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Figure 1. Selection of Studies in the Systematic Review and Meta-Analysis

Thirty-nine studies were included, which involved 85 comparisons.

Figure 2. Pooled Overall Survival by Quality Adjustment Scores

All studies are listed in the References list at the end of this article. Quality was assessed on a 16-point scale with higher scores indicating less potential for confounding or bias. Studies were classified as low-quality (≤6 points), medium-quality (7 points), and high-quality (≥8 points). See eTable 5 in Supplement 1 for factors used in scoring and eTable 9 in Supplement 1 for quality scores of individual studies. Some studies had multiple comparisons and, to differentiate them, superscript letters are noted by each comparison. The distinctions can be found in eTable 8 in Supplement 1.

Table 1. Study Characteristics of 85 Included Comparisons
Table 2. Results of Subgroup Analyses of Comparisons by Various Characteristics That Indicate Quality of Comparison in Publications
Original Investigation
Ѳ20, 2024

Survival Benefit Associated With Participation in Clinical Trials of Anticancer Drugs: A Systematic Review and Meta-analysis

Author Affiliations
  • 1Department of Equity, Ethics and Policy, McGill University, Montreal, Quebec, Canada
  • 2Department of Medicine, Temple University, Philadelphia, Pennsylvania
  • 3Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
JAMA. Published online May 20, 2024. doi:10.1001/jama.2024.6281
Key Points

Question Is patient participation in cancer drug trials associated with longer survival?

Findings In this systematic review and meta-analysis of 39 studies (85 comparisons), cancer patient participation in trials was associated with greater survival benefit compared with routine care (hazard ratio [HR], 0.76). However, survival benefit was not significantly greater when only high-quality studies were pooled (HR, 0.9) or when the sample was adjusted for possible publication bias (HR, 0.94).

Meaning After accounting for biases and confounders, cancer clinical trial participation was not associated with longer survival.


Importance Many cancer clinical investigators view clinical trials as offering better care for patients than routine clinical care. However, definitive evidence of clinical benefit from trial participation (hereafter referred to as the participation effect) has yet to emerge.

Objective To conduct a systematic review and meta-analysis of the evidence examining whether patient participation in cancer trials was associated with greater survival benefit compared with routine care.

Data Sources Studies were found through PubMed and Embase (January 1, 2000, until August 31, 2022), as well as backward and forward citation searching.

Study Selection Studies were included that compared overall survival of trial participants and routine care patients.

Data Extraction and Synthesis Data extraction and methodological quality assessment were completed by 2 independent coders using Covidence software. Data were pooled using a random-effects model and analyzed based on the quality of the comparison between trial participants and routine care patients (ie, extent to which studies controlled for bias and confounders).

Main Outcomes and Measures The hazard ratio (HR) for overall survival of trial participants vs routine care patients.

Results Thirty-nine publications were included, comprising 85 comparisons of trial participants and routine care patients. The meta-analysis revealed a statistically significant overall survival benefit for trial participants (HR, 0.76 [95% CI, 0.69-0.82]) when all studies were pooled, regardless of design or quality. However, survival benefits diminished in study subsets that matched trial participants and routine care patients for eligibility criteria (HR, 0.85 [95% CI, 0.75-0.97]) and disappeared when only high-quality studies were pooled (HR, 0.91 [95% CI, 0.80-1.05]). They also disappeared when estimates were adjusted for potential publication bias (HR, 0.94 [95% CI, 0.86-1.03]).

Conclusions and Relevance Many studies suggest a survival benefit for cancer trial participants. However, these benefits were not detected in studies using designs addressing important sources of bias and confounding. Pooled results of high-quality studies are not consistent with a beneficial effect of trial participation on its own.


Quiz Ref IDMany people believe that patients achieve better clinical outcomes because of participation in clinical trials.1-8 This phenomenon is commonly called the trial effect, which is often attributed to closer monitoring and access to new treatments in trials. However, the assertion that trials confer medical benefits for participants rests on observational methodologies that are notoriously prone to bias and confounding.1-4,9-11 These observational methodologies are precisely the forms of evidence that motivate the conduct of rigorous trials.

Two reviews of cancer clinical trials based mostly on studies published in the 1990s found inconclusive evidence that participation in trials confers survival benefits.1,5 Two other major systematic reviews in other disease areas have been published,12,13 and neither showed evidence that patients in trials have better outcomes than patients outside of trials.

Numerous reports supporting a trial effect have been published since these reviews.14-20 Changes in trial practices since the 1990s, such as improved patient monitoring or greater inclusivity, might influence survival benefit for trial participation. Recent studies also vary in the extent to which they control for factors that might produce a spurious suggestion of survival benefit. For example, survival benefits associated with trial participation may reflect the confounding influence of trials selecting for patients with better prognoses, rather than the benefits of closer monitoring.

The primary aim of this study was to use meta-analytic methods to probe estimates of overall survival differences for patients participating in trials (hereafter referred to as trial participants) relative to patients who did not participate in trials (hereafter referred to as routine care patients) considering various sources of bias or confounding. The study assessed pooled overall survival based on subgroups related to quality of the comparison between trial participants and routine care patients (eg, accounting for trial eligibility, treatment effect, prognostic confounders) to explore design features associated with detection of survival benefits in trial participants.

Theoretical Framework

We defined treatment effect as the effects of trial participation on outcomes that are mediated by assignment to the experimental intervention in the trial. We defined participation effect as the effects of trial participation that are not mediated by assignment to the experimental intervention in the trial. We refer to the combination of these 2 as the trial effect, which includes all outcome differences between trial participants and routine care patients that are attributable to trial participation (eTable 1 and eTable 2 in Supplement 1). The present study is primarily aimed at isolating the participation effect, which we defined as outcome differences (eg, in overall survival time) attributed to trial participation that are unrelated to receiving an investigational intervention and not the result of confounding or measurement errors. Participation effects in trials might arise because of better management of medication adverse effects or improved medication adherence.

Search Strategy and Selection Criteria

We performed searches of PubMed and Embase (for articles published until August 31, 2022) for studies comparing survival outcomes for trial participants and routine care patients in cancer, limiting our search to studies published on or after January 1, 2000. We limited our search to 2000 and onward because previous systematic reviews have included older publications.5,13 Also, older studies are not representative of the current landscape of clinical trials and often lack the detailed information needed for the present systematic review. Reference lists of found articles were also searched for eligible articles. The following keywords and variants were used to identify relevant articles: cancer, oncology, clinical trial, retrospective cohort, trial participation, trial effect, participation bias, nonparticipant, and nontrial (full list provided in eTable 3 in Supplement 1). Because the literature on trial effect uses different terminologies, we also conducted backward citation searches (searching publications cited in publications we found) and forward citation searches (searching publications that cited publications we found).

Publications were first screened based on titles and abstracts and then full texts were screened by 2 authors (R.I. and H.M.) for studies meeting the following inclusion criteria: (1) use of hazard ratios (HRs) to compare overall survival in a group of trial participants to a group of routine care patients (regardless of whether trial participant groups derived from a randomized trial), (2) treatment includes a drug/biologic, and (3) studies were conducted in patients with cancer. We excluded publications that were (1) focused on trials examining surgical procedures or indirect interventions (eg, programs); (2)commentaries, editorials, letters, or other nonresearch articles; and (3) non–English-language studies (eTable 4 in Supplement 1). Screening was completed using Covidence software.21 Disagreements were resolved through consensus.

Data Extraction

We extracted information from all studies for the following domains: patient demographics, treatment characteristics, and various quality items deemed important for adjustment when measuring participation effects (see below). We also extracted overall survival HRs for trial participants and routine care patients (receiving the same treatment, if available), regardless of whether the former derived from a treatment or control arm. When multiple HRs were available for a comparison, we extracted the HR that reflected the most adjustments for quality factors. Following extraction, study authors were contacted for missing information. All studies were extracted by 2 independent coders with disagreements reconciled by discussion.

Quality scoring in meta-analyses of observational studies using standard methods (eg, ROBINS-I) involves conceptual and practical difficulties22-24 and is not customized to discern factors that specifically bear on estimating trial and participation effects. Also, some items in such tools involve many different components that are likely to be at play with routine care patient groups (eg, bias in selection of participants into studies might reflect differences in eligibility, prognostic factors, or consent in the trial participant and routine care patient groups). Instead, we began our study by creating a directed acyclic graph (eFigure 1 in Supplement 1) to identify factors that could cause a participation effect as well as factors that might confound or bias estimates thereof. Sixteen factors associated with confounding or bias were identified. From this, we created a 16-point scoring scale, assigning 1 quality point for each factor addressed in the primary study (eTable 5 in Supplement 1). Of note, some primary studies were not focused on measuring participation effects; our scale reflected quality with respect to estimating participation effects, not the primary objectives of original studies. A leave-one-out meta-analysis of the quality factors is included in eTable 6 in Supplement 1. Quality factors included differences between trial participants and routine care patients in cancer treatment, eligibility criteria, timeframe, demographics (eg, age, sex, race and ethnicity), and medical history (comorbidities, cancer stage, histology, performance status, and line of treatment). To examine the impact of quality scores on estimates of participation effects, we categorized primary studies into 3 similarly sized subgroups of low (≤6 points in our score), medium (7 points), and high (≥8 points) quality levels. Additional post hoc analyses include grouping primary studies into 2 and 5 quality score subgroups and are reported in eFigures 2 and 3 in Supplement 1.

Statistical Analysis

Because studies in our sample involved different indications and drugs, a standard meta-analysis would not be possible. Instead, meta-analytic methods were used to explore the impacts of quality factors (eg, accounting for treatment and eligibility) and general study characteristics (eg, sponsorship, study location) on pooled effects and heterogeneity.25 For this, a DerSimonian and Laird random-effects model was used to pool survival hazard ratios of trial participants vs routine care patients.26 This model is appropriate when pooling estimates from heterogeneous studies employing multiple designs and cancer types. All statistical analyses were completed using R version 4.3.0 (R Foundation).27 We used funnel plots, Begg28 and Egger29 tests, and the trim-and-fill method30 to explore potential publication biases.

This systematic review followed the PRISMA reporting guidelines (eTable 7 in Supplement 1).31 A protocol was preregistered on Open Science Framework ().32 Other information, including the dataset and codebook, are available on Open Science Framework.


Following screening, 39 studies (85 total comparisons) were eligible for inclusion in the meta-analysis (Figure 1). Of these studies, 32 comparisons comparing trial participants and routine care patients aimed to measure the trial effect. The median sample sizes for the trial participant and routine care patient groups were 209 and 409 patients, respectively (Table 1). Characteristics of individual publications are available in eTable 8 and quality scores of individual studies are available in eTable 9 in Supplement 1. Examples of studies included in the sample are provided in the Box,33-37 along with an explanation of their quality.

Box Section Ref ID

Examples of Included Studies and Results

  • Abdel-Rahman (2019)37 compared survival of localized prostate cancer patients treated in clinical trials and patients registered in the Surveillance, Epidemiology, and End Results Program database. This study was deemed high quality: it accounted for age, sex, race and ethnicity, comorbidities, stage, performance status, line of treatment, treatment, eligibility, and timeframe. The study did not show evidence of a survival difference between patients treated in a clinical trial (n = 397) vs routine care (n = 1718) (hazard ratio [HR], 0.79 [95% CI, 0.45-1.39]; quality score = 12 points).

  • Tanai et al (2011)36 compared survival of patients with unresectable or recurrent gastric cancer treated with chemotherapy in trials and patients who were offered trial participation using medical records. This study was deemed high quality: it accounted for age, sex, race and ethnicity, stage, histology, performance status, line of treatment, treatment, eligibility, timeframe, and data source for patient groups. They found no evidence of a survival difference in patients treated in a clinical trial (n = 190) vs patients treated in routine care who refused trial participation (n = 96) (HR, 0.83 [95% CI, 0.62-1.10]; quality score = 12 points).

  • Le Du et al (2016)35 was scored as medium quality. It compared survival of patients with breast cancer in and out of clinical trials using medical records, and it accounted for age, sex, race and ethnicity, comorbidities, line of treatment, eligibility, timeframe, data source for patient groups, but not performance status, histology, stage, or treatment. They found no evidence of a survival difference in patients treated in a clinical trial (n = 285) vs patients treated in routine care (n = 367) (HR, 0.89 [95% CI, 0.72-1.10]; quality score = 7 points).

  • Elumalai et al (2022)34 compared survival of patients treated with docetaxel for metastatic castration-resistant prostate cancer in and out of clinical trials using medical records. This study was low quality with respect to estimation of participation effects. It accounted for sex, performance status, treatment, and timeframe, but not for factors including age, race and ethnicity, comorbidities, stage, histology, line of treatment, eligibility, or data source for patient groups. They found a survival benefit for trial participants (n = 2070) compared with routine care patients (n = 178) (HR, 0.57 [95% CI, 0.48-0.68]; quality score = 3 points).

  • Mayers et al (2001)33 compared survival of patients with breast carcinoma who participated in a clinical trial with those who did not using medical records. This study was low quality with respect to estimation of participation effects. It accounted for sex, stage, treatment, timeframe, data source for patient groups, but not for factors including age, race and ethnicity, comorbidities, histology, performance status, line of treatment, or eligibility. They reported a survival benefit for trial participants (n = 160) compared with routine care patients (n = 519), but results were not statistically significant (HR, 0.77 [95% CI, 0.57-1.05]; quality score = 3 points).

Quiz Ref IDThe original pooled HR for all included studies without regard to quality subgroups was 0.76 (95% CI, 0.69-0.82), suggesting a statistically significant survival benefit for trial participants in the highly heterogeneous sample (I2 = 88%; Figure 2). When studies were grouped according to their overall quality score based on susceptibility to bias or confounding, the lowest scoring group reported the largest survival benefit for trial participants (HR, 0.64 [95% CI, 0.58-0.72]) and high heterogeneity (I2 = 83%). The intermediate group reported results between the low and high groups (HR, 0.85 [95% CI, 0.73-0.98]; I2 = 53%), and the highest scoring group reported no significant survival benefit (HR, 0.91 [95% CI, 0.80-1.05]) and showed high heterogeneity (I2 = 89%) (Figure 2). Other groupings by quality produced consistent trends toward null estimates with greater quality (eFigures 2 and 3 in Supplement 1).

Significant evidence of publication bias was found using 2 of the 3 methods deployed. A funnel plot (eFigure 4 in Supplement 1) and Egger test (P = .007) suggested possible publication bias against studies failing to show a participation effect. The Begg test did not show publication bias (P = .48). The trim-and-fill method added 30 comparisons to the 85 (n = 115) with a random effects pooled HR regressing to 0.94 (95% CI, 0.86-1.03).

All studies matched trial participants and routine care patients by cancer type, but varied in the extent to which they addressed other confounders and biases. This might explain the observed survival benefit for trial participants in the overall pooled estimate (Table 2). Overall, high heterogeneity is a possible indication of studies that were lower-quality for estimating participation effects. For instance, studies that lacked information on whether trial participants and routine care patients were matched on trial eligibility were the most heterogeneous and had the strongest effects (HR, 0.76 [95% CI, 0.63-0.92]; Q = 266.5), whereas studies known to have matched on trial eligibility had less heterogeneity and a higher HR (0.85 [95% CI, 0.75-0.97]; Q = 37.0). Similarly, studies that did not report whether they included individuals who chose not to participate in the trial were included in the routine care group (Q = 653.9) were also highly heterogeneous. In all cases but 1 (line of treatment), studies that accounted for prognostic confounders (another set of quality factors) produced smaller estimates of survival benefit for trial participants.

Subgroup analyses based on different study characteristics were also conducted, without regard for quality indicators. Comparisons from studies in the US (n = 31) showed an HR of 0.87 (95% CI, 0.75-1.02), while those conducted in other countries (n = 54) had an HR of 0.72 (95% CI, 0.65-0.79) (eTable 10 in Supplement 1).


In this systematic review and meta-analysis of 39 studies comparing outcomes among cancer patients participating in clinical trials with those receiving routine care, typical pooled analyses suggest that trial participation is associated with greater survival benefit. However, survival benefits for participation diminish or disappear in studies that account for various sources of bias and confounding. Accounting for factors such as eligibility and some prognostic confounders reduced the magnitude of overall participation effects. For example, no statistically significant trial effect was observed for studies that accounted for comorbidities, histology, or race and ethnicity. When a subgroup analysis was conducted based on quality score to isolate participation effects, there was a greater apparent survival benefit in low-quality studies than high-quality studies. The high-quality studies made more efforts to reduce heterogeneity between trial participants and routine care patients.

Because preregistries are not widely used for observational studies, there are no direct ways to test for publication bias. However, indirect statistical tests suggest a disproportionate number of small studies indicating large survival benefits for trial participants, which is consistent with publication bias. Statistically correcting for potential publication bias produced an HR that was inconsistent with a significant survival benefit for trial participants.

Previous systematic reviews, including those focused on cancer, have not detected clear evidence that patients in randomized trials have better outcomes than patients outside them.1,5,9,12,13 Despite this, many factors may contribute to the perception that patients have better outcomes in trials. One is that participants in trials often experience better care processes, including more frequent imaging. Another is the notorious efficacy-effectiveness gap. Trials often show survival outcomes that exceed those in clinical care.38,39 Indeed, the literature on efficacy-effectiveness gaps complements our findings: prognostic variables such as performance status that appear to explain better outcomes in trials, when adjusted for in comparisons of trial participants and routine care patients, diminish apparent participation effects. A third factor is the regular publication of studies, similar to many in the current sample, that suggest a participation effect but do not account for relevant confounders. Similar to prior studies,1,5 the current study suggests that methodological rigor and quality continues to present a challenge for some publications asserting estimates of the participation effect.

This study leaves many questions about participation effects unresolved. Some studies suggest that patients have improved quality of life, greater cost savings, or benefit from incidental findings when participating in trials.40-42 Other studies have suggested that patients have worse quality of life outcomes.43,44 The current protocol set out to measure such benefits. However, outcomes such as quality of life or anxiety are often not measured in trials, much less in routine care patients. It was not possible to identify a critical mass of studies on patient-reported outcomes to include in the meta-analysis. The analysis also does not address whether participation effects might occur with interventions involving greater skill with administration (eg, surgical procedures).


This meta-analysis has limitations. First, many studies in the review were missing data or provided unclear information leading to exclusions and/or difficulties in interpreting studies. Authors were contacted to mitigate these difficulties. Due to incomplete descriptions on how trial participants and routine care patients were matched, it was also difficult to determine how well quality factors were implemented. For example, treatments were often very poorly described and therefore treatment similarity was not always clear for studies claiming to have matched treatments between groups. Second, the scale used for measuring study quality was created expressly for this study. Although the scoring scale was systematic and reflected sources of bias for trial effect studies, scales with different sets of factors, factor prioritization with weighted scores, or score cut-offs might produce findings that differ from those reported above. However, the main purpose of stratifying by quality was to explore relationships between low adjustment and detection of participation effects.

Third, as previously noted,1 the absence of standard terminologies makes it extremely difficult to conduct literature searches for primary reports of the participation effect. It cannot be ruled out that some reports were not captured in the search. Fourth, the study provides a picture of participation benefit associated with current research practices. The possibility that other trial approaches might produce different results cannot be excluded. For example, trials often select for patients that have fewer comorbidities or better performance status. Participation effects might be more manifest where trials relax such criteria. Fifth, any attempt to study the benefits of trial participation is subject to the fact that there is no random assignment among trial participants and routine care patients. Even ideally designed studies may be confounded, and such confounders could either mask or amplify participation effect estimates. The best that can be concluded is that participation effect estimates grow narrower the more analyses account for various confounders. It cannot be ruled out that benefits of participation might be revealed were it possible to conduct a randomized trial testing the benefits of participating in a randomized trial.


Quiz Ref IDIn this meta-analysis, evidence that cancer trial participation results in survival benefits is mostly driven by studies that do not account for factors that could bias or confound such estimates. When analysis is restricted to studies that account for such factors (eg, eligibility, prognostic confounders), effects regress toward a null effect. These findings may strike some trial advocates as discouraging, given how hard they work to improve patient outcomes within trials. However, a more reassuring interpretation is that there is no evidence that excluding patients from trials due to geography, nonavailability of trials in their condition, or ineligibility deprives them of survival opportunities.

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Article Information

Accepted for Publication: March 26, 2024.

Published Online: May 20, 2024. doi:10.1001/jama.2024.6281

Corresponding Author: Jonathan Kimmelman, PhD, Department of Equity, Ethics and Policy, McGill University, 2001 McGill College Ave, Room 1155, Montreal, QC H3A 1G1, Canada (jonathan.kimmelman@mcgill.ca).

Author Contributions: Ms Iskander and Dr Kimmelman had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Iskander, Moyer, Mahmud, Kimmelman.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Iskander, Kimmelman.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Iskander.

Obtained funding: Kimmelman.

Administrative, technical, or material support: Kimmelman.

Supervision: Mahmud, Kimmelman.

Conflict of Interest Disclosures: Ms Iskander reported her PhD being partially funded by CIHR and the Rossy Cancer Network (Philip Kuok Graduate Fellowship). Dr Mahmud reported receiving fees as a consultant and advisory board member for GlaxoSmithKline, Merck, Sanofi Pasteur, and Seqirus. Dr Kimmelman reported receiving personal fees from Amylyx Pharmaceuticals outside the submitted work. No other disclosures were reported.

Funding/Support: This work was funded by CIHR. Ms Iskander was supported by the Philip Kuok Graduate Fellowship established at McGill’s Faculty of Medicine and Health Sciences in partnership with the Rossy Cancer Network. Dr Mahmud is supported, in part, by funding from the Canada Research Chairs Program.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Meeting Presentation: This paper was presented at the Society of Clinical Trials meeting; May 20, 2024; Boston, MA.

Data Sharing Statement: See Supplement 2.

Braunholtz DA, Edwards SJL, Lilford RJ. Are randomized clinical trials good for us (in the short term)? evidence for a “trial effect”. J Clin Epidemiol. 2001;54(3):217-224. doi:
Sedgwick P, Greenwood N. Understanding the Hawthorne effect. BMJ. Published online September 4, 2015. doi:
McCambridge J, Witton J, Elbourne DR. Systematic review of the Hawthorne effect: new concepts are needed to study research participation effects. J Clin Epidemiol. 2014;67(3):267-277. doi:
Menezes P. Trial effect: the road from efficacy to effectiveness. Clin Investig (Lond). 2012;2(5):443-445. doi:
Peppercorn JM, Weeks JC, Cook EF, Joffe S. Comparison of outcomes in cancer patients treated within and outside clinical trials: conceptual framework and structured review. Lancet. 2004;363(9405):263-270. doi:
Engelbak Nielsen Z, Eriksson S, Schram Harsløf LB, et al. Are cancer patients better off if they participate in clinical trials? a mixed methods study. BMC Cancer. 2020;20(1):401. doi:
Godskesen TE, Petri S, Eriksson S, Halkoaho A, Mangset M, Nielsen ZE. The culture of hope and ethical challenges in clinical trials: a qualitative study of oncologists and haematologists’ views. Clin Ethics. 2020;15(1):29-38. doi:
Godskesen TE, Petri S, Eriksson S, et al. When nursing care and clinical trials coincide: a qualitative study of the views of nordic oncology and hematology nurses on ethical work challenges. J Empir Res Hum Res Ethics. 2018;13(5):475-485. doi:
Bouzalmate-Hajjaj A, Massó Guijarro P, Khan KS, Bueno-Cavanillas A, Cano-Ibáñez N. Benefits of participation in clinical trials: an umbrella review. Int J Environ Res Public Health. 2022;19(22):15368. doi:
Lantos JD. The “inclusion benefit” in clinical trials. J Pediatr. 1999;134(2):130-131. doi:
Joffe S. Framing the benefits of cancer clinical trials. Arch Pediatr Adolesc Med. 2010;164(3):293-294. doi:
Fernandes N, Bryant D, Griffith L, et al. Outcomes for patients with the same disease treated inside and outside of randomized trials: a systematic review and meta-analysis. CMAJ. 2014;186(16):E596-E609. doi:
Vist GE, Bryant D, Somerville L, Birminghem T, Oxman AD. Outcomes of patients who participate in randomized controlled trials compared to similar patients receiving similar interventions who do not participate. Published online July 16, 2008. Cochrane Database of Syst Rev. doi:
Arrieta O, Carmona A, Ramírez-Tirado LA, et al. Survival of patients with advanced non-small cell lung cancer enrolled in clinical trials. Oncology. 2016;91(4):185-193. doi:
Esteban D, Tovar N, Jiménez R, et al. Patients with relapsed/refractory chronic lymphocytic leukaemia may benefit from inclusion in clinical trials irrespective of the therapy received: a case-control retrospective nalysis. Blood Cancer J. 2015;5(10):e356-e356. doi:
Goldman C, Tchack J, Robinson EM, et al. Outcomes in melanoma patients treated with BRAF/MEK-directed therapy or immune checkpoint inhibition stratified by clinical trial versus standard of care. Oncology. 2017;93(3):164-176. doi:
Han JJ, Kim JW, Suh KJ, et al. Clinical characteristics and outcomes of patients enrolled in clinical trials compared with those of patients outside clinical trials in advanced gastric cancer. Asia Pac J Clin Oncol. 2019;15(3):158-165. doi:
Melnick KF, Miller P, Carmichael E, et al. The trial effect in patients with glioblastoma: effect of clinical trial enrollment on overall survival. J Neurooncol. 2022;159(2):479-484. doi:
Ohno S, Mukai H, Narui K, et al. Participants in a randomized controlled trial had longer overall survival than non-participants: a prospective cohort study. Breast Cancer Res Treat. 2019;176(3):631-635. doi:
Strahlendorf C, Pole JD, Barber R, et al. Enrolling children with acute lymphoblastic leukaemia on a clinical trial improves event-free survival: a population-based study. Br J Cancer. 2018;118(5):744-749. doi:
Covidence systematic review software. Accessed May 1, 2024.
West S, King V, Carey T, et al. Systems to rate the strength of scientific evidence. In: AHRQ Evidence Report Summaries. Vol 47. Agency for Healthcare Research and Quality; 2002:1-11.
Jüni P, Witschi A, Bloch R, Egger M. The hazards of scoring the quality of clinical trials for meta-analysis. JAMA. 1999;282(11):1054-1060. doi:
Igelström E, Campbell M, Craig P, Katikireddi SV. Cochrane’s risk of bias tool for non-randomized studies (ROBINS-I) is frequently misapplied: a methodological systematic review. J Clin Epidemiol. 2021;140:22-32. doi:
Rao G, Lopez-Jimenez F, Boyd J, et al; American Heart Association Council on Lifestyle and Cardiometabolic Health; Council on Cardiovascular and Stroke Nursing; Council on Cardiovascular Surgery and Anesthesia; Council on Clinical Cardiology; Council on Functional Genomics and Translational Biology; and Stroke Council. Methodological standards for meta-analyses and qualitative systematic reviews of cardiac prevention and treatment studies: a scientific statement from the American Heart Association. Circulation. 2017;136(10):e172-e194. doi:
Higgins JPT; Cochrane Collaboration, ed. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. Wiley-Blackwell; 2020.
R Core Team. R: A Language and Environment for Statistical Computing. Published online 2023.
Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088-1101. doi:
Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629-634. doi:
Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455-463. doi:
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Published online March 29, 2021. doi:
OSF Home. Measuring participation effects on survival in cancer clinical trials: a systematic review and meta-analysis. Updated February 1, 2024. Accessed May 1, 2024.
Mayers C, Panzarella T, Tannock IF. Analysis of the prognostic effects of inclusion in a clinical trial and of myelosuppression on survival after adjuvant chemotherapy for breast carcinoma. Cancer. 2001;91(12):2246-2257.
Elumalai T, Barker C, Elliott T, et al. Translation of Prognostic and pharmacodynamic biomarkers from trial to non-trial patients with metastatic castration-resistant prostate cancer treated with docetaxel. Clin Oncol. 2022;34(7):e291-e297. doi:
Le Du F, Fujii T, Park M, et al. Impact of clinical trial on survival outcomes. Breast Cancer Res Treat. 2016;159(2):273-281. doi:
Tanai C, Nakajima TE, Nagashima K, et al. Characteristics and outcomes of patients with advanced gastric cancer who declined to participate in a randomized clinical chemotherapy trial. J Oncol Pract. 2011;7(3):148-153. doi:
Abdel-Rahman O. Comparison of outcomes of radiotherapy-treated localized prostate cancer patients within a clinical trial setting versus real-life setting. Future Oncol. 2019;15(11):1269-1277. doi:
Boyle JM, Hegarty G, Frampton C, et al. Real-world outcomes associated with new cancer medicines approved by the Food and Drug Administration and European Medicines Agency: A retrospective cohort study. Eur J Cancer. 2021;155:136-144. doi:
Templeton AJ, Booth CM, Tannock IF. Informing Patients About Expected Outcomes: The Efficacy-Effectiveness Gap. J Clin Oncol. 2020;38(15):1651-1654. doi:
McGrath-Lone L, Ward H, Schoenborn C, Day S. The effects of cancer research participation on patient experience: a mixed-methods analysis. Eur J Cancer Care (Engl). 2016;25(6):1056-1064. doi:
Mandelblatt JS, Makgoeng SB, Luta G, et al. A planned, prospective comparison of short-term quality of life outcomes among older patients with breast cancer treated with standard chemotherapy in a randomized clinical trial vs. an observational study: CALGB #49907 and #369901. J Geriatr Oncol. 2013;4(4):353-361. doi:
Julian-Reynier C, Genève J, Dalenc F, et al; Patients’ Committee for Clinical Trials of the Ligue Nationale Contre le Cancer. Assessment of care by breast cancer patients participating or not participating in a randomized controlled trial: a report with the Patients’ Committee for Clinical Trials of the Ligue Nationale Contre le Cancer. J Clin Oncol. 2007;25(21):3038-3044. doi:
Thompson CA, Hugo SE, Swetz KM, et al. End-of-life care in a population-based cohort of cancer patients: clinical trial participation versus standard of care. BMJ Support Palliat Care. 2013;3(2):181-187. doi:
Enzinger AC, Zhang B, Weeks JC, Prigerson HG. Clinical trial participation as part of end-of-life cancer care: associations with medical care and quality of life near death. J Pain Symptom Manage. 2014;47(6):1078-1090. doi: