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The placebo response in clinical trials has four components: regression to the mean (RTM), measurement artifacts, natural tendency (NT) of the disease, and the genuine placebo effect. Our objective is to determine what contributes to the size of the placebo-effect in clinical trials by meta-regressions of randomized placebo-controlled clinical trials. We identified five diseases where data on the rates of NT were available to search for a sample of n = 150 (5x30) randomized controlled trials. We extracted various study descriptors and performed meta-regressions to predict improvement in treatment and placebo groups. We sampled 30 trials each from the following diagnoses: osteoarthritis of the knee, irritable bowel syndrome, depression, sleep disorders, migraine, and extracted relevant information. We estimated the effects due to RTM and NT and analyzed the improvement in placebo and treatment groups by fitting two regression models. Both models were highly significant, explaining 72% of the variance. Improvement in the placebo group can be significantly predicted by improvement in the treatment group (beta = .84), whether a study was analyzed according to intention to treat (beta = -.10) or was a multicenter study (beta = .12). Improvement in the treatment group can be explained by the improvement in the placebo group (beta = .83), whether a study was a multicenter trial (beta = -.16), and by RTM (beta = -.18). The treatment effect is smaller in sleep studies (beta = -.17). The high correlation of r = .73 between placebo improvement and treatment improvement rates is genuine and not explainable by study or disease characteristics. We conclude from our data that the placebo-effect is the major driver of treatment effects in clinical trials that alone explains 69% of the variance. This leaves only limited space for effects due to pharmacological substances. Context effects are more important than pharmacological ones in the conditions studied by us.

Background This study investigated the body features underpinning accurate size judgments of female bodies, and whether judgment accuracy varies with body size. Previous research indicates several body features can influence size judgments; however, there is uncertainty around which specific parts are important, if those vary with size, and whether body perception involves holistic processing (i.e., the whole body) or relies on specific cues (i.e., certain body parts). To examine these questions, we used the bodyline task, which measures two underlying sources of perceptual error in body size judgments: regression to the mean and serial dependence. Results In Experiment 1 ( N  = 99), we compared judgments of whole bodies to those made viewing the top-half or bottom-half only. Viewing the bottom-half only produced judgments as accurate as those for the whole body, suggesting holistic processing of the whole body is not required for body size judgments. Experiment 2 ( N  = 116) built on that result by comparing judgments when only the inner or outer thigh region were visible, compared to the whole body. Both isolated thigh regions led to significantly poorer accuracy in judgments compared to whole-body stimuli, indicating that accurate size judgments require more body features than those alone. Conclusions Our findings demonstrate that accurate judgment of female body size does not require holistic processing but does involve integration of multiple features within the lower body region. These results have implications for understanding the perceptual processes involved in body size estimation with potential for broader considerations of body image disorders.

Lifjeld’s comment provides an opportunity to illustrate the intricacies of the “regression to the mean” (RTM) effect, to clarify the difficulty in teasing apart RTM from allocation bias, and to re-examine our results in relation to RTM and in the context of related evidence. Here, we show that (a) the correlations between paternity change and initial paternity are mathematically expected and can equally be produced when changes are caused by the experimental manipulation itself. (b) The approach taken by Lifjeld to control for RTM is overly conservative because it is based on the unrealistic assumption of zero correlation between individuals’ repeated measurements. Yet, even when using this conservative method, the main effects we originally reported are still detectable. (c) The combined effect of color darkening and tail elongation in Israel is additionally supported by an increase in the number of extra-pair young in other nests and by three independent studies of this population. (d) The experimental effect of color darkening in North America has been replicated successfully and is consistent with multiple correlative studies. Thus, divergent sexual selection in barn swallow populations is supported by both a conservative reanalysis and multiple, independent analyses of experimental and observational datasets.

The influence of early pregnancy weight and gestational weight gain (GWG) on neonatal outcomes among Indian slum‐dwellers remains understudied. A prospective cohort study summarised maternal weight trajectories using the longitudinal clustering technique and explored associations between these clusters and neonatal outcomes (low birthweight, small for gestational age [SGA] and preterm births) among 423 pregnant slum‐dwelling women in Pune, India. Sociodemographic data, height and weight were measured at enrolment (< 12 weeks, ‘early pregnancy’). Weight was additionally measured at 23 ± 1 (‘mid‐pregnancy’), 33 ± 1 (‘late pregnancy’), 36–37 and 39–40 weeks. The mean age was 24.7 (95% CI, 23.3, 25.1) years and the mean BMI at enrolment was 22.3 (95% CI, 21.9, 22.7) kg/m2. Underweight women had the highest GWG rates and total GWG, while obese women had the lowest. Four clusters were identified: Cluster 1 (n = 124, 97% normal and overweight women, GWG rate: 0.27 (95% CI, 0.24, 0.30) kg/week early‐late pregnancy) was the reference group. Women in Cluster 2 (n = 146, 93% underweight and normal weight women, GWG rate: 0.31 (95% CI, 0.28, 0.34) kg/week early‐late pregnancy) had a higher risk of having SGA and preterm newborns and women in Cluster 3 (n = 68, 100% overweight and obese women, GWG rate: 0.17, 95% CI, 0.12, 0.22 kg/week early‐late pregnancy) had a higher risk of having preterm newborns than Cluster 1. The women in Cluster 4 (n = 85, 100% underweight and normal weight, mean early‐late pregnancy GWG rate of 0.47, 95% CI, 0.44, 0.50 kg/week) showed no higher risk of adverse neonatal outcomes. This study highlights the need to monitor both pre‐pregnancy BMI and weight throughout pregnancy to enhance the possibility of favourable neonatal outcomes. This prospective study among pregnant slum‐dwelling Indian women highlights the need to address both pre‐pregnancy BMI and regular weight monitoring throughout pregnancy to enhance the likelihood of favourable neonatal outcomes. Summary The mean rates of gestational weight gain at mid‐ and late‐pregnancy, as well as total GWG, were highest among underweight women and lowest among obese women in this cohort of urban slum‐dwelling women. Our study highlights the need to address both pre‐pregnancy BMI and regular weight monitoring throughout pregnancy to enhance the likelihood of favourable neonatal outcomes. Given the pre‐pregnancy weight and weight during the first trimester, we can predict the weight trajectory for the later part of the pregnancy and predict the risk of adverse neonatal outcomes. Such predictive modelling can be valuable for counselling expectant mothers. However, to ensure the generalisability of this approach, larger‐scale studies are necessary.

Background: Negative public attitudes promote undesirable stereotypes and stigma in stutterers. Method: To mitigate negative attitudes, 403 respondents combined from 16 international samples filled out the Public Opinion Survey of Human Attributes–Stuttering (POSHA–S) before and after interventions to improve attitudes and were compared to 249 respondents from seven control groups. Investigators aimed (a) to replicate an extreme case of regression to the mean (i.e., “crossover” effect) reported earlier in larger combined samples in which respondents with high pre-scores ended with low post-scores, respondents with low pre-scores finished with high post-scores, and intermediate scorers were unchanged; and (b) to identify individual POSHA–S items related to overall attitude change and among the high and low scorers. Results: As in previous studies, stuttering attitudes improved in the intervention group but not in the control group. Intervention and control respondents demonstrated “crossover” but less than the earlier samples due to lower pre–post correlations. Item contributions to pre–post change and differences among the three change groups were inconsistent; however, high agreement items by respondents were less likely to vary than low agreement items. Conclusion: The “crossover” effect was replicated, and future research should explore its presence in other measures or conditions.

This paper presents a discussion regarding regression models, especially those belonging to the location class. Our main motivation is that, with simple distributions having simple interpretations, in some cases, one gets better results than the ones obtained with overly complex distributions. For instance, with the reverse Gumbel (RG) distribution, it is possible to explain response variables by making use of the generalized additive models for location, scale, and shape (GAMLSS) framework, which allows the fitting of several parameters (characteristics) of the probabilistic distributions, like mean, mode, variance, and others. Three real data applications are used to compare several location models against the RG under the GAMLSS framework. The intention is to show that the use of a simple distribution (e.g., RG) based on a more sophisticated regression structure may be preferable than using a more complex location model.

The Vasicek distribution is a two-parameter probability model with bounded support on the open unit interval. This distribution allows for different and flexible shapes and plays an important role in many statistical applications, especially for modeling default rates in the field of finance. Although its probability density function resembles some well-known distributions, such as the beta and Kumaraswamy models, the Vasicek distribution has not been considered to analyze data on the unit interval, especially when we have, in addition to a response variable, one or more covariates. In this paper, we propose to estimate quantiles or means, conditional on covariates, assuming that the response variable is Vasicek distributed. Through appropriate link functions, two Vasicek regression models for data on the unit interval are formulated: one considers a quantile parameterization and another one its original parameterization. Monte Carlo simulations are provided to assess the statistical properties of the maximum likelihood estimators, as well as the coverage probability. An R package developed by the authors, named vasicekreg, makes available the results of the present investigation. Applications with two real data sets are conducted for illustrative purposes: in one of them, the unit Vasicek quantile regression outperforms the models based on the Johnson-SB, Kumaraswamy, unit-logistic, and unit-Weibull distributions, whereas in the second one, the unit Vasicek mean regression outperforms the fits obtained by the beta and simplex distributions. Our investigation suggests that unit Vasicek quantile and mean regressions can be of practical usage as alternatives to some well-known models for analyzing data on the unit interval.

In this paper, we present an extension of the spatially-clustered linear regression models, namely, the spatially-clustered spatial regression (SCSR) model, to deal with spatial heterogeneity issues in clustering procedures. In particular, we extend classical spatial econometrics models, such as the spatial autoregressive model, the spatial error model, and the spatially-lagged model, by allowing the regression coefficients to be spatially varying according to a cluster-wise structure. Cluster memberships and regression coefficients are jointly estimated through a penalized maximum likelihood algorithm which encourages neighboring units to belong to the same spatial cluster with shared regression coefficients. Motivated by the increase of observed values of the Gini index for the agricultural production in Europe between 2010 and 2020, the proposed methodology is employed to assess the presence of local spatial spillovers on the market concentration index for the European regions in the last decade. Empirical findings support the hypothesis of fragmentation of the European agricultural market, as the regions can be well represented by a clustering structure partitioning the continent into three-groups, roughly approximated by a division among Western, North Central and Southeastern regions. Also, we detect heterogeneous local effects induced by the selected explanatory variables on the regional market concentration. In particular, we find that variables associated with social, territorial and economic relevance of the agricultural sector seem to act differently throughout the spatial dimension, across the clusters and with respect to the pooled model, and temporal dimension. Supplementary materials accompanying this paper appear online.

For inferring a treatment effect from the difference between a treated and untreated group on a quantitative outcome measured before and after treatment, current methods are analysis of covariance (ANCOVA) of the outcome with the baseline as covariate, and analysis of variance (ANOVA) of change from baseline. This article compares both methods on power and bias, for randomized and nonrandomized studies. The methods are compared by writing both as a regression model and as a repeated measures model, and are applied to a nonrandomized study of preventing depression. In randomized studies both methods are unbiased, but ANCOVA has more power. If treatment assignment is based on the baseline, only ANCOVA is unbiased. In nonrandomized studies with preexisting groups differing at baseline, the two methods cannot both be unbiased, and may contradict each other. In the study of depression, ANCOVA suggests absence, but ANOVA of change suggests presence, of a treatment effect. The methods differ because ANCOVA assumes absence of a baseline difference. In randomized studies and studies with treatment assignment depending on the baseline, ANCOVA must be used. In nonrandomized studies of preexisting groups, ANOVA of change seems less biased than ANCOVA, but two control groups and two baseline measurements are recommended.

This study considers the statistical estimation of relations presented by implicit functions. Such structures define mutual interconnections of variables rather than outcome variable dependence by predictor variables considered in regular regression analysis. For a simple case of two variables, pairwise regression modeling produces two different lines of each variable dependence using another variable, but building an implicit relation yields one invertible model composed of two simple regressions. Modeling an implicit linear relation for multiple variables can be expressed as a generalized eigenproblem of the covariance matrix of the variables in the metric of the covariance matrix of their errors. For unknown errors, this work describes their estimation by the residual errors of each variable in its regression by the other predictors. Then, the generalized eigenproblem can be reduced to the diagonalization of a special matrix built from the variables’ covariance matrix and its inversion. Numerical examples demonstrate the eigenvector solution’s good properties for building a unique equation of the relations between all variables. The proposed approach can be useful in practical regression modeling with all variables containing unobserved errors, which is a common situation for the applied problems.