Bias In Clinical Research Research Articles

Bias in Clinical Research: A Review Article

Bias in Clinical Research: A Review Article

Tools to assess the risk of bias in clinical research

Tools to assess the risk of bias in clinical research

Be Positive! International Stroke Conference Oral Presentations ‐ 2015 to 2020

Be Positive! International Stroke Conference Oral Presentations ‐ 2015 to 2020

Impact of the timing of tumor assessments on median progression-free survival in clinical trials in advanced cancer patients

BackgroundSurvival-based surrogate endpoints such as progression-free survival (PFS) are commonly used in oncology clinical trials. The evaluation-time bias in the assessment of median disease progression in randomized trials has been suggested by several simulation studies, but never demonstrated in the clinic. We aimed to demonstrate the existence of potential evaluation-time bias by assessing the impact of the timing of tumor assessments on median PFS from control arms without any active treatment of randomized controlled trials involving advanced cancer patients.Materials and methodsA systematic literature search of English language publications from 1 January 2000 to 7 January 2021 was performed using MEDLINE (PubMed). Eligible trials for our meta-analysis included all randomized clinical trials evaluating anticancer drugs in adult patients with advanced cancers with a control arm without any anticancer drug consisting of best supportive care with or without a placebo. We performed a meta-regression analysis to analyze the correlation between the timing of the first tumor assessment and median PFS in patients randomized in the control arms without any active treatment.ResultsOf 3551 studies screened, 97 eligible trials were retrieved involving 36 747 patients, including 14 229 patients randomized into the control arms. A later first tumor assessment correlated with a prolonged median PFS (R2 = 0.44, P < 10−5).ConclusionsOur results confirm the existence of potential evaluation-time bias in clinical research that had been suggested by simulation studies. The timing of tumor assessments should be kept the same in precision medicine trials using the PFS ratio as an efficacy endpoint.

Pitfalls in Study Interpretation.

Background: The goal of a randomized or observational study is to develop an unbiased and reliable answer to a therapeutic question. However, there are multiple pitfalls in the reporting and interpretation of data that can compromise our ability to evaluate the pragmatism and the effectiveness of the intervention being studied. Researchers must be conscious of these biases when designing their studies, just as readers must be aware of these potential pitfalls when interpreting results. Results: The purpose of this review is to highlight some of the more common sources of bias in clinical research, including internal and external validity, type 1 and type 2 error, reporting of secondary outcomes, the use of subgroup analyses, and multiple comparisons. This article also discusses potential solutions to these issues, including using the fragility index to understand the robustness of study conclusions, and generating an E value to determine the degree of unmeasured confounding in a study. Conclusions: With an understanding of these pitfalls, readers can critically review scientific literature and ascertain the validity of the conclusions.

Definitions of Bias in Clinical Research.

In this chapter, a catalog of the various types of bias that can affect the validity of clinical epidemiologic studies is presented. The biases are classified by stage of research: literature review and publication, design of the study and selection of subjects, execution of the intervention, measurement of exposures and outcomes, data analysis , and interpretation and publication. Definitions are provided for each type of bias listed.

Bias in Clinical Research.

Clinical epidemiological research entails assessing the burden and etiology of disease, the diagnosis and prognosis of disease, the efficacy of preventive measures or treatments, the analysis of the risks and benefits of diagnostic and therapeutic maneuvers, and the evaluation of health care services. In all areas, the main focus is to describe the relationship between exposure and outcome and to determine one of the following: prevalence, incidence, cause, prognosis, or effect of treatment. The accuracy of these conclusions is determined by the validity of the study. Validity is determined by addressing potential biases and possible confounders that may be responsible for the observed association. Therefore, it is important to understand the types of bias that exist and also to be able to assess their impact on the magnitude and direction of the observed effect. The following chapter reviews the epidemiological concepts of selection bias, information bias, intervention bias, and confounding and discusses ways in which these sources of bias can be minimized.

Comprehensive Review of Effective Application of Questionnaires for Clinical Research on Lower Urinary Tract Symptoms With Translation and Cultural Adaptation to the Korean Language.

Lower urinary tract symptoms are highly prevalent and closely related to patients’ quality of life. Clinical research on urologic disease is essential for accumulating evidence on patient management; however, the major obstacle is converting patients’ subjective symptoms to objective parameters. The optimal application of well-developed and validated questionnaires is vital in achieving objectivity and minimizing bias in clinical research. Numerous questionnaires for measuring symptoms and quality of life in urologic diseases have been developed worldwide; however, they cannot be directly used in clinical studies without validation processes. This review aimed to explain the common procedures for translation, linguistic, and psychometric validation of developed questionnaires from other languages. Furthermore, we comprehensively reviewed currently available questionnaires for evaluating lower urinary tract symptoms in the Korean population.

Stakeholder views on publication bias in health services research.

ObjectivesWhile the presence of publication bias in clinical research is well documented, little is known about its role in the reporting of health services research. This paper explores stakeholder perceptions and experiences with regard to the role of publication and related biases in quantitative research relating to the quality, accessibility and organization of health services.MethodsWe present findings from semi-structured interviews with those responsible for the funding, publishing and/or conduct of quantitative health services research, primarily in the UK. Additional data collection includes interviews with health care decision makers as ‘end users’ of health services research, and a focus group with patient and service user representatives. The final sample comprised 24 interviews and eight focus group participants.ResultsMany study participants felt unable to say with any degree of certainty whether publication bias represents a significant problem in quantitative health services research. Participants drew broad contrasts between externally funded and peer reviewed research on the one hand, and end user funded quality improvement projects on the other, with the latter perceived as more vulnerable to selective publication and author over-claiming. Multiple study objectives, and a general acceptance of ‘mess and noise’ in the data and its interpretation was seen to reduce the importance attached to replicable estimates of effect sizes in health services research. The relative absence of external scrutiny, either from manufacturers of interventions or health system decision makers, added to this general sense of ‘low stakes’ of health services research. As a result, while many participants advocated study pre-registration and using protocols to pre-identify outcomes, others saw this as an unwarranted imposition.ConclusionsThis study finds that incentives towards publication and related bias are likely to be present, but not to the same degree as in clinical research. In health services research, these were seen as being offset by other forms of ‘novelty’ bias in the reporting and publishing of research findings.

Bias In Clinical Research For Antimicrobial Stewardship Programs

Bias In Clinical Research For Antimicrobial Stewardship Programs

A guide to missing data for the pediatric nephrologist.

Missing data is an important and common source of bias in clinical research. Readers should be alert to and consider the impact of missing data when reading studies. Beyond preventing missing data in the first place, through good study design and conduct, there are different strategies available to handle data containing missing observations. Complete case analysis is often biased unless data are missing completely at random. Better methods of handling missing data include multiple imputation and models using likelihood-based estimation. With advancing computing power and modern statistical software, these methods are within the reach of clinician-researchers under guidance of a biostatistician. As clinicians reading papers, we need to continue to update our understanding of statistical methods, so that we understand the limitations of these techniques and can critically interpret literature.

If the Facts Were Not Untruths, Their Implications Were: Sponsorship Bias and Misleading Communication.

Sponsorship bias occurs when the financial interests of funders of scientific research influence claims made by scientists, especially in peer-reviewed publications. This article examines the relationship between sponsorship bias and misleading claims, understood as claims that are not necessarily false but which encourage those exposed to them to infer false conclusions. Misleading claims are relevant to how the term "bias" should be understood and thereby to evaluating a recent dispute about whether there is evidence of sponsorship bias in clinical research on statins. The concept of inferential asymmetry is introduced as an aid for understanding the relationship between misleading claims and sponsorship bias.

The Early Use of Blinding in Therapeutic Clinical Research of Neurological Disorders.

We sought to identify early uses of blinding in therapeutic clinical trials of neurological disorders by multiple search methods. A 1784 report by Benjamin Franklin and others described the evaluation of the use of Mesmerism to treat neurological and other syndromes including headache and epilepsy, using blindfolds and screens. This report demonstrated the usefulness of blinding to reduce bias in clinical research, yet despite this early discovery, blinding was not widely accepted or routinely used until the 20th century. Blinded clinical trials began to be used for various neurological syndromes in the 1950s, sporadically at first and then increasing in frequency in subsequent years. The reason for this delay is unclear, but we propose several hypotheses.

Evidence-informed recommendations to reduce dissemination bias in clinical research: conclusions from the OPEN (Overcome failure to Publish nEgative fiNdings) project based on an international consensus meeting

BackgroundDissemination bias in clinical research severely impedes informed decision-making not only for healthcare professionals and patients, but also for funders, research ethics committees, regulatory bodies and other stakeholder groups that...

Definitions of bias in clinical research.

In this chapter a catalog of the various types of bias that can affect the validity of clinical epidemiologic studies is presented. The biases can be grouped into those associated with selection of subjects, misclassification or misinformation, and finally confounding. Definitions are provided for each type of bias listed.

Publication bias in clinical research sponsored by pharmaceutical industry

An increasing number of clinical trials are funded by the pharmaceutical industry, and the industry as strong commercial interests in research publications that present their products positively and enhance their sales. As main sponsor of clinical trials, the pharmaceutical industry has gained unprecedented control over the evaluation of its own products and communication of research results. There is mounting evidence that industry-sponsored trials are biased in favor of the sponsor`s products. Various potential ways have been described in which pharmaceutical concerns exert influence on the outcome of a study and its communication, however, publication bias proved to be a major cause for bias in industry-funded trials. Pharmaceutical industry and its sponsored investigators selectively report favorable outcomes, fail to publish whole studies with unfavorable results, or publish studies with favorable results multiple times. Such manipulation of research communication might greatly jeopardize the scientific basis for good clinical practice. This article explores the aspects of clinical trial performance that can be affected by pharmaceutical company sponsorship, particularly emphasizing on results dissemination and publication. It is aimed at describing the relationship between pharmaceutical industry sponsorship and bias in research publication. By revealing the scope of the problem and its essence - it attempts to sensitize the scientific, professional and publishing societies and provoke

Neglecting the null: the pitfalls of underreporting negative results in preclinical research

ABSTRACTHeightened competition for funding and increased pressure to publish in high-impact journals has led to a modern-day publication culture that favours positive results. The underreporting of negative, or null, results is a form of publication bias that occurs when researchers and/or reviewers fail to communicate findings due to unfavourable directionality or perceived unimportance. For nearly three decades, recognition of this bias in clinical research has led to revised policies and guidelines in an effort to improve reporting transparency and accuracy. Only recently has the existence of this reporting bias been fully appreciated as a formidable problem in preclinical research. Considering that preclinical research provides the foundation on which many clinical trials are conceived, finding solutions to increase the reporting accuracy of preclinical studies is of paramount importance. In this commentary, we will explore how the underreporting of negative results in preclinical research distorts scientific knowledge and subsequently misguides clinical research. We will conclude with several suggestions for reducing this bias with the intention of transitioning towards a truly transparent and objective publishing landscape.

Clinical trial design for orthodontists

High-quality research should form the basis of all clinical practice. Randomized controlled trials currently provide the gold standard for investigating the effectiveness of treatment interventions and these are increasingly being used in orthodontics. Here we discuss the reasons why this form of investigation provides the most useful evidence for assessing treatment outcome. The methods available to achieve true randomization, a fundamental component in the design of these trials, are also discussed. In addition, we focus on how to minimize bias in clinical research, not only during the design and management of a trial, but also when disseminating results. We focus on the importance of using control groups correctly and describe methods that are available to adequately power a trial. Finally, we emphasise the importance of accurate and transparent reporting, which facilitates correct communication and assessment of the evidence.

Sponsorship bias in clinical research

Pharmaceutical companies fund the vast majority of the clinical research that is undertaken on medications but face a conflict of interest between producing good science and results that will enhance the sales of their products. To document concrete examples of bias in clinical research induced by pharmaceutical industry sponsorship. This paper uses a thematic approach to documenting the extent of these biases in the following areas: research question/topic, choice of doses and comparator agents, control over trial design and changes in protocols, early termination of clinical trials, reporting to regulatory authorities, reinterpretation of data, restrictions on publication rights, use of fake journals, journal supplements and symposia, ghostwriting, publication and reporting of results and outcomes. Bias in favour of industry is apparent in every one of the themes examined with the result that research funded by industry undermines confidence in medical knowledge. Bias induced by commercial concerns can be countered in one of two ways. The first is to erect a firewall between the money and the people doing the research and the data analysis. The other approach is to develop an entirely separate funding source that is independent of the pharmaceutical industry.

Selection Bias and Information Bias in Clinical Research

The internal validity of an epidemiological study can be affected by random error and systematic error. Random error reflects a problem of precision in assessing a given exposure-disease relationship and can be reduced by increasing the sample size. On the other hand, systematic error or bias reflects a problem of validity of the study and arises because of any error resulting from methods used by the investigator when recruiting individuals for the study, from factors affecting the study participation (selection bias) or from systematic distortions when collecting information about exposures and outcomes (information bias). Another important factor which may affect the internal validity of a clinical study is confounding. In this article, we focus on two categories of bias: selection bias and information bias. Confounding will be described in a future article of this series.