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  • Risk of Bias Assessment During Systematic Literature Reviews: Why and How?

    Risk of Bias Assessment During Systematic Literature Reviews: Why and How?

    A systematic literature review (SLR) is considered the highest form of evidence due to its rigorous approach through which every relevant piece of published or unpublished literature that is currently available to address a specific research issue is pooled and analyzed to answer a research question. For this reason, it is essential that the risk of bias (ROB) is assessed for all studies that are included in the SLRs. ROB assessment ensures the transparency of the outcomes, the validity of evidence, confidence, and reproducibility of the SLR process. It is typically done by identifying systematic errors or limitations in the conduct, design, or analysis of each included study in SR. Further, ROB assessment of the included studies is also a requirement for optimal reporting of SLRs, recommended by the PRISMA 2020 statement. (1,2)

    The tool for ROB assessment depends on the study design of the included studies. For SLRs including RCTs, different tools are available for ROB assessment; one such tool is the Cochrane risk of bias tool version 2.0 (RoB-2), that was published in 2019 as an upgrade to the previous Cochrane RoB tool. According to the developers, the Cochrane RoB-2 is suitable for assessing ROB in individually-randomized, parallel-group, and cluster- randomized trials. (3) Other tools that are used for assessing ROB of RCTs include the EPOC RoB Tool for complex interventions randomized trials, the Critical Appraisal Skills Programme (CASP) checklist, the Joanna Briggs Institute (JBI) critical appraisal checklist, and the Scottish Intercollegiate Guidelines Network (SIGN) critical appraisal checklists for assessing methodological quality of different study types, including RCT. (4-6) Out of all of these available tools, the choice of the most appropriate tools depends on the research question, domain coverage of the tool, availability of the tool, the type of scoring system used, and any specific regulatory requirement.

    For SLRs including non-randomized studies, a frequently used tool is the ROBINS-I (Risk Of Bias In Non-randomized Studies – of Interventions) tool. Apart from this, other popular tools include the JBI critical appraisal checklist for non-randomized experimental studies, the EPOC RoB tool, and the methodological index for non-randomized studies (MINORS) tool. (1,5,6)

    There are several available tools for observational studies. To name a few, the CASP cohort study checklist, the SIGN critical appraisal checklists, the NIH quality assessment tool, the Newcastle-Ottawa Scale, and JBI critical appraisal checklist are the recommended tools for cohort study and Case-control studies. The Appraisal tool for Cross-Sectional Studies (AXIS) is another recommended tool for cross-sectional studies. Additionally, MINORS and the JBI Critical Appraisal tool can be used for ROB assessment of case series and case reports. (4-7) If an SLR includes more than one type of study design, the ROB assessment must use multiple tools based on the study design. Alternatively, a recently developed mixed methods appraisal tool (MMAT) can be used in such a situation.(8) Finally, for SLRs of SLRs, tools such as ROBINS and AMSTAR-2 are available, which can assess the risk of bias or methodological quality of the included SLR.(9,10)

    While assessing ROB of the included studies in an SLR, it is essential to ensure that the latest version of the appropriate tool is used, and that the tool is validated and reliable. It is also recommended that independent assessment of ROB is carried out, and the results consolidated so that a comprehensive ROB assessment is performed. Finally, the reporting of ROB of the included studies must follow the recommendations of the tool.

    While systematic reviews are the gold standard of evidence synthesis, their findings highly depend on the validity of their assessed studies. Therefore, the researchers must ensure that these studies are reliable and free from bias, leading to more accurate conclusions and evidence-based recommendations.

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    References

    1. Zeng X, Zhang Y, Kwong JS, et al. The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review. J Evid Based Med. 2015 Feb;8(1):2-10.
    2. Jüni P, Altman DG, Egger M. Systematic reviews in health care: Assessing the quality of controlled clinical trials. BMJ. 2001 Jul 7;323(7303):42-6.
    3. Sterne JAC, Savović J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019 Aug 28;366:l4898
    4. Nadelson S, Nadelson LS. Evidence‐based practice article reviews using CASP tools: a method for teaching EBP. Worldviews on Evidence‐Based Nursing. 2014 Oct;11(5):344-6.
    5. Baker A, Young K, Potter J, Madan I. A review of grading systems for evidence-based guidelines produced by medical specialties. Clinical medicine. 2010 Aug;10(4):358.
    6. Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. Int J Evid Based Healthc. 2015;13(3):147–153.
    7. Downes MJ, Brennan ML, Williams HC, Dean RS. Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS). BMJ Open. 2016 Dec 8;6(12):e011458. doi: 10.1136/bmjopen-2016-011458.
    8. Hong QN, Gonzalez-Reyes A, Pluye P. Improving the usefulness of a tool for appraising the quality of qualitative, quantitative and mixed methods studies, the Mixed Methods Appraisal Tool (MMAT). J Eval Clin Pract. 2018 Jun;24(3):459-467.
    9. Whiting P, Savović J, Higgins JP, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J, Churchill R; ROBIS group. ROBIS: A new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol. 2016 Jan;69:225-34.
    10. Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, Moher D, Tugwell P, Welch V, Kristjansson E, Henry DA. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017 Sep 21;358:j4008.

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  • Using ‘Limits’ While Developing SLR Search Strategy: Advantages and Disadvantages

    Using ‘Limits’ While Developing SLR Search Strategy: Advantages and Disadvantages

    Systematic literature reviews (SLR) are a crucial tool to identify high-quality evidence for research, policy, and practice. The success of SLRs heavily relies on the performance of the literature search strategy, which involves utilizing appropriate information retrieval (IR) techniques to locate relevant studies. (1,2) Appropriate IR techniques are essential to capture all relevant studies, and to avoid retrieving irrelevant studies as far as possible. (3) In this direction, systematic reviewers often use combination of specific search strings, Boolean operators, limits and search filters to optimize the search strategy, and also to ensure that the search is comprehensive, systematic, and reproducible. (4,5)

    Search filters are tools that help to retrieve certain types of records based on their characteristics, such as methodology, study design, or publication type. Filters can be applied to various aspects of the search, such as the population, intervention, study design, and so on, and are usually guided by the PICOS of the SLR. For example, while conducting an SLR on the clinical efficacy and safety of a specific intervention, a filter may be applied to only include randomized controlled trials (RCTs) involving the specific intervention and a predefined set of comparators. (5) On the other hand, ‘limits’ in a search strategy restricts the number of search results by setting a predefined maximum number of results, and can be based on aspects such as publication type, language, or publication year. For example, for updating a previously published SLR, a limit may be used to restrict the search results for the past 5 years. Other examples for limits include country restrictions and language restrictions. (6)

    Having a comprehensive search strategy without any restrictions or limits is highly desirable for pooling together all relevant research to help unbiassed evidence synthesis. However, at times, researchers will need to use limits in search strategy to optimize the literature search process and make the SLR more time-efficient:

    • By narrowing the scope of search to specific fields, such as title, author, descriptors, language, publication year, and country of publication, limits help to conduct more focused and targeted searches. (7)
    • Since the same limits can be applied across multiple searches, consistency and reproducibility of search results can be enhanced. (8)
    • Using limits can be particularly useful in cases where a large amount of literature is available on a given topic, and a rapid evidence synthesis is needed to support quick decision-making.

    That said, using limits are associated with certain disadvantages:

    • Since limits can reduce the sensitivity of the search, some relevant studies may be missed out, thereby decreasing the completeness of the SLR. (3)
    • Bias may be introduced towards certain type of studies, especially if too many limiting commands are used. (3)
    • Over-reliance on search limits can decrease the transparency of the systematic review process. (7)
    • Limits may potentially cause an oversimplification of the research question, and this may reflect in incomplete or inaccurate SLR conclusions. (6)

    At times it becomes essential to use limits in search strategy for various reasons. In such cases, the following guidelines may be helpful to utilise the advantages of the limits while reducing their disadvantages:

    • The research question and the SLR objectives musts be considered carefully for determining which limits are appropriate for the search strategy, and which limits are to be avoided. (9)
    • Limits must be judiciously selected in order to avoid bias and ensure a comprehensive and systematic search without missing relevant information. (5)
    • Limits must be combined with appropriate search strings to maximize the effectiveness of the search strategy, thus ensuring relevant results. (5)
    • The appropriateness of each limit used must be carefully considered against the backdrop of the potential benefits of limiting the search and the potential loss of valuable studies. (10)
    • The limits applied to the search strategy must be recorded diligently, including the rationale for selecting the limits, to evaluate their impact on the search results, and also to ensure that the search strategy is transparent and reproducible. (7)

    The use of limits in literature searching requires a balanced approach. If used with proper justification, they can help refine search results and improve the review’s precision, and also ensure timely completion of SLR. (10) Systematic reviewers must weigh the benefits and potential drawbacks of limiting their search, make informed decisions based on the research question and the scope of the review. By using limits appropriately and judiciously, it is possible to improve the accuracy and efficiency of literature searches and thus produce a high-quality SLR.

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    References

    1. Hemingway P, Brereton N. What is a systematic review? Hayward Medical Communications. What is…? series. 2009 Available from http://www.bandolier.org.uk/painres/download/whatis/Syst-review.pdf 
    2. del Amo IF, Erkoyuncu JA, Roy R, Palmarini R, Onoufriou D. A systematic review of Augmented Reality content-related techniques for knowledge transfer in maintenance applications. Computers in Industry. 2018 Dec 1;103:47-71.
    3. McGowan J, Sampson M. Systematic reviews need systematic searchers. Journal of the Medical Library Association. 2005 Jan;93(1):74
    4. Kugley S, Wade A, Thomas J, Mahood Q, Jørgensen AM, Hammerstrøm K, Sathe N. Searching for studies: A guide to information retrieval for Campbell. Campbell Systematic Reviews, 13: 1-73.
    5. Papaioannou D, Sutton A, Booth A. Systematic approaches to a successful literature review. Systematic approaches to a successful literature review. London, Sage, 2016:1-336..
    6. Papaioannou D, Sutton A, Carroll C, Booth A, Wong R. Literature searching for social science systematic reviews: consideration of a range of search techniques. Health Information & Libraries Journal. 2010 Jun;27(2):114-22.
    7. Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. Cochrane handbook for systematic reviews of interventions. John Wiley & Sons; 2019 Sep 23. Available from https://training.cochrane.org/handbook 
    8. Koffel JB, Rethlefsen ML. Reproducibility of search strategies is poor in systematic reviews published in high-impact pediatrics, cardiology and surgery journals: a cross-sectional study. PLoS One. 2016 Sep 26;11(9):e0163309.
    9. Greenhalgh T. How to read a paper: the basics of evidence-based medicine and healthcare. Wiley Blackwell; 2019 May 6.
    10. Glanville J, Bayliss S, Booth A, Dundar Y, Fernandes H, Fleeman ND, Foster L, Fraser C, Fry-Smith A, Golder S, Lefebvre C. So many filters, so little time: the development of a search filter appraisal checklist. Journal of the Medical Library Association: JMLA. 2008 Oct;96(4):356.

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  • The Importance of CHARMS Checklist in the SLRs of Clinical Prediction Models

    The Importance of CHARMS Checklist in the SLRs of Clinical Prediction Models

    Clinical prediction models (CPMs) are statistical models that use patient characteristics and clinical variables to estimate the probability of a particular health outcome, such as a disease or adverse event. CPMs can be diagnostic prediction models that aid in diagnosis, by predicting the likelihood that a person is currently having a particular health condition (for example, Wells score for pulmonary embolism). Another type of CPMs is the prognostic prediction models, which aid in prognosis by predicting the likelihood that a person will experience a particular health outcome over a specific period (for example, the Framingham Risk Score for cardiovascular disease). (1) These days CPMs have become an essential part of evidence-based clinical practice, and thus it becomes important that the CPMs provide accurate estimation of the disease condition for which they are used. (2, 3) Towards this direction, systematic literature reviews (SLRs) are often conducted to determine the quality and validity of CPMs, as well as to identify gaps in the literature to inform the development of new CPMs. (4)

    The CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist is a tool that has been developed to facilitate the critical evaluation and data extraction while performing SLRs of CPMs. First published in 2014, this checklist provides explicit guidance to help reviewers and users to frame the right review question. It also provides a data extraction list with explicit guidance on which items to extract from CPM studies for evaluating the risk of bias and applicability. It can be used to critically appraise all types of primary prediction model studies for all kinds of the target population, outcomes, and predictors, regardless of the statistical techniques used. (5) Thus, the CHARMS checklist helps the researchers in two critical areas of conducting an SLR: framing the review question, and critical appraisal of included articles.

    The contents of the CHARMS checklist are arranged in two sections. First, there is a list of 7 key items to help the researcher frame a well-defined, proper, and focussed review questions; these key items include the intended scope of the review, type of prediction model studies, prognostic versus diagnostic prediction model, target population to whom the prediction model applies, outcome to be predicted, the period of the prediction, and the intended moment of using the model. Next, the checklist provides guidance for data extraction and critical appraisal of the included articles in the SLR by means of 35 key items that are organized in 11 domains: source of data, participants, outcome to be predicted, candidate predictors, sample size, missing data, model development, model performance, model evaluation, results, interpretation and discussion. (4, 5) 

    Ever since its publication in 2014, the CHARMS checklist has been used by various SLRs of CPMs. (6-10) To further facilitate the easier application of the CHARMS tool, an Excel template for data extraction and risk of bias assessment of clinical prediction models has been recently published; this template makes it possible for the user to apply both the CHARMS and the PROBAST (Prediction model Risk Of Bias Assessment Tool) while conducting critical appraisal of SLRs of CPMs. It will also encourage more accurate and thorough reporting of these systematic reviews. (11)

    Critical appraisal of included studies is an essential part of any SLR, and SLRs of CPMs is not an exception. It is crucial that the tool used for the critical appraisal is validated, asks the correct questions, is user-friendly, and gives accurate results. The CHARMS checklist, standing the test of the time, fits the bill perfectly, and along with the PROBAST tool, has become an invaluable resource in the conduct of SLRs of CPMs.

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    References:

    1. Vogenberg FR. Predictive and prognostic models: implications for healthcare decision-making in a modern recession. Am Health Drug Benefits. 2009 Sep;2(6):218-22. 
    2. Van Smeden M, Reitsma JB, Riley RD, et al. Clinical prediction models: diagnosis versus prognosis. Journal of clinical epidemiology. 2021 Apr 1;132:142-5.
    3. Hendriksen JM, Geersing GJ, Moons KG, de Groot JA. Diagnostic and prognostic prediction models. J Thromb Haemost. 2013 Jun;11 Suppl 1:129-41. 
    4. Damen JAA, Moons KGM, van Smeden M, Hooft L. How to conduct a systematic review and meta-analysis of prognostic model studies. Clin Microbiol Infect. 2023 Apr;29(4):434-440. Doi: 10.1016/j.cmi.2022.07.019. Epub 2022 Aug 4. 
    5. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PloS Med. 2014Oct 14;11(10):e1001744. 
    6. Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. Bmj. 2020 Apr 7;369.
    7. Viswanathan M, Patnode CD, Berkman ND, et al. Assessing the risk of bias in systematic reviews of health care interventions. Methods guide for effectiveness and comparative effectiveness reviews [Internet]. 2017 Dec 13.
    8. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. bmj. 2016 May 16;353.
    9. Smith EE, Kent DM, Bulsara KR, et al. Accuracy of prediction instruments for diagnosing large vessel occlusion in individuals with suspected stroke: a systematic review for the 2018 guidelines for the early management of patients with acute ischemic stroke. Stroke. 2018 Mar;49(3):e111-22.
    10. Meehan AJ, Lewis SJ, Fazel S, et al. Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges. Molecular Psychiatry. 2022 Jun;27(6):2700-8.
    11. Fernandez-Felix BM, López-Alcalde J, Roqué M, Muriel A, Zamora J. CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models. BMC Medical Research Methodology. 2023 Dec;23(1):1-8.

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  • The Role and Importance of SLRs and RWE in Drug Price Negotiations in the USA

    The Role and Importance of SLRs and RWE in Drug Price Negotiations in the USA

    The cost of prescription drugs is a significant burden on patients and the healthcare system, especially in countries such as the USA. High drug prices can strain government programs, such as Medicare and Medicaid, and private insurers, which can lead to higher premiums for consumers. Additionally, high drug prices are responsible for increased out-of-pocket expenses for patients, which further lead to medication non-adherence, and thus result in poorer health outcomes. On the other hand, the research and development activities in pharmaceutical industries depend on their profit from sales, and an extremely harsh reduction in drug prices can have adverse consequences in terms of a lack of incentive for innovation in the pharmaceutical industry. (1)

    All these factors make responsible and reasonable price negotiation an extremely important process in the market access cycle for drugs. By negotiating drug prices appropriately, the government and payers can help ensure that patients have access to affordable medications while also promoting competition and innovation in the industry. (1)

    Price negotiations for drugs in the US are typically done by insurers and government programs, such as Medicare and Medicaid. The negotiation process is complex, involves multiple factors, and largely depends on the payer and the drug; the typical steps and factors considered include formulary placement, rebates and discounts, value-based arrangements, price controls, and competitive bidding. (2) To this effect, evidence on clinical effectiveness becomes extremely important, and it is essential that there is a robust and ethical body of evidence to display that the new innovation is efficacious, safe, and brings about enough value to justify the premium that the patients and payers are asked to pay for accessing the intervention.

    The traditional sources for clinical effectiveness evidence for the purpose of price negotiation of drugs are the same as those for marketing approval, and are largely constituted by Randomized Clinical Trials (RCTs), which are often conducted by pharmaceutical companies to demonstrate the safety and efficacy of their drugs. These trials are designed to meet regulatory requirements and are often submitted to the FDA as part of the drug approval process. (3)

    However, it is being increasingly realized that evidence in addition to traditional RCTs can play a crucial role in determining the true extent of efficacy, safety, and value of an intervention in a particular therapy area or patient population. Specifically, increasing interest is being shown towards using evidence from systematic literature reviews (SLRs) and real-world evidence (RWE) for informing clinical effectiveness data for drug price negotiations. (3, 4)

    SLRs, being comprehensive evaluations of existing research studies, provide a balanced summary of the available evidence on a drug’s safety, efficacy, and cost-effectiveness, and thus can be an invaluable resource for drug price negotiations. However, using SLRs is associated with some challenges pertaining to the varying quality of evidence of studies included in the SLR, publication bias (by which there is an overestimation of a drug’s effectiveness due to non-publication of many studies with negative results), time and resource constraints, conflicts of interest, and lack of generalizability. (4)

    RWE coming from the analysis of different sources such as electronic health records, claims data, and patient registries, can provide insights into how drugs are used in actual clinical practice, including their safety and effectiveness over time. By demonstrating the value of a drug in real-world settings, RWE can provide details about the actual usage pattern of an intervention post its marketing, compared to RCTs which offer a view of clinical effectiveness from a restricted population, prior to marketing. However, using RWE is also associated with certain challenges, such as quality of data, reliability, lack of data standardization, data interoperability, privacy concerns, and concerns about the quality of data analysis leading to generation of RWE. (3)

    Health authorities worldwide have taken several initiatives to include Systematic Literature Review and RWE as key elements in market authorization and in price and reimbursement negotiations. Of more credit, is the fact that in the USA, the 21st Century Cures Act specified that RWD could be used to support the approval of a new indication for a drug that is already approved or to support or satisfy post-approval study requirements. (5-7)

    Interestingly, the Inflation Reduction Act (IRA) is the biggest landmark set by United States federal law to curb inflation by reducing the deficit and lowering prescription drug prices. (5) This Act established a drug price negotiation program within the department of Health and Human Services (HHS), enabling the Secretary to negotiate the prices of certain costly drugs within the Medicare program. The Centres for Medicare & Medicaid Services (CMS), through the U.S. Department of Health and Human Services (HHS), released initial guidance outlining the conditions and limitations of the new Medicare Drug Price Negotiation Program for 2026. The Medicare Drug Pricing Negotiation Program and other provisions in the new drug law will improve Medicare’s capacity to serve those enrolled in the program and future generations of Medicare recipients. (5-8)

    SLRs and RWE have an important role to play in generating clinical evidence for drug price negotiations, and the USFDA is in the process of regulating the steps needed for this by drafting the guidance document.  Together with other provisions in the new drug law, the Medicare Drug Price Negotiation Program will increase Medicare’s ability to serve current Medicare beneficiaries as well as future generations. (8)

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    References

    1. Papanicolas I, Woskie LR, Jha AK. Health care spending in the United States and other high-income countries. Jama. 2018 Mar 13;319(10):1024-39.
    2. Gruber J. Delivering public health insurance through private plan choice in the United States. Journal of Economic Perspectives. 2017 Nov 1;31(4):3-22.
    3. Pulini AA, Caetano GM, Clautiaux H, et al. Impact of Real-World Data on Market Authorization, Reimbursement Decision & Price Negotiation. Ther Innov Regul Sci. 2021 Jan;55(1):228-238.
    4. Tarsilla M. Cochrane handbook for systematic reviews of interventions. Journal of Multidisciplinary Evaluation. 2010;6(14):142-8.
    5. Levitt. The Inflation Reduction Act Is a Foot in the Door for Containing Health Care Costs, JAMA Health Forum. 3 (2022) e223575.
    6. A Turning Point for U.S. Climate Progress: Assessing the Climate and Clean Energy Provisions in the Inflation Reduction Act | Policy Commons. https://policycommons.net/artifacts/2649285/a-turning-point-for-us-climate-progress_inflation-reduction-act/3672158/
    7. Inflation Reduction Act Guidebook | Clean Energy | The White House. https://www.whitehouse.gov/cleanenergy/inflation-reduction-act-guidebook
    8. Sullivan SD. Medicare Drug Price Negotiation in the United States: Implications and Unanswered Questions. Value Health. 2023 Mar;26(3):394-399.

     

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  • An Overview of Methods for Indirect Treatment Comparisons in Healthcare Decision-making

    An Overview of Methods for Indirect Treatment Comparisons in Healthcare Decision-making

    Meta-analyses summarize data from head-to-head trials to evaluate pairs of treatments that have been directly compared.[1] However, in certain circumstances, multiple therapies are of interest, and no data is available on their direct comparison. In such cases, indirect treatment comparison (ITC) is performed for synthesizing evidence surrounding treatments of interest.[2] ITC assumes that the studies are similar and homogenous regarding the administered therapies, patient characteristics, and observed effects, and works best when the inconsistency between indirect and direct evidence is minimal or absent. 

    Various methods of ITC have been developed depending on the availability of individual patient data (IPD) and summary level data (SLD). Some of these methods include naïve ITC, network meta-analysis (NMA), population-adjusted indirect comparisons (PAIC), simulated treatment comparisons (STCs), and matching-adjusted indirect comparisons (MAICs). The choice of these methods depends on the study design, the number of comparators available, and the degree to which the outcomes are measured. In addition, the extent of assumptions employed, methodological limitations, and inherent biases associated with each method also determine the choice of the ITC method.

    Naïve ITC is based on SLD and is used when the treatments cannot be connected by a common comparator. It does not account for heterogeneity and excludes information from the placebo arms when comparing treatments, thereby introducing bias. Hence, this method is mainly avoided to preserve the randomization in trials during the analyses. 

    Network Meta-Analysis (NMA) is perhaps the most popular of the ITC methods. It works with SLD, and compares treatments by combining indirect and direct evidence connected by a network of studies.[3] NMA is considered as the gold standard for ITCs. It offers more an exact estimate of the relative effects of treatments in the network than a single direct or indirect estimate. It also enables for the assessment of intervention ranking and hierarchy. To some extent, this bias can be reduced by using meta-regression, that addresses heterogeneity in treatment effects. It can assess how the effect of treatment changes with a covariate (a patient or methodological attribute). Unfortunately, the usage of meta-regression with NMAs becomes questionable in cases where the number of studies in a network is limited. Furthermore, this approach can only be used when there is a variance study or comparison with only minor variations in impact modifiers.[4,5] Moreover, covariate correction in aggregate-level data may result in ecological bias, which limits the interpretation of estimated results for subgroups. In such cases, Individual Patient Data (IPD) provides adjustments for covariates that cause inconsistencies (e.g., prognostic factors, effect modifiers, etc.). Hence, NMA that leverages IPD can be put to use for conducting analyses that can provide adjustments to reduce such inconsistencies.[6] 

    The application of NMAs and their associated methods are often limited by insufficient evidence networks and heterogeneity across trials. This is resolved to certain extent through population-adjusted indirect comparison (PAIC), which is a targeted approach to enhance ITC.[7] It allows to overcome the challenges faced by NMAs by carrying out a targeted comparison between outcomes for specific treatments and factors. It includes two methods: simulated treatment comparisons (STCs) and matching-adjusted indirect comparisons (MAICs). These methods can help reduce the ambiguity in the comparisons with statistical adjustment. STCs do this by applying predictive equations, whereas MAIC relies on patient reweighting. 

    STCs or MAICs can be used to conduct either “anchored” indirect comparison, in which each trial has a common comparator arm, or “unanchored” indirect comparison, in which the treatment network is disconnected (single-arm investigations). An anchored approach relies on “conditional constancy of relative effects”. In contrast, an unanchored approach works on a stringent assumption of “conditional constancy of absolute effects”. which is more demanding than the former and is not a widely accepted approach [8]  STCs are often appropriate in analyses where numerous comparators are available for a small set of outcomes, whereas MAICs are often suitable in cases with only one comparator but multiple outcomes. The precision of the equations in STC and the effective matching of populations in MAIC determine the dependability of the studies.

    A task force report released in 2011 by the Professional Society for Health Economics and Outcomes Research (ISPOR) defines the fundamentals of conducting ITCs and assessing these studies for informed and efficient decision-making.[4,5] Though the methodological aspects of NMAs have received much attention from researchers, the other ITC methods are yet to be refined to a similar extent. The standardization of these methods is vital to increase their reliability and application.

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    References

    [1] Ahn E, Kang H. Introduction to systematic review and meta-analysis. Korean J Anesthesiol. 2018 Apr;71(2):103-112. doi: 10.4097/kjae.2018.71.2.103.  [2] Veroniki AA, Straus SE, Soobiah C, et al. A scoping review of indirect comparison methods and applications using individual patient data. BMC Med Res Methodol. 2016 Apr 27;16:47. doi: 10.1186/s12874-016-0146-y.  [3] Tonin FS, Rotta I, Mendes AM, Pontarolo R. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons. Pharm Pract (Granada). 2017 Jan-Mar;15(1):943. doi: 10.18549/PharmPract.2017.01.943 [4] Jansen JP, Fleurence R, Devine B, et al. Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 1. Value Health. 2011 Jun;14(4):417-28. doi: 10.1016/j.jval.2011.04.002.  [5] Hoaglin DC, Hawkins N, Jansen JP, et al. Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2. Value Health. 2011 Jun;14(4):429-37. doi: 10.1016/j.jval.2011.01.011.  [6] Riley RD, Dias S, Donegan S, et al. Using individual participant data to improve network meta-analysis projects. BMJ Evid Based Med. 2022 Aug 10:bmjebm-2022-111931. doi: 10.1136/bmjebm-2022-111931. Epub ahead of print.  [7] Phillippo DM, Dias S, Elsada A, et al. Population Adjustment Methods for Indirect Comparisons: A Review of National Institute for Health and Care Excellence Technology Appraisals. Int J Technol Assess Health Care. 2019 Jan;35(3):221-228. doi: 10.1017/S0266462319000333. Epub 2019 Jun 13.  [8] Jiang Y, Ni W. Performance of unanchored matching-adjusted indirect comparison (MAIC) for the evidence synthesis of single-arm trials with time-to-event outcomes. BMC Med Res Methodol. 2020 Sep 29;20(1):241. doi: 10.1186/s12874-020-01124-6.

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  • Certainty of Evidence in Systematic Reviews: The GRADE Approach

    Certainty of Evidence in Systematic Reviews: The GRADE Approach

    When seeking answers to clinical questions about efficacy or other aspects of a proposed intervention, health professionals often look for reliable evidence. Systematic literature reviews (SLRs) serve this purpose, by virtue of their ability to generate high-quality, verifiable, and trustworthy evidence in a systematic, transparent, and impartial manner.(1) In the new evidence pyramid, systematic reviews are visualized as the lens through which certainty can be measured in all other primary research reported in multiple study designs.(1,2) The SLR process is most often used by reputed professional organizations worldwide for generating practice guidelines for diagnosis and management of different disorders.(1) However, since the primary research used for generating such guidelines are often of variable quality, the resulting guidelines may also be of a non-uniform quality. Therefore, it becomes essential to quantify and grade the quality of evidence and strength of the recommendations.(1,2)

    Out of the different available frameworks for doing this, perhaps the most frequently used system is the GRADE (Grading quality of evidence and strength of recommendations) framework. The GRADE approach starts with formulating the research question into appropriate PICOS headings, and selecting the most relevant outcomes for research. After performing an SLR gathering evidence about each of the selected outcomes, the certainty (alternatively called level or quality) of the evidence so gathered is graded based on various factors under four headings:(1–5)

    1. High: Authors are confident that the true effect lies closer to that of the estimate of the effect.
    2. Moderate: the true effect is probably close to the estimated effect
    3. Low: The true effect is probably substantially different from the estimated effect
    4. Very Low: The true effect is likely to be substantially different from the estimated effect

    To begin with, certainty of evidence is considered to be higher from RCTs than observational studies. Other factors which impact evidence certainty include:

    1. Risk of bias in individual studies: this occurs when the results of a study do not represent the truth because of inherent limitations in the design or conduct of a study. Examples for this would be methodological issues such as inadequate randomization, lack of blinding, confounding, loss to follow-up, and other such factors.(2)
    2. Consistency of results between studies: When multiple studies show consistent effects, overlap of confidence intervals, and low levels of heterogeneity, the resulting quality of evidence will be high. Consistency between studies is measured using heterogeneity of point estimates, statistical measures such as I2 values, and confidence intervals (CIs).(2,4)
    3. Indirectness of evidence: When the population of interest for which the recommendations are being prepared is different from the population found in the included studies (for example, if the study participants are adults, but the recommendations are being prepared for children), the certainty of evidence will be lower.(2)
    4. Imprecision: Explained as the ‘range of plausible effect sizes’, imprecision depends on the number of included patients/ events and the confidence interval. If the confidence interval of the plausible effect is too wide in a manner hindering a decision, then there is imprecision.(2)
    5. Publication Bias: It is common knowledge that small studies with no statistically significant results are less likely to be published; source of funding also contributes to publication bias. The resulting synthesized evidence is skewed towards published results.(2)

    Evidence that is initially rated as ‘high’ may be downgraded after consideration of the aforementioned criteria; the opposite is also possible.(3)

    At the end of the grading process, all outcomes of interest will be associated with a certain level of certainty. Based on the evidence so collected, recommendations are often drawn by a guideline panel. Various considerations that go into this process include balancing of benefits and risks, certainty of evidence, values and preferences, costs, feasibility, acceptability, and equity.(1) The resulting practice recommendations are assigned a ‘strength ranking’ under the GRADE approach: the recommendation can be ‘strong’ or ‘conditional’, and ‘for’ or ‘against’ a specific action in a specific situation. Strong recommendations suggest that most, if not all, people would choose this intervention. Weak recommendations imply that there is significant variation likely to be made by investigators in the decision.(2,4)

    It is important to acknowledge that using GRADE will commonly involve some subjective judgments, and assessments may vary between individuals. Despite this, the GRADE approach has proven to be an essential component of all clinical practice guidelines resulting from high-quality SLRs, since it provides a systematic, explicit, and transparent approach for grading the certainty of evidence and quality of practice recommendations.

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    References

    1. Granholm A, Alhazzani W, Møller MH. Use of the GRADE approach in systematic reviews and guidelines. Br J Anaesth. 2019 Nov;123(5):554–9.
    2. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008 Apr 26;336(7650):924–6.
    3. Goldet G, Howick J. Understanding GRADE: an introduction. J Evid Based Med. 2013 Feb 28;6(1):50–4.
    4. Kirmayr M, Quilodrán C, Valente B, Loezar C, Garegnani L, Franco JVA. The GRADE approach, Part 1: how to assess the certainty of the evidence. Medwave. 2021 Mar 31;21(02):e8109–e8109.
    5. Balshem H, Helfand M, Schünemann HJ, Oxman AD, Kunz R, Brozek J, et al. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol. 2011 Apr;64(4):401–6.

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  • Different Types of Systematic Literature Review: A Beginner’s Guide

    Different Types of Systematic Literature Review: A Beginner’s Guide

    Literature reviews play a vital role in fulfilling the primary goal of any evidence-based research by aiding in decision-making with transparent and impacting results.[1] With advances in all medical fields and with increased access to medical literature, different methodologies have been conceptualized for performing literature reviews, leading to the advent of different types of literature reviews. The main types of systematic review of literature include systematic literature reviews (SLRs) and its subtypes such as targeted literature reviews (TLRs), scoping reviews (ScR), and rapid reviews (RR); meta-analyses (MA) and its subtypes such as indirect treatment comparison (ITC), network meta-analysis (NMA), and individual patient data MA (IPD-MA).[1,2] While the expanded range of approaches have the potential to address broader types of evidence designs to cater to the increasing complexity of healthcare, the challenges in choosing the best review approach to meet the purpose of the review should also be acknowledged.[1,2] Often a new reviewer (and sometimes even the seasoned one) is confused about which review methodology is best suited for addressing the research question s/he has.

    Systematic literature reviews are based on explicit, reproducible methods to search all sources of evidence and critically appraise a highly focused clinical question. SLRs are performed to compare two different interventions, to confirm relevant evidence, to assess the quality of evidence, and to address any variation in practice. SLRs generally involve two independent reviewers for screening, data extraction, and quality assessment, and are time and resource-intensive.[2,4,5]

    Scoping reviews are performed to identify, map, and synthesize the available research if it is not yet comprehensively reviewed. They identify key characteristics or factors and usually act as a precursor to an SLR for identifying and analyzing knowledge gaps. ScRs may require larger teams because of the volume of literature.[2,4,6]

    Rapid reviews are rigorous in their methodology, but set limits on the process for shortening the timeframe of review completion. They are mostly used for emerging, critical research topics, updates of previous reviews. However, curtailing the time for research may introduce inconsistencies and biases.[1,4,7]

    Targeted/ focused literature reviews address clearly formulated questions by using explicit methods to identify, critically appraise, and qualitatively analyze key relevant research, and are usually performed by a single reviewer. However, TLRs are often not as comprehensive as SLRs, and may be less reproducible than an SLR.[4,8]

    Meta-analysis is a systematic statistical procedure for combining data from multiple studies and then quantitatively synthesizing them. Results are often depicted graphically in the form of forest plots, and specialized statistical methods and software are often used. They are more time and resource-intensive than other forms of SLRs.[1,9]

    Individual participant data (IPD) meta-analysis uses individual participant data from studies and performs statistical analyses to generate powerful and uniformly consistent results. The quality of data from IPD-MAs are quite high and the results are highly reliable, but the statistical methods of IPD-MA require specialized software and highly qualified statisticians. IPD-MAs are more lengthier and costlier than traditional MAs.[10,11]

    Network meta-analysis is an umbrella term to describe indirect treatment comparisons (ITC) and mixed treatment comparisons (MTC). It extends the principles of meta-analysis by comparing multiple treatments simultaneously in a single analysis by combining direct and indirect evidence within a network of randomized controlled trials. The methodological approaches are complex, and the results are displayed graphically in the form of network diagrams.[12]

    To conclude, selecting the appropriate literature review approach largely depends on the research question, availability of resources, cost, time availability, and the intention of the research.

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    References

    1. Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal. 2009 May 27;26(2):91–108.
    2. Gopalakrishnan S, Ganeshkumar P. Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare. J Family Med Prim Care. 2013 Jan;2(1):9–14.
    3. Tonin FS, Rotta I, Mendes AM, Pontarolo R. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons. Pharmacy Practice. 2017 Mar 31;15(1):943–3.
    4. Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic Review or Scoping review? Guidance for Authors When Choosing between a Systematic or Scoping Review Approach. BMC Medical Research Methodology. 2018 Nov 19;18(1).
    5. Page AS, Page G, Deprest J. Cervicosacropexy or vaginosacropexy for urinary incontinence and apical prolapse: A systematic review. Eur J Obstet Gynecol Reprod Biol. 2022 Oct 6;279:60-71.
    6. Bhowmick S, Dang A, Vallish BN, Dang S. Safety and Efficacy of Ivermectin and Doxycycline Monotherapy and in Combination in the Treatment of COVID-19: A Scoping Review. Drug Saf. 2021 Jun;44(6):635-644.
    7. Dzinamarira T, Moyo E, Pierre G, Mpabuka E, Kahere M, Tungwarara N, Chitungo I, Murewanhema G, Musuka G. Postnatal care services availability and utilization during the COVID-19 era in sub-Saharan Africa: A rapid review. Women Birth. 2022 Oct 11:S1871-5192(22)00330-4.
    8. Igarashi A, Ueyama M, Idehara K, Nomoto M. Burden of illness associated with pneumococcal infections in Japan – a targeted literature review. J Mark Access Health Policy. 2021 Dec 27;10(1):2010956.
    9. Dang A, Dang S, Vallish BN. Efficacy and Safety of EGFR Inhibitors in the Treatment of EGFR Positive NSCLC Patients: A Meta-Analysis. Rev Recent Clin Trials. 2021;16(2):193-201.
    10. Tierney JF, Vale C, Riley R, Smith CT, Stewart L, Clarke M, et al. Individual Participant Data (IPD) Meta-analyses of Randomised Controlled Trials: Guidance on Their Use. PLoS Med. 2015 Jul;12(7):e1001855. 
    11. AlRasheed MM, Fekih-Romdhane F, Jahrami H, Pires GN, Saif Z, Alenezi AF, Humood A, Chen W, Dai H, Bragazzi N, Pandi-Perumal SR, BaHammam AS, Vitiello MV; COMITY investigators. The prevalence and severity of insomnia symptoms during COVID-19: A global systematic review and individual participant data meta-analysis. Sleep Med. 2022 Aug 8;100:7-23.
    12. Zhao M, Shao T, Ren Y, Zhou C, Tang W. Identifying optimal PD-1/PD-L1 inhibitors in first-line treatment of patients with advanced squamous non-small cell lung cancer in China: Updated systematic review and network meta-analysis. Front Pharmacol. 2022 Sep 29;13:910656.

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  • How to Report a Systematic Review? The PRISMA Statement and its Extensions

    How to Report a Systematic Review? The PRISMA Statement and its Extensions

    Healthcare decisions for patients and public health policies should be supported by the best available research evidence. To ensure the hierarchy of the evidence, an evidence pyramid was conceptualized, with in-vitro studies and animal studies forming the bottom of the pyramid, expert opinions, case series, case reports, and observational studies forming the middle layer, and RCTs occupying the top. Systematic literature reviews (SLRs), which are a synthesis of multiple studies, including observational studies and RCTs, are conceptualized to occupy a place even above the RCTs in the evidence pyramid.[1] Thus, the evidence from SLRs is considered to be the most trustworthy form of scientific evidence as they are associated with high statistical power and precise results.[2,3]

    However, there are concerns over the overproduction and poor quality of SLRs which has led to several international and multidisciplinary groups collaborating to develop guidelines.[2] Various solutions have been developed to ensure transparency and consistency in the conduct and reporting of SLRs:

    • The Cochrane Collaboration introduced a handbook to guide the conduct and reporting of SLRs.[2]
    • The Quality of Reporting of Meta-analyses (QUOROM) guideline was published in 1999[4]
    • Various portals were launched for prospective registration of SLR protocols prior to data collection, such as the PROSPERO registry in 2011[2]
    • The publication of the Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) guidelines in 2009[4]

    The PRISMA guidelines were developed to help reviewers report their findings completely and transparently. The PRISMA 2009 statement comprised a checklist of 27 items for reporting in SLRs including the title, abstract, methods, results, discussion, and funding. It also had a section that provided an explanation and elaboration for each item of the guideline. To understand the flow of information through the different phases, a flow chart was supplemented to ensure that aspects such as identification, screening, eligibility, and inclusion are not missed.[4]

    The PRISMA guidelines were updated in 2020, and the PRISMA 2020 statement has a 27-item checklist, which also includes an abstract checklist and a revised flow diagram format. In the methods section, the protocol and registration items have moved to a new section with an additional subsection. Authors are required to present the complete search strategies for all the databases. For the ‘Selection process’ item, it is now required to report how many reviewers have screened each record and whether they have worked independently and details of any automation tools used. There are new sub-items in the results section as well.[4] The flow diagram template has been updated with optional boxes that are applicable for review updates. To reflect this, there are four variations of the flow diagram template to choose from.

    In addition, several ‘extensions’ of the PRISMA Statement have been developed to facilitate reporting of different types of SLRs.[4] For example, the PRISMA-ScR Statement is developed with the aim of facilitating better reporting of scoping reviews. This extension adds a few points relevant to scoping reviews, and removes some aspects that are not relevant.[5] Next, there is the PRISMA-NMA statement which is a 32-item checklist, and has the flow diagram in a different format that suits comparison of multiple treatments using direct and indirect evidence in network meta-analyses. Likewise, there are separate PRISMA extensions for SLR abstracts, SLRs of acupuncture therapies, SLRs of diagnostic studies, SLRs for IPD (individual patient data) meta-analysis, SLR protocols, SLRs for assessing treatment harms, and so on.[6] Several new PRISMA statements are in development, including SLRs of pediatric population, and SLRs of outcome measurement instruments.[7]

    The PRISMA guidelines and its extensions help ensure that SLRs are reported transparently and accurately. Today, most journals and regulatory agencies including HTA bodies require that the SLRs submitted to them strictly adhere to the relevant PRISMA guidelines, regardless of whether the SLR is conducted for academic or regulatory purposes. It is highly essential that all researchers are aware of these guidelines, and more importantly, follow the appropriate extension of the guidelines according to their research objectives, to ensure the highest quality of evidence.

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    References

    1. Pandis N. The evidence pyramid and introduction to randomized controlled trials. Am J Orthod Dentofacial Orthop. 2011 Sep;140(3):446-7 
    2. Gopalakrishnan S, Ganeshkumar P. Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare. J Family Med Prim Care. 2013 Jan;2(1):9–14. 
    3. IOANNIDIS JPA. The Mass Production of Redundant, Misleading, and Conflicted Systematic Reviews and Meta-analyses. Milbank Q. 2016 Sep;94(3):485–514. 
    4. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021 Dec 29;10(1):89. 
    5. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018 Oct 2;169(7):467–73.
    6. https://www.prisma-statement.org/Extensions/
    7. https://www.prisma-statement.org/Extensions/InDevelopment

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  • Importance of Searching Clinical Trial Registries for Systematic Reviews

    Importance of Searching Clinical Trial Registries for Systematic Reviews

    Results from over half of all conducted randomized controlled trials (RCTs), especially those with negative or unfavorable results, never get published. This means, searching only electronic databases that index published literature does not provide the entire spectrum of information; additionally, a bias is induced, since most trials having negative results get omitted. This is extremely crucial while doing literature search for informing a systematic review (SR).(1) Any kind of information bias, such as publication bias, selective outcome reporting bias, selective analysis bias, and time-lag bias, can result in an SR with a biased result, which can significantly hamper the validity and applicability of an SR.(2)

    Identification of all the previous relevant research, along with its informative quality, is an essential tool that validates the SR findings. This has led to the development of certain methods to identify all the literature to avoid publication bias. These methods, such as those based on funnel plots, are now regularly performed and are a part of the SR reporting guidelines.(3) However, these methods are found to be inadequate, since they can only suggest the presence of unpublished studies, assuming that the largest studies on the subject are published, which might not always be the case.(4,5) Furthermore, findings from empirical analyses have shown these methods to not consistently detect publication bias.(5,6)

    There are two approaches preferred by the systematic reviewers to deal with the information bias. One is detecting (and perhaps correcting results for) the bias based only on the identified studies (e.g., applying funnel-plot-based methods(7) or sensitivity analyses to represent possibly missing information(8) or comparing outcomes listed under Methods and Results sections in published manuscripts.(9) The second one is assessing trial registries, survey researchers, and scrutinize the gray literature to identify missing information from unpublished study results or ongoing studies.(2)

    Clinical trial registries basically enable clinical researchers, physicians, as well as the general public, to learn about clinical studies/trials being conducted on a subject, irrespective of the publication of findings of those studies. The International Committee of Medical Journal Editors (ICMJE) has, since 2005, made it mandatory for all the prospective human trials to undergo registration prior to commencing study enrollment. This requirement also makes for the condition ICMJE has put forth for publication in member journals.(10) Prospective registration of trials has also become a requirement under United States law for several interventional studies after the United States Food and Drug Administration Amendments Act (FDAAA) was passed in 2007.(11) Therefore, clinical trial registries are an all-inclusive storehouse of recently initiated clinical trials. Policy-makers and regulators believe that pre-registration of studies, if done consistently, will help all the healthcare stakeholders justify publication bias and other forms of selective reporting.(5,10)

    Clinical trial registries not only make available the findings from prospectively registered studies, but they also have result summaries (e.g., the National Library of Medicine ClinicalTrials.gov registry and registry networks), which can account for a lot of unpublished information. Policies that mandate prior registration often comprise of requirements for registering studies that include documentation of the study type, intervention, trial phase, information on the funding source, and outcomes, and the kind of information to be included within a study record. These steps have evolved after the launch of ClinicalTrials.gov in 2000. Explanations on what information to include in ClinicalTrials.gov have been provided to the research community to warrant compliance and timely submission of appropriate data to the registry.(12) The Clinical Trial Registry of India (CTRI) does a similar function with respect to clinical trials conducted in India, and it is now mandatory for RCTs performed in India to prospectively register with CTRI.(13) Researchers in favor of clinical trial registration underline the role of registry platforms to broadcast gathered results to researchers, clinicians, and study participants. Clinical trial registries improve transparency by providing a record of studies, which are in progress or have been completed.(2)

    Clinical trial registries have been developed essentially to reduce waste in research, as well as publication bias. Even policy makers and editors have emphasized on their use. Furthermore, the use of registries have shown to provide greater transparency, thus improving the value of research. Therefore, searching clinical trial registries should be promoted and mandated while conducting SRs.(14)

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    References

    1. Schmucker C, Schell LK, Portalupi S, et al. OPEN consortium. Extent of non-publication in cohorts of studies approved by research ethics committees or included in trial registries. PLoS One 2014; 356:e114023.
    2. Adam GP, Springs S, Trikalinos T, et al. Does information from ClinicalTrials.gov increase transparency and reduce bias? Results from a five-report case series. Syst Rev 2018; 7(59).
    3. Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 2009; 62(10):e1-e34.
    4. Jones CW, Handler L, Crowell KE, et al. Non-publication of large randomized clinical trials: cross sectional analysis. BMJ 2013; 347:f6104.
    5. Jones CW, Keil LG, Weaver MA, et al. Clinical trials registries are under-utilized in the conduct of systematic reviews: a cross-sectional analysis. Syst Rev 2014; 3(126).
    6. Lau J, Ioannidis JP, Terrin N, et al. The case of the misleading funnel plot. BMJ 2006; 333:597-600.
    7. Rucker G, Carpenter JR, Schwarzer G. Detecting and adjusting for small-study effects in meta-analysis. Biom J 2011; 53:351–68.
    8. Copas JB, Shi JQ. A sensitivity analysis for publication bias in systematic reviews. Stat Methods Med Res 2001; 10:251–65.
    9. Dwan K, Altman DG, Clarke M, et al. Evidence for the selective reporting of analyses and discrepancies in clinical trials: a systematic review of cohort studies of clinical trials. PLoS Med 2014; 11:e1001666.
    10. De Angelis C, Drazen JM, Frizelle FA, et al. Clinical trial registration: a statement from the International Committee of Medical Journal Editors. N Engl J Med 2004; 351(12):1250-1251.
    11. Food and Drug Administration Amendments Act of 2007. US Public Law 110–85. (2007, Sept 27); 21 USC 301.
    12. Zarin DA, Tse T, Sheehan J. The proposed rule for U.S. clinical trial registration and results submission. N Engl J Med 2015; 372:174–80.
    13. Vardhana Rao MV, Maulik M, Gupta J, Panchal Y, Juneja A, Adhikari T, Pandey A. Clinical Trials Registry – India: An overview and new developments. Indian J Pharmacol. 2018 Jul-Aug;50(4):208-211.
    14. Baudard M, Yavchitz A, Ravaud P, et al. Impact of searching clinical trial registries in systematic reviews of pharmaceutical treatments: methodological systematic review and reanalysis of meta-analyses. BMJ 2017; 356.

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