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Risk of bias in observational studies using routinely collected data of comparative effectiveness research: a meta-research study

Abstract

Background

To assess the completeness of reporting, research transparency practices, and risk of selection and immortal bias in observational studies using routinely collected data for comparative effectiveness research.

Method

We performed a meta-research study by searching PubMed for comparative effectiveness observational studies evaluating therapeutic interventions using routinely collected data published in high impact factor journals from 01/06/2018 to 30/06/2020. We assessed the reporting of the study design (i.e., eligibility, treatment assignment, and the start of follow-up). The risk of selection bias and immortal time bias was determined by assessing if the time of eligibility, the treatment assignment, and the start of follow-up were synchronized to mimic the randomization following the target trial emulation framework.

Result

Seventy-seven articles were identified. Most studies evaluated pharmacological treatments (69%) with a median sample size of 24,000 individuals. In total, 20% of articles inadequately reported essential information of the study design. One-third of the articles (n = 25, 33%) raised some concerns because of unclear reporting (n = 6, 8%) or were at high risk of selection bias and/or immortal time bias (n = 19, 25%). Only five articles (25%) described a solution to mitigate these biases. Six articles (31%) discussed these biases in the limitations section.

Conclusion

Reporting of essential information of study design in observational studies remained suboptimal. Selection bias and immortal time bias were common methodological issues that researchers and physicians should be aware of when interpreting the results of observational studies using routinely collected data.

Peer Review reports

Background

Though randomized control trials (RCTs) are considered to provide the best evidence in comparative effectiveness research (CER), they also have some limitations [1, 2]. They can often be resource-intensive and time-consuming. As such, RCTs may not be able to detect effects on long-term outcomes or rare events [3,4,5]. Observational studies using routinely collected data have been used to complement RCTs [5,6,7,8]. Routinely collected health data (RCD) are generated from the daily operations of healthcare systems, recorded without a priori research question [6]. A broad range of sources (e.g., disease registries, health administrative data, quality/safety surveillance databases, electronic health records, and pharmacy data) hosts such routinely collected data and contains both drug exposure and clinical outcomes to be used to provide evidence on treatment effectiveness.

However, observational studies are limited by their susceptibility to bias [5, 9,10,11]. Hernán et al. published a framework for using observational data to emulate a target trial, a hypothetical pragmatic trial [4, 12]. The framework suggested researcher explicitly specifying key components of this hypothetical trial such as eligibility criteria, treatment assignment, and the start of follow-up. The time when patients fulfill the eligibility criteria is assigned to one of the treatment strategies (i.e., fulfill the criteria to be classified as exposure or control), and starting the follow-up should be aligned to mimic the randomization process in an RCT [3, 4, 12]. By avoiding methodological pitfalls, this approach reduces the risk of bias of the effect estimate and hence produces more reliable results [13]. Cochrane has adopted this framework in the assessment of the risk of bias for non-randomized intervention studies [14].

This study aimed to assess the completeness of reporting essential information of study design and risk of bias due to failure to mimic the randomization in observational studies using routinely collected data for comparative effectiveness research. We did not aim to assess the extent that the bias could influence the conclusion of the included studies. After systematically reviewing the reporting and conducting of observational studies, we propose a checklist to help readers and reviewers to identify common methodological pitfalls of observational studies.

Methods

Study design

We conducted a meta-research study and reviewed the comparative effectiveness observational studies evaluating the therapeutic interventions with the use of routinely collected data published in high impact factor journals. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [15].

Search strategy

We identified a convenience sample of the 7 highest impact factor journals of the InCites Journal Citation Reports categories medicine, general, and internal (New England Journal of Medicine, Lancet, JAMA, BMJ, Annals of Internal Medicine, BMC Medicine, and PLoS Medicine) and 3 highest impact factor journals in endocrinology and metabolism (Lancet Diabetes & Endocrinology, Diabetes Care, and Diabetes) and cardiac and cardiovascular systems (European Heart Journal, Journal of American College of Cardiology, and Circulation) that cover research on high prevalent diseases.

As all these ten journals were indexed on PubMed, we conducted a search on PubMed to identify the observational studies evaluating a comparative effectiveness question. To reflect contemporary reporting practices and methodological conduct, the search was narrowed to studies published between 01/06/2018 and 30/06/2020. The full search strategy is presented in Additional file 1: Table S1.

Eligibility criteria

We included cohort studies which evaluated a therapeutic intervention by using RCD [6]. Studies were eligible for inclusion if they (1) evaluated a therapeutic intervention, defined as a treatment-related to healing a disease, i.e., pharmaceuticals, surgery; (2) used RCD as the data source; and (3) answered a comparative effectiveness question, i.e., research aiming to identify which interventions work best for improving health. Studies that did not answer CER questions, studies without an abstract, and retracted papers were excluded. The inclusion and exclusion criteria for study selection are provided in Additional file 1: Table S2.

Study screening and selection

One reviewer (ME) screened all the titles and abstracts of the studies retrieved. A second reviewer (VNT) screened a sample of 775 (57%) of 1357 articles excluded by ME. There was good agreement between the two reviewers with only 1 conflict. Then, each of the full texts was assessed by two of three reviewers (ME, VNT, MD) to ensure the eligibility of the study for data extraction. All conflicts were resolved through discussion, and a third reviewer was available to adjudicate. Literature search results were imported into Mendeley (https://www.mendeley.com) to store, organize, and manage all references. The screening process was aided by the use of the Rayyan software [16].

Data extraction

Data from each article were extracted independently by two of the three reviewers (ME, VTN, and MD) using a standardized form created based on the framework for emulating a target trial proposed by Hernán et al. and RECORD-PE reporting guideline for observational studies using routinely collected data for pharmacoepidemiology [4, 12, 14, 17]. The form was initially piloted and refined throughout the process (Additional file 1: Table S3 – data extraction form and Additional file 1: Table S4 – explanation of data items). Any disagreement was discussed with senior researchers (RP, IB) to reach a consensus. The following data were extracted from the selected papers:

  1. 1.

    Study characteristics: title, year of publication, author, location of the corresponding author, name of the journal, study design (longitudinal study), treatment type, comparator, funding source (i.e., public, private funding), and data source

  2. 2.

    Research transparency practices: use of reporting guidelines, access to codes and algorithms to classify exposures and outcomes, and data sharing policy

  3. 3.

    Reporting of essential items:

    1. (a)

      Diagram to illustrate the study design (i.e., describing the time of eligibility, treatment assignment, and follow-up).

    2. (b)

      Eligibility criteria, and particularly whether individuals with contraindication to one of the evaluated treatments, were explicitly excluded as in an RCT.

    3. (c)

      Methods used to adjust for confounding (i.e., regression, propensity score, inverse probability weighting).

    4. (d)

      Causal contrast of interest (i.e., intention-to-treat effect, per-protocol effect).

    5. (e)

      Time points of eligibility (i.e., when individuals were evaluated regarding their eligibility), treatment assignment (i.e., when individuals were classified to one of the treatment groups), and the start of follow-up (i.e., when individuals started outcome assessment).

  4. 4.

    After determining the time points of eligibility, treatment assignment, and the start of follow-up, we assessed if these time points were aligned to avoid bias. We identified the type of bias that might arise when they were not aligned (Table 1) and whether the authors described a solution to address bias.

Table 1 Situations when time points of eligibility, treatment assignment, and the start of follow-up are not aligned

Data synthesis

Categorical data were summarized using frequencies and percentages. Interrater reliability was tested using Cohen’s kappa [18]. Descriptive analysis was completed in R (version 4.0.2).

Data sharing

Data of this study will be available on Zenodo after the publication of the article.

Patient involvement

Patients and public members were not involved in this study.

Results

Study characteristics

Among the 1465 articles retrieved from the search, 77 articles were selected for data extraction after screening for the title, abstract, and full text (Fig. 1).

Fig. 1
figure1

Study selection process

Most of the studies were from North America and Europe and with a median sample size of 24,000 individuals. Ten articles (13%) did not report the study design. Fifty-three studies (69%) evaluated the pharmacological treatment. Forty-nine studies (63%) compared against active comparators. The sources of data were registry (n = 34/77, 44%), electronic health record (n = 17/77, 22%), administration data (n = 14/77, 18%), and health insurance claims (n = 20, 26%). Fifty-six percent of studies (43/77) received funding from not-for-profit organizations, and 13% (10/77) did not report the type of funding.

Research transparency practices

Only seven articles (9%) mentioned the use of a reporting guideline. Fifty-three articles (69%) provided codes (e.g., ICD-10 codes) used to classify both exposures and outcomes. Ten articles (13%) indicated that data were available upon request (Table 2).

Table 2 Characteristics of included articles

Reporting essential information of the target trial

Only 18% (n = 14/77) reported a diagram to illustrate the study design and reported the three essential time points (i.e., eligibility, treatment initiation, start of follow-up). Eighteen percent (n = 14/77) did not report completely essential time points, i.e., the start of follow-up, when individuals completed the eligibility criteria and when patients started the treatments of interest. Regarding the inclusion criteria, only 12% (n = 9/77) reported the exclusion of patients with contraindication to one of the evaluated interventions. Only one article explained the reason for not excluding patients with such a contraindication, due to the inability to identify these patients from the dataset. Sixty-five percent of articles (n = 50/77) did not specify the type of causal contrast estimated (Table 3).

Table 3 Reporting of essential information

Risk of bias due to failure of specifying a target trial

Overall, 33% (n = 25/77) raised concerns about the risk of bias. Of these, in one-fourth (n = 6/25), as the start of follow-up was not clearly reported, we could not determine if eligibility, treatment assignment, and the start of follow-up were synchronized (Fig. 1). In 76% (n = 19/25), the time when patients completed the eligibility criteria, initiated the treatments, and the start of follow-up was not aligned (Fig. 1). Among these 19 articles, in four articles (n = 4/19, 21%), the follow-up started when patients met eligibility but after patients initiated treatment (Table 1 (b)), which led to prevalent user bias and selection bias due to post-treatment eligibility [19,20,21,22]. The authors did not describe any solutions to address these biases in these four articles.

In seven articles (n = 7/19, 37%), the follow-up started when patients initiated treatment but before patients met the eligibility criteria leading to immortal time bias and selection bias due to post-treatment eligibility (Table 1 (c)) [23,24,25,26,27,28,29]. Among these, one article reported handling treatment exposure as a time-dependent variable to account for immortal time bias; however, this strategy was inadequate to account for selection bias due to post-treatment eligibility [25]. One article performed a sensitivity analysis to include participants who were excluded based on the post-treatment eligibility criteria and yielded similar results to the main analysis [27].

In seven articles (n = 7/19, 37%), follow-up started when patients met the eligibility criteria, but patients were assigned to one of the treatment groups after the start of the follow-up, a situation both at risk of immortal time bias and misclassification of treatment (Table 1 (d)) [30,31,32,33,34,35,36,37]. Of these, four articles did not mention any solutions leading to high risk of selection bias [31, 32, 35, 37]; three articles treated treatment exposure as a time-dependent variable [30, 33, 36] which was inadequate to address the risk of misclassification, and one article randomly assigned individuals who had outcomes before treatment initiation to one of the two treatment groups [34] to mitigate the risk of bias. In one article (n = 1/19, 5%), individuals could start the treatment both before and after eligibility and the start of follow-up (Table 1 (b and d)); thus, the study was at risk of prevalent user bias and immortal time bias [38]. No solution was described in this article. Among these 19 articles that we identified biases, six articles (32%) discussed these biases in the limitations section (Fig. 2).

Fig. 2
figure2

The number of studies at risk of bias due to lack of synchronization. Nineteen (25%) studies had a high risk of bias due to the lack of synchronization. Of these, 14 proposed no solution, and 5 used inadequate methods to address the bias. Six studies inadequately reported to enable the assessment of synchronization. Fifty-two (68%) studies had low risk of bias

Table 4 presents the main features of 19 studies without synchronization of eligibility, treatment assignment, and follow-up.

Table 4 Studies without synchronization of eligibility, treatment assignment, and follow-up

Discussion

Our review showed that 20% (n = 14/77) of the articles did not adequately report essential information of the study design. A third of reviewed articles had unclear risk of bias or high risk of selection bias and/or immortal time bias due to the choice of the time of eligibility, treatment assignment, and the start of follow-up that failed to mimic the randomization. In only 25% of the articles at risk of bias, a solution was described; however, these solutions were not adequate to eliminate the risk of bias completely. The lack of synchronization arises when investigators want to select individuals who might have better treatment adherence, i.e., select only individuals who adhered to the treatment for a given period (Table 5 (c)), or only individuals who have adhered to the treatment for a given period are classified as exposed (Table 5 (d)). To address the selection bias caused by using a post-treatment event to include individuals or predict treatment strategies in the future, Hernan et al. proposed creating a clone, i.e., an exact copy of the population, assign them to one of the treatment groups and censor when they deviate from the assigned treatment [12].

Table 5 Solutions proposed by Hernan et al. to address the risk of bias when time points of eligibility, treatment assignment, and the start of follow-up are not aligned

Another common reason for the lack of synchronization in observational studies using routinely collected data is due to having a grace period, i.e., individuals start to use treatment within a given period after the start of follow-up and eligibility (Table 5 (d)); thus, investigators can increase the number of eligible individuals. For example, to compare the effectiveness of hydroxychloroquine versus standard of care in the treatment for COVID-19 patients, the number of patients who initiated hydroxychloroquine immediately after hospital admission would be quite low. To increase the number of eligible patients for the analysis, investigators allowed for a grace period to assign patients who started hydroxychloroquine within 48 h since admission to the intervention group [34, 35]. However, a challenge of having a grace period is that we could not assign patients to one of the intervention groups at the start of the follow-up as in an RCT. If a patient had an outcome within 48 h since admission, it is uncertain if they should be classified as exposed or control group. To overcome the challenge of having a grace period, Hernan et al. also recommended following the strategy as above, i.e., to create an exact copy of the population, assign them to one of the intervention group, censor when they start to deviate from assigned treatment, and use inverse probability weighting to adjust for post-treatment censoring bias [12, 39] (Table 5). However, the use of such an approach was never reported in our sample. Although Hernan et al. proposed this approach in 2016, there are only a few studies applying this approach due to methodological and logistical challenges. Maringe et al. provided a detailed tutorial to perform the cloning strategy [40].

Additionally, the emulated trial framework highlights the importance of the new-user design by identifying all eligible in the defined population who started the study treatments to avoid these biases. The selection of only new users, however, might reduce the sample size and the study power [41, 42]. To address this challenge, sensitivity analysis could be used to assess the magnitude of potential bias related to including prevalent users [41, 42].

Furthermore, some other essential information was missing in the report of observational studies in our sample, particularly specifying if patients with contraindication with one of the evaluated treatments were excluded from the analysis. This issue could be problematic as we are uncertain if patients in different treatment groups were comparable. For example, in a study, patients who had contraindication with evaluated treatments were classified as the control group [43]. It means that patients in the intervention and control groups were not exchangeable, which violated a fundamental condition of causal inference.

Previous studies have also highlighted the incomplete reporting and potential bias in the implementation of observational studies. Luijken et al. found that 6% of the evaluated observational studies did not specify if new users or prevalent users were included, and in only half of the studies using new user design, time point of eligibility, treatment initiation, and start of follow-up were synchronized [44]. Due to these avoidable methodological pitfalls, the results of observational studies could be biased and mislead healthcare decisions [45]. The emulated trial framework which relies on synchronization of eligibility, treatment assignment, and the start of follow-up to mimic the randomization of RCT can help in reducing the risk of bias. However, the approach proposed by Hernan has also some limitations particularly in some situations, synchronization of the time points of the eligibility criteria, start of treatment, and start of follow-up is not feasible. By explicitly reporting these components and the decision made when emulating the target trials, researchers could help readers in assessing the extent that results might be influenced by bias and whether the choice of methodology to address this bias was appropriate to ensure the validity of results. We propose a checklist following the framework of emulated trials to help readers and reviewers to identify the common pitfalls of observational studies (Table 6).

Table 6 Checklist to determine the potential risk of bias in observational studies

Our study has some limitations. First, to ensure the feasibility of the study, we restricted the search to high impact factor journals, which might underestimate the prevalence of bias due to the lack of synchronization of eligibility, treatment assignment, and start of follow-up. However, our aim is to raise awareness of the common problems of reporting and conducting observational studies using RCD that need to be addressed in future research. Second, we were unable to determine the magnitude of the bias. For example, if there are more individuals who have outcomes during the grace period, the effect estimates would be at higher risk of bias, because these individuals are more likely to be classified in the control group. Third, we did not evaluate the risk of confounding in the included studies. Nevertheless, the emulated trial framework and the cloning strategy can address the confounding bias.

Conclusions

In conclusion, reporting of essential information of the study design in observational studies remained suboptimal. The lack of synchronization of eligibility, treatment assignment, and the start of follow-up is common among observational studies, which leads to different types of bias such as prevalent user bias, immortal time bias, and selection bias due to post-treatment eligibility. Researchers and physicians should critically appraise the results of observational studies using routinely collected data.

Availability of data and materials

The dataset is available at https://doi.org/10.5281/zenodo.5543469.

Abbreviations

PRISMA:

Preferred reporting items for systematic reviews and meta-analyses

RCD:

Routinely collected data

RCTs:

Randomized controlled trials

RECORD-PE:

Reporting Guideline for observational studies using routinely collected data for pharmacoepidemiology

References

  1. 1.

    Stuart EA, Ackerman B, Westreich D. Generalizability of randomized trial results to target populations: design and analysis possibilities. Res Soc Work Pract. 2017;28(5):532–7. https://doi.org/10.1177/1049731517720730.

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    McDonald AM, Knight RC, Campbell MK, Entwistle VA, Grant AM, Cook JA, et al. What influences recruitment to randomized controlled trials? A review of trials funded by two UK funding agencies. Trials. 2006;7(1):9. https://doi.org/10.1186/1745-6215-7-9.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Hernan M, Robins JM. Causal inference: what if. Boca Raton: Chapman & Hall/CRC; 2020.

    Google Scholar 

  4. 4.

    Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183(8):758–64. https://doi.org/10.1093/aje/kwv254.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Gershman B, Guo DP, Dahabreh IJ. Using observational data for personalized medicine when clinical trial evidence is limited. Fertil Steril. 2018;109(6):946–51. https://doi.org/10.1016/j.fertnstert.2018.04.005.

    Article  PubMed  Google Scholar 

  6. 6.

    Hemkens LG, Contopoulos-Ioannidis DG, Ioannidis JPA. Routinely collected data and comparative effectiveness evidence: promises and limitations. Can Med Assoc J. 2016;188(8):E158–64. https://doi.org/10.1503/cmaj.150653.

    Article  Google Scholar 

  7. 7.

    Dreyer NA. Making observational studies count: shaping the future of comparative effectiveness research. Epidemiology. 2011;22(3):295–7.

  8. 8.

    Concato J, Lawler EV, Lew RA, Gaziano JM, Aslan M, Huang GD. Observational methods in comparative effectiveness research. Am J Med. 2010;123(12):e16–23. https://doi.org/10.1016/j.amjmed.2010.10.004.

    Article  PubMed  Google Scholar 

  9. 9.

    D’Agostino RB. Estimating treatment effects using observational data. JAMA. 2007;297(3):314–6. https://doi.org/10.1001/jama.297.3.314.

    Article  PubMed  Google Scholar 

  10. 10.

    Lévesque LE, Hanley JA, Kezouh A, Suissa S. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. BMJ. 2010;340(mar12 1):b5087. https://doi.org/10.1136/bmj.b5087.

    Article  PubMed  Google Scholar 

  11. 11.

    Iudici M, Porcher R, Riveros C, Ravaud P. Time-dependent biases in observational studies of comparative effectiveness research in rheumatology. A methodological review. Ann Rheum Dis. 2019;78(4):562–9. https://doi.org/10.1136/annrheumdis-2018-214544.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016;79:70–5. https://doi.org/10.1016/j.jclinepi.2016.04.014.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    García-Albéniz X, Hsu J, Hernán MA. The value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening. Eur J Epidemiol. 2017;32(6):495–500. https://doi.org/10.1007/s10654-017-0287-2.

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Sterne JAC, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomized studies of interventions. BMJ. 2016;355:i4919. https://doi.org/10.1136/bmj.i4919.

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339(jul21 1):b2700. https://doi.org/10.1136/bmj.b2700.

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. https://doi.org/10.1186/s13643-016-0384-4.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Langan SM, Schmidt SAJ, Wing K, Ehrenstein V, Nicholls SG, Filion KB, et al. The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology (RECORD-PE). BMJ. 2018;363:k3532. https://doi.org/10.1136/bmj.k3532.

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276–82. https://doi.org/10.11613/BM.2012.031.

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Converse MP, Sobhanian M, Taber DJ, Houston BA, Meadows HB, Uber WE. Effect of angiotensin II inhibitors on gastrointestinal bleeding in patients with left ventricular assist devices. J the Am Coll Cardiol. 2019;73(14):1769–78. https://doi.org/10.1016/j.jacc.2019.01.051.

    CAS  Article  Google Scholar 

  20. 20.

    Skriver C, Dehlendorff C, Borre M, Brasso K, Larsen SB, Dalton SO, et al. Use of low-dose aspirin and mortality after prostate cancer diagnosis: a nationwide cohort study. Ann Intern Med. 2019;170(7):443–52. https://doi.org/10.7326/M17-3085.

    Article  PubMed  Google Scholar 

  21. 21.

    Friberg L, Andersson T, Rosenqvist M. Less dementia and stroke in low-risk patients with atrial fibrillation taking oral anticoagulation. Eur Heart J. 2019;40(28):2327–35. https://doi.org/10.1093/eurheartj/ehz304.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Siontis KC, Zhang X, Eckard A, Bhave N, Schaubel DE, He K, et al. Outcomes associated with apixaban use in patients with end-stage kidney disease and atrial fibrillation in the United States. Circulation. 2018;138(15):1519–29. https://doi.org/10.1161/CIRCULATIONAHA.118.035418.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Brauer R, Lau WCY, Hayes JF, Man KKC, Osborn DPJ, Howard R, et al. Trazodone use and risk of dementia: a population-based cohort study. PLOS Medicine. 2019;16(2):e1002728. https://doi.org/10.1371/journal.pmed.1002728.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Xie Y, Bowe B, Yan Y, Xian H, Li T, Al-Aly Z. Estimates of all cause mortality and cause specific mortality associated with proton pump inhibitors among US veterans: cohort study. BMJ. 2019;365:l1580. https://doi.org/10.1136/bmj.l1580.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Brown JWL, Coles A, Horakova D, Havrdova E, Izquierdo G, Prat A, et al. Association of initial disease-modifying therapy with later conversion to secondary progressive multiple sclerosis. JAMA. 2019;321(2):175–87. https://doi.org/10.1001/jama.2018.20588.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Kim NH, Han KH, Choi J, Lee J, Kim SG. Use of fenofibrate on cardiovascular outcomes in statin users with metabolic syndrome: propensity matched cohort study. BMJ. 2019;366:l5125. https://doi.org/10.1136/bmj.l5125.

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Lin Y-C, Chien S-C, Hsieh Y-C, Shih C-M, Lin F-Y, Tsao N-W, et al. Effectiveness and safety of standard- and low-dose rivaroxaban in Asians with atrial fibrillation. J Am Coll Cardiol. 2018;72(5):477–85. https://doi.org/10.1016/j.jacc.2018.04.084.

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Rea F, Corrao G, Merlino L, Mancia G. Early cardiovascular protection by initial two-drug fixed-dose combination treatment vs. monotherapy in hypertension. Eur Heart J. 2018;39(40):3654–61. https://doi.org/10.1093/eurheartj/ehy420.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Ramos R, Comas-Cufí M, Martí-Lluch R, Balló E, Ponjoan A, Alves-Cabratosa L, et al. Statins for primary prevention of cardiovascular events and mortality in old and very old adults with and without type 2 diabetes: retrospective cohort study. BMJ. 2018;362:k3359. https://doi.org/10.1136/bmj.k3359.

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Axtell AL, Bhambhani V, Moonsamy P, Healy EW, Picard MH, Sundt TM, et al. Surgery does not improve survival in patients with isolated severe tricuspid regurgitation. J Am Coll Cardiol. 2019;74(6):715–25. https://doi.org/10.1016/j.jacc.2019.04.028.

    Article  PubMed  Google Scholar 

  31. 31.

    Gharbi M, Drysdale JH, Lishman H, Goudie R, Molokhia M, Johnson AP, et al. Antibiotic management of urinary tract infection in elderly patients in primary care and its association with bloodstream infections and all cause mortality: population based cohort study. BMJ. 2019;364:l525. https://doi.org/10.1136/bmj.l525.

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Gray E, Marti J, Wyatt JC, Brewster DH, Hall PS, Group Sa. Chemotherapy effectiveness in trial-underrepresented groups with early breast cancer: a retrospective cohort study. Plos Medicine. 2020;16(12):e1003006.

    Article  Google Scholar 

  33. 33.

    van Rein N, Heide-Jørgensen U, Lijfering Willem M, Dekkers Olaf M, Sørensen Henrik T, Cannegieter SC. Major bleeding rates in atrial fibrillation patients on single, dual, or triple antithrombotic therapy. Circulation. 2019;139(6):775–86. https://doi.org/10.1161/CIRCULATIONAHA.118.036248.

    Article  PubMed  Google Scholar 

  34. 34.

    Mahévas M, Tran V-T, Roumier M, Chabrol A, Paule R, Guillaud C, et al. Clinical efficacy of hydroxychloroquine in patients with COVID-19 pneumonia who require oxygen: observational comparative study using routine care data. BMJ. 2020;369:m1844.

    Article  Google Scholar 

  35. 35.

    Rosenberg ES, Dufort EM, Udo T, Wilberschied LA, Kumar J, Tesoriero J, et al. Association of treatment with hydroxychloroquine or azithromycin with in-hospital mortality in patients with COVID-19 in New York State. JAMA. 2020;323(24):2493–502. https://doi.org/10.1001/jama.2020.8630.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Jorge A, Wallace ZS, Lu N, Zhang Y, Choi HK. Renal transplantation and survival among patients with lupus nephritis: a cohort study. Ann Intern Med. 2019;170(4):240–7. https://doi.org/10.7326/M18-1570.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Larochelle MR, Bernson D, Land T, Stopka TJ, Wang N, Xuan Z, et al. Medication for opioid use disorder after nonfatal opioid overdose and association with mortality. Ann Intern Med. 2018;169(3):137–45. https://doi.org/10.7326/M17-3107.

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Geleris J, Sun Y, Platt J, Zucker J, Baldwin M, Hripcsak G, et al. Observational study of hydroxychloroquine in hospitalized patients with COVID-19. N Engl J Med. 2020;382(25):2411–8. https://doi.org/10.1056/NEJMoa2012410.

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Hernán MA. How to estimate the effect of treatment duration on survival outcomes using observational data. BMJ. 2018;360:k182. https://doi.org/10.1136/bmj.k182.

    Article  PubMed  Google Scholar 

  40. 40.

    Maringe C, Benitez Majano S, Exarchakou A, Smith M, Rachet B, Belot A, et al. Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data. Int J Epidemiol. 2020;49(5):1719–29. https://doi.org/10.1093/ije/dyaa057.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol. 2003;158(9):915–20. https://doi.org/10.1093/aje/kwg231.

    Article  PubMed  Google Scholar 

  42. 42.

    Yoshida K, Solomon DH, Kim SC. Active-comparator design and new-user design in observational studies. Nat Rev Rheumatol. 2015;11(7):437–41. https://doi.org/10.1038/nrrheum.2015.30.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Lagier J-C, Million M, Gautret P, Colson P, Cortaredona S, Giraud-Gatineau A, et al. Outcomes of 3,737 COVID-19 patients treated with hydroxychloroquine/azithromycin and other regimens in Marseille, France: a retrospective analysis. Travel Med Infect Dis. 2020;36:101791.

    Article  Google Scholar 

  44. 44.

    Luijken K, Spekreijse JJ, van Smeden M, Gardarsdottir H, Groenwold RHH. New-user and prevalent-user designs and the definition of study time origin in pharmacoepidemiology: a review of reporting practices. Pharmacoepidemiol Drug Saf. 2021;30(7):960–74. https://doi.org/10.1002/pds.5258.

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Gueyffier F, Cucherat M. The limitations of observation studies for decision making regarding drugs efficacy and safety. Therapies. 2019;74(2):181–5. https://doi.org/10.1016/j.therap.2018.11.001.

    Article  Google Scholar 

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Funding

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Study conception: VTN, IB, PR, RP, and ME. Data collection: VTN, ME, and MD; Data analysis and interpretation: VTN, IB, RP, PR, ME, and MD. Writing: VTN, IB, RP, and PR. All authors read and approved the final version of the manuscript.

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Correspondence to Van Thu Nguyen.

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Supplementary Information

Additional file 1: Table S1.

Search strategy. Table S2. Eligibility criteria. Table S3. Data extraction form. Table S4. Data Extraction Form Explanation.

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Nguyen, V.T., Engleton, M., Davison, M. et al. Risk of bias in observational studies using routinely collected data of comparative effectiveness research: a meta-research study. BMC Med 19, 279 (2021). https://doi.org/10.1186/s12916-021-02151-w

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Keywords

  • Observational studies
  • Routinely collected data
  • Emulated trial
  • Meta-research
  • Risk of bias