 COMMENTARY 
 

Improving the Reporting Quality of Nonrandomized Evaluations 

of Behavioral and Public Health Interventions: 

The TREND Statement 

Developing an evidence base 

for making public health decisions will require using data 

from evaluation studies with 

randomized and nonrandomized designs. Assessing individual studies and using studies in quantitative research 

syntheses require transparent 

reporting of the study, with sufficient detail and clarity to readily see differences and similarities among studies in the 

same area. The Consolidated 

Standards of Reporting Trials 

(CONSORT) statement provides 

guidelines for transparent reporting of randomized clinical 

trials. 

We present the initial version of the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) 

statement. These guidelines 

emphasize the reporting of theories used and descriptions of 

intervention and comparison 

conditions, research design, 

and methods of adjusting for 

possible biases in evaluation 

studies that use nonrandomized designs. (Am J Public 

Health. 2004;94:361–366) 

| Don C. Des Jarlais, PhD, Cynthia Lyles, PhD, Nicole Crepaz, PhD, and the TREND Group 

OVER THE PAST SEVERAL 

decades, a strong movement toward evidence-based medicine 

has emerged.1–3 In the context of 

evidence-based medicine, clinical 

decisions are based on the best 

available scientific data rather 

than on customary practices or 

the personal beliefs of the health 

care provider. There is now a 

parallel movement toward evidence-based public health practices.4,5 The movement is intended to utilize the best 

available scientific knowledge as 

the foundation for public 

health–related decisionmaking. 

In the context of evidencebased medicine, the randomized 

controlled trial (RCT) is usually 

considered of greatest evidentiary value for assessing the efficacy of interventions. Indeed, 

the preference for this design is 

sufficiently strong that when empirical evidence from RCTs is 

available, “weaker” designs are 

often considered to be of little or 

no evidentiary value. In this 

issue, Victora et al.6 make a 

strong argument that evidencebased public health will necessarily involve the use of research 

designs other than RCTs. Most 

important, they argue that RCTs 

are often not practical or not 

ethical for evaluating many public health interventions and discuss methods for drawing causal 

inferences from nonrandomized 

evaluation designs (“plausibility” 

and “adequacy” designs in their 

terminology). 

Also in this issue, Donner and 

Klar,7 Murray et al.,8 and Varnell et al.9 provide overviews of 

the benefits and pitfalls of the 

group-randomized trial, which, 

in some situations, may be a 

reasonable alternative to the 

RCT. There are also a wide variety of nonrandomized evaluation designs that can contribute 

important data on the efficacy 

or effectiveness of interventions, 

such as quasi-experimental designs,10 nonrandomized trials, 

and natural experiments. Including these types of designs in developing evidence-based recommendations can provide a more 

integrated picture of the existing 

evidence and could help to 

strengthen public health practice. Excluding data collected 

under such designs would undoubtedly bias the evidence 

base toward interventions that 

are “easier” to evaluate but not 

necessarily more effective or 

cost-effective. 

If nonrandomized designs are 

to be systematically used in 

building evidence-based public 

health practices, it will be necessary to improve the reporting 

quality of these types of studies. 

The transparency, or clarity, in 

the reporting of individual studies is key. Sufficient detail and 

clarity in the report allow readers 

to understand the conduct and 

findings of the intervention study 

and how the study was different 

from or similar to other studies 

in the field. 

Furthermore, evidence-based 

practice may often rely on metaanalyses of large numbers of 

studies, some of which may report negative results. Metaanalysis requires full reporting 

of methods and outcomes to enable assessment of comparability 

of different studies. Inadequate, 

or nontransparent, reporting 

may make it difficult to understand the variables that affect intervention outcomes and the 

central elements in intervention 

success or failure over multiple 

studies. 

In recent years, efforts have 

been made to improve the quality of reporting of RCTs. The 

Consolidated Standards of Reporting Trials (CONSORT) statement11 provides a 22-item checklist and subject flow chart for the 

transparent reporting of RCTs. 

This statement has been adopted 

as a framework for the reporting 

of RCTs by a large number of 

medical, clinical, and psychological journals (153, according to 

http://www.consort-statement. 

org, as of September 16, 2003). 

Use of the CONSORT statement 

has improved the quality of RCT 

reports over the past several 

years.12 There is yet, however, no 

agreed-upon framework for the 

transparent reporting of nonrandomized research evaluations. 

The HIV/AIDS Prevention 

Research Synthesis (PRS) team 

of the Centers for Disease Control and Prevention (CDC) has 

been synthesizing evidence from 

March 2004, Vol 94, No. 3 | American Journal of Public Health Des Jarlais et al. | Peer Reviewed | Commentary | 361 
 COMMENTARY 
 

HIV behavioral intervention 

studies involving RCT and nonrandomized designs. The PRS 

team found that many study reports failed to include critical information (e.g., intervention timing and dosage, effect size data) 

necessary for research syntheses.13–17 To improve their ability 

to synthesize HIV behavioral 

prevention research, the PRS 

team convened the CDC’s Journal Editors Meeting in Atlanta, 

Ga, on July 24–25, 2003; this 

meeting was attended by editors 

and representatives of 18 journals that publish HIV behavioral 

intervention studies (a complete 

list of the journals is available 

from the authors and at http:// 

www.TREND-statement.org). 

The main goals of the meeting 

were to (1) communicate the 

usefulness and importance of adequate reporting standards, 

(2) reach consensus on reporting 

standards for behavioral interventions, (3) develop a checklist 

of reporting standards to guide 

authors and journal reviewers, 

and (4) develop strategies to disseminate the resulting reporting 

standards. 

The discussions at the meeting broadened to include standardized reporting of behavioral 

and public health interventions 

in general, rather than focusing 

only on HIV behavioral interventions. There was strong consensus at the meeting in regard 

to more standardized and transparent reporting of research 

evaluations using other than randomized designs, particularly 

those with some form of comparison group. This agreement 

was reached with the realization 

that additional input would be 

needed from a wide variety of 

researchers, other journal editors, and practitioners in the 

public health field before the 

adoption of a final set of reporting standards. 

Table 1 presents a proposed 

checklist—the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) 

checklist—for reporting standards 

of behavioral and public health 

intervention evaluations involving nonrandomized designs. The 

TREND checklist is meant to be 

consistent with the CONSORT 

checklist for the reporting of 

RCTs. Items presented in boldface type in the table are particularly relevant to behavioral and 

public health intervention studies, whether or not randomized 

designs are used. Thus, we would 

suggest that they be used to expand the information requested 

by CONSORT for RCTs of behavioral and public health interventions. Some of the items 

(8, 10, and 15) presented in the 

proposed TREND checklist are 

not relevant to RCTs and, thus, 

not included in the CONSORT 

checklist, but they are extremely 

relevant to nonrandomized designs. We also refer readers to 

CONSORT elaboration reports11,18 that provide rationales 

and examples for items in 

Table 1 that are shared with the 

CONSORT checklist. 

The TREND checklist is proposed for intervention evaluation 

studies using nonrandomized designs, not for all research using 

nonrandomized designs. Intervention evaluation studies would 

necessarily include (1) a defined 

intervention that is being studied 

and (2) a research design that 

provides for an assessment of the 

efficacy or effectiveness of the intervention. Thus, our proposed 

checklist emphasizes description 

of the intervention, including the 

theoretical base; description of 

the comparison condition; full reporting of outcomes; and inclusion of information related to the 

design needed to assess possible 

biases in the outcome data. Brief 

comments may be helpful for a 

few of the items included in the 

proposed TREND checklist. 

• Use of theory (item 2). Behavioral and social science theories 

provide a framework for generating cumulative knowledge. 

Thus, it would be very helpful to 

include references to the theoretical bases of the intervention 

being evaluated. This would permit identification of theories that 

are useful in developing interventions in different fields. Some 

interventions, however, are 

based on atheoretical needs assessments or simply the experience of the individuals who designed the intervention. In these 

situations, a post hoc application 

of a theory is not likely to be 

helpful. 

• Description of the intervention 

condition and the services provided in a comparison condition 

(item 4). Although space is limited in many journals, it is still 

critical to provide sufficient detail so that a reader has an understanding of the content and 

delivery of both the experimental intervention and the services 

in the comparison condition. For 

example, “usual care” is not a 

helpful description of a comparison condition. 

• Description of the research design (item 8). We recognize that 

there can be meaningful disagreement about what research 

design was actually used in an 

intervention study, including 

whether an RCT design was 

used. To minimize confusion, it 

would be helpful for authors to 

specify the design they intended 

to use, particularly the method 

of assignment, and any variations 

or deviations from the design. 

It is important to note that the 

TREND checklist is not intended 

to serve as a criterion for evaluating papers for publication. 

Rather, it is intended to improve 

the quality of data reporting in 

peer-reviewed publications so 

that the conduct and the findings 

of research are transparent. As 

the volume of public health literature is consistently expanding, 

research synthesis becomes an 

important tool for creating a cumulative body of knowledge and 

making evidence-based recommendations of effective interventions. Reporting standards will 

help ensure that fewer intervention trials with nonrandomized 

designs are missing information 

critical for research synthesis and 

that comparable information 

across studies can be more easily 

consolidated and translated into 

generalizable knowledge and 

practice. 

We recognize several challenges in promoting and disseminating reporting standards for 

nonrandomized intervention 

evaluations. Most important, the 

TREND checklist is only a suggested set of guidelines and 

should be considered a work in 

progress. It is highly likely that 

improvements will be necessary; 

moreover, adaptations may be 

needed to refine the standards 

for specific fields of intervention 

research, and additional specifications for specific types of nonrandomized evaluation designs 

are likely to be needed. Furthermore, page limitations in many 

journals create strong pressure 

toward shorter rather than 

longer articles. Some alternatives 

were recommended in the CDC’s 

Journal Editors Meeting to resolve the space issue, such as 

having additional information 

from a published study provided 

on a journal’s Web site or on an 

362 | Commentary | Peer Reviewed | Des Jarlais et al. American Journal of Public Health | March 2004, Vol 94, No. 3 
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TABLE 1—The TREND Checklist (Version 1.0) 

Paper Section/Topic Item No. Descriptor Examples From HIV Behavioral Prevention Research 

Title and abstract 1 • Information on how units were allocated to interventions Example (title): A nonrandomized trial of a clinic-based HIV counseling intervention 

• Structured abstract recommended for African American female drug users 

• Information on target population or study sample 

Introduction • Scientific background and explanation of rationale 

Background 2 • Theories used in designing behavioral interventions Example (theory used): the community-based AIDS intervention was based on social 

learning theory 

Methods 

Participants 3 • Eligibility criteria for participants, including criteria at different 

levels in recruitment/sampling plan (e.g., cities, clinics, subjects) 

• Method of recruitment (e.g., referral, self-selection), including the Example (sampling method): using an alphanumeric sorted list of possible venues 

sampling method if a systematic sampling plan was implemented and times for identifying eligible subjects, every tenth venue–time unit was 

selected for the location and timing of recruitment 

• Recruitment setting Examples (recruitment setting): subjects were approached by peer opinion leaders 

• Settings and locations where the data were collected during conversations at gay bars 

Interventions 4 • Details of the interventions intended for each study condition and how 

and when they were actually administered, specifically including: 

Content: what was given? 

Delivery method: how was the content given? 

Unit of delivery: how were subjects grouped during delivery? Example (unit of delivery): the intervention was delivered to small groups of 5–8 subjects 

Deliverer: who delivered the intervention? 

Setting: where was the intervention delivered? Examples (setting): the intervention was delivered in the bars; the intervention was 

delivered in the waiting rooms of sexually transmitted disease clinics 

Exposure quantity and duration: how many sessions or episodes or Examples (exposure quantity and duration): the intervention was delivered in five 

events were intended to be delivered? How long were they 1-hour sessions; the intervention consisted of standard HIV counseling and 

intended to last? testing (pretest and posttest counseling sessions, each about 30 minutes) 

Time span: how long was it intended to take to deliver the intervention Examples (time span): each intervention session was to be delivered (in five 1-hour 

to each unit? sessions) once a week for 5 weeks; the intervention was to be delivered over a 

1-month period. 

Activities to increase compliance or adherence (e.g., incentives) Example (activities to increase compliance or adherence): bus tokens and food 

stamps were provided 

Objectives 5 • Specific objectives and hypotheses 

Outcomes 6 • Clearly defined primary and secondary outcome measures 

• Methods used to collect data and any methods used to enhance Examples (method used to collect data): self-report of behavioral data using a 

the quality of measurements face-to-face interviewer-administered questionnaire; audio-computer-assisted 

• Information on validated instruments such as psychometric and self-administered instrument 

biometric properties 

Sample size 7 • How sample size was determined and, when applicable, explanation 

of any interim analyses and stopping rules 

Assignment 8 • Unit of assignment (the unit being assigned to study condition, Example 1 (assignment method): subjects were assigned to study conditions 

method e.g., individual, group, community) using an alternating sequence wherein every other individual enrolled 

• Method used to assign units to study conditions, including details (e.g., 1, 3, 5, etc.) was assigned to the intervention condition and the 

of any restriction (e.g., blocking, stratification, minimization) alternate subjects enrolled (e.g., 2, 4, 6, etc.) were assigned to the 

• Inclusion of aspects employed to help minimize potential bias comparison condition 

induced due to nonrandomization (e.g., matching) 

Example 2 (assignment method): for odd weeks (e.g. 1, 3, 5), subjects attending the 

clinic on Monday, Wednesday, and Friday were assigned to the intervention 

condition and those attending the clinic on Tuesday and Thursday were assigned 

to the comparison condition; this assignment was reversed for even weeks 

Continued 

March 2004, Vol 94, No. 3 | American Journal of Public Health Des Jarlais et al. | Peer Reviewed | Commentary | 363 
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TABLE 1—Continued 

Blinding 9 • Whether or not participants, those administering the interventions, and Example (blinding): the staff member performing the assessments was not involved in 

(masking) those assessing the outcomes were blinded to study condition implementing any aspect of the intervention and knew the participants only by 

assignment; if so, statement regarding how the blinding was their study identifier number 

accomplished and how it was assessed 

Unit of analysis 10 • Description of the smallest unit that is being analyzed to assess Example 1 (unit of analysis): since groups of individuals were assigned to study 

intervention effects (e.g., individual, group, or community) conditions, the analyses were performed at the group level, where mixed effects 

• If the unit of analysis differs from the unit of assignment, the analytical models were used to account for random subject effects within each group 

method used to account for this (e.g., adjusting the standard Example 2 (unit of analysis): since analyses were performed at the individual level and 

error estimates by the design effect or using multilevel analysis) communities were randomized, a prior estimate of the intraclass correlation coefficient 

was used to adjust the standard error estimates before calculating confidence intervals 

Statistical 11 • Statistical methods used to compare study groups for primary 

methods outcome(s), including complex methods for correlated data 

• Statistical methods used for additional analyses, such as subgroup 

analyses and adjusted analysis 

• Methods for imputing missing data, if used 

• Statistical software or programs used 

Results 

Participant flow 12 • Flow of participants through each stage of the study: enrollment, 

assignment, allocation and intervention exposure, follow-up, analysis 

(a diagram is strongly recommended) 

Enrollment: the numbers of participants screened for eligibility, found to be 

eligible or not eligible, declined to be enrolled, and enrolled in the study 

Assignment: the numbers of participants assigned to a study condition 

Allocation and intervention exposure: the number of participants 

assigned to each study condition and the number of participants 

who received each intervention 

Follow-up: the number of participants who completed the follow-up or did 

not complete the follow-up (i.e., lost to follow-up), by study condition 

Analysis: the number of participants included in or excluded from the 

main analysis, by study condition 

• Description of protocol deviations from study as planned, along with reasons 

Recruitment 13 • Dates defining the periods of recruitment and follow-up 

Baseline data 14 • Baseline demographic and clinical characteristics of participants in 

each study condition 

• Baseline characteristics for each study condition relevant to specific Example (baseline characteristics specific to HIV prevention research): HIV serostatus 

disease prevention research and HIV testing behavior 

• Baseline comparisons of those lost to follow-up and those retained, 

overall and by study condition 

• Comparison between study population at baseline and target 

population of interest 

Baseline 15 • Data on study group equivalence at baseline and statistical methods Example (baseline equivalence): the intervention and comparison groups did not 

equivalence used to control for baseline differences statistically differ with respect to demographic data (gender, age, race/ethnicity; 

P > .05 for each), but the intervention group reported a significantly greater 

baseline frequency of injection drug use (P = .03); all regression analyses 

included baseline frequency of injection drug use as a covariate in the model 

Numbers 16 • Number of participants (denominator) included in each analysis for each Example (number of participants included in the analysis): the analysis of condom use 

analyzed study condition, particularly when the denominators change for different included only those who reported at the 6-month follow-up having had vaginal or anal 

outcomes; statement of the results in absolute numbers when feasible sex in the past 3 months (75/125 for intervention group and 35/60 for standard group) 

• Indication of whether the analysis strategy was “intention to treat” or, Example (“intention to treat”): the primary analysis was intention to treat and included 

if not, description of how noncompliers were treated in the analyses all subjects as assigned with available 9-month outcome data (125 of 176 

assigned to the intervention and 110 of 164 assigned to the standard condition) 

Continued 

364 | Commentary | Peer Reviewed | Des Jarlais et al. American Journal of Public Health | March 2004, Vol 94, No. 3 
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TABLE 1—Continued 

Outcomes and 17 • For each primary and secondary outcome, a summary of results for each 

estimation study condition, and the estimated effect size and a confidence 

interval to indicate the precision 

• Inclusion of null and negative findings 

• Inclusion of results from testing prespecified causal pathways 

through which the intervention was intended to operate, if any 

Ancillary 18 • Summary of other analyses performed, including subgroup or restricted Example (ancillary analyses): although the study was not powered for this hypothesis, 

analyses analyses, indicating which are prespecified or exploratory an exploratory analysis shows that the intervention effect was greater among 

women than among men (although not statistically significant) 

Adverse events 19 • Summary of all important adverse events or unintended effects in each Example (adverse events): police cracked down on prostitution, which drove the target 

study condition (including summary measures, effect size estimates, population, commercial sex workers, to areas outside the recruitment/sampling area 

and confidence intervals) 

Discussion 

Interpretation 20 • Interpretation of the results, taking into account study hypotheses, 

sources of potential bias, imprecision of measures, multiplicative 

analyses, and other limitations or weaknesses of the study 

• Discussion of results taking into account the mechanism by which 

the intervention was intended to work (causal pathways) or 

alternative mechanisms or explanations 

• Discussion of the success of and barriers to implementing the 

intervention, fidelity of implementation 

• Discussion of research, programmatic, or policy implications 

Generalizability 21 • Generalizability (external validity) of the trial findings, taking into 

account the study population, the characteristics of the intervention, 

length of follow-up, incentives, compliance rates, specific 

sites/settings involved in the study, and other contextual issues 

Overall evidence 22 • General interpretation of the results in the context of current evidence 

and current theory 

Note. Masking (blinding) of participants or those administering the intervention may not be relevant or possible for many behavioral interventions. Theories used to design the interventions (see 

item 2) could also be reported as part of item 4. The comparison between study population at baseline and target population of interest (see item 14) could also be reported as part of item 21. 

Descriptors appearing in boldface are specifically added, modified, or further emphasized from the CONSORT statement. Boldface topic and descriptors are not included in the CONSORT statement 

but are relevant for behavioral interventions using nonrandomized experimental designs. The CONSORT statement11 or the explanation document for the CONSORT statement18 provides relevant 

examples for any topic or descriptor that is not in boldface. A structured format of the discussion is presented in Annals of Internal Medicine (information for authors; www.annals.org, accessed 

September 16, 2003). 

author’s Web site or having authors send additional information 

to relevant research synthesis 

groups or a central repository. 

Finally, the process has so far 

involved only CDC scientists and 

journal editors in a single meeting along with the preparation of 

this commentary. Although many 

of the journal editors included 

are notable researchers in the 

fields of HIV, public health, and 

drug abuse prevention, we realize that successful promotion and 

dissemination of these guidelines 

must involve an ongoing dialogue and must be extended to a 

large number of other researchers, methodologists, and statisticians across various healthrelated research fields. 

In an effort to initiate this dialogue, we invite all editors, reviewers, authors, and readers to 

provide comments and feedback 

to help us revise the standards. 

Comments can be sent to 

TREND@cdc.gov, and the 

TREND group will periodically 

revise the guidelines accordingly. Also, journals are encouraged to endorse this effort by 

publishing editorials or commentaries on the TREND statement 

or by referencing it in their publication guidelines for authors 

and reviewers. To increase accessibility and ease of use, the 

revised versions of the TREND 

statement will be posted on an 

open access Web site (http:// 

www.TREND-statement.org). 

If the movement toward evidence-based public health is to 

succeed, it will be necessary to 

improve our ability to synthesize 

research on public health interventions. As Victora and colleagues note, this will include 

using data from intervention 

evaluations that do not involve 

randomized designs. The 

TREND statement presented 

here is proposed as a first step toward developing standardized 

and transparent reporting for 

nonrandomized intervention research evaluations in public 

health–related fields. 

About the Authors 

Don C. Des Jarlais is with the Baron 

Edmond de Rothschild Chemical Dependency Institute, Beth Israel Medical Center, 

New York City. Cynthia Lyles and Nicole 

Crepaz are with the Centers for Disease 

Control and Prevention, Atlanta, Ga. 

TREND Group members are listed below 

in the Acknowledgments. 

Requests for reprints should be sent to 

Don C. Des Jarlais, PhD, Beth Israel Medical Center, Chemical Dependency Institute, First Ave at 16 St, New York, NY 

10003 (e-mail: dcdesjarla@aol.com). 

March 2004, Vol 94, No. 3 | American Journal of Public Health Des Jarlais et al. | Peer Reviewed | Commentary | 365 
 COMMENTARY 
 

This article was accepted October 26, 

2003. 

Contributors 

Don Des Jarlais, Cynthia Lyles, and 

Nicole Crepaz developed the initial draft 

of the article and made the successive 

revisions. All members of the TREND 

group contributed to the ideas in the article and reviewed the initial draft and 

revisions. 

Acknowledgments 

Members of the TREND Group are as 

follows: Kamran Abbasi, MBChB, MRCP 

(BMJ), William Blattner, MD (University 

of Maryland, Baltimore; Journal of Acquired Immune Deficiency Syndromes), 

Jay Bernhardt, PhD, MPH (Emory University; Health Education Research), 

Bruce Bullington, PhD (Florida State 

University; Journal of Drug Issues), Raul 

Caetano, MD, PhD, MPH (University of 

Texas; Addiction), Terry Chambers, BA 

(Journal of Psychoactive Drugs), Harris 

Cooper, PhD (Duke University; Psychological Bulletin), Roel A. Coutinho, MD, 

PhD (University of Amsterdam; AIDS), 

Nicole Crepaz, PhD (Centers for Disease 

Control and Prevention), John D. DeLamater, PhD (University of Wisconsin, 

Madison; Journal of Sex Research), Don 

C. Des Jarlais, PhD (Beth Israel Medical 

Center; American Journal of Public 

Health), Jeffrey H. Herbst, PhD (Centers 

for Disease Control and Prevention), 

David Holtgrave, PhD (Emory University), Barry Hong, PhD (Washington University at St. Louis; Journal of Consulting 

and Clinical Psychology), Angela B. 

Hutchinson, PhD (Centers for Disease 

Control and Prevention), Seth Kalichman, PhD (University of Connecticut; 

Health Psychology), Linda Kay, MPH 

(Centers for Disease Control and Prevention), Cynthia Lyles, PhD (Centers 

for Disease Control and Prevention), 

Robert McNutt, MD (Rush St. Luke’s 

Medical Center at Chicago; Journal of the 

American Medical Association), Thomas 

L. Patterson, PhD (University of California, San Diego; AIDS and Behavior), 

Michael Ross, PhD, MPH, MA, MHPEd 

(University of Texas, Houston; AIDS 

Care), Theo Sandfort, PhD (Columbia 

University; Archives of Sexual Behavior), 

Ron Stall, PhD, MPH (Centers for Disease Control and Prevention), Francisco 

S. Sy, MD, DrPH (Centers for Disease 

Control and Prevention; AIDS Education 

and Prevention), Cesar G. Victora, MD, 

PhD (Universidade Federal de Pelotas), 

and David Vlahov, PhD (New York 

Academy of Medicine; American Journal 

of Epidemiology, Journal of Urban 

Health). 

We also thank the following Prevention Research Synthesis team members 

for their assistance in the meeting preparation and contribution to the data reporting presentation: Julia Britton, 

Tanesha Griffin, Angela Kim, Mary 

Mullins, Paola Marrero-Gonzalez, Sima 

Rama, R. Thomas Sherba, and Sekhar 

Thadiparthi. 

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