In a randomized experiment, allocation concealment hides the sorting of trial participants into treatment groups so that this knowledge cannot be exploited. Adequate allocation concealment serves to prevent study participants from influencing treatment allocations for subjects. Studies with poor allocation concealment (or none at all) are prone to selection bias. [1]
Some standard methods of ensuring allocation concealment include sequentially numbered, opaque, sealed envelopes (SNOSE); sequentially numbered containers; pharmacy controlled randomization; and central randomization. [2] CONSORT guidelines recommend that allocation concealment methods be included in a study's protocol, and that the allocation concealment methods be reported in detail in their publication; however, a 2005 study determined that most clinical trials have unclear allocation concealment in their protocols, in their publications, or both. [3] A 2008 study of 146 meta-analyses concluded that the results of randomized controlled trials with inadequate or unclear allocation concealment tended to be biased toward beneficial effects only if the trials' outcomes were subjective as opposed to objective. [4]
Allocation concealment is different from blinding. An allocation concealment method prevents influence on the randomization process, while blinding conceals the outcome of the randomization. [1] However, allocation concealment may also be called "randomization blinding".
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Without the use of allocation concealment, researchers may (consciously or unconsciously) place subjects expected to have good outcomes in the treatment group, and those expected to have poor outcomes in the control group. This introduces considerable bias in favor of treatment.
Allocation concealment has also been called randomization blinding, blinded randomization, and bias-reducing allocation among other names. The term 'allocation concealment' was first introduced by Shultz et al. The authors justified the introduction of the term:
“The reduction of bias in trials depends crucially upon preventing foreknowledge of treatment assignment. Concealing assignments until the point of allocation prevents foreknowledge, but that process has sometimes been confusingly referred to as 'randomization blinding'. This term, if used at all, has seldom been distinguished clearly from other forms of blinding (masking) and is unsatisfactory for at least three reasons. First, the rationale for generating comparison groups at random, including the steps taken to conceal the assignment schedule, is to eliminate selection bias. By contrast, other forms of blinding, used after the assignment of treatments, serve primarily to reduce ascertainment bias. Second, from a practical standpoint, concealing treatment assignment up to the point of allocation is always possible, regardless of the study topic, whereas blinding after allocation is not attainable in many instances, such as in trials conducted to compare surgical and medical treatments. Third, control of selection bias pertains to the trial as a whole, and thus to all outcomes being compared, whereas control of ascertainment bias may be accomplished successfully for some outcomes, but not for others. Thus, concealment up to the point of allocation of treatment and blinding after that point address different sources of bias and differ in their practicability. In light of those considerations, we refer to the former as 'allocation concealment' and reserve the term 'blinding' for measures taken to conceal group identity after allocation” [5]
Traditionally, each patient's treatment allocation data was stored in a sealed envelopes, which was to be opened to determine treatment allocation. However, this system is prone to abuse. Reports of researchers opening envelopes prematurely or holding the envelopes up to lights to determine their contents has led some researchers to say that the use of sealed envelopes is no longer acceptable. [6] [7] As of 2016 [update] , sealed envelopes were still in use in some clinical trials. [8]
Modern clinical trials often use centralized allocation concealment. Although considered more secure, central allocations are not completely immune from subversion. Typical and sometimes successful strategies include keeping a list of previous allocations (up to 15% of study personnel report keeping lists). [9]
A randomized controlled trial is a form of scientific experiment used to control factors not under direct experimental control. Examples of RCTs are clinical trials that compare the effects of drugs, surgical techniques, medical devices, diagnostic procedures or other medical treatments.
In a blind or blinded experiment, information which may influence the participants of the experiment is withheld until after the experiment is complete. Good blinding can reduce or eliminate experimental biases that arise from a participants' expectations, observer's effect on the participants, observer bias, confirmation bias, and other sources. A blind can be imposed on any participant of an experiment, including subjects, researchers, technicians, data analysts, and evaluators. In some cases, while blinding would be useful, it is impossible or unethical. For example, it is not possible to blind a patient to their treatment in a physical therapy intervention. A good clinical protocol ensures that blinding is as effective as possible within ethical and practical constraints.
A case–control study is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. Case–control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have that condition/disease with patients who do not have the condition/disease but are otherwise similar. They require fewer resources but provide less evidence for causal inference than a randomized controlled trial. A case–control study is often used to produce an odds ratio, which is an inferior measure of strength of association compared to relative risk, but new statistical methods make it possible to use a case-control study to estimate relative risk, risk differences, and other quantities.
Clinical study design is the formulation of trials and experiments, as well as observational studies in medical, clinical and other types of research involving human beings. The goal of a clinical study is to assess the safety, efficacy, and / or the mechanism of action of an investigational medicinal product (IMP) or procedure, or new drug or device that is in development, but potentially not yet approved by a health authority. It can also be to investigate a drug, device or procedure that has already been approved but is still in need of further investigation, typically with respect to long-term effects or cost-effectiveness.
In statistics, (between-) study heterogeneity is a phenomenon that commonly occurs when attempting to undertake a meta-analysis. In a simplistic scenario, studies whose results are to be combined in the meta-analysis would all be undertaken in the same way and to the same experimental protocols. Differences between outcomes would only be due to measurement error. Study heterogeneity denotes the variability in outcomes that goes beyond what would be expected due to measurement error alone.
A hierarchy of evidence, comprising levels of evidence (LOEs), that is, evidence levels (ELs), is a heuristic used to rank the relative strength of results obtained from experimental research, especially medical research. There is broad agreement on the relative strength of large-scale, epidemiological studies. More than 80 different hierarchies have been proposed for assessing medical evidence. The design of the study and the endpoints measured affect the strength of the evidence. In clinical research, the best evidence for treatment efficacy is mainly from meta-analyses of randomized controlled trials (RCTs). Systematic reviews of completed, high-quality randomized controlled trials – such as those published by the Cochrane Collaboration – rank the same as systematic review of completed high-quality observational studies in regard to the study of side effects. Evidence hierarchies are often applied in evidence-based practices and are integral to evidence-based medicine (EBM).
In medicine an intention-to-treat (ITT) analysis of the results of a randomized controlled trial is based on the initial treatment assignment and not on the treatment eventually received. ITT analysis is intended to avoid various misleading artifacts that can arise in intervention research such as non-random attrition of participants from the study or crossover. ITT is also simpler than other forms of study design and analysis, because it does not require observation of compliance status for units assigned to different treatments or incorporation of compliance into the analysis. Although ITT analysis is widely employed in published clinical trials, it can be incorrectly described and there are some issues with its application. Furthermore, there is no consensus on how to carry out an ITT analysis in the presence of missing outcome data.
Zelen's design is an experimental design for randomized clinical trials proposed by Harvard School of Public Health statistician Marvin Zelen (1927-2014). In this design, patients are randomized to either the treatment or control group before giving informed consent. Because the group to which a given patient is assigned is known, consent can be sought conditionally.
In science, randomized experiments are the experiments that allow the greatest reliability and validity of statistical estimates of treatment effects. Randomization-based inference is especially important in experimental design and in survey sampling.
In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints. One common observational study is about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator. This is in contrast with experiments, such as randomized controlled trials, where each subject is randomly assigned to a treated group or a control group. Observational studies, for lacking an assignment mechanism, naturally present difficulties for inferential analysis.
In epidemiology, reporting bias is defined as "selective revealing or suppression of information" by subjects. In artificial intelligence research, the term reporting bias is used to refer to people's tendency to under-report all the information available.
Consolidated Standards of Reporting Trials (CONSORT) encompasses various initiatives developed by the CONSORT Group to alleviate the problems arising from inadequate reporting of randomized controlled trials. It is part of the larger EQUATOR Network initiative to enhance the transparency and accuracy of reporting in research.
Clinical trials are medical research studies conducted on human subjects. The human subjects are assigned to one or more interventions, and the investigators evaluate the effects of those interventions. The progress and results of clinical trials are analyzed statistically.
The Jadad scale, sometimes known as Jadad scoring or the Oxford quality scoring system, is a procedure to assess the methodological quality of a clinical trial by objective criteria. It is named after Canadian-Colombian physician Alex Jadad who in 1996 described a system for allocating such trials a score of between zero and five (rigorous). It is the most widely used such assessment in the world, and as of 2022, its seminal paper has been cited in over 23,000 scientific works.
Within the field of clinical trials, rating is the process by which a human evaluator subjectively judges the response of a patient to a medical treatment. The rating can include more than one treatment response. The accessor is normally an independent observer other than the patient, but the accessor can also be the patient. Furthermore, some clinical outcomes can only be assessed by the patient.
The Enhancing the Quality and Transparency of health research Network is an international initiative aimed at promoting transparent and accurate reporting of health research studies to enhance the value and reliability of medical research literature. The EQUATOR Network was established with the goals of raising awareness of the importance of good reporting of research, assisting in the development, dissemination and implementation of reporting guidelines for different types of study designs, monitoring the status of the quality of reporting of research studies in the health sciences literature, and conducting research relating to issues that impact the quality of reporting of health research studies. The Network acts as an "umbrella" organisation, bringing together developers of reporting guidelines, medical journal editors and peer reviewers, research funding bodies, and other key stakeholders with a mutual interest in improving the quality of research publications and research itself. The EQUATOR Network comprises four centres at the University of Oxford, Bond University, Paris Descartes University, and Ottawa Hospital Research Institute.
PRISMA is an evidence-based minimum set of items aimed at helping scientific authors to report a wide array of systematic reviews and meta-analyses, primarily used to assess the benefits and harms of a health care intervention. PRISMA focuses on ways in which authors can ensure a transparent and complete reporting of this type of research. The PRISMA standard superseded the earlier QUOROM standard. It offers the replicability of a systematic literature review. Researchers have to figure out research objectives that answer the research question, states the keywords, a set of exclusion and inclusion criteria. In the review stage, relevant articles were searched, irrelevant ones are removed. Articles are analyzed according to some pre-defined categories.
Isabelle Boutron is a professor of epidemiology at the Université Paris Cité and head of the INSERM- METHODS team within the Centre of Research in Epidemiology and Statistics (CRESS). She was originally trained in rheumatology and later switched to a career in epidemiology and public health. She is also deputy director of the French EQUATOR Centre, member of the SPIRIT-CONSORT executive committee, director of Cochrane France and co-convenor of the Bias Methods group of the Cochrane Collaboration.
Allegiance bias in behavioral sciences is a bias resulted from the investigator's or researcher's allegiance to a specific school of thought. Researchers/investigators have been exposed to many types of branches of psychology or schools of thought. Naturally they adopt a school or branch that fits with their paradigm of thinking. More specifically, allegiance bias is when this leads therapists, researchers, etc. believing that their school of thought or treatment is superior to others. Their superior belief to these certain schools of thought can bias their research in effective treatments trials or investigative situations leading to allegiance bias. Reason being is that they may have devoted their thinking to certain treatments they have seen work in their past experiences. This can lead to errors in interpreting the results of their research. Their “pledge” to stay within their own paradigm of thinking may affect their ability to find more effective treatments to help the patient or situation they are investigating.
A code-break procedure is a set of rules which determine when planned unblinding should occur in a blinded experiment. FDA guidelines recommend that sponsors of blinded trials include a code-break procedure in their standard operating procedure. A code-break procedure should only allow a participant to be unblinded before the conclusion of a trial in the event of an emergency. Code-break usually refers to the unmasking of treatment allocation, but can refer to any form of unblinding.