Manufacturers' compliance with the US Food and Drug Administration's Over‐the‐counter Human Drugs: Labeling Requirements

The use of over‐the‐counter (OTC) medications is increasing every year among consumers for symptomatic relief of ailments. Consumers have difficulties in reading and comprehending information on the OTC medication packages. A standardized format to present information on OTC medication packages was necessary; hence the FDA announced guidelines, Over‐the‐counter Human Drugs: Labeling Requirements, on 11 March 1999. Manufacturers have been given 6 years since 1999 to comply with the FDA guidelines. The objective of our study was to evaluate marketed OTC medication packages to determine the extent of adoption of the FDA guidelines by manufacturers. Packages (n ± 104) of OTC medication products from three different categories, viz. pain relievers, cough and cold remedies and stomach remedies, were evaluated for general information, written information and product claims. The Compugraphic point system scale was used to measure the font size of written information. In addition variables recommended by the FDA, such as bullets, paragraphs and hyphenations, were also evaluated. All the variables were coded in a database and analysed using the SAS (Version 8.2) statistical package. The study found that some manufacturers have adopted the FDA guidelines. However, many manufacturers did not implement several aspects of the guidelines. Although manufacturers still have a few more years to adhere to the guidelines, early implementation could benefit the consumers. Copyright © 2003 John Wiley & Sons, Ltd.     

Review of Package Warning Labels and Their Effect on Consumer Behaviour with Insights to Future Anticounterfeit Strategy of Label and Communication Systems

This paper investigates and reviews literature regarding a range of topics important to the research question – the themes apply across all the topics. The topics include label and package warnings, including tamper‐evident features, to review their effect on the consumer behaviour of changing usage or purchase decisions. This information was then applied to gain insight into anticounterfeit product/package efforts. Based on this research, it appears that there is value for brand marketers to enhance their anticounterfeit efforts and consumer confidence by utilizing warning labels and information labels on their labels or packages. Currently, consumer perception of a domestic counterfeit product threat (promoted as genuine products), even for pharmaceuticals by healthcare professionals, although increasing has still been very low. Generally, the consumers believe and expect that the product purchased from retailers is safe and genuine. The study reviewed many key warning and label communication areas: consumer behaviour review, technical specifications, anticounterfeit, tamper evidence/tamper resistance, over‐the‐counter drugs, alcohol, cigarette, nutrition food and nutrition supplements. To complete the insight on communicating with the consumer, a review of label legibility was included. Before brand marketers consider adding anticounterfeit warning labels, extensive consumer behaviour research should be conducted in areas such as fear appeals and involuntary risk outrage. Copyright © 2011 John Wiley & Sons, Ltd.     

Orthogonal second‐order space‐filling designs with insights from simulation experiments to support test planning

Frequency‐based designs are presented for exploring large numbers of factors in simulation experiments. This approach yields completely orthogonal full second‐order space‐filling designs. We describe how they are generated, explore their space‐filling properties, and compare their performance to other designs of similar sizes. We illustrate their use for test planning on a simulation model of a live counter‐IED (improved explosive device) test event and present some ideas about ways in which simulation experiments can be used to support planning for live tests.     

Separation in D‐optimal experimental designs for the logistic regression model

The D‐optimality criterion is often used in computer‐generated experimental designs when the response of interest is binary, such as when the attribute of interest can be categorized as pass or fail. The majority of methods in the generation of D‐optimal designs focus on logistic regression as the base model for relating a set of experimental factors with the binary response. Despite the advances in computational algorithms for calculating D‐optimal designs for the logistic regression model, very few have acknowledged the problem of separation, a phenomenon where the responses are perfectly separable by a hyperplane in the design space. Separation causes one or more parameters of the logistic regression model to be inestimable via maximum likelihood estimation. The objective of this paper is to investigate the tendency of computer‐generated, nonsequential D‐optimal designs to yield separation in small‐sample experimental data. Sets of local D‐optimal and Bayesian D‐optimal designs with different run (sample) sizes are generated for several “ground truth” logistic regression models. A Monte Carlo simulation methodology is then used to estimate the probability of separation for each design. Results of the simulation study confirm that separation occurs frequently in small‐sample data and that separation is more likely to occur when the ground truth model has interaction and quadratic terms. Finally, the paper illustrates that different designs with identical run sizes created from the same model can have significantly different chances of encountering separation.     

High Electrocatalytic and Wettable Nitrogen‐Doped Microwave‐Exfoliated Graphene Nanosheets as Counter Electrode for Dye‐Sensitized Solar Cells

In this paper, high electrocatalytic and wettable nitrogen‐doped microwave‐exfoliated graphene (N‐MEG) nanosheets are used as Pt‐free counter electrode (CE) for dye‐sensitized solar cells (DSSCs). A low cost solution‐based process is developed by using cyanamide (NH₂CN) at room temperature and normal pressure. The pyrrolic and pyridinic N atoms are doped into the carbon conjugated lattice to enhance electrocatalytic activity. N‐MEG film having N‐doping active sites and large porosity provides a wettable surface to facilitate electrolyte diffusion so that improves fill factor. Moreover, the control of the air exposure time after completing N‐MEG film is found to be crucial to obtain a reliable N‐MEG CE. A high DSSC efficiency up to 7.18% can be achieved based on N‐MEG CE, which is nearly comparable to conventional Pt CE.     

Measurement and analysis of the second‐day air small and light‐weight package shipping environment within Federal Express

This study measured and analysed the parcel shipping environment within Federal Express. The package sizes and weights monitored in this study represent the ‘light‐weight’ and 'small‐size' packages within the FedEx handling system. Five packages, ranging in size from 0.18 × 0.19 × 0.13 m to 0.36 × 0.37 × 0.31 m, and weighing 1.9–2.5 kg, were shipped, both with and without warning labels with the text ‘Fragile – Handle With Care’. The shipments were done between Michigan and California or Florida using the second‐day air shipment mode. The data showed that neither the package size/weight nor the labels had any significant effect on the severity of drop heights. The highest drop height measured was 1.85 m. The data is presented in terms of drop heights associated with the 90, 95 and 99th percentile of occurrence. Impact orientation is also discussed. Copyright © 2004 John Wiley & Sons, Ltd.     

Investigation of the effect of column stacked corrugated boxes on load bridging using partial four‐way stringer class wooden pallets

Pallets are the foundation of the global packaging supply chain. They provide a way to store and transport products in an efficient manner. The load capacity of pallets greatly depends on the type of packages carried by the pallet; however, current pallet design methods do not consider the effect of packages on the load carrying capacity of the pallet. This results in excessive use of materials which reduces the sustainability of unit loads, drives costs up, and creates potential safety issues. The objective of this study was to investigate the effect of corrugated box size and headspace on pallet deflection and stress distribution on the top of the pallet as a function of pallet stiffness across multiple pallet support conditions. Data analysis identified that box size had a significant effect on the deflection of the pallet. This effect was only significant for warehouse racking across the width and length support conditions. As much as a 53% reduction in pallet deflection was observed for high stiffness pallets supporting corrugated boxes with 25.4‐mm headspace when the size was increased from small to large. The redistribution of vertical compression stresses towards the supports as a function of the increasing box size was observed. The increased concentration of compression stresses on top of the supports and the resulting lower pallet deflection could significantly increase the actual load carrying capacity of some pallet designs. The effect of box headspace was significant in some scenarios but inconsistent; thus, more investigation with a larger sample size is recommended.     

Munchy Monster: Using video gaming to objectively evaluate front‐of‐pack labelling strategies for school‐aged children

Marketers have long targeted children in an attempt to influence food purchases. This is not the case for regulators; nutrition information is complex, using decimal places, percentages, and units of measure. It has been suggested that the approach to nutrition labelling in the United States is difficult for some adults to interpret, let alone children. This is unfortunate as children influence purchase decisions and childhood is a critical time for developing long‐lasting eating habits. An alternative approach to traditional nutrition labelling employs the use of front‐of‐pack (FOP) nutrition labels. FOPs provide simplified, truncated nutrition information on the front of packages. The objective of this work was to evaluate how four different FOP label designs impact the ability of children to assess product healthfulness and time to assessment. Children aged 6 to 10 played a video game where they fed “Munchy Monster” the healthier of two products. The principal display panels (PDPs) of two mock brands of cereal appeared together on a computer screen, and children were instructed to feed Munchy Monster the healthier of the two options as quickly as they could by pressing one of two arrows. Across trials, the FOP format varied in a 2 (colour/no colour) × 2 (facial icon/no facial icon) factorial design. Within a trial, both cereals presented the same FOP format, with one healthier than the other. Two groups of children participated in trials; those in the uninstructed group were simply asked to feed the monster the healthier cereal (n = 38); the “minimally instructed group” (n = 41) was told that “this part of the package” (the FOP) might help you decide which is healthier.” Accuracy of selection and time to selection were dependent variables. With regard to accuracy, both groups showed evidence of a significant face by colour interaction (P < .001), with the colour or facial icon presence improving accuracy. For uninstructed participants, accuracy of selection significantly improved with any combination of colour or facial icon, and all other labels were improved when accuracy was compared with the treatments with no face/no colour, but none containing colour and/or facial icons differed from one another. Minimally instructed participants were also more accurate in identifying the healthier product for all FOP label designs compared with the no face/no colour condition (P < .001). However, the trials with FOPs including both face with colour also performed better than the face with no colour label, P = .001. A main effect of colour was evident for both groups when time to correct selection was the dependent variable (α = .01). Results demonstrated that colour coding and/or facial icons significantly benefit selection accuracy and speed, particularly for the youngest children. Minimal training further improved accuracy and speed of responses. FOPs that leverage visual indicators assist even young children in assessing the nutritional value of a product. This should be considered as FOPs are debated and standard practices regarding these labels emerge.     

Cost‐penalized estimation and prediction evaluation for split‐plot designs

Comparisons between different designs have traditionally focused on balancing the quality of estimation or prediction relative to the overall size of the design. For split‐plot designs with two levels of randomization, the total number of observations may not accurately summarize the true cost of the experiment, because different costs are likely associated with setting up the whole and subplot levels. In this paper, we present several flexible measures for design assessment based on D‐, G‐ and V‐optimality criteria that take into account potentially different cost structures for the split‐plot designs. The new measures are illustrated with two examples: a 2³ factorial experiment for first‐order models, where all possible designs are considered, and selective designs for a three‐factor second‐order model. Copyright © 2006 John Wiley & Sons, Ltd.     

Nonparametric cost‐minimized Shewhart‐type process monitoring with restricted false alarm probability

Traditional Duncan‐type models for cost‐efficient process monitoring often inflate type I error probability. Nevertheless, controlling the probability of type I error or false alarms is one of the key issues in sequential monitoring of specific process characteristics. To this end, researchers often recommend economic‐statistical designs. Such designs assign an upper bound on type I error probability to avoid excessive false alarms while achieving cost optimality. In the context of process monitoring, there is a plethora of research on parametric approaches of controlling type I error probability along with the cost optimization. In the nonparametric setup, most of the existing works on process monitoring address one of the two issues but not both simultaneously. In this article, we present two distribution‐free cost‐efficient Shewhart‐type schemes for sequentially monitoring process location with restricted false alarm probability, based, respectively, on the sign and Wilcoxon rank‐sum statistics. We consider the one‐sided shift in location parameter in an unknown continuous univariate process. Nevertheless, one can easily extend our proposed schemes to monitor the two‐sided process shifts. We evaluate and compare the actual performance of the two monitoring schemes employing extensive computer simulation based on Monte Carlo. We investigate the effects of the size of the reference sample and the false alarm constraint. Finally, we provide two illustrative examples, each based on a realistic situation in the industry.     

Developing a model‐based sampling plan

Statistical process modeling is widely used in industry for forecasting the production outcomes, for process control and for process optimization. Applying a prediction model in a production process allows the user to calibrate/predict the mean of the distribution of the process outcomes and to partition the overall variation in the distribution of the process outcomes into explained (by the model) and unexplained (residuals) variations; thus, reducing the unexplained variability. The additional information about the process behavior can be used prior to the sampling procedure and may help to reduce the required sample size to classify a lot. This research focuses on the development of a model‐based sampling plan based ontextitCpk (process capability index). It is an extension of a multistage acceptance sampling plan also based on Cpk (Negrin et al., Quality Engineering 2009; 21 :306–318; Quality and Reliability Engineering International 2011; 27 :3–14). The advantage of this sampling plan is that the sample size needed depends directly and quantitatively on the quality of the process (Cpk), whereas other sampling plans such as MIL‐STD‐414 (Sampling Procedures and Tables for Inspection by Variables for Percent Defective, Department of Defense, Washington, DC, 1957.) use only qualitative measures. The objective of this paper is to further refine the needed sample size by using a predictive model for the lot's expectation. We developed model‐based sample size formulae which depend directly on the quality of the prediction model (as measured by R²) and adjust the ‘not model‐based’ multistage sampling plan developed in Negrin et al. (Quality Engineering 2009; 21 :306–318; Quality and Reliability Engineering International 2011; 27 :3–14) accordingly. A simulation study was conducted to compare between the model‐based and the ‘not model‐based’ sampling plans. It is found that when R² = 0, the model‐based and ‘not model‐based’ sampling plans require the same sample sizes in order to classify the lots. However, as R² becomes larger, the sample size required by the model‐based sampling plan becomes smaller than the one required by the ‘not model‐based’ sampling plan. In addition, it is found that the reduction of the sample size achieved by the model‐based sampling plan becomes more significant as Cpk tends to 1 and can be achieved without increasing the proportion of the classification errors. Finally, the suggested sampling plan was applied with areal data set from a chemicals manufacturing process for illustration. Copyright © 2011 John Wiley & Sons, Ltd.     

Considerations for D‐Optimal Sequential Design

Classical D‐optimal design is used to create experimental designs for situations in which an underlying system model is known or assumed known. The D‐optimal strategy can also be used to add additional experimental runs to an existing design. This paper demonstrates a study of variable choices related to sequential D‐optimal design and how those choices influence the D‐efficiency of the resulting complete design. The variables studied are total sample size, initial experimental design size, step size, whether or not to include center points in the initial design, and complexity of initial model assumption. The results indicate that increasing total sample size improves the D‐efficiency of the design, less effort should be placed in the initial design, especially when the true underlying system model isn't known, and it is better to start off with assuming a simpler model form, rather than a complex model, assuming that the experimenter can reach the true model form during the sequential experiments. Copyright © 2013 John Wiley & Sons, Ltd.     

Three‐level and mixed‐level orthogonal arrays for lean designs

Orthogonal arrays (OA's) are widely used in design of experiments. Each OA has a specific number of rows that is fixed by the number of factors in the OA and the number of levels in each factor. In a practical application of an industrial experiment, however, because of various operational constraints it could happen that the number of runs of the experiment cannot be set exactly equal to the number of rows of an OA. In this case, a lean design can be used. A lean design is obtained by removing some specific rows and columns from the extended design matrix formed from an OA, so that the resulting sub‐matrix still allows efficient estimation of the effects of some of the factors. Tables for 2‐level lean designs are already available in the literature. In this paper, the authors will investigate 3‐level lean designs and mixed‐level lean designs, and construct tables for such designs for convenient use. Copyright © 2009 John Wiley & Sons, Ltd.     

Correlation‐Based r‐plot for Evaluating Supersaturated Designs

Orthogonality or near‐orthogonality is an important property in the design of experiments. Supersaturated designs are natural when we wish to investigate the main effects for a large number of factors but are restricted to a small number of runs. These supersaturated designs, by definition, cannot satisfy pairwise orthogonality of all the factor columns in the design matrix. Hence, we need a means to evaluate the degree of near‐orthogonality of different alternative supersaturated designs. It is usual to use numerical measures that condense the rich information from many pairwise column measures to assess the degree of orthogonality of given supersaturated designs, but we propose using graphical methods to better understand patterns between sets of columns and evaluate the degree of near‐orthogonality to compare and select between alternative supersaturated designs. The methods are illustrated with a number of diverse examples to illustrate the information that can be extracted from the summary. Copyright © 2013 John Wiley & Sons, Ltd.     

On the Analysis of Balanced Two‐Level Factorial Whole‐Plot Saturated Split‐Plot Designs

This paper considers an experimentation strategy when resource constraints permit only a single design replicate per time interval and one or more design variables are hard to change. The experimental designs considered are two‐level full‐factorial or fractional‐factorial designs run as balanced split plots. These designs are common in practice and appropriate for fitting a main‐effects‐plus‐interactions model, while minimizing the number of times the whole‐plot treatment combination is changed. Depending on the postulated model, single replicates of these designs can result in the inability to estimate error at the whole‐plot level, suggesting that formal statistical hypothesis testing on the whole‐plot effects is not possible. We refer to these designs as balanced two‐level whole‐plot saturated split‐plot designs. In this paper, we show that, for these designs, it is appropriate to use ordinary least squares to analyze the subplot factor effects at the ‘intermittent’ stage of the experiments (i.e., after a single design replicate is run); however, formal inference on the whole‐plot effects may or may not be possible at this point. We exploit the sensitivity of ordinary least squares in detecting whole‐plot effects in a split‐plot design and propose a data‐based strategy for determining whether to run an additional replicate following the intermittent analysis or whether to simply reduce the model at the whole‐plot level to facilitate testing. The performance of the proposed strategy is assessed using Monte Carlo simulation. The method is then illustrated using wind tunnel test data obtained from a NASCAR Winston Cup Chevrolet Monte Carlo stock car. Copyright © 2012 John Wiley & Sons, Ltd.     

The use of change detection as a method of objectively evaluating labels

This study introduces the use of change detection, a technique used in cognitive psychology to measure attentional scan paths, as a way to objectively evaluate the prominence of varied label elements. There are two major objectives related to this work: (a) to develop change detection software and methodology for label use; and (b) to compare the relative prominence of different label elements on a beverage container. Six label elements (i.e. treatments) were analysed, namely: the manufacturer name, the product name and a warning dot with text in three colours. Study results suggest that experimental set‐up can significantly impact results, specifically the position of the change (p = 0.0078) and the order of appearance (p = 0.069). This is not only important from an experimental design perspective, but also could lead to insights regarding the attentive behaviours of people as they purchase, select and use products. With regard to the elements of the labels tested, we identified a significant difference on time to detect a change (p < 0.0001). Time required for the manufacturer's name, Asahi Breweries, was significantly longer than for any of the other label elements (p < 0.0001). Pairwise comparisons indicated that for the warning dot, red text was located marginally faster than the warning printed in black (p = 0.0566). Change detection offers the promise to objectively evaluate the relative prominence of a label (or a scene) but is quicker and cheaper than other methods, such as eye tracking, that are currently utilized for this purpose. Copyright © 2010 John Wiley & Sons, Ltd.     

Biocompatibility of a nickel‐free austenitic steel assayed by osteoblastic MC3T3‐E1 cells

The in vitro biocompatibility of the nickel‐free austenitic steel (brand name P2000) characterized by extreme strength, high ductility and superior corrosion resistance was tested. As the material appears promising for hard tissue implant development we employed osteoblastic cells (MC3T3‐E1) as test specimens. Cells growing directly on this metal were undistinguishable from control cells on cell culture tested plastic material with respect to morphology and growth parameters. P2000 can, therefore, be classified as a biocompatible material. In line with that application of bone morphogentic protein 2 (BMP‐2) increased expression of alkaline phosphatase as it does under control conditions. However, the slope of the dose response curve was diminished, indicating partially impaired BMP‐2 signaling on P2000. Since P2000 contains nitrogen and may release alkalizing amounts of NH₃ we compared steady state intracellular pH (pHi) of cells on P2000 vs. controls, but were unable to find any differences. Further studies are needed to understand how P2000 can influence cellular functions of directly contacting cells.     

Estimation of missing observations in two‐level split‐plot designs

Inserting estimates for the missing observations from split‐plot designs restores their balanced or orthogonal structure and alleviates the difficulties in the statistical analysis. In this article, we extend a method due to Draper and Stoneman to estimate the missing observations from unreplicated two‐level factorial and fractional factorial split‐plot (FSP and FFSP) designs. The missing observations, which can either be from the same whole plot, from different whole plots, or comprise entire whole plots, are estimated by equating to zero a number of specific contrast columns equal to the number of the missing observations. These estimates are inserted into the design table and the estimates for the remaining effects (or alias chains of effects as the case with FFSP designs) are plotted on two half‐normal plots: one for the whole‐plot effects and the other for the subplot effects. If the smaller effects do not point at the origin, then different contrast columns to some or all of the initial ones should be discarded and the plots re‐examined for bias. Using examples, we show how the method provides estimates for the missing observations that are very close to their actual values. Copyright © 2007 John Wiley & Sons, Ltd.     

Effect of heat treatment on microstructure and property of Fe‐Cr‐B alloy

In this article, the effect of heat treatment in different quenching temperature on microstructure and hardness of Fe‐Cr‐B alloy was studied, by contrast with boron‐free Fe‐Cr alloy. The results indicated that microstructure of boron‐free Fe‐Cr alloy consisted of the martensite and a few (Cr, Fe)₇C₃ type carbide. The microstructures had no obvious change with the increase of quenching temperature, but its hardness increased from 51.5 HRC to 60.8 HRC. When boron element was added into the Fe‐Cr alloy, the netlike eutectic structure began to break and spheroidizing after quenching, in which the borocarbide turned into spherical groups and network Fe₂B phase was broken. Moreover, the portion of martensite increased, and the amount of secondary carbide decreased, and the size of secondary carbide began to largen after quenching. When the quenching temperature reached 1100°C, secondary carbide particles dissolved in the matrix wholly. The hardness of Fe‐Cr‐B alloy increased with the increase of quenching temperature below 1050°C. The hardness of sample containing 2.0% B and quenching at 1050°C reached 66.7 HRC. The hardness of Fe‐Cr‐B alloy had no obvious change when quenching temperature continued to increase. After tempered at 200°C, the microstructure of Fe‐Cr‐B alloy had no significant change and its hardness had slight decrease. The hardness of sample containing 2.0% B tempered at 200°C reached 63.9 HRC.     

Selecting a near‐optimal design for multiple criteria with improved robustness to different user priorities

In a decision‐making process, relying on only one objective can often lead to oversimplified decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical for balancing trade‐offs on different aspects of performance. When multiple objectives are considered, it is often hard to make a precise decision on how to weight the different objectives when combining their performance for ranking and selecting designs. We show that there are situations when selecting a design with near‐optimality for a broad range of weight combinations of the criteria is a better test selection strategy compared with choosing a design that is strictly optimal under very restricted conditions. We propose a new design selection strategy that identifies several top‐ranked solutions across broad weight combinations using layered Pareto fronts and then selects the final design that offers the best robustness to different user priorities. This method involves identifying multiple leading solutions based on the primary objectives and comparing the alternatives using secondary objectives to make the final decision. We focus on the selection of screening designs because they are widely used both in industrial research, development, and operational testing. The method is illustrated with an example of selecting a single design from a catalog of designs of a fixed size. However, the method can be adapted to more general designed experiment selection problems that involve searching through a large design space.