Contact Us

Home

About us

Services

AI Excellence

case studies

publications

HEOR Certificationheor Certification

AI for Medical Writing

Contact

  • The significance of STROBE and RECORD Reporting Guidelines for Observational Studies

    The significance of STROBE and RECORD Reporting Guidelines for Observational Studies

    The significance of STROBE and RECORD Reporting Guidelines for Observational Studies

    Observational studies are instrumental in health research as they provide important insights into disease patterns, treatment efficacy, and patient outcomes in real-world settings. However, the reporting of these studies has been a persistent challenge in the scientific community, thanks to inadequate documentation that usually hinders the evaluation of a study’s strengths, limitations, and generalizability to wider contexts. Observational research, unlike randomized controlled trials (RCTs), is especially prone to bias and confounding, necessitating transparent and systematic reporting for credibility and applicability.(1)

    Identifying this need, the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) Statement was developed by collaborative efforts of epidemiologists, methodologists, and journal editors. First published in 2007, STROBE gives an exhaustive 22-item checklist to lead researchers, journal editors, and readers through the key components of cohort, case-control, and cross-sectional studies.(2) By promoting clarity in study objectives, selection of participant, data sources, variables, and analytical methods, STROBE empowers authors to accurately put forth their research design, methodology, and outcomes. Consequently, it enhances the clarity, generalizability, and validity of observational research while helping readers critically evaluate study reliability.(2, 3)

    The increasing dependence on routinely collected health data, including data from electronic health records (EHRs), insurance claims, and registries, gave rise to new challenges that were not fully addressed by STROBE alone. To address these, the RECORD (REporting of studies Conducted using Observational Routinely collected health Data) guidelines were created as an extension to STROBE in 2015.(4) RECORD introduced a 13-item checklist addressing concerns pertinent to these data sources, such as database connections, coding procedures, algorithms, population hierarchies, and data cleaning methods. Considering that routinely collected data are mainly intended for administrative or clinical objectives rather than research, RECORD plays a significant role in ensuring transparent, complete, and accurate reporting, making findings more understandable and actionable for both health policy and clinical decision-making.(4)

    Although the original STROBE and RECORD guidelines have not been updated, new extensions like STROBE-Equity,(5) STROBE-Vision,(6) and STROBE-MetEpi (6) have been or are being developed to address particular areas. Researchers are encouraged to use these extensions along with the main STROBE and RECORD checklists, as the basic frameworks remain the same.(5, 6)

    A critical development in reporting standards is the emphasis on health equity; since  observational studies offer important equity-related data, although with limited guidance on reporting. The STROBE-Equity extension addresses this by including 10 additional equity-specific items to the STROBE checklist, enabling clearer reporting of discrepancies and their consequences. Its implementation by researchers and endorsement by journals can reinforce the inclusion of health equity in observational research to guide fairer and more socially relevant decision-making.(5)

    Endorsement and execution of both STROBE and RECORD by authors, reviewers, and journals have already been linked to enhanced reporting quality. These guidelines promote full disclosure of study strengths and limitations, while mitigating risks of bias and misinterpretation originating from incomplete reporting. With evolving research approaches and data sources, the enhancement and consistent adoption of reporting standards like STROBE and RECORD continues to remain crucial. By reinforcing transparency, reproducibility, and reliability, these guidelines allow observational studies to efficiently contribute important and dependable evidence to the advancement of science, healthcare practice, and policy.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Grosman S, Scott IA. Quality of observational studies of clinical interventions: a meta-epidemiological review. BMC Med Res Methodol. 2022 Dec 7;22(1):313.
    2. von Elm E, Altman DG, Egger M, et al; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007 Oct 16;4(10):e296.
    3. Vandenbroucke JP, von Elm E, Altman DG, et al; STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration. Value Health. 2014; 12(12):1500-1524.
    4. Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, Sørensen HT, von Elm E, Langan SM; RECORD Working Committee. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015 Oct 6;12(10):e1001885.
    5. Dewidar O, Shamseer L, Melendez-Torres GJ, et al. Improving the reporting on health equity in observational research (STROBE-Equity): extension checklist and elaboration. BMJ 2025;390:e083882.
    6. Equator Network. Reporting guidelines under development for observational studies2021. [Accessed on 16th September 2025]. Available online at: https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-observational-studies/#STROBEV

    MarksMan Healthcare

    , ,

  • Addressing Confounding in RWE studies

    Addressing Confounding in RWE studies
    Addressing Confounding in RWE studies

    Real-world evidence (RWE) studies strongly complement the conventional randomized controlled trials (RCTs), offering crucial insights into the performance of interventions in routine clinical settings. However, one of the most persistent practical concerns in RWE studies is the issue of confounding, which occurs owing to the inherent biases in real-world data (RWD). Unlike RCTs, where randomization factors in both known and unknown covariates amongst study groups, RWE studies are usually based on observational data, where treatment allocation is not arbitrary.[1] This results in confounding variables, i.e. factors that are influenced by both the treatment and the outcome, which can distort the estimated effects of interventions.[2]

    The first step in addressing confounding in RWE studies is a robust study design. Researchers must be cautious while evaluating the data source, conditions for cohort selection, and timing of assessment of covariates to ensure that potential confounders are well-defined. It is essential to recognize a distinct temporal connection between exposure, confounders, and outcome. Mispositioning in these time points can result in biased associations, especially if covariates are influenced by the treatment itself or are quantified post-exposure. Careful design selection can lower this risk before applying any statistical adjustment.[2, 3]

    Statistical methods play a key role in addressing confounding in RWE. Techniques like multivariable regression, inverse probability of treatment weighting (IPTW), instrumental variable analysis, and propensity score matching (PSM) are commonly applied; each method has its set of assumptions and limitations. For example, PSM can compare observed covariates between treatment groups, but they cannot justify unmeasured confounding. Instrumental variable methods, while theoretically strong, need even robust instruments, which may not be widely available in real-world datasets. These techniques seek to improve causal inference by simulating the balance achieved in RCTs. The choice of an appropriate method relies on the nature of data, the credibility of assumptions, and the research question.[2, 4, 5]

    Sensitivity analyses are important tools in assessing the strength and validity of findings in the presence of residual confounding. With variable assumptions, such as the robustness of unmeasured confounding or the model specifics, researchers can evaluate how much their results might be influenced by factors not included directly. Quantitative bias analysis, E-values, and negative control outcomes are some methods that can improve the reliability of study findings. These methods do not remove confounding but help analyse the potential extent of bias.[2, 6]

    Finally, transparent reporting is also essential for addressing confounding in RWE studies. Researchers should precisely define their methods for identifying, measuring, and adjusting for confounders, including the reasoning behind selected techniques and any limitations in the data. Communicating code lists, model specifications, and sensitivity analyses improves robustness and enables others to evaluate the authenticity of the findings. Established reporting guidelines, such as the STRengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement [7] for observational studies, provide a solid basis for transparency of findings. For studies considering routinely collected RWD, the REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement, which is an extension of the STROBE statement, provides additional guidance particularly to RWE complexities.[8] Implementing such frameworks facilitates clearer communication of study design and results, making RWE more reliable and actionable.[6, 8]

    With the growing use of RWE in regulatory, clinical, and policy decision-making, carefully addressing confounding will be vital for ensuring reliable and actionable evidence generation.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Tashkin DP, Amin AN, Kerwin EM. Comparing Randomized Controlled Trials and Real-World Studies in Chronic Obstructive Pulmonary Disease Pharmacotherapy. Int J Chron Obstruct Pulmon Dis. 2020 Jun 2;15:1225-1243.
    2. Wang SV, Schneeweiss S. Assessing and Interpreting Real-World Evidence Studies: Introductory Points for New Reviewers. Clin Pharmacol Ther. 2022 Jan;111(1):145-149.
    3. Laurent T, Lambrelli D, Wakabayashi R, et al. Strategies to Address Current Challenges in Real-World Evidence Generation in Japan. Drugs Real World Outcomes. 2023 Jun;10(2):167-176.
    4. European Network of Centres for Pharmacoepidemiology and Pharmacovigilance. Chapter 6: Methods to address bias and confounding. Available at: https://encepp.europa.eu/encepp-toolkit/methodological-guide/chapter-6-methods-address-bias-and-confounding_en
    5. Chandramouli R. Statistical Methodologies in Real-World Evidence (RWE) for Medical Product Development. 2024. Available at: https://www.linkedin.com/pulse/statistical-methodologies-real-world-evidence-rwe-medical-r-bbglc
    6. Assimon MM. Confounding in Observational Studies Evaluating the Safety and Effectiveness of Medical Treatments. Kidney360. 2021 May 14;2(7):1156-1159.
    7. Von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Preventive medicine. 2007;45(4):247–51.
    8. Nicholls SG, Quach P, von Elm E, et al. The REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) Statement: Methods for Arriving at Consensus and Developing Reporting Guidelines. PLoS One. 2015 May 12;10(5):e0125620.

    MarksMan Healthcare

    , , ,