Valid Adjustment of Randomized Comparisons for Binary Covariates

In randomized trials, the treatment influences not only endpoints but also other variables measured after randomization which, when used as covariates to adjust for the observed imbalance, become pseudo‐covariates. There is a logical circularity in adjusting for a pseudo‐covariate because the variability in the endpoint that is attributed not to the treatment but rather to the pseudo‐covariate may actually represent an effect of the treatment modulated by the pseudo‐covariate. This potential bias is well known, but we offer new insight into how it can lead to reversals in the direction of the apparent treatment effect by way of stage migration. We then discuss a related problem that is not generally appreciated, specifically how the absence of allocation concealment can lead to this reversal of the direction of the apparent treatment effect even when adjustment is for a true covariate measured prior to randomization. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Blinded and unblinded sample size reestimation in crossover trials balanced for period

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under‐ or overpowered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re‐estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multitreatment crossover trials. Specifically, regulators favor reestimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomization. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomization are unbiased. We further provide a formula for the bias of the estimators following simple randomization. The performance of these procedures, along with that of an unblinded approach, is then examined utilizing three motivating examples, including one based on a recently completed four‐treatment four‐period crossover trial. Simulation results show that the performance of the proposed blinded procedures is in many cases similar to that of the unblinded approach, and thus they are an attractive alternative.     

African HIV/AIDS trials are more likely to report adequate allocation concealment and random generation than North American trials

Background

Adherence to good methodological quality is necessary to minimise bias in randomised conrolled trials (RCTs). Specific trial characteristics are associated with better trial quality, but no studies to date are specific to HIV/AIDS or African trials. We postulated that location may negatively impact on trial quality in regions where resources are scarce.

Methods

1) To compare the methodological quality of all HIV/AIDS RCTs conducted in Africa with a random sample of similar trials conducted in North America; 2) To assess whether location is predictive of trial quality. We searched MEDLINE, EMBASE, CENTRAL and LILACS. Eligible trials were 1) randomized, 2) evaluations of preventive or treatment interventions for HIV/AIDS, 3) reported before 2004, and 4) conducted wholly or partly (if multi-centred) in Africa or North America. We assessed adequacy of random generation, allocation concealment and masking of assessors. Using univariate and multivariate logistic regression analyses we evaluated the association between location (Africa versus North America) and these domains.

Findings

The African search yielded 12,815 records, from which 80 trials were identified. The North American search yielded 13,158 records from which 785 trials were identified and a random sample of 114 selected for analysis. African trials were three times more likely than North American trials to report adequate allocation concealment (OR = 3.24; 95%CI: 1.59 to 6.59; p<0.01) and twice as likely to report adequate generation of the sequence (OR = 2.36; 95%CI: 1.20 to 4.67; p = 0.01), after adjusting for other confounding factors. Additional significant factors positively associated with quality were an a priori sample size power calculation, restricted randomization and inclusion of a flow diagram detailing attrition. We did not detect an association between location and outcome assessor masking.

Conclusions

The higher quality of reporting of methodology in African trials is noteworthy. Most African trials are externally funded, and it is possible that stricter agency requirements when leading trials in other countries and greater experience and training of principal investigators of an international stature, may account for this difference.     

Participant informed consent in cluster randomized trials: review

Background

The Nuremberg code defines the general ethical framework of medical research with participant consent as its cornerstone. In cluster randomized trials (CRT), obtaining participant informed consent raises logistic and methodologic concerns. First, with randomization of large clusters such as geographical areas, obtaining individual informed consent may be impossible. Second, participants in randomized clusters cannot avoid certain interventions, which implies that participant informed consent refers only to data collection, not administration of an intervention. Third, complete participant information may be a source of selection bias, which then raises methodological concerns. We assessed whether participant informed consent was required in such trials, which type of consent was required, and whether the trial was at risk of selection bias because of the very nature of participant information.

Methods and Findings

We systematically reviewed all reports of CRT published in MEDLINE in 2008 and surveyed corresponding authors regarding the nature of the informed consent and the process of participant inclusion. We identified 173 reports and obtained an answer from 113 authors (65.3%). In total, 23.7% of the reports lacked information on ethics committee approval or participant consent, 53.1% of authors declared that participant consent was for data collection only and 58.5% that the group allocation was not specified for participants. The process of recruitment (chronology of participant recruitment with regard to cluster randomization) was rarely reported, and we estimated that only 56.6% of the trials were free of potential selection bias.

Conclusions

For CRTs, the reporting of ethics committee approval and participant informed consent is less than optimal. Reports should describe whether participants consented for administration of an intervention and/or data collection. Finally, the process of participant recruitment should be fully described (namely, whether participants were informed of the allocation group before being recruited) for a better appraisal of the risk of selection bias.     

Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology?

Establishing causal relationships between environmental exposures and common diseases is beset with problems of unresolved confounding, reverse causation and selection bias that may result in spurious inferences. Mendelian randomization, in which a functional genetic variant acts as a proxy for an environmental exposure, provides a means of overcoming these problems as the inheritance of genetic variants is independent of—that is randomized with respect to—the inheritance of other traits, according to Mendel’s law of independent assortment. Examples drawn from exposures and outcomes as diverse as milk and osteoporosis, alcohol and coronary heart disease, sheep dip and farm workers’ compensation neurosis, folate and neural tube defects are used to illustrate the applications of Mendelian randomization approaches in assessing potential environmental causes of disease. As with all genetic epidemiology studies there are problems associated with the need for large sample sizes, the non-replication of findings, and the lack of relevant functional genetic variants. In addition to these problems, Mendelian randomization findings may be confounded by other genetic variants in linkage disequilibrium with the variant under study, or by population stratification. Furthermore, pleiotropy of effect of a genetic variant may result in null associations, as may canalisation of genetic effects. If correctly conducted and carefully interpreted, Mendelian randomization studies can provide useful evidence to support or reject causal hypotheses linking environmental exposures to common diseases.     

Randomized,replicated, staggered clonal-row (R2SCR) seed orchard design

Spatial randomization of clones across a seed orchard’s grid is commonly applied to promote cross-fertilization and minimize selfing. The high selection differential attained from advanced-generation breeding programs sets high premier on the genetic gain and diversity delivery from seed orchards, thus clonal allocation is important and even more challenging when clones share common ancestry. Evidences of low selfing in many conifers’ seed orchards, as a result of their high genetic load, inbreeding depression, and polyembryony are abundant and call for orchards’ design re-evaluation, specifically when randomization is associated with added managerial burden. Clonal-rows represent a viable option for simplifying orchards management; however, they are often associated with elevated correlated matings between adjacent clones. Here, we propose a modified clonal-row design that replicates, staggers, and randomizes the rows, thus doubling the number of adjacent clones and providing different set of neighboring clones at each replication, thus allowing accommodating related parents more readily than any single-tree arrangement. We present a novel algorithm packaged in user-friendly software for executing various seed orchards’ designs. The developed program is interactive and suitable for any orchard size and configuration, accommodates any number of clones that are allocated to rows with variable length (ranging from a single tree to any even number) and pre-set separation zone between ramets of the same clone. The program offers three deployment modes (equal, linear, and custom) each with multiple layouts determined by the number of iterations requested. The resulting layouts are ranked based on four criteria including: (1) the number of empty positions, (2) deviation between expected and observed clone size, (3) minimum inbreeding, and (4) a neighborhood index that expresses the efficiency of clonal distribution.     

The Impact of Purifying and Background Selection on the Inference of Population History: Problems and Prospects

Current procedures for inferring population history generally assume complete neutrality—that is, they neglect both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically. We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the distribution of fitness effect as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy.     

Comparison of sequence masking algorithms and the detection of biased protein sequence regions

MOTIVATION: Separation of protein sequence regions according to their local information complexity and subsequent masking of low complexity regions has greatly enhanced the reliability of function prediction by sequence similarity. Comparisons with alternative methods that focus on compositional sequence bias rather than information complexity measures have shown that removal of compositional bias yields at least as sensitive and much more specific results. Besides the application of sequence masking algorithms to sequence similarity searches, the study of the masked regions themselves is of great interest. Traditionally, however, these have been neglected despite evidence of their functional relevance. RESULTS: Here we demonstrate that compositional bias seems to be a more effective measure for the detection of biologically meaningful signals. Typical results on proteins are compared to results for sequences that have been randomized in various ways, conserving composition and local correlations for individual proteins or the entire set. It is remarkable that low-complexity regions have the same form of distribution in proteins as in randomized sequences, and that the signal from randomized sequences with conserved local correlations and amino acid composition almost matches the signal from proteins. This is not the case for sequence bias, which hence seems to be a genuinely biological phenomenon in contrast to patches of low complexity.     

How avian nest site selection responds to predation risk: testing an 'adaptive peak hypothesis'

1. Nest predation limits avian fitness, so birds should favour nest sites that minimize predation risk. Nevertheless, preferred nest microhabitat features are often uncorrelated with apparent variation in predation rates. 2. This lack of congruence between theory-based expectation and empirical data may arise when birds already occupy 'adaptive peaks'. If birds nest exclusively in low-predation microhabitats, microhabitat and nest predation may no longer be correlated even though predation ultimately shaped microhabitat selection. 3. This 'adaptive peak hypothesis' was tested for a population of Yellow Warblers (Dendroica petechia) focusing on two nest microhabitat features: concealment and height. Experimental nests measured relative predation risk both within and outside the microhabitat range typically occupied by natural nests to examine whether nest site choices made by birds restricted our ability to detect microhabitat effects on predation. 4. Within the natural range (30-80% concealment, >75 cm height), microhabitat-predation relationships were weak and inconsistent, and similar for experimental and natural nests. Over an extended range, however, experimental predation rates were elevated in exposed sites (<30% concealed), indicating a concealment-related 'adaptive plateau'. 5. Clay egg bite data revealed a concealment effect on avian predators, and the abundance of one avian predator group correlated with nest concealment among years, suggesting these predators may cue birds to modulate nest concealment choices. 6. This study demonstrates how avian responses to predation pressure can obscure the adaptive significance of nest site selection, so predation influences may be more important than apparent from published data.     

Conditional Markov Chain Design for Accrual Clinical Trials

Randomization in a comparative experiment has, as one aim, the control of bias in the initial selection of experimental units. When the experiment is a clinical trial employing the accrual of patients, two additional aims are the control of admission bias and control of chronologic bias. This can be accomplished by using a method of randomization, such as the “biased coin design” of Efron, which sequentially forces balance. As an extension of Efron's design, this paper develops a class of conditional Markov chain designs. The detailed randomization employed utilizes the sequential imbalances in the treatment allocation as states in a Markov process. Through the use of appropriate transition probabilities, a range of possible designs can be attained. An additional objective of physical randomization is to provide a model for data analysis. Such a randomization theoretic analysis is presented for the current designs. In addition, Monte Carlo sampling results are given to support the proposed normal theory approximation to the exact randomization distribution.     

Differential dropout among SNP genotypes and impacts on association tests

BACKGROUND: Current biotechnologies are able to achieve high accuracy and call rates. Concerns are raised on how differential performance on various genotypes may bias association tests. Quantitatively, we define differential dropout rate as the ratio of no-call rate among heterozygotes and homozygotes. METHODS: The hazard ofdifferential dropout is examined for population- and family-based association tests through a simulation study. Also, we investigate detection approaches such as Hardy-Weinberg Equilibrium (HWE) and testing for correlation between sample call rate and sample heterozygosity. Finally, we analyze two public datasets and evaluate the magnitudes of differential dropout. RESULTS: In case-control settings, differential dropout has negligible effect on power and odds ratio (OR) estimation. However, the impact on family-based tests range from minor to severe depending on the disease parameters. Such impact is more prominent when disease allele frequency is relatively low (e.g., 5%), where a differential dropout rate of 2.5 can dramatically bias OR estimation and reduce power even at a decent 98% overall call rate and moderate effect size (e.g., OR(true) = 2.11). Both of the two public datasets follow HWE; however, HapMap data carries detectable differential dropout that may endanger family-based studies. CONCLUSIONS: Case-control approach appears to be robust to differential dropout; however, family-based association tests can be heavily biased. Both of the public genotype data show high call rate, but differential dropout is detected in HapMap data. We suggest researchers carefully control this potential confounder even using data of high accuracy and high overall call rate.     

Restricted randomization designs in clinical trials.

Though therapeutic clinical trials are often categorized as using either "randomization" or "historical controls" as a basis for treatment evaluation, pure random assignment of treatments is rarely employed. Instead various restricted randomization designs are used. The restrictions include the balancing of treatment assignments over time and the stratification of the assignment with regard to covariates that may affect response. Restricted randomization designs for clinical trials differ from those of other experimental areas because patients arrive sequentially and a balanced design cannot be ensured. The major restricted randomization designs and arguments concerning the proper role of stratification are reviewed here. The effect of randomization restrictions on the validity of significance tests is discussed.     

The large sample bounds on the principal strata effect with application to a prostate cancer prevention trial

Issues of post-randomization selection bias and truncation-by-death can arise in randomized clinical trials; for example, in a cancer prevention trial, an outcome such as cancer severity is undefined for individuals who do not develop cancer. Restricting analysis to a subpopulation selected after randomization can give rise to biased outcome comparisons. One approach to deal with such issues is to consider the principal strata effect (PSE, or equally, the survivor average causal effect). PSE is defined as the effect of treatment on the outcome among the subpopulation that would have been selected under either treatment arm. Unfortunately, the PSE cannot generally be estimated without the identifying assumptions; however, the bounds can be derived using a deterministic causal model. In this paper, we propose a number of assumptions for deriving the bounds with narrow width. The assumptions and bounds, which differ from those introduced by Zhang and Rubin (2003), are illustrated using data from a randomized prostate cancer prevention trial.     

Pair-switching rerandomization

Rerandomization discards assignments with covariates unbalanced in the treatment and control groups to improve estimation and inference efficiency. However, the acceptance-rejection sampling method used in rerandomization is computationally inefficient. As a result, it is time-consuming for rerandomization to draw numerous independent assignments, which are necessary for performing Fisher randomization tests and constructing randomization-based confidence intervals. To address this problem, we propose a pair-switching rerandomization (PSRR) method to draw balanced assignments efficiently. We obtain the unbiasedness and variance reduction of the difference-in-means estimator and show that the Fisher randomization tests are valid under PSRR. Moreover, we propose an exact approach to invert Fisher randomization tests to confidence intervals, which is faster than the existing methods. In addition, our method is applicable to both nonsequentially and sequentially randomized experiments. We conduct comprehensive simulation studies to compare the finite-sample performance of the proposed method with that of classical rerandomization. Simulation results indicate that PSRR leads to comparable power of Fisher randomization tests and is 3–23 times faster than classical rerandomization. Finally, we apply the PSRR method to analyze two clinical trial datasets, both of which demonstrate the advantages of our method.     

Disruptive contrast in animal camouflage

Camouflage typically involves colour patterns that match the background. However, it has been argued that concealment may be achieved by strategic use of apparently conspicuous markings. Recent evidence supports the theory that the presence of contrasting patterns placed peripherally on an animal's body (disruptive coloration) provides survival advantages. However, no study has tested a key prediction from the early literature that disruptive coloration is effective even when some colour patches do not match the background and have a high contrast with both the background and adjacent pattern elements (disruptive contrast). We test this counter-intuitive idea that conspicuous patterns might aid concealment, using artificial moth-like targets with pattern elements designed to match or mismatch the average luminance (lightness) of the trees on which they were placed. Disruptive coloration was less effective when some pattern elements did not match the background luminance. However, even non-background-matching disruptive patterns reduced predation relative to equivalent non-disruptive patterns or to unpatterned controls. Therefore, concealment may still be achieved even when an animal possesses markings not found in the background. Disruptive coloration may allow animals to exploit backgrounds on which they are not perfectly matched, and to possess conspicuous markings while still retaining a degree of camouflage.     

Sensitivity analyses comparing outcomes only existing in a subset selected post-randomization, conditional on covariates, with application to HIV vaccine trials

In many experiments, researchers would like to compare between treatments and outcome that only exists in a subset of participants selected after randomization. For example, in preventive HIV vaccine efficacy trials it is of interest to determine whether randomization to vaccine causes lower HIV viral load, a quantity that only exists in participants who acquire HIV. To make a causal comparison and account for potential selection bias we propose a sensitivity analysis following the principal stratification framework set forth by Frangakis and Rubin (2002, Biometrics58, 21-29). Our goal is to assess the average causal effect of treatment assignment on viral load at a given baseline covariate level in the always infected principal stratum (those who would have been infected whether they had been assigned to vaccine or placebo). We assume stable unit treatment values (SUTVA), randomization, and that subjects randomized to the vaccine arm who became infected would also have become infected if randomized to the placebo arm (monotonicity). It is not known which of those subjects infected in the placebo arm are in the always infected principal stratum, but this can be modeled conditional on covariates, the observed viral load, and a specified sensitivity parameter. Under parametric regression models for viral load, we obtain maximum likelihood estimates of the average causal effect conditional on covariates and the sensitivity parameter. We apply our methods to the world's first phase III HIV vaccine trial.     

On the assignment of fitness to parents and offspring: whose fitness is it and when does it matter?

There has been a long‐standing conceptual debate over the legitimacy of assigning components of offspring fitness to parents for purposes of evolutionary analysis. The benefits and risks inherent in assigning fitness of offspring to parents have been given primarily as verbal arguments and no explicit theoretical analyses have examined quantitatively how the assignment of fitness can affect evolutionary inferences. Using a simple quantitative genetic model, we contrast the conclusions drawn about how selection acts on a maternal character when components of offspring fitness (such as early survival) are assigned to parents vs. when they are assigned directly to the individual offspring. We find that there are potential shortcomings of both possible assignments of fitness. In general, whenever there is a genetic correlation between the parental and direct effects on offspring fitness, assigning components of offspring fitness to parents yields incorrect dynamical equations and may even lead to incorrect conclusions about the direction of evolution. Assignment of offspring fitness to parents may also produce incorrect estimates of selection whenever environmental variation contributes to variance of the maternal trait. Whereas assignment of offspring fitness to the offspring avoids these potential problems, it introduces the possible problem of missing components of kin selection provided by the mother, which may not be detected in selection analyses. There are also certain conditions where either model can be appropriate because assignment of offspring fitness to parents may yield the same dynamical equations as assigning offspring fitness directly to offspring. We discuss these implications of the alternative assignments of fitness for modelling, selection analysis and experimentation in evolutionary biology.     

Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIV vaccine trials

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002, Biometrics 58, 21-29), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.     

Host concealment: a determinant for host acceptance and feeding in an ectoparasitoid wasp

In general, ectoparasitoids attack concealed hosts in protected situations whereas endoparasitoids use both concealed and exposed hosts. The difference is assumed to be the consequence of ecological constraints; ectoparasitic larvae are vulnerable both to predation and to climatic factors such as rainfall, and, hence, require some structures to protect themselves. I hypothesized that such ecological constraints should act as a within-species selection pressure to female ectoparasitoids, and hence that females should recognize the degree of concealment of the host and prefer concealed over exposed hosts for oviposition. To test this hypothesis, I examined 1) whether host concealment could influence host acceptance by the ectoparasitoid wasp Agrothereutes lanceolatus and 2) whether host concealment could influence the fitness of the offspring. Female wasps recognized and attacked (probed) both cocooned and exposed host prepupae in equal proportions, but discriminated between them after ovipositor insertion, and preferred the former for oviposition and the latter for host-feeding. They also selected to oviposit on hosts concealed in paper tubes. Thus host concealment was important for host selection in A. lanceolatus . Offspring fitness (measured as survival and size) was much lower on exposed hosts than on cocooned and paper-concealed hosts, even under laboratory conditions. Thus, host concealment influenced the fitness of wasp offspring, and, hence, is a good indicator of host quality for female wasps. Adaptiveness of host selection and host-feeding in A. lanceolatus in relation to host concealment is discussed.     

Sensitivity analyses comparing time-to-event outcomes only existing in a subset selected postrandomization and relaxing monotonicity

In randomized studies researchers may be interested in the effect of treatment assignment on a time-to-event outcome that only exists in a subset selected after randomization. For example, in preventative HIV vaccine trials, it is of interest to determine whether randomization to vaccine affects the time from infection diagnosis until initiation of antiretroviral therapy. Earlier work assessed the effect of treatment on outcome among the principal stratum of individuals who would have been selected regardless of treatment assignment. These studies assumed monotonicity, that one of the principal strata was empty (e.g., every person infected in the vaccine arm would have been infected if randomized to placebo). Here, we present a sensitivity analysis approach for relaxing monotonicity with a time-to-event outcome. We also consider scenarios where selection is unknown for some subjects because of noninformative censoring (e.g., infection status k years after randomization is unknown for some because of staggered study entry). We illustrate our method using data from an HIV vaccine trial.