In clinical and agricultural trials, there is the danger that an experimental outcome appears to arise from the causal process or treatment one is interested in when, in reality, it was produced by some extraneous variation in the experimental conditions. The remedy prescribed by classical statisticians involves the procedure of randomization, whose effectiveness and appropriateness is criticized. An alternative, Bayesian analysis of experimental design, is shown, on the other hand, to provide a coherent and intuitively satisfactory solution to the (...) problem. (shrink)

Randomization procedures are used in legal and statistical applications, aiming to shield important decisions from spurious influences. This article gives an intuitive introduction to randomization and examines some intended consequences of its use related to truthful statistical inference and fair legal judgment. This article also presents an open-code Java implementation for a cryptographically secure, statistically reliable, transparent, traceable, and fully auditable randomization tool.

In this paper, I will argue that, while the ethical issues raised by cluster randomization can be challenging, they are not new. My thesis divides neatly into two parts. In the first, easier part I argue that many of the ethical challenges posed by cluster randomized human subjects research are clearly present in other types of human subjects research, and so are not novel. In the second, more difficult part I discuss the thorniest ethical challenge for cluster randomized research (...) – cases where consent is genuinely impractical to obtain. I argue that once again these cases require no new analytic insight; instead, we should look to political philosophy for guidance. In other words, the most serious ethical problem that arises in cluster randomized research also arises in political philosophy. (shrink)

In the recent literature across philosophy, medicine and public health policy, many influential arguments have been put forward to support the use of randomization procedures to allocate scarce life-saving resources. In this paper, I provide a systematic categorization and a critical evaluation of these arguments. I shall argue that those arguments justify using RAND to allocate SLSR in fewer cases than their proponents maintain and that the relevant decision-makers should typically allocate SLSR directly to the individuals with the strongest (...) claims to these resources rather than use RAND to allocate such resources. (shrink)

Why do classic biostatistical studies, alleged to provide causal explanations of effects, often fail? This article argues that in statistics-relevant areas of biology—such as epidemiology, population biology, toxicology, and vector ecology—scientists often misunderstand epistemic constraints on use of the statistical-significance rule (SSR). As a result, biologists often make faulty causal inferences. The paper (1) provides several examples of faulty causal inferences that rely on tests of statistical significance; (2) uncovers the flawed theoretical assumptions, especially those related to randomization, that (...) likely contribute to flawed biostatistics; (3) re-assesses the three classic (SSR-warrant, avoiding-selection-bias, and avoiding-confounders) arguments for using SSR only with randomization; and (4) offers five new reasons for biologists to use SSR only with randomized experiments. (shrink)

Peter Urbach has argued, on Bayesian grounds, that experimental randomization serves no useful purpose in testing causal hypothesis. I maintain that he fails to distinguish general issues of statistical inference from specific problems involved in identifying causes. I concede the general Bayesian thesis that random sampling is inessential to sound statistical inference. But experimental randomization is a different matter, and often plays an essential role in our route to causal conclusions.

This article attempts to comprehend the problem within the methodology of science. The authors compare the concepts of creativity and heuristics and suggest a semantic differentiation between them, and also offer their own viewpoint on the main types of activity corresponding to these concepts. The problem of creativity is associated with the characteristics that a person must have in order to solve tasks and problems. The authors consider the relationship between the problem and the task, as well as some major (...) techniques to tackle them. Here they substantiate the idea of a wider adaptation of randomization as a special tactic, that is, going beyond the narrow framework of mathematical statistics and empirical research. In this context, the authors introduce the notion of “epistemological randomization”, designed to denote the following of open rationality without abandoning the rational way of solving scientific problems. This technique is viewed as a phenomenon related to the counterfactual thinking. The last part of the article proposes a typology of personalities as problem and task solvers – adaptive, heuristic and creative personalities. It is assumed that the “heuristic” personality aims to complete tasks, which, unlike problems, have a final solution, while the “creative” personality aims to expand the problem field. The latter type is characterized as capable and inclined to use the “epistemological randomization”, the techniques of lateral thinking and other techniques that suggest several methods of solving problems and tasks at once. (shrink)

Researchers may be concurrently recruiting for several multi-site clinical trials at the same time, placing them in the position of potentially recruiting a patient-participant into one of several competing trials. This article examines how researchers should go about making equipoise calculations when competition emerges, creating ethical dilemmas about randomizing among, and not merely within, clinical trials.

Decoupling is a general principle that allows us to separate simple components in a complex system. In statistics, decoupling is often expressed as independence, no association, or zero covariance relations. These relations are sharp statistical hypotheses, that can be tested using the FBST - Full Bayesian Significance Test. Decoupling relations can also be introduced by some techniques of Design of Statistical Experiments, DSEs, like randomization. This article discusses the concepts of decoupling, randomization and sparsely connected statistical models in (...) the epistemological framework of cognitive constructivism. (shrink)

While the HVTN 505 trial showed no overall efficacy of the tested vaccine to prevent HIV infection over placebo, markers measuring immune response to vaccination were strongly correlated with infection. This finding generated the hypothesis that some marker-defined vaccinated subgroups were partially protected whereas others had their risk increased. This hypothesis can be assessed using the principal stratification framework (Frangakis and Rubin, 2002) for studying treatment effect modification by an intermediate response variable, using methods in the sub-field of principal surrogate (...) (PS) analysis that studies multiple principal strata. Unfortunately, available methods for PS analysis require an augmented study design not available in HVTN 505, and make untestable structural risk assumptions, motivating a need for more robust PS methods. Fortunately, another sub-field of principal stratification, survivor average causal effect (SACE) analysis (Rubin, 2006) – which studies effects in a single principal stratum – provides many methods not requiring an augmented design and making fewer assumptions. We show how, for a binary intermediate response variable, methods developed for SACE analysis can be adapted to PS analysis, providing new and more robust PS methods. Application to HVTN 505 supports that the vaccine partially protected individuals with vaccine-induced T-cells expressing certain combinations of functions. (shrink)

Randomization is an integral part of well-designed statistical trials, and is also a required procedure in legal systems. Implementation of honest, unbiased, understandable, secure, traceable, auditable and collusion resistant randomization procedures is a mater of great legal, social and political importance. Given the juridical and social importance of randomization, it is important to develop procedures in full compliance with the following desiderata: (a) Statistical soundness and computational efficiency; (b) Procedural, cryptographical and computational security; (c) Complete auditability and (...) traceability; (d) Any attempt by participating parties or coalitions to spuriously influence the procedure should be either unsuccessful or be detected; (e) Open-source programming; (f) Multiple hardware platform and operating system implementation; (g) User friendliness and transparency; (h) Flexibility and adaptability for the needs and requirements of multiple application areas (like, for example, clinical trials, selection of jury or judges in legal proceedings, and draft lotteries). This paper presents a simple and easy to implement randomization protocol that assures, in a formal mathematical setting, full compliance to the aforementioned desiderata for randomization procedures. (shrink)

Outcome-adaptive randomization has been proposed as a corrective to certain ethical difficulties inherent in the traditional randomized clinical trial using fixed-ratio randomization. In particular, it has been suggested that OAR redresses the balance between individual and collective ethics in favour of the former. In this paper, I examine issues of welfare and autonomy arising in relation to OAR. A central issue in discussions of welfare in OAR is equipoise, and the moral status of OAR is crucially influenced by (...) the way in which this concept is construed. If OAR is based on a model of equipoise that demands strict indifference between competing interventions throughout the trial, such equipoise is disturbed by accruing data favouring one treatment over another; OAR seeks to redress this by weighting randomization to the seemingly superior treatment. However, this is a partial response, as patients continue to be allocated to the inferior therapy. Moreover, it rests upon considerations of aggregate harms and benefits, and does not therefore uphold individual ethics. Issues of fairness also arise, as early and late enrollees are randomized on a different basis. Fixed-ratio randomization represents a fuller and more consistent response to a loss of equipoise, as so construed. With regard to consent, the complexity of OAR poses challenges to adequate disclosure and comprehension. Additionally, OAR does not offer a remedy to the therapeutic misconception—participants’ tendency to attribute treatment allocation in an RCT to individual clinical judgments, rather than to scientific considerations—and, if anything, accentuates rather than alleviates this misconception. In relation to these issues, OAR fails to offer ethical advantages over fixed-ratio randomization. More broadly, the ethical basis of OAR can be seen to lie more in collective than in individual ethics, and overall it fares worse in this territory than fixed-ratio randomization. (shrink)

Randomization is the “gold standard” design for clinical research trials and is accepted as the best way to reduce bias. Although some controversy remains over this matter, we believe equipoise is the fundamental ethical requirement for conducting a randomized clinical trial. Despite much attention to the ethics of randomization, the moral psychology of this study design has not been explored. This article analyzes the ethical tensions that arise from conducting these studies and examines the moral psychology of this (...) design from the perspectives of physician-investigators and patient-subjects. We conclude with a discussion of the practical implications of this analysis. (shrink)

In standard medical care, physicians select treatments for patients based on clinical judgment, considering which treatment is best for the individual patient, given the patient's history and circumstances. In contrast, investigators conducting randomized clinical trials select treatments for participants based on a random selection process. Because this process represents a significant departure from the norms of standard medical care, it is widely assumed that potential research participants must understand randomization to give valid informed consent. This assumption, together with data (...) that many research participants do not understand randomization, implies that randomized clinical trials often fail to obtain adequately informed consent. Before accepting this conclusion, and before initiating extensive efforts to improve research participants' understanding of randomization, we should assess the plausible, but rarely analyzed assumption that participants need to understand randomization to give valid informed consent for randomized clinical trials. (shrink)

A key role in inference is played by randomization, which has been extensively used in clinical trials designs. Randomization is primarily intended to prevent the source of bias in treatment allocation by producing comparable groups. In the frequentist framework of inference, randomization allows also for the use of probability theory to express the likelihood of chance as a source for the difference of end outcome. In the Bayesian framework, its role is more nuanced. The Bayesian analysis of (...) clinical trials can afford a valid rationale for selective controls, pointing out a more limited role for randomization than it is generally accorded. This paper is aimed to offer a view of randomization from the perspective of both frequentist and Bayesian statistics and discussing the role of randomization also in theoretical decision models. (shrink)

There are two uses of randomization in efforts to control systematic bias in experimental design: (a) Alchemical uses seek to convert unavoidable systematic errors into random errors. (b) Hygienic uses seek to reduce the prospect of the experimenter's involvement with the implementation of the experiment contributing to bias. A few remarks are made at the end of the paper about the hygienic use of randomization as a preventative against sticky fingers. The bulk of the discussion addresses the alchemical (...) applications. The thesis is that attitudes towards the cogency of Fisher's alchemical use of randomization ought to depend on views concerning statistical deduction or direct inference. (shrink)

Urbach (1985) has concluded that the use of randomization in the design of clinical and agricultural trials is both inappropriate and ineffective. It is argued here that it is appropriate, as it eliminates the dependence of inference on the unknown precise physical model that underlies a set of observations, and effective, in that it is relatively simple to apply in practice compared with any competing method. Furthermore, it has been proven in practice.

Recently, there has been increasing interest in the use of heavily restricted randomization designs which enforce balance on observed covariates in randomized controlled trials. However, when restrictions are strict, there is a risk that the treatment effect estimator will have a very high mean squared error. In this article, we formalize this risk and propose a novel combinatoric-based approach to describe and address this issue. First, we validate our new approach by re-proving some known properties of complete randomization (...) and restricted randomization. Second, we propose a novel diagnostic measure for restricted designs that only use the information embedded in the combinatorics of the design. Third, we show that the variance of the MSE of the difference-in-means estimator in a randomized experiment is a linear function of this diagnostic measure. Finally, we identify situations in which restricted designs can lead to an increased risk of getting a high MSE and discuss how our diagnostic measure can be used to detect such designs. Our results have implications for any restricted randomization design and can be used to evaluate the trade-off between enforcing balance on observed covariates and avoiding too restrictive designs. (shrink)

t Intentional sampling methods are non-randomized procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. In this paper we extend previous works related to intentional sampling, and address the problem of sequential allocation for clinical trials with few patients. Roughly speaking, patients are enrolled sequentially, according to the order in which they start the treatment at the clinic or hospital. The allocation problem consists in assigning each new patient to one, and (...) only one, of the alternative treatment arms. The main requisite is that the profiles in the alternative arms remain similar with respect to some relevant patients’ attributes (age, gender, disease, symptom severity and others). We perform numerical experiments based on a real case study and discuss how to conveniently set up perturbation parameters, in order to yield a suitable balance between optimality – the similarity among the relative frequencies of patients in the several categories for both arms, and decoupling – the absence of a tendency to allocate each pair of patients consistently to the same arm. (shrink)

In a recent paper in this journal, John Worrall (2008) used the example of a series of trials involving extracorporeal membrane oxygenation (ECMO), a technology for the treatment of respiratory failure in newborns, to illustrate the relationship between ethics and epistemology in medical research. One of the issues considered was whether or not it was ethical to perform a particular clinical trial at all, and he showed clearly that the answer was intimately related to epistemological judgments about the weight to (...) be given existing evidence concerning treatment effectiveness. In the case of ECMO, a trial was initiated at the University of Michigan (Bartlett et al. 1985), despite the fact that the researchers had .. (shrink)

Governments are increasingly using randomized controlled trials (RCTs) to evaluate policy interventions. RCTs are often understood to provide the highest quality evidence regarding the causal efficacy of an intervention. While randomization plays an essential epistemic role in the context of policy RCTs however, it also plays an important distributive role. By randomly assigning participants to either the intervention or control arm of an RCT, people are subject to different policies and so, often, to different types and levels of benefits. (...) In this paper, I identify one necessary condition as well as a set of sufficient conditions for the permissible use of random assignment by government agencies. I argue first that random assignment is permissible only if it is consistent with governments’ duty to realize morally important outcomes. I argue second that random assignment is permissible in cases where investigators are in a state of genuine equipoise regarding all arms of the experiment and the policy to which people have a claim of justice. Finally, I defend a set of conditions under which random assignment is permissible in cases where one or more arms of a policy RCT are reasonably expected to be either superior or inferior to this policy. (shrink)

A formulation of probabilistic causality is given in terms of the theory of abstract dynamical systems. Causal factors are identified as invariants of motion of a system. Repetition of an experiment leads to the notion of stationarity, and causal factors yield a decomposition of the stationary probability law of the experiment into ergodic components. In these, statistical behaviour is uniform. Control of identified causal factors leads to a corresponding statistical law for the events, which is offered as a notion of (...) probabilistic causality. After a suggestion by Feller, randomization is identified as mixing, formulated in above terms. (shrink)

The growing prevalence of health care ethics consultation (HCEC) services in the U.S. has been accompanied by an increase in calls for accountability and quality assurance, and for the debates surrounding why and how HCEC is evaluated. The objective of this study was to evaluate the effectiveness of HCEC as indicated by several novel outcome measurements in East Asian medical encounters.

The requirement of randomization in experimental design was first stated by R. A. Fisher, statistician and geneticist, in 1925 in his book Statistical Methods for Research Workers. Earlier designs were systematic and involved the judgment of the experimenter; this led to possible bias and inaccurate interpretation of the data. Fisher's dictum was that randomization eliminates bias and permits a valid test of significance. Randomization in experimenting had been used by Charles Sanders Peirce in 1885 but the practice (...) was not continued. Fisher developed his concepts of randomizing as he considered the mathematics of small samples, in discussions with "Student," William Sealy Gosset. Fisher published extensively. His principles of experimental design were spread worldwide by the many "voluntary workers" who came from other institutions to Rothamsted Agricultural Station in England to learn Fisher's methods. (shrink)

Government agencies and nonprofit organizations have increasingly turned to randomized controlled trials (RCTs) to evaluate public policy interventions. Random assignment is widely understood to be fair when there is equipoise; however, some scholars and practitioners argue that random assignment is also permissible when an intervention is reasonably expected to be superior to other trial arms. For example, some argue that random assignment to such an intervention is fair when the intervention is scarce, for it is sometimes fair to use a (...) lottery to allocate scarce goods. We investigate the permissibility of randomization in public policy RCTs when there is no equipoise, identifying two sets of conditions under which it is fair to allocate access to a superior intervention via random assignment. We also reject oft-made claims that alternative study designs, including stepped-wedge designs and uneven randomization, offer fair ways to allocate beneficial interventions. (shrink)

Mayo (1987) sought to discredit Urbach's (1985) arguments against randomization as a universal requirement in clinical and agricultural trials. The present reply rebuts Mayo's criticisms.

The scheme of abstract dynamical systems will represent repetitive experimentation: There is a basic space of events X1 and the denumerable product … contains all possible sequences of events x = (x1, x2, … ). There are projections qn which give the nth member of x: qn (x) = xn. A transformation T is defined over X by the equation qn (Tx)= q n+1 (x). It removes the sequence by one step, T(x1,x2,…) = (x2,x3,…) and is known as the shift (...) transformation. It comes as an abstraction of the dynamical transformations of classical theories. Here it represents the performance of ‘the next’ experiment. (shrink)

Randomization is a generally accepted principle of sound experimental design and common practice among working scientists. But Bayesian statisticians reject it, most often because of decision theoretic argument against randomization. I trace it back to Abraham Wald's Theory of Inductive Behavior and argue that Bayesians should concur with Ronald Fisher 's criticism of Wald's analysis of randomization. The paper ends with a Bayesian argument in favor of randomization: randomization can lead to an increase in expected (...) utility. (shrink)