Harnessing placebo: Lessons from psychedelic science

The randomized controlled trial (RCT) research design assumes that a drug's "specific" effect can be isolated, added, and subtracted from the "nonspecific" effect of context and person. While RCTs are helpful in assessing the added benefit of a novel drug, they tend to obscure the curative potential of extra-pharmacological variables, known as "the placebo effect." Ample empirical evidence suggests that person/context-dependent physical, social, and cultural variables not only add to, but also shape drug effects, making them worth harnessing for patient benefits. Nevertheless, utilizing placebo effects in medicine is challenging due to conceptual and normative obstacles. In this article, we propose a new framework inspired by the field of psychedelic science and its employment of the "set and setting" concept. This framework acknowledges that drug and nondrug factors have an interactive and synergistic relationship. From it, we suggest ways to reintegrate nondrug variables into the biomedical toolbox, to ethically harness the placebo effect for improved clinical care.

Cross-Attribute Level Effects Models for Modeling Modified 5-Level Version of EQ-5D Health State Values: Is Less Still More?

OBJECTIVES

Cross-attribute level effects (CALE) model has demonstrated better predictive accuracy for out-of-sample health states than the conventional additive main-effects model in cross-validation analysis of the 5-level version of EQ-5D (EQ-5D-5L) composite time trade-off (cTTO) datasets. In this study, we aimed to further test the performance of CALE model using a different design and modified EQ-5D-5L states.

METHODS

A total of 29 EQ-5D-5L self-care bolt-off states, 30 EQ-5D-5L states, and 31 EQ-5D-5L vision bolt-on states were selected from the same orthogonal array. A total of 600 university students were interviewed face-to-face to value a subset of these health states using the cTTO method. For each type of health state, we fitted both the conventional main-effects model and the CALE model. Predictive accuracy was assessed in a series of cross-validation analysis using the leave-one-state-out method.

RESULTS

Overall, the CALE model outperformed the conventional model for each of the 3 types of health states in predicting the cTTO values of out-of-sample health states. The prediction accuracy of using the CALE model improved with the number of dimensions in health states, for example, the MAE decreased about 24%, 67%, and 77% for the EQ-5D-5L self-care bolt-off, EQ-5D-5L, and EQ-5D-5L vision bolt-on states, respectively, when using CALE models.

CONCLUSION

Our study supported the strengths of the CALE model for modelling the utility values of both original and modified EQ-5D-5L health states. Investigators with limited resources may consider using the CALE model to lower the costs for their valuation studies for EQ-5D-5L or similar health state descriptive systems.

Identifying important gene signatures of BMI using network structure-aided nonparametric quantile regression

We focus on identifying genomics risk factors of higher body mass index (BMI) incorporating a priori information, such as biological pathways. However, the commonly used methods to incorporate prior information provide a model for the mean function of the outcome and rely on unmet assumptions. To address these concerns, we propose a method for nonparametric additive quantile regression with network regularization to incorporate the information encoded by known networks. To account for nonlinear associations, we approximate the unknown additive functional effect of each predictor with the expansion of a B-spline basis. We implement the group Lasso penalty to obtain a sparse model. We define the network-constrained penalty by the totalℓ 2 $$ {\ell}_2 $$ norm of the difference between the effect functions of any two linked genes in the known network. We further propose an efficient computation procedure to solve the optimization problem that arises in our model. Simulation studies show that our proposed method performs well in identifying more truly associated genes and less falsely associated genes than alternative approaches. We apply the proposed method to analyze the microarray gene-expression dataset in the Framingham Heart Study and identify several 75 percentile BMI associated genes. In conclusion, our proposed approach efficiently identifies the outcome-associated variables in a nonparametric additive quantile regression framework by leveraging known network information.

Co-Occurrence of Tic Disorders and Attention-Deficit/Hyperactivity Disorder-Does It Reflect a Common Neurobiological Background?

BACKGROUND

The co-existence of tic disorders and attention-deficit/hyperactivity disorder (TD + ADHD) has proven to be highly important in daily clinical practice. The factor ADHD is not only associated with further comorbidities, but also has a long-term negative psychosocial effect, while the factor TD is usually less disturbing for the major part of the patients. It remains unclear how far this is related to a different neurobiological background of the associated disorders or whether TD + ADHD reflects a common one.

OBJECTIVE

This review provides an update on the neurobiological background of TD + ADHD in order to better understand and treat this clinical problem, while clarifying whether an additive model of TD + ADHD holds true and should be used as a basis for further clinical recommendations.

METHOD

A comprehensive research of the literature was conducted and analyzed, including existing clinical guidelines for both TD and ADHD. Besides genetical and environmental risk factors, brain structure and functions, neurophysiological processes and neurotransmitter systems were reviewed.

RESULTS

Only a limited number of empirical studies on the neurobiological background of TD and ADHD have taken the peculiarity of co-existing TD + ADHD into consideration, and even less studies have used a 2 × 2 factorial design in order to disentangle the impact/effects of the factors of TD versus those of ADHD. Nevertheless, the assumption that TD + ADHD can best be seen as an additive model at all levels of investigation was strengthened, although some overlap of more general, disorder non-specific aspects seem to exist.

CONCLUSION

Beyond stress-related transdiagnostic aspects, separate specific disturbances in certain neuronal circuits may lead to disorder-related symptoms inducing TD + ADHD in an additive way. Hence, within a classificatory categorical framework, the dimensional aspects of multilevel diagnostic-profiling seem to be a helpful precondition for personalized decisions on counselling and disorder-specific treatment in TD + ADHD.

[Developing biomass estimation models of young trees in typical plantation on the Qinghai-Tibet Plateau, China.]

Calculation of forest biomass is the basis for global carbon stock estimation, which has been included in national forest inventory projects. The volume-derived biomass method is generally used for trees with diameter at breast height (DBH) larger than 5 cm in most forest carbon sink measurement, which omits young trees (diameter at breast height <6 cm, height >0.3 m) and thus may underestimate ecosystem carbon sink capacity. Based on the biomass data of 137 young trees in five typical plantations on the Tibetan Plateau, independent biomass models were developed using the weighted generalized least squares method, with basic diameter as the predictor instead of DBH. Additive biomass models of controlling directly by proportion functions and controlling by the sum of equations were selected. Additive biomass models for the whole plant and each component were developed by applying weighted nonlinear seemingly uncorrelated regression. The results showed that the binary additive biomass model (R² reached 0.90-0.99) performed better than the monadic biomass models and independent biomass models for the estimation of total biomass. For different tree species, two forms of the additive models had their own advantages, with neglectable difference in accuracy. From the perspective of forestry production, models of controlling directly by proportion functions were more practical. From the perspective of predictors extraction by remote sensing technology, suitable young tree biomass models were developed for remote sensing estimation. In this study, the additive model had high overall fitting accuracy and could accurately estimate the whole plant and component biomass of young trees in similar climatic environments.

Interaction Contrasts and Collider Bias

Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove this statement and, along with intuitions, formally examine the direction and magnitude of the associations between 2 risk factors of a binary collider using interaction contrasts. Among level one of the collider, 2 variables are independent, positively associated, and negatively associated if multiplicative risk interaction contrast is equal to, more than, and less than 0, respectively; the same results hold for the other level of the collider if the multiplicative survival interaction contrast, equal to multiplicative risk interaction contrast minus the additive risk interaction contrast, is compared with 0. The strength of the association depends on the magnitude of the interaction contrast: The stronger the interaction is, the larger the magnitude of the association will be. However, the common conditional odds ratio under the homogeneity assumption will be bounded. A figure is presented that succinctly illustrates our results and helps researchers to better visualize the associations introduced upon conditioning on a collider.

[Analysis of carbon concentration and allometric growth model of carbon content for Larix olgensis]

Forest carbon storage accounts for about 45% of terrestrial carbon storage. Accurate assessment of forest carbon storage is of great significance to the scientific management and planning of forests. Based on the data of 77 sampling Larix olgensis trees from Mengjiagang, Shangzhi Maoershan, Xiaojiu Forest Farm and Dongjing, Lin-kou Forestry Bureaus of Jiamusi, Heilongjiang Province from 2015 to 2018, we analyzed the partition of carbon content and variation of carbon concentration for five tree components (i.e., wood, bark, branch, leaf, and root). The mono-element and dual-element additive models of carbon content for each component of L. olgensis were deve-loped. The nonlinear seemly unrelated regression was used to estimate the parameters in the additive models, while the jackknife resampling technique was used to verify and evaluate the developed models. The results showed that the weighted mean carbon concentration of each component differed significantly, branches (49.3%) > bark (48.7%) > foliage (48.5%) > wood (48.2%) > root (47.1%). The aboveground and belowground carbon content accounted for about 80% and 20% of the total carbon content, respectively. The adjusted coefficient of determination (R_a²) of additive models of carbon content was greater than 0.89, the mean absolute error was less than 4.1 kg, and the mean absolute error percentage for most models was less than 30%. Adding tree height in the additive models of carbon content could significantly improve model fitting performance and predicting precision. The additive models of carbon content of total, aboveground, wood and bark were better than that of carbon content of branch, foliage, root and crown.

An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.

Finding Liebig's law of the minimum

Liebig's law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co-limited growth. However, its mechanistic foundation is rarely discussed, even though its validity has been questioned since its introduction in the 1820s. Here we first show that LLM is a crude approximation of the law of mass action, the state of art theory of biochemical reactions, and the LLM model is less accurate than two other approximations of the law of mass action: the synthesizing unit model and the additive model. We corroborate this conclusion using empirical data sets of algae and plants grown under two co-limiting substrates. Based on our analysis, we show that when growth is modeled directly as a function of substrate uptake, the LLM model improperly restricts the organism to be of fixed elemental stoichiometry, making it incapable of consistently resolving biological adaptation, ecological evolution, and community assembly. When growth is modeled as a function of the cellular nutrient quota, the LLM model may obtain good results at the risk of incorrect model parameters as compared to those inferred from the more accurate synthesizing unit model. However, biogeochemical models that implement these three formulations are needed to evaluate which formulation is acceptably accurate and their impacts on predicted long-term ecosystem dynamics. In particular, studies are needed that explore the extent to which parameter calibration can rescue model performance when the mechanistic representation of a biogeochemical process is known to be deficient.

[Construction of the additive model system for heartwood, sapwoodand bark taper of Pinus koraiensis plantation in different regions of Heilongjiang Province, China]

With the commonly used variable-exponent taper models developed by Kozak (1988), Muhairwe (1999), Lee (2003) and Kozak (2004), we aimed to develop taper functions of diameter outside bark (DOB), heartwood diameter (HD), sapwood width (SW), and bark thickness (BT) based on the data of 2977 discs from 103 sample trees of Pinus koraiensis plantations under different conditions in Mengjiagang Forest Farm, Dongjingcheng and Linkou Forestry Bureau in Heilongjiang Province. We selected the optimal basic model after comparison. By using seemingly unrelated regression (SUR) method in the PROC MODEL of SAS software, we established an additive model system for the taper equations of DOB, HD, SW and BT. By introducing the region as a dummy variable, the models were evaluated by coefficient determination (R_adj²), root mean square error (RMSE), Akaike information criterion (AIC) and Bayesian information criterion (BIC). The results showed that the taper model developed by Kozak (2004) was the optimal one for the DOB, HD, SW and BT. When satisfying the additivity of each component and total amount, the additivity model system got better prediction effect, with a prediction precision of more than 98%. The additive model system with dummy variables improved the prediction ability to varying degrees, especially the prediction precision of HD and SW models. Minor differences existed in DOB and BT among different regions, while enormous differences in HD and SW. The additive model system with dummy variables not only had high prediction accuracy but also satisfied the additive logic of DOB, HD, SW and BT, which provided a basis for accurate estimation of heartwood, sapwood and bark volume of P. koraiensis.

Functional Horseshoe Priors for Subspace Shrinkage

We introduce a new shrinkage prior on function spaces, called the functional horseshoe prior (fHS), that encourages shrinkage towards parametric classes of functions. Unlike other shrinkage priors for parametric models, the fHS shrinkage acts on the shape of the function rather than inducing sparsity on model parameters. We study the efficacy of the proposed approach by showing an adaptive posterior concentration property on the function. We also demonstrate consistency of the model selection procedure that thresholds the shrinkage parameter of the functional horseshoe prior. We apply the fHS prior to nonparametric additive models and compare its performance with procedures based on the standard horseshoe prior and several penalized likelihood approaches. We find that the new procedure achieves smaller estimation error and more accurate model selection than other procedures in several simulated and real examples. The supplementary material for this article, which contains additional simulated and real data examples, MCMC diagnostics, and proofs of the theoretical results, is available online.

Data-adaptive additive modeling

In this paper, we consider fitting a flexible and interpretable additive regression model in a data-rich setting. We wish to avoid pre-specifying the functional form of the conditional association between each covariate and the response, while still retaining interpretability of the fitted functions. A number of recent proposals in the literature for nonparametric additive modeling are data adaptive, in the sense that they can adjust the level of flexibility in the functional fits to the data at hand. For instance, the sparse additive model makes it possible to adaptively determine which features should be included in the fitted model, the sparse partially linear additive model allows each feature in the fitted model to take either a linear or a nonlinear functional form, and the recent fused lasso additive model and additive trend filtering proposals allow the knots in each nonlinear function fit to be selected from the data. In this paper, we combine the strengths of each of these recent proposals into a single proposal that uses the data to determine which features to include in the model, whether to model each feature linearly or nonlinearly, and what form to use for the nonlinear functions. We establish connections between our approach and recent proposals from the literature, and we demonstrate its strengths in a simulation study.

Replication of Type 2 diabetes-associated variants in a Saudi Arabian population

Over 120 Type 2 diabetes (T2D) loci have been identified from genome-wide association studies (GWAS), mainly from Caucasian populations. Very limited knowledge is available on the Saudi Arabian population. In this study, 122 previously reported T2D-related variants from 84 loci were examined in a Saudi Arabian cohort of 1,578 individuals (659 T2D cases and 919 controls). Eleven single nucleotide polymorphisms (SNPs) corresponding to nine independent loci had a P value <0.05. If a more stringent Bonferroni threshold of P = 4.1 × 10⁻⁴ ( = 0.05/122) were applied, none of the SNPs would have reached the significance level. Nine of the SNPs with a P value <0.05 showed similar odds ratios as previously described, but rs11605924 (CRY2) and rs9470794 (ZFAND3) were in the opposite direction. This study demonstrates the importance of large-scale GWAS in the Saudi Arabian population to identify ethnicity-specific disease-associated variants.

A simulations approach for meta-analysis of genetic association studies based on additive genetic model

Meta-analysis of genetic association studies is being increasingly used to assess phenotypic differences between genotype groups. When the underlying genetic model is assumed to be dominant or recessive, assessing the phenotype differences based on summary statistics, reported for individual studies in a meta-analysis, is a valid strategy. However, when the genetic model is additive, a similar strategy based on summary statistics will lead to biased results. This fact about the additive model is one of the things that we establish in this paper, using simulations. The main goal of this paper is to present an alternate strategy for the additive model based on simulating data for the individual studies. We show that the alternate strategy is far superior to the strategy based on summary statistics.

Fused Lasso Additive Model

We consider the problem of predicting an outcome variable using p covariates that are measured on n independent observations, in a setting in which additive, flexible, and interpretable fits are desired. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant with a small number of adaptively-chosen knots. FLAM is the solution to a convex optimization problem, for which a simple algorithm with guaranteed convergence to a global optimum is provided. FLAM is shown to be consistent in high dimensions, and an unbiased estimator of its degrees of freedom is proposed. We evaluate the performance of FLAM in a simulation study and on two data sets. Supplemental materials are available online, and the R package flam is available on CRAN.

A century after Fisher: time for a new paradigm in quantitative genetics

Quantitative genetics traces its roots back through more than a century of theory, largely formed in the absence of directly observable genotype data, and has remained essentially unchanged for decades. By contrast, molecular genetics arose from direct observations and is currently undergoing rapid changes, making the amount of available data ever greater. Thus, the two disciplines are disparate both in their origins and their current states, yet they address the same fundamental question: how does the genotype affect the phenotype? The rapidly accumulating genomic data necessitate sophisticated analysis, but many of the current tools are adaptations of methods designed during the early days of quantitative genetics. We argue here that the present analysis paradigm in quantitative genetics is at its limits in regards to unraveling complex traits and it is necessary to re-evaluate the direction that genetic research is taking for the field to realize its full potential.

Semiparametric additive marginal regression models for multiple type recurrent events

Recurrent event data are often encountered in biomedical research, for example, recurrent infections or recurrent hospitalizations for patients after renal transplant. In many studies, there are more than one type of events of interest. Cai and Schaube (Lifetime Data Anal 10:121-138, 2004) advocated a proportional marginal rate model for multiple type recurrent event data. In this paper, we propose a general additive marginal rate regression model. Estimating equations approach is used to obtain the estimators of regression coefficients and baseline rate function. We prove the consistency and asymptotic normality of the proposed estimators. The finite sample properties of our estimators are demonstrated by simulations. The proposed methods are applied to the India renal transplant study to examine risk factors for bacterial, fungal and viral infections.

The essential role of psychosocial risk and protective factors in pediatric traumatic brain injury research

This article builds upon Traumatic Brain Injury Common Data Elements (TBI CDE) version 1.0 and the pediatric CDE Initiative by emphasizing the essential role of psychosocial risk and protective factors in pediatric TBI research. The goals are to provide a compelling rationale for including psychosocial risk and protective factors in addition to socioeconomic status (SES), age, and sex in the study design and analyses of pediatric TBI research and to describe recommendations for core common data elements in this domain. Risk and protective factor research is based on the ecological theory of child development in which children develop through a series of interactions with their immediate and more distant environments. Home, school, religious, and social influences are conceptualized as risk and/or protective factors. Child development and TBI researchers have interpreted risk and protective variables as main effects or as interactions and have used cumulative risk indices and moderation models to describe the relationship among these variables and outcomes that have to do with development and with recovery from TBI. It is likely that the number, type, and interaction among risk and protective factors each contribute unique variance to study outcomes. Longitudinal designs in TBI research will be essential to understanding the reciprocal relationships between risk/protective factors and the recovery/outcome made by the child. The search for effective interventions to hasten TBI recovery mandates the need to target modifiable risks and to promote protective factors in the child's environment.

Polymorphisms associated with the risk of lung cancer in a healthy Mexican Mestizo population: Application of the additive model for cancer

Lung cancer is the leading cause of cancer mortality in Mexico and worldwide. In the past decade, there has been an increase in the number of lung cancer cases in young people, which suggests an important role for genetic background in the etiology of this disease. In this study, we genetically characterized 16 polymorphisms in 12 low penetrance genes (AhR, CYP1A1, CYP2E1, EPHX1, GSTM1, GSTT1, GSTPI, XRCC1, ERCC2, MGMT, CCND1 and TP53) in 382 healthy Mexican Mestizos as the first step in elucidating the genetic structure of this population and identifying high risk individuals. All of the genotypes analyzed were in Hardy-Weinberg equilibrium, but different degrees of linkage were observed for polymorphisms in the CYP1A1 and EPHX1 genes. The genetic variability of this population was distributed in six clusters that were defined based on their genetic characteristics. The use of a polygenic model to assess the additive effect of low penetrance risk alleles identified combinations of risk genotypes that could be useful in predicting a predisposition to lung cancer. Estimation of the level of genetic susceptibility showed that the individual calculated risk value (iCRV) ranged from 1 to 16, with a higher iCRV indicating a greater genetic susceptibility to lung cancer.

Single-trial normalization for event-related spectral decomposition reduces sensitivity to noisy trials

In electroencephalography, the classical event-related potential model often proves to be a limited method to study complex brain dynamics. For this reason, spectral techniques adapted from signal processing such as event-related spectral perturbation (ERSP) - and its variant event-related synchronization and event-related desynchronization - have been used over the past 20 years. They represent average spectral changes in response to a stimulus. These spectral methods do not have strong consensus for comparing pre- and post-stimulus activity. When computing ERSP, pre-stimulus baseline removal is usually performed after averaging the spectral estimate of multiple trials. Correcting the baseline of each single-trial prior to averaging spectral estimates is an alternative baseline correction method. However, we show that this method leads to positively skewed post-stimulus ERSP values. We eventually present new single-trial-based ERSP baseline correction methods that perform trial normalization or centering prior to applying classical baseline correction methods. We show that single-trial correction methods minimize the contribution of artifactual data trials with high-amplitude spectral estimates and are robust to outliers when performing statistical inference testing. We then characterize these methods in terms of their time-frequency responses and behavior compared to classical ERSP methods.

Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models

A variable screening procedure via correlation learning was proposed in Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under general nonparametric models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, a data-driven thresholding and an iterative nonparametric independence screening (INIS) are also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data analysis demonstrate that the proposed procedure works well with moderate sample size and large dimension and performs better than competing methods.

Additive transformation models for clustered failure time data

We propose a class of additive transformation risk models for clustered failure time data. Our models are motivated by the usual additive risk model for independent failure times incorporating a frailty with mean one and constant variability which is a natural generalization of the additive risk model from univariate failure time to multivariate failure time. An estimating equation approach based on the marginal hazards function is proposed. Under the assumption that cluster sizes are completely random, we show the resulting estimators of the regression coefficients are consistent and asymptotically normal. We also provide goodness-of-fit test statistics for choosing the transformation. Simulation studies and real data analysis are conducted to examine the finite-sample performance of our estimators.

Relationship between epistasis and aggressiveness in resistance of pepper (Capsicum annuum L.) to Phytophthora nicotianae

This study evaluated the types of gene action governing the inheritance of resistance to Phytophthora nicotianae necrosis in populations derived from two crosses involving two susceptible (Beldi and Nabeul II) and one resistant (CM334) cultivars of pepper (Capsicum annuum L.). Populations, composed of Pr, Ps, F(1) , F (2) , BC (1) Pr, and BC (1) Ps generations, were inoculated with six P. nicotianae isolates. Generation means analysis indicated that an additive-dominance model was appropriate for P. nicotianae isolates Pn (Ko1) , Pn (Ko2) and Pn (Kr1) , which showed low aggressiveness in the two crosses. For the more aggressive isolates Pn (Bz1) , Pn (Bz2) and Pn (Kr2) , epistasis was an integral component of resistance in the two crosses. The presence of epistasis in the resistance of pepper to P. nicotianae was dependent on the level of aggressiveness of the isolates. Selection in pepper with less aggressive isolates was efficient, but not with more aggressive isolates; on the other hand, selection with more aggressive isolates was more stable. The minimum number of genes controlling resistance was estimated at up to 2.71. In the majority of cases, the additive variance was significant and greater than the environmental and dominance variance.