The use of fixed study main effects in arm-based network meta-analysis.

Methods of network meta-analysis (NMA) can be classified as arm-based and contrast-based approaches. There are several arm-based approaches, and some of these have been criticized because they recover inter-study information and hence do not obey the principle of concurrent control. Here, we point out that recovery of inter-study information in arm-based NMA can be prevented by fitting a fixed main effect for studies. Advantages of arm-based NMA are discussed.

Spot It! and balanced block designs: keys to better debate architecture for a plethora of candidates in presidential primaries?

U.S. presidential primary debates are influential but under-researched. Before 2015, all of these debates, both Democratic and Republican, had 10 candidates or fewer. The first Republican debate in 2015, however, abided 17 candidates. They were split into two segments, with the 10 best-polling candidates in the main (prime-time) segment and the others in an 'undercard' session. A comparable pattern applied for the next six Republican debates. Concern arose not only because many candidates were crowded into a session but also because the undercard candidates were seen as receiving inferior exposure. The Democratic presidential primary debates that started four years later encountered similar difficulty. An authorized policy caused their candidates in each of the first two debates to be limited to 20, randomly divided into two groups of 10 appearing on successive nights. For remedy, this paper examines innovative debate plans, for different numbers of candidates, that feature symmetry among all candidates and entail many short segments with relatively few candidates in each. We apply combinatorial designs-balanced incomplete block designs and regular pairwise balanced designs, which are analogous to the games Spot It Jr.! Animals and (full-fledged) Spot It!, respectively.

Innovative Design and Analysis for PK/PD Biosimilar Bridging Studies with Multiple References.

When there are multiple reference products, (e.g., EU-approved product and US-licensed product), a pharmacokinetic/pharmacodynamic (PK/PD) bridging study is often conducted in order to bridge the clinical data from the original region (e.g., Europe) to the new region (e.g., USA) in support of the biosimilar regulatory submission in the new region. The purpose is to avoid duplicated clinical trials for clinical similarity between a proposed biosimilar product and the reference product in the new region provided that there is no ethnic concern in the two regions. In this article, some innovative statistical designs for PK/PD biosimilar bridging studies are proposed. Statistical model and methods under the proposed statistical designs are studied. Power analysis for sample size requirement based on Schuirmann's two one-sided tests procedure is also derived and compared to pairwise testing using simulation.

Assigning readers to cases in imaging studies using balanced incomplete block designs.

In many imaging studies, each case is reviewed by human readers and characterized according to one or more features. Often, the inter-reader agreement of the feature indications is of interest in addition to their diagnostic accuracy or association with clinical outcomes. Complete designs in which all participating readers review all cases maximize efficiency and guarantee estimability of agreement metrics for all pairs of readers but often involve a heavy reading burden. Assigning readers to cases using balanced incomplete block designs substantially reduces reading burden by having each reader review only a subset of cases, while still maintaining estimability of inter-reader agreement for all pairs of readers. Methodology for data analysis and power and sample size calculations under balanced incomplete block designs is presented and applied to simulation studies and an actual example. Simulation studies results suggest that such designs may reduce reading burdens by >40% while in most scenarios incurring a <20% increase in the standard errors and a <8% and <20% reduction in power to detect between-modality differences in diagnostic accuracy and κ statistics, respectively.

An algorithm to minimize the number of blocks in incomplete block designs.

Incomplete block designs are experimental designs in which a subset of treatments are included in each block. The researcher must decide which conditions are assigned to each block. This design concept is quite general. At the level of the experiment, a treatment is a condition in an experiment, blocks are different groups of subjects, and the researcher must decide how to assign a subset of conditions to each block of subjects. At the level of the subject, the treatments correspond to individual stimuli, blocks correspond to experimental trials, and the researcher must decide which subset of stimuli to include in each trial. In this article, we present an efficient algorithm that assigns treatments to blocks in an incomplete block design according to two criteria: Each pair of treatments must appear together in at least one block, and the number of blocks in the experiment is minimized. We discuss details and applications of the algorithm and provide software and a web application to generate designs according to the needs of the researcher.

Efficient baseline utilization for incomplete block crossover clinical trials.

Incomplete block crossover trials with period-specific baseline and post-baseline (outcome) measures for each subject are often used in clinical drug development; without loss of generality, we focus on the three-treatment two-period ( 3×2 ) crossover. Data from such trials are commonly analyzed using a mixed effects model with indicator terms for treatment and period, and an unstructured covariance matrix for the vector of intra-subject measurements. It is well-known that treatment effect estimates from this analysis are complex functions of both within-subject and between-subject treatment contrasts. We caution that the associated type I error rate and power for hypothesis testing can be non-trivially influenced by how the baselines are utilized. Specifically, the mixed effects analysis which uses change from baseline as the dependent variable is shown to consistently underperform corresponding analyses in which the outcome is the dependent variable and linear combinations of the baselines are used as period-specific and/or period-invariant covariates. A simpler fixed effects analysis of covariance involving only within-subject contrasts is also described for small sample situations in which the mixed effects analyses can suffer from increased type I error rates. Theoretical insights, simulation results and an illustrative example with real data are used to develop the main points.

Improving the Assessment of Differential Item Functioning in Large-Scale Programs With Dual-Scale Purification of Rasch Models: The PISA Example.

By design, large-scale educational testing programs often have a large proportion of missing data. Since the effect of missing data on differential item functioning (DIF) assessment has been investigated in recent years and it has been found that Type I error rates tend to be inflated, it is of great importance to adapt existing DIF assessment methods to the inflation. The DIF-free-then-DIF (DFTD) strategy, which originally involved one single-scale purification procedure to identify DIF-free items, has been extended to involve another scale purification procedure for the DIF assessment in this study, and this new method is called the dual-scale purification (DSP) procedure. The performance of the DSP procedure in assessing DIF in large-scale programs, such as Program for International Student Assessment (PISA), was compared with the DFTD strategy through a series of simulation studies. Results showed the superiority of the DSP procedure over the DFTD strategy when tests consisted of many DIF items and when data were missing by design as in large-scale programs. Moreover, an empirical study of the PISA 2009 Taiwan sample was provided to show the implications of the DSP procedure. The applications as well as further studies of DSP procedure are also discussed.

A Key Pre-Distribution Scheme Based on µ-PBIBD for Enhancing Resilience in Wireless Sensor Networks.

Many key pre-distribution (KPD) schemes based on combinatorial design were proposed for secure communication of wireless sensor networks (WSNs). Due to complexity of constructing the combinatorial design, it is infeasible to generate key rings using the corresponding combinatorial design in large scale deployment of WSNs. In this paper, we present a definition of new combinatorial design, termed “µ-partially balanced incomplete block design (µ-PBIBD)”, which is a refinement of partially balanced incomplete block design (PBIBD), and then describe a 2-D construction of µ-PBIBD which is mapped to KPD in WSNs. Our approach is of simple construction which provides a strong key connectivity and a poor network resilience. To improve the network resilience of KPD based on 2-D µ-PBIBD, we propose a KPD scheme based on 3-D Ex-µ-PBIBD which is a construction of µ-PBIBD from 2-D space to 3-D space. Ex-µ-PBIBD KPD scheme improves network scalability and resilience while has better key connectivity. Theoretical analysis and comparison with the related schemes show that key pre-distribution scheme based on Ex-µ-PBIBD provides high network resilience and better key scalability, while it achieves a trade-off between network resilience and network connectivity.

Model-free tests of equality in binary data under an incomplete block design.

Using Prescott's model-free approach, we develop an asymptotic procedure and an exact procedure for testing equality between treatments with binary responses under an incomplete block crossover design. We employ Monte Carlo simulation and note that these test procedures can not only perform well in small-sample cases but also outperform the corresponding test procedures accounting for only patients with discordant responses published elsewhere. We use the data taken as a part of the crossover trial comparing two different doses of an analgesic with placebo for the relief of primary dysmenorrhea to illustrate the use of test procedures discussed here.

Exact tests in binary data under an incomplete block crossover design.

To improve the power of a parallel groups design and reduce the time length of a crossover trial, we may consider an incomplete block crossover design. Under a distribution-free random effects logistic regression model, we derive an exact test and a Mantel-Haenszel Type of summary test procedure for testing non-equality in binary data when comparing three treatments. We employ Monte Carlo simulation to evaluate the performance of these test procedures. We find that both test procedures developed here can perform well in a variety of situations. We use the data taken as a part of the crossover trial comparing the low and high doses of an analgesic with a placebo for the relief of pain in primary dysmenorrhea to illustrate the use of the proposed test procedures.

Testing Equality of Treatments under an Incomplete Block Crossover Design with Ordinal Responses.

The generalized odds ratio (GOR) for paired sample is considered to measure the relative treatment effect on patient responses in ordinal data. Under a three-treatment two-period incomplete block crossover design, both asymptotic and exact procedures are developed for testing equality between treatments with ordinal responses. Monte Carlo simulation is employed to evaluate and compare the finite-sample performance of these test procedures. A discussion on advantages and disadvantages of the proposed test procedures based on the GOR versus those based on Wald's tests under the normal random effects proportional odds model is provided. The data taken as a part of a crossover trial studying the effects of low and high doses of an analgesic versus a placebo for the relief of pain in primary dysmenorrhea over the first two periods are applied to illustrate the use of these test procedures.

Estimation of the treatment effect under an incomplete block crossover design in binary data - A conditional likelihood approach.

A random effects logistic regression model is proposed for an incomplete block crossover trial comparing three treatments when the underlying patient response is dichotomous. On the basis of the conditional distributions, the conditional maximum likelihood estimator for the relative effect between treatments and its estimated asymptotic standard error are derived. Asymptotic interval estimator and exact interval estimator are also developed. Monte Carlo simulation is used to evaluate the performance of these estimators. Both asymptotic and exact interval estimators are found to perform well in a variety of situations. When the number of patients is small, the exact interval estimator with assuring the coverage probability larger than or equal to the desired confidence level can be especially of use. The data taken from a crossover trial comparing the low and high doses of an analgesic with a placebo for the relief of pain in primary dysmenorrhea are used to illustrate the use of estimators and the potential usefulness of the incomplete block crossover design.

Effects of Design Properties on Parameter Estimation in Large-Scale Assessments.

The selection of an appropriate booklet design is an important element of large-scale assessments of student achievement. Two design properties that are typically optimized are the balance with respect to the positions the items are presented and with respect to the mutual occurrence of pairs of items in the same booklet. The purpose of this study is to investigate the effects of these two design properties on bias and root mean square error of item parameter estimates from the Rasch model. First, position effects are estimated using data from a large-scale assessment study measuring the competencies of 19,107 ninth graders in science. These results were then used for a simulation study with 1,540 booklet designs with systematically varied position balance and cluster pair balance. The simulation results showed a small effect of position balancing on bias and root mean square error of the item parameter estimates while the cluster pair balance was ignorable. This null effect is actually good news for test designers since it allows for deliberately reducing the degree of cluster pair balance without negative effects on item parameter estimates. However, it is recommended to try to achieve a high position balance when designing large-scale assessment studies.

Test Equality between Three Treatments under an Incomplete Block Crossover Design.

Under a random effects linear additive risk model, we compare two experimental treatments with a placebo in continuous data under an incomplete block crossover trial. We develop three test procedures for simultaneously testing equality between two experimental treatments and a placebo, as well as interval estimators for the mean difference between treatments. We apply Monte Carlo simulations to evaluate the performance of these test procedures and interval estimators in a variety of situations. We note that the bivariate test procedure accounting for the dependence structure based on the F-test is preferable to the other two procedures when there is only one of the two experimental treatments has a non-zero effect vs. the placebo. We note further that when the effects of the two experimental treatments vs. a placebo are in the same relative directions and are approximately of equal magnitude, the summary test procedure based on a simple average of two weighted-least-squares (WLS) estimators can outperform the other two procedures with respect to power. When one of the two experimental treatments has a relatively large effect vs. the placebo, the univariate test procedure with using Bonferroni's equality can be still of use. Finally, we use the data about the forced expiratory volume in 1 s (FEV1) readings taken from a double-blind crossover trial comparing two different doses of formoterol with a placebo to illustrate the use of test procedures and interval estimators proposed here.