMM  
Generalized linear mixed model 
HAC 
 
Hub Advisory Committee 
HPSA 
 
Health professional shortage area 
HTE 
 
Heterogeneity of treatment effect 
LMM 
 
Linear mixed model 
MSA 
 
Metropolitan statistical area 
MUA 
 
Medically underserved area 
MUAP  
Medically underserved area and population 
MUP 
 
Medically underserved population 
OMB 
 
Office of Management and Budget 
PCO 
 
Patient centered outcome 
PCP 
 
Primary care practitioner (prescribing provider) 
PCORI  
Patient-Centered Outcomes Research Institute 
PI 
 
Principal Investigator 
PSEC 
 
Patient Stakeholder Engagement Core 
RRC 
 
Recruitment and Retention Center 
RUCA  
Rural urban commuting area 
RUCC  
Rural urban continuum code 
SAC 
 
Study Advisory Committee 
SC 
 
Steering Committee 
SD 
 
Standard deviation 
SHUTi  
Sleep Healthy Using the Internet 
UCSF 
 
University of California, San Francisco 
UIC 
 
Urban influence code 
UPMC  
University of Pittsburgh Medical Center