Shopping Context and the Impulsive and Compulsive Buyer

Impulsive and compulsive buying are behaviors with unique antecedents and consequences. Each has been studied at length but not in the duel context of offline and online retail environments. This current research examines the interaction of shopping context (online or offline) in relation to impulsive and compulsive buying behaviors. We find evidence that compulsive buying tendency is positively associated with online shopping, while impulse buying tendency is positively associated with offline shopping. The implications of this research suggest that purchasing as a result of compulsive and impulsive buying tendencies vary as a result of the shopping context which includes physical proximity to product and store atmospherics. This study reports the behavior of 353 young adults who provide a survey and shopping diary data over a two-week period during the U.S. holiday of Thanksgiving

Can emergency medicine research benefit from adaptive design clinical trials?

Background: Adaptive design clinical trials use preplanned interim analyses to determine whether studies should be stopped or modified before recruitment is complete. Emergency medicine trials are well suited to these designs as many have a short time to primary outcome relative to the length of recruitment. We hypothesised that the majority of published emergency medicine trials have the potential to use a simple adaptive trial design. Methods: We reviewed clinical trials published in three emergency medicine journals between January 2003 and December 2013. We determined the proportion that used an adaptive design as well as the proportion that could have used a simple adaptive design based on the time to primary outcome and length of recruitment. Results: Only 19 of 188 trials included in the review were considered to have used an adaptive trial design. A total of 154/165 trials that were fixed in design had the potential to use an adaptive design. Conclusions: Currently, there seems to be limited uptake in the use of adaptive trial designs in emergency medicine despite their potential benefits to save time and resources. Failing to take advantage of adaptive designs could be costly to patients and research. It is recommended that where practical and logistical considerations allow, adaptive designs should be used for all emergency medicine clinical trials

Mapping from visual acuity to EQ-5D, EQ-5D with vision bolt-on, and VFQ-UI in patients with macular edema in the LEAVO trial

Objectives Mappings to convert clinical measures to preference-based measures of health such as the EQ-5D-3L are sometimes required in cost-utility analyses. We developed mappings to convert best-corrected visual acuity (BCVA) to the EQ-5D-3L, the EQ-5D-3L with a vision bolt-on (EQ-5D V), and the Visual Functioning Questionnaire-Utility Index (VFQ-UI) in patients with macular edema caused by central retinal vein occlusion. Methods We used data from Lucentis, Eylea, Avastin in vein occlusion (LEAVO), which is a phase-3 randomized controlled trial comparing ranibizumab, aflibercept, and bevacizumab in 463 patients with observations at 6 time points. We estimated adjusted limited dependent variable mixture models consisting of 1 to 4 distributions (components) using BCVA in each eye, age, and sex to predict utility within the components and BCVA as a determinant of component membership. We compared model fit using mean error, mean absolute error, root mean square error, Akaike information criteria, Bayesian information criteria, and visual inspection of mean predicted and observed utilities and cumulative distribution functions. Results Mean utility scores were 0.82 for the EQ-5D-3L, 0.79 for the EQ-5D V, and 0.88 for the VFQ-UI. The best-fitting models for the EQ-5D and EQ-5D V had 2 components (with means of approximately 0.44 and 0.85), and the best-fitting model for VFQ-UI had 3 components (with means of approximately 0.95, 0.74, and 0.90). Conclusions Models with multiple components better predict utility than those with single components. This article provides a valuable addition to the literature, in which previous mappings in visual acuity have been limited to linear regressions, resulting in unfounded assumptions about the distribution of the dependent variable

Comprehensive review of statistical methods for analysing patient-reported outcomes (PROs) used as primary outcomes in randomised controlled trials (RCTs) published by the UK’s Health Technology Assessment (HTA) journal (1997–2020)

Objectives: To identify how frequently patient-reported outcomes (PROs) are used as primary and/or secondary outcomes in randomised controlled trials (RCTs) and to summarise what statistical methods are used for the analysis of PROs. Design: Comprehensive review. Setting: RCTs funded and published by the United Kingdom’s (UK) National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme. Data sources and eligibility: HTA reports of RCTs published between January 1997 and December 2020 were reviewed. Data extraction: Information relating to PRO use and analysis methods was extracted. Primary and secondary outcome measures: The frequency of using PROs as primary and/or secondary outcomes; statistical methods that were used for the analysis of PROs as primary outcomes. Results: In this review, 37.6% (114/303) of trials used PROs as primary outcomes, and 82.8% (251/303) of trials used PROs as secondary outcomes from 303 NIHR HTA reports of RCTs. In the 114 RCTs where the PRO was the primary outcome, the most used PRO was the Short-Form 36 (8/114); the most popular methods for multivariable analysis were linear mixed model (45/114), linear regression (29/114) and analysis of covariance (13/114); logistic regression was applied for binary and ordinal outcomes in 14/114 trials; and the repeated measures analysis was used in 39/114 trials. Conclusion: The majority of trials used PROs as primary and/or secondary outcomes. Conventional methods such as linear regression are widely used, despite the potential violation of their assumptions. In recent years, there is an increasing trend of using complex models (eg, with mixed effects). Statistical methods developed to address these violations when analysing PROs, such as beta-binomial regression, are not routinely used in practice. Future research will focus on evaluating available statistical methods for the analysis of PROs

Recruitment and retention of participants in randomised controlled trials: a review of trials funded and published by the United Kingdom Health Technology Assessment Programme

Background Substantial amounts of public funds are invested in health research worldwide. Publicly funded randomised controlled trials (RCTs) often recruit participants at a slower than anticipated rate. Many trials fail to reach their planned sample size within the envisaged trial timescale and trial funding envelope. Objectives To review the consent, recruitment and retention rates for single and multicentre randomised control trials funded and published by the UK's National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme. Data sources and study selection HTA reports of individually randomised single or multicentre RCTs published from the start of 2004 to the end of April 2016 were reviewed. Data extraction Information was extracted, relating to the trial characteristics, sample size, recruitment and retention by two independent reviewers. Main outcome measures Target sample size and whether it was achieved; recruitment rates (number of participants recruited per centre per month) and retention rates (randomised participants retained and assessed with valid primary outcome data). Results This review identified 151 individually RCTs from 787 NIHR HTA reports. The final recruitment target sample size was achieved in 56% (85/151) of the RCTs and more than 80% of the final target sample size was achieved for 79% of the RCTs (119/151). The median recruitment rate (participants per centre per month) was found to be 0.92 (IQR 0.43–2.79) and the median retention rate (proportion of participants with valid primary outcome data at follow-up) was estimated at 89% (IQR 79–97%). Conclusions There is considerable variation in the consent, recruitment and retention rates in publicly funded RCTs. Investigators should bear this in mind at the planning stage of their study and not be overly optimistic about their recruitment projections

Recruitment and retention of participants in randomised controlled trials: a review of trials funded by the United Kingdom health technology assessment programme

Background Substantial amounts of public funds are invested in health research worldwide. Publicly funded randomised controlled trials (RCTs) often recruit participants at a slower than anticipated rate. Many trials fail to reach their planned sample size within the envisaged trial timescale and trial funding envelope. Objectives To review the consent, recruitment and retention rates for single and multicentre randomised control trials funded and published by the UK's National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme. Data sources and study selection HTA reports of individually randomised single or multicentre RCTs published from the start of 2004 to the end of April 2016 were reviewed. Data extraction Information was extracted, relating to the trial characteristics, sample size, recruitment and retention by two independent reviewers. Main outcome measures Target sample size and whether it was achieved; recruitment rates (number of participants recruited per centre per month) and retention rates (randomised participants retained and assessed with valid primary outcome data). Results This review identified 151 individually RCTs from 787 NIHR HTA reports. The final recruitment target sample size was achieved in 56% (85/151) of the RCTs and more than 80% of the final target sample size was achieved for 79% of the RCTs (119/151). The median recruitment rate (participants per centre per month) was found to be 0.92 (IQR 0.43–2.79) and the median retention rate (proportion of participants with valid primary outcome data at follow-up) was estimated at 89% (IQR 79–97%). Conclusions There is considerable variation in the consent, recruitment and retention rates in publicly funded RCTs. Investigators should bear this in mind at the planning stage of their study and not be overly optimistic about their recruitment projections

London region atlas of topsoil geochemistry

The London Region Atlas of Topsoil Geochemistry (LRA) is a further step towards understanding the chemical quality of soils in London, following a previous project called London Earth carried out by the British Geological Survey (BGS) (Johnson et al., 2010[1]). The main advantage of the LRA is that it includes soil geochemical data from the counties surrounding London; placing the city within the context of its rural hinterland, allowing assessments of the impact of urbanisation on soil quality. The London Region Atlas of Topsoil Geochemistry is a product derived from the BGS Geochemical Baseline Survey of the Environment (G-BASE[2]) project. The London Region Geochemical Dataset (LRD, n=8400), on which the atlas is based, includes TOPSOIL data from two complementary surveys: i) the urban London Earth (LOND) and ii) the rural South East England (SEEN). The LRA covers the Greater London Authority (GLA) and its outskirts in a rectangular area of 80x62 km. This extends from British National Grid coordinates Easting 490000–570000, and Northing 153000–215000. The urban LOND and the rural SEEN surveys contribute with 6801 and 1599 samples respectively to the LRD. The concentrations of 44 inorganic chemical elements (Al2O3, CaO, Fe2O3, K2O, MgO, MnO, Na2O, P2O5, SiO2, TiO2, Ag, As, Ba, Bi, Br, Cd, Ce, Co, Cr, Cs, Cu, Ga, Ge, Hf, I, La, Mo, Nb, Nd, Ni, Pb, Rb, Sb, Sc, Se, Sn, Sr, Th, U, V, W, Y, Zn and Zr), loss on ignition (LOI) and pH in topsoil are included in the LRA. For each element, a map showing the distribution in topsoil across the atlas area and a one-page sketch of descriptive statistics and graphs are presented. Statistics and graphs for whole dataset (LRD), London urban subset (LOND) and London surroundings rural subset (SEEN), as well as graphs of topsoil element concentrations over each simplified geology unit are shown. The LRD has been used already in a study aiming to detect geogenic (geological) signatures and controls on soil chemistry in the London region (Appleton et al., 2013[3]). It includes maps showing the distribution of Al, Si, La and I (and Th, Ca, Mn, As, Pb and Zr in supplementary material) and it is concluded that the spatial distribution of a range of elements is primarily controlled by the rocks from where soil derives, and that these geogenic patterns are still recognisable inside the urban centre. Other studies have been done that are based on data in the LRD, namely using the LOND subset or part of it. The main focus of these studies was the mercury content (Scheib et al., 2010[4]), the influence of land use on geochemistry (Knights and Scheib, 2011[5]; Lark and Scheib, 2013[6]); the bioaccessibility of pollutants such as As and Pb (Appleton et al., 2012[7]; Appleton et al., 2012[8]; Cave, 2012[9]; Appleton et al., 2013[10]; Cave et al., 2013[11]) and the lability of lead in soils (Mao et al., 2014[12]); the determination of normal background concentrations of contaminants in English soil (Ander et al., 2013[13]) and the contribution of geochemical and other environmental data to the future of the cities (Ludden et al., 2015[14]). The London Region Atlas of Topsoil Geochemistry formally presents detailed information for all chemical elements in the LRD. This information can be easily visualised and elements compared as its production and layout is standardised. Differences in topsoil element concentrations between the centre of the city and its outskirts can be assessed by observing the map and comparing statistics and graphs reported for the LOND and SEEN subsets respectively. This urban/rural contrast is particularly evident for elements such as Pb, Sb, Sn, Cu and Zn, for which mean concentrations in the urban environment are two to three times higher than those observed in the rural environment. This is a typical indicator suite of urban soil pollution reported in several other cities in the UK also (Fordyce et al., 2005[15])

Point estimation for adaptive trial designs II: practical considerations and guidance

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred