The placebo response in clinical trials: more questions than answers

Meta-analyses and re-analyses of trial data have not been able to answer some of the essential questions that would allow prediction of placebo responses in clinical trials. We will confront these questions with current empirical evidence. The most important question asks whether the placebo response rates in the drug arm and in the placebo arm are equal. This 'additive model' is a general assumption in almost all placebo-controlled drug trials but has rarely been tested. Secondly, we would like to address whether the placebo response is a function of the likelihood of receiving drug/placebo. Evidence suggests that the number of study arms in a trial may determine the size of the placebo and the drug response. Thirdly, we ask what the size of the placebo response is in 'comparator' studies with a direct comparison of a (novel) drug against another drug. Meta-analytic and experimental evidence suggests that comparator studies may produce higher placebo response rates when compared with placebo-controlled trials. Finally, we address the placebo response rate outside the laboratory and outside of trials in clinical routine. This question poses a serious challenge whether the drug response in trials can be taken as evidence of drug effects in clinical routine.     

Risk of hospitalization among survivors of childhood and adolescent acute lymphoblastic leukemia compared to siblings and a general population sample

BackgroundAcute Lymphoblastic Leukemia (ALL) has a high survival rate, but cancer-related late effects in the early post-treatment years need documentation. Hospitalizations are an indicator of the burden of late effects. We identify rates and risk factors for hospitalization from five to ten years after diagnosis for childhood and adolescent ALL survivors compared to siblings and a matched population sample.Methods176 ALL survivors were diagnosed at ≤22 years between 1998 and 2008 and treated at an Intermountain Healthcare facility. The Utah Population Database identified siblings, an age- and sex-matched sample of the Utah population, and statewide inpatient hospital discharges. Sex- and birth year-adjusted Poisson models with Generalized Estimating Equations and robust standard errors calculated rates and rate ratios. Cox proportional hazards models identified demographic and clinical risk factors for hospitalizations among survivors.ResultsHospitalization rates for survivors (Rate:3.76, 95% CI = 2.22–6.36) were higher than siblings (Rate:2.69, 95% CI = 1.01–7.18) and the population sample (Rate:1.87, 95% CI = 1.13–3.09). Compared to siblings and population comparisons, rate ratios (RR) were significantly higher for survivors diagnosed between age 6 and 22 years (RR:2.87, 95% CI = 1.03–7.97 vs siblings; RR:2.66, 95% CI = 1.17–6.04 vs population comparisons). Rate ratios for diagnosis between 2004 and 2008 were significantly higher compared to the population sample (RR:4.29, 95% CI = 1.49, 12.32), but not siblings (RR:2.73, 95% CI = 0.54, 13.68). Survivors originally diagnosed with high-risk ALL did not have a significantly higher risk than siblings or population comparators. However, high-risk ALL survivors (Hazard ratio [HR]:3.36, 95% CI = 1.33–8.45) and survivors diagnosed from 2004 to 2008 (HR:9.48, 95% CI = 1.93–46.59) had the highest risk compared to their survivor counterparts.ConclusionsFive to ten years after diagnosis is a sensitive time period for hospitalizations in the ALL population. Survivors of childhood ALL require better long-term surveillance.     

Sex-biased genetic component distribution among populations: additive genetic and maternal contributions to phenotypic differences among populations of Chinook salmon

An approach frequently used to demonstrate a genetic basis for population-level phenotypic differences is to employ common garden rearing designs, where observed differences are assumed to be attributable to primarily additive genetic effects. Here, in two common garden experiments, we employed factorial breeding designs between wild and domestic, and among wild populations of Chinook salmon (Oncorhynchus tshawytscha). We measured the contribution of additive (V(A)) and maternal (V(M)) effects to the observed population differences for 17 life history and fitness-related traits. Our results show that, in general, maternal effects contribute more to phenotypic differences among populations than additive genetic effects. These results suggest that maternal effects are important in population phenotypic differentiation and also signify that the inclusion of the maternal source of variation is critical when employing models to test population differences in salmon, such as in local adaptation studies.     

Osteochondral tissue engineering: scaffolds, stem cells and applications

Osteochondral tissue engineering has shown an increasing development to provide suitable strategies for the regeneration of damaged cartilage and underlying subchondral bone tissue. For reasons of the limitation in the capacity of articular cartilage to self-repair, it is essential to develop approaches based on suitable scaffolds made of appropriate engineered biomaterials. The combination of biodegradable polymers and bioactive ceramics in a variety of composite structures is promising in this area, whereby the fabrication methods, associated cells and signalling factors determine the success of the strategies. The objective of this review is to present and discuss approaches being proposed in osteochondral tissue engineering, which are focused on the application of various materials forming bilayered composite scaffolds, including polymers and ceramics, discussing the variety of scaffold designs and fabrication methods being developed. Additionally, cell sources and biological protein incorporation methods are discussed, addressing their interaction with scaffolds and highlighting the potential for creating a new generation of bilayered composite scaffolds that can mimic the native interfacial tissue properties, and are able to adapt to the biological environment.     

A Variance Components Model for Analyzing Repeated Measures Designs with Non-Stationary Error Structure

When data are collected in the form of multiple measurements on several subjects, they are often analyzed as repeated measures data with some stationary error structure assumed for the errors. For data with non-stationary error structure, the multivariate model is often used. The multivariate model imposes restrictions that are often not met in practice by data of such type. At the same time, they ignore valuable information in the data that are related to time dependencies and time relations. In this paper, we propose a model that is a reparametrization of the multivariate model and is suitable to analyze general repeated measures designs with non-stationary error structure. The model is shown to be a variance components model whose components are estimated using the method of maximum likelihood. Several other properties of the model are derived and discussed including tests of significance. Finally, an example on neurological data is included to demonstrate its application in biological sciences.     

Assessing exposure-time treatment effect heterogeneity in stepped-wedge cluster randomized trials

A stepped-wedge cluster randomized trial (CRT) is a unidirectional crossover study in which timings of treatment initiation for clusters are randomized. Because the timing of treatment initiation is different for each cluster, an emerging question is whether the treatment effect depends on the exposure time, namely, the time duration since the initiation of treatment. Existing approaches for assessing exposure-time treatment effect heterogeneity either assume a parametric functional form of exposure time or model the exposure time as a categorical variable, in which case the number of parameters increases with the number of exposure-time periods, leading to a potential loss in efficiency. In this article, we propose a new model formulation for assessing treatment effect heterogeneity over exposure time. Rather than a categorical term for each level of exposure time, the proposed model includes a random effect to represent varying treatment effects by exposure time. This allows for pooling information across exposure-time periods and may result in more precise average and exposure-time-specific treatment effect estimates. In addition, we develop an accompanying permutation test for the variance component of the heterogeneous treatment effect parameters. We conduct simulation studies to compare the proposed model and permutation test to alternative methods to elucidate their finite-sample operating characteristics, and to generate practical guidance on model choices for assessing exposure-time treatment effect heterogeneity in stepped-wedge CRTs.     

STATISTICAL OPTIMIZATION OF A NOVEL LOW-COST MEDIUM BASED ON REGIONAL AGRO-INDUSTRIAL BY-PRODUCTS FOR THE PRODUCTION OF PROTEOLYTIC ENZYMES BY Bacillus cereus

Bacillus sp. are specific producers of peptidase amongst bacteria and peptidase enzymes and are of significant ones due to their multifarious applications. Advances in industrial biotechnology offer potential opportunities for economic utilization of agro-industrial by-products for many biochemical reactions. Due to their rich organic nature, they can serve as an ideal substrate for the production of different value added products like peptidases. In the present work, an attempt was made to optimize different variables by Taguchi methodology for the production of peptidase using agro-industrial by-products hydrolyzed by a Bacillus cereus strain, resulting in brewer's spent grain (BSG) being the optimal organic substrate. Subsequently, operative variables for the BSG were investigated using Taguchi methodology in order to maximize the enzyme production. Additionally, the main medium components were optimized using a mixture design. Finally, the production of peptidase by B. cereus was investigated; also the possible interaction with other proteolytic microbial strains was evaluated. A notorious synergistic effect was observed when B. cereus was inoculated with Pseudomonas sp. These brought a triple benefit, first, opening the possibility to produce technical enzymes at low cost, second, giving greater value to a food industry by-product, and third, reducing the environmental impact caused by the product removal directly into the environment.     

Comparative genetic analyses of metric traits using diallel and factorial mating designs in bread wheat

Summary For studying the inheritance of metric traits, diallel cross and factorial mating designs are commonly used. Since factorial mating design is less restrictive in crossing plans, the genetic information drawn from it was compared with that from a diallel cross. The comparison was made using graphical, genetic components and combining ability analyses for grain yield, grain weight and spike length in a field experiment of bread wheat (Triticum aestivum L.). Analyses were made on a nine parent diallel cross and a 4 × 5 factorial mating design which was sampled from the diallel cross. In general, there was a high degree of agreement between the results obtained from factorial mating design and diallel cross analyses showing thereby that the former provides almost equivalent genetic information to the latter.     

Non-Iterative Least Squares Estimation of Missing Values in Replicated Latin Square Designs

In this paper an attempt has been made to estimate several missing values in replicated latin square designs. The explicit computable expressions for the non-iterative least squares estimates of the missing values are presented for particular patterns of missing values.