Use of Statistics in Plant Cell Tissue Culture.

Statistics and experimental design are important tools for plant cell and tissue culture researchers and should be used when planning and conducting experiments as well as during the analysis and interpretation of experimental results. The chapter provides basic concepts important to the statistical analysis of data obtained from plant tissue culture experiments and illustrates the application of common statistical procedures to analyze binomial, count, and continuous data for experiments with different treatment factors as well as identifying trends of dosage treatment factors.

Child Welfare Outcomes After Attachment and Biobehavioral Catch-Up Intervention Participation: A Quasi-Experimental Study.

Limited research is available examining distal child welfare outcomes after participation in evidence-based parenting interventions. To address this gap, this study employed a multi-tiered analytic approach to examine child welfare outcomes after participation in Attachment and Biobehavioral Catch-up (ABC). Using propensity score analytic techniques to establish a matched comparison group, logistic regressions examined subsequent maltreatment reports and substantiation, and survival analyses observed time to and likelihood of reunification for children who received one of three ABC curriculums compared to comparison group children (child welfare services as usual). In total, 205 children were included in the impact analysis (n = 66 treatment; n = 139 comparison); the majority of the children were White (53.7%), non-Hispanic (84.4%), males (59.5%) with an average age of 6 months (M [SD] = .50 [1.0]). Over half (56.1%) of the study sample was in out-of-home placement; 23.5% of the removed children experienced reunification. No statistically significant group differences were observed on the likelihood of subsequent or substantiated maltreatment reports. All three ABC curriculums were associated with a statistically significant increased likelihood of reunification, when compared to their matched counterpart. Additional research is warranted, though results indicate ABC may be a promising intervention to help enhance the likelihood of reunification.

The perspective of the logarithmic field sprayer technology.

A logarithmic sprayer was suggested about 70 years ago, but it has not yet been seriously used in research and development, and subsequent registration of plant protection products. Logarithmic sprayers have resorted to mere demonstration experiments to show end users and others how plant protection products work. Fitting dose-response curves in field experiments, however, generates much essential information, e.g., extraction of various effective field rate levels (e.g., ED20, ED50, and ED80). One of the reasons for it rarely being used in the registration of plant protection products is that the dose-response curve regression was hitherto difficult to fit; the registration requirement solely focuses on analyses of variance. Another alleged obstacle is that the logarithmic plots have systematically, not randomly distributed field rates. This paper goes through some of the problems of how to non-randomly analyze field rates by taking autocorrelation into account to make the logarithmic sprayer palatable as registration documentation by assessing efficacy, selectivity, environmental side effects, general toxicity of plant protection products, and cost-effectiveness. The development in precision agriculture, drone technology, and automation of data capture and subsequent analysis could make the logarithmic sprayer a cost-effective alternative to numerous ANOVA experiments with very few fixed field rates to aid the precision spraying of pesticides and thus reduce unnecessary environmental side effects. © 2024 Society of Chemical Industry.

Do Methodological Differences in Experiments with Stream Shredders Imply Variability in Outputs? A Microcosm Approach.

Experiments are useful scientific tools for testing hypotheses by manipulating variables of interest while controlling for other factors that can bias or confuse the results and their interpretation. To ensures accuracy and reproducibility, experiments must have transparent and repeatable methodologies. Due to the importance of shredder invertebrates in organic matter processing, carbon cycling, and nutrient cycling, we tested experimentally the effect of different methodological approaches in microcosm experiments on the consumption and survival of shredders. We found that the shredder species, the presence or absence of the case, and the use or non-use of air-pumps in the microcosms did not affect shredder performance (i.e., consumption and survival). Furthermore, the type of water (stream or bottled) did not affect shredder performance. On the other hand, the amount of light had a negative effect on shredder performance, with constant light (i.e., 24 h) reducing shredder consumption and survival. Our results demonstrate that the use of different methodologies does not always result in changes in outcomes, thus ensuring comparability. However, luminosity is a critical factor that deserves attention when conducting microcosm experiments. Our findings provide valuable insights that can assist researchers in designing experiments with shredders from neotropical streams and conducting systematic reviews and meta-analyses.

Measuring aliphatic hydrocarbons in sediments by direct thermal desorption-gas chromatography-mass spectrometry: Matrix effects and quantification challenges.

Direct sample introduction thermal desorption (TD) coupled to GC-MS was investigated for the analysis of paraffinic hydrocarbons (HCs) from polluted sediments. TD-GC-MS is sometimes used for analysing paraffinic HCs from atmospheric particles but rarely for their direct desorption from sediments. So, the new TD methodology, applied to sediments, required development, optimization and validation. A definitive screening experimental design was performed to discriminate the critical factors on TD efficiency, from model sediments containing various organic matter (OM) amounts. Low molecular weight HCs had extraction behaviours markedly different from high molecular ones (HMW-HCs), but a compromise was found using very few sediment amount (5 mg), high temperature rate (55 °C min-1) and final temperature (350 °C). Linear HCs (n-C10 to n-C40) could be quantified using the matrix-matched calibration method, with very low detection limits (3.8-13.4 ng). The amount of the overall paraffinic alkanes was also determined as a sum of unresolved components between predefined equivalent carbon ranges. The developed solventless methodology was compared to an optimized solvent microwave assisted extraction (MAE). Matrix effects could be higher for TD compared to MAE but it depended on sediment matrix. When matrix effect was strong, particularly on HMW-HCs signal depletion, a dilution with pure non-porous sand was favourable for accurate quantification. The sum of resolved and unresolved HCs gave comparable results between MAE and TD extractions, with an exception of alkanes greater than C30 which were less quantitatively extracted via TD. However, TD-GC-MS was more sensitive than MAE-GC-MS. So TD-GC-MS is useful for analyzing sediments containing a great range of paraffinic HCs (C9-C34) and it has the advantages of being fully automated, with few sample preparation and operator intervention, using very low amounts of solvent, and generating few wastes.

A Description of Missing Data in Single-Case Experimental Designs Studies and an Evaluation of Single Imputation Methods.

Missing data is inevitable in single-case experimental designs (SCEDs) studies due to repeated measures over a period of time. Despite this fact, SCEDs implementers such as researchers, teachers, clinicians, and school psychologists usually ignore missing data in their studies. Performing analyses without considering missing data in an intervention study using SCEDs or a meta-analysis study including SCEDs studies in a topic can lead to biased results and affect the validity of individual or overall results. In addition, missingness can undermine the generalizability of SCEDs studies. Considering these drawbacks, this study aims to give descriptive and advisory information to SCEDs practitioners and researchers about missing data in single-case data. To accomplish this task, the study presents information about missing data mechanisms, item level and unit level missing data, planned missing data designs, drawbacks of ignoring missing data in SCEDs, and missing data handling methods. Since single imputation methods among missing data handling methods do not require complicated statistical knowledge, are easy to use, and hence are more likely to be used by practitioners and researchers, the present study evaluates single imputation methods in terms of intervention effect sizes and missing data rates by using a real and hypothetical data sample. This study encourages SCEDs implementers, and also meta-analysts to use some of the single imputation methods to increase the generalizability and validity of the study results in case they encounter missing data in their studies.

Democratising the design and delivery of large-scale randomised, controlled clinical trials in primary care: A personal view.

BACKGROUND: Rapid identification of effective treatments for use in the community during a pandemic is vital for the well-being of individuals and the sustainability of healthcare systems and society. Furthermore, identifying treatments that do not work reduces research wastage, spares people unnecessary side effects, rationalises the cost of purchasing and stockpiling medication, and reduces inappropriate medication use. Nevertheless, only a small minority of therapeutic trials for SARS-CoV-2 infections have been in primary care: most opened too late, struggled to recruit, and few produced actionable results. Participation in research is often limited by where one lives or receives health care, and trial participants may not represent those for whom the treatments are intended. INNOVATIVE TRIALS: The ALIC4E, PRINCIPLE and the ongoing PANORAMIC trial have randomised over 40,500 people with COVID-19. This personal view describes how these trials have innovated in: trial design (by using novel adaptive platform designs); trial delivery (by complementing traditional site-based recruitment ('the patient comes to the research') with mechanisms to enable sick, infectious people to participate without having to leave home ('taking research to the people'), and by addressing the 'inverse research participation law,' which highlights disproportionate barriers faced by those who have the most to contribute, and benefit from, research, and; in transforming the evidence base by evaluating nine medicines to support guidelines and care decisions world-wide for COVID-19 and contribute to antimicrobial stewardship. CONCLUSION: The PRINCIPLE and PANORAMIC trials represent models of innovation and inclusivity, and exemplify the potential of primary care to lead the way in addressing pressing global health challenges.

Adaptive platform trials can efficiently evaluate several treatments in parallel and sequentially'Taking research to people' can democratise participation by enabling sick, contagious people to contribute from home, country-wideThe PRINCIPLE and PANORAMIC Trials innovated in trial design and delivery to produce evidence on nine treatments for COVID-19 in the community.

Rise of single-case experimental designs: A historical overview of the necessity of single-case methodology.

ABSTRACTWindelband ([1894]1980) advocated that two approaches are used for accumulating scientific knowledge. The first is the idiographic approach that derives knowledge from a single unit, and the second is the nomothetic approach that accumulates knowledge of a group. Given these two approaches, the former matches case studies while the latter is more appropriate with experimental group studies. Scientists have criticized both methodologies for their various limitations. Later, the single-case methodology emerged as an alternative that potentially allays these limitations. In this context, this narrative review aims to describe the historical roots of single-case experimental designs (SCEDs) that have emerged to eliminate the tension of nomothetic and idiographic approaches over time. First, the review focuses on the emergence of SCEDs. Second, the strengths and challenges of SCEDs are reviewed, including those to address the limitations of group experimental and case studies. Third, the use and analyses of SCEDs are outlined, considering their current status. Fourth, this narrative review continues to delineate the dissemination of SCEDs in the modern scientific world. As a result, SCEDs can be evaluated as a method that has the potential to overcome the issues encountered in case description and group experimental research. Thus, that helps accumulate nomothetic and idiographic knowledge in determining evidence-based practices.

ABkPowerCalculator: An App to Compute Power for Balanced (AB)k Single Case Experimental Designs.

Single case experimental designs are an important research design in behavioral and medical research. Although there are design standards prescribed by the What Works Clearinghouse for single case experimental designs, these standards do not include statistically derived power computations. Recently we derived the equations for computing power for (AB)k designs. However, these computations and the software code in R may not be accessible to applied researchers who are most likely to want to compute power for their studies. Therefore, we have developed an (AB)k power calculator Shiny App (https://abkpowercalculator.shinyapps.io/ABkpowercalculator/) that researchers can use with no software training. These power computations assume that the researcher would be interested in fitting multilevel models with autocorrelations or conduct similar analyses. The purpose of this software contribution is to briefly explain how power is derived for balanced (AB)k designs and to elaborate on how to use the Shiny App. The app works well on not just computers but mobile phones without installing the R program. We believe this can be a valuable tool for practitioners and applied researchers who want to plan their single case studies with sufficient power to detect appropriate effect sizes.

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data.

Single-case experimental designs (SCEDs) have grown in popularity in the fields such as education, psychology, medicine, and rehabilitation. Although SCEDs are valid experimental designs for determining evidence-based practices, they encounter some challenges in analyses of data. One of these challenges, missing data, is likely to be occurred frequently in SCEDs research due to repeated measurements over time. Since missing data is a critical factor that can weaken the validity and generalizability of a study, it is important to determine the characteristics of missing data in SCEDs, which are especially conducted with a small number of participants. In this regard, this study aimed to describe missing data features in SCEDs studies in detail. To accomplish this goal, 465 published SCEDs studies within the recent 5 years in six journals were included in the investigation. The overall results showed that the prevalence of missing data among SCEDs articles in at least one phase, as at least one data point, was approximately 30%. In addition, the results indicated that the missing data rates were above 10% within most studies where missing data occurred. Although missing data is so common in SCEDs research, only a handful of studies (5%) have handled missing data; however, their methods are traditional. In analyzing SCEDs data, several methods are proposed considering missing data ratios in the literature. Therefore, missing data rates determined in this study results can shed light on the analyses of SCEDs data with proper methods by improving the validity and generalizability of study results.

Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design.

Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person's daily emotional state). The hybrid experimental design (HED) is a new experimental approach that enables researchers to answer scientific questions about the construction of psychological interventions in which components are delivered and adapted on different timescales. These designs involve sequential randomizations of study participants to intervention components, each at an appropriate timescale (e.g., monthly randomization to different intensities of coaching sessions and daily randomization to different forms of motivational messages). The goal of the current manuscript is twofold. The first is to highlight the flexibility of the HED by conceptualizing this experimental approach as a special form of a factorial design in which different factors are introduced at multiple timescales. We also discuss how the structure of the HED can vary depending on the scientific question(s) motivating the study. The second goal is to explain how data from various types of HEDs can be analyzed to answer a variety of scientific questions about the development of multicomponent psychological interventions. For illustration, we use a completed HED to inform the development of a technology-based weight loss intervention that integrates components that are delivered and adapted on multiple timescales.

Impact of Safe@Home on Placement and Permanency Outcomes: Results of a Quasi-Experimental Study.

This study assessed the effectiveness of Safe@Home, an in-home intervention to (1) prevent out-of-home placement for children at imminent risk of placement, and (2) minimize time in out-of-home care for children already in foster care. Using Coarsened Exact Matching, children who received Safe@Home were matched to a comparison group of children served before Safe@Home was available in their community. All children were determined by the child welfare agency to be unsafe and in need of immediate intervention. The matched samples (Safe@Home n = 510, Comparison n = 851) showed strong baseline equivalence on child age, race/ethnicity, previous history of child welfare involvement, and safety threats. Children who received Safe@Home experienced lower rate of out-of-home placements, higher rate of permanency with a parent (sustained for 12 months after the end of Safe@Home), fewer days in out-of-home care, and shorter time to case closure relative to children who received treatment as usual. There was no effect of Safe@Home on post-permanency outcomes of maltreatment and entry or re-entry after case closure.

A Scoping Review to Explore the Potential Benefits of Nutrition Interventions for Latino/a Adult Cancer Survivors in the US.

Despite evidence for the role of healthy diets in preventing cancer, little is known about how nutrition can support positive health outcomes after a cancer diagnosis for Latino/a cancer survivors in the United States (U.S.). The purpose of this scoping review is to understand the potential benefits of nutrition interventions in supporting healthy survivorship among Latino/a cancer survivors in the U.S. A team compiled, evaluated, and summarized the available evidence. Potentially relevant studies were identified from a comprehensive search of peer-reviewed databases and the gray literature. Eligible studies included Latino/a adult cancer survivors with a nutrition education, dietary change, or behavioral intervention; and a nutrition-related health outcome. Data were extracted and summarized using tables. The review included 10 randomized controlled trials, with samples or subsamples of Latino/a cancer survivors. Interventions mostly focused on breast cancer survivors. The results showed some evidence that dietary behaviors, like fruit and vegetable intake, were related to positive outcomes, like a decreased risk of cancer (through changes in DNA methylation), decreased risk breast cancer recurrence (through changes in inflammatory biomarkers), or improved perception of health status. The findings highlight a need for community-engaged and culturally relevant nutrition interventions for Latino/a adults, especially for rural communities; and innovative intervention approaches, including m/ehealth approaches with long-term follow-up.

Testing delayed, gradual, and temporary treatment effects in randomized single-case experiments: A general response function framework.

Randomization tests represent a class of significance tests to assess the statistical significance of treatment effects in randomized single-case experiments. Most applications of single-case randomization tests concern simple treatment effects: immediate, abrupt, and permanent changes in the level of the outcome variable. However, researchers are confronted with delayed, gradual, and temporary treatment effects; in general, with "response functions" that are markedly different from single-step functions. We here introduce a general framework that allows specifying a test statistic for a randomization test based on predicted response functions that is sensitive to a wide variety of data patterns beyond immediate and sustained changes in level: different latencies (degrees of delay) of effect, abrupt versus gradual effects, and different durations of the effect (permanent or temporary). There may be reasonable expectations regarding the kind of effect (abrupt or gradual), entailing a different focal data feature (e.g., level or slope). However, the exact amount of latency and the exact duration of a temporary effect may not be known a priori, justifying an exploratory approach studying the effect of specifying different latencies or delayed effects and different durations for temporary effects. We provide illustrations of the proposal with real data, and we present a user-friendly freely available web application implementing it.

Aerated lagoon/adsorption combination method for the treatment of olive mill wastewater: optimizing parameters using study design.

The current study focuses on investigating how to improve the efficacy of the combined process of aerated lagooning and adsorption for the treatment of olive mill wastewater (OMWW) from the olive industries in the Loukkos region using the design of experiments approach. The latter made it possible to optimize the experimental conditions, such as the mass concentration of lime, the mass concentration of powdered activated carbon (AC), and the speed of agitation (Va), which are required for the envisaged treatment, in order to control the results after the evaluation of the necessary physicochemical parameters, namely pH, total suspended solids (TSS), chemical oxygen demand (COD), rate of discoloration, and content of polyphenols. The experimental conditions necessary to carry out this study were between 1.4 and 1.75 g/l for AC, between 23 and 32 rpm for the stirring speed, and between 0.88 and 1.3 g/l for the mass concentration of lime. The results of this experiment showed that treating OMWW with the prototype II after adding lime to the mixture resulted in an estimated 85% reduction in the analyzed parameters. The pH, TSS, COD, discoloration rate, and polyphenol content were all reduced from initial values of 4.87, 0.63 (g/l), 80.3 (g (O2/l)), 0.8%, and 1.45 (g/l), respectively, to final values of 6.92, 0.12 (g/l), 12, 0.16%, and 0.25 (g/l). These results are highly significant when compared to those obtained during the treatment of prototype I using only powdered activated carbon, which showed a reduction rate of around 70%.

How to Evaluate a Tailor-made Social Work Intervention? Some Practice-Based Solutions with Single-Case Designs.

PURPOSE: With the increased attention to the principles of evidence-based practice (EBP), social workers are challenged to adapt their daily interventions accordingly when treating clients. They usually work with individual clients, all with their own specificities. Single-Case Experimental Designs (SCEDs) can be used to inform a social worker about the effectiveness of an intervention at the individual client level. In everyday social work practice, however, it is difficult to meet methodological requirements of SCEDs to find causal explanations. A concern is that repeated measurements prior to an intervention are required in most situations. This study aims to provide researchers with alternatives to repeated measurement when using the logic of SCED to apply EBP in their everyday practice. METHODS: In this study, we reviewed published single-case designs between January 1 and December 31, 2019, on types of SCEDs in the social domain, and how is dealt with baseline conditions. RESULTS: SCEDs and quasi-experimental alternatives are hardly published in situations when baseline data are not available. Four underused quasi-experimental strategies that can be employed when repeated measurement during baseline is not possible are as follows: retrospective baselines, theoretical inference, multiraters, and triangulation with qualitative data. DISCUSSION AND CONCLUSION: The suggestions to work with single-case designs with quasi-experimental elements are meant to enable social workers to evaluate their interventions in a way that enhances mere narrative evaluations of the experiences of an intervention.

Emotionally focused couple therapy in cancer survivor couples with marital and sexual problems: a replicated single-case experimental design.

Objective: The current research examined the effect of Emotionally Focused Couples Therapy (EFCT) on perceived intimacy, affect, and dyadic connection in cancer survivor couples with relationship challenges. Method: In this longitudinal replicated single-case study, positive and negative affect, intimacy, partner responsiveness, and expression of attachment-based emotional needs were reported every 3 days before and during treatment. Thirteen couples, with one partner having survived colorectal cancer or breast cancer, participated for the full duration of the study. Statistical analysis of the data was performed using randomization tests, piecewise regression, and multilevel analyses. Results: Adherence to the therapeutic protocol was tested and found adequate. Compared with baseline, significant positive effects on affect variables were found during the therapeutic process. Positive affect increased and negative affect decreased. Partner responsiveness, perceived intimacy, and the expression of attachment-based emotional needs improved, but only in the later phase of treatment. Results at the group level were statistically significant, whereas effects at the individual level were not. Discussion: This study found positive group-level effects of EFCT on affect and dyadic outcome measures in cancer survivors. The positive results warrant further research, including randomized clinical trials, to replicate these effects of EFCT in cancer survivor couples experiencing marital and sexual problems.

A unified strategy to rebalance multifactorial designs with unequal group sizes: application to analysis of variance multiblock orthogonal partial least squares.

BACKGROUND: Adequately handling unbalanced groups remains one of the major challenges for the analysis of multivariate data collected from multifactorial experimental designs. While partial least squares-based methods, such as analysis of variance multiblock orthogonal partial least squares (AMOPLS), can offer better discrimination between factor levels, they can be more heavily affected by this issue, and unbalanced designs of experiments may lead to a substantial confusion of the effects. Even state-of-the-art analysis of variance (ANOVA) decomposition methodologies using general linear models (GLM) lack the ability to efficiently disentangle these sources of variation when combined with AMOPLS. RESULTS: A versatile solution developed as an extension of a prior rebalancing strategy is proposed for the first decomposition step based on ANOVA. This approach has the advantage of yielding an unbiased estimation of the parameters and retaining the within-group variation in the rebalanced design, while preserving the orthogonality of effect matrices, even in presence of unequal group sizes. This property is of utmost importance for model interpretation because it avoids mixing sources of variation related to the different effects in the design. A real case study involving metabolomic data from in vitro toxicological experiments was used to demonstrate the potential of this strategy to handle unequal group sizes using a supervised approach. Primary 3D rat neural cell cultures were exposed to trimethyltin following a multifactorial design of experiments involving three fixed effect factors. SIGNIFICANCE AND NOVELTY: The rebalancing strategy was demonstrated as a novel and potent solution to handle unbalanced experimental designs by offering unbiased parameter estimators and orthogonal submatrices, thus avoiding confusion of the effects and facilitating model interpretation. Moreover, it can be combined with any multivariate method used for the analysis of high-dimensional data collected from multifactorial designs.

Does the choice of a linear trend-assessment technique matter in the context of single-case data?

Trend is one of the data aspects that is an object of assessment in the context of single-case experimental designs. This assessment can be performed both visually and quantitatively. Given that trend, just like other relevant data features such as level, immediacy, or overlap does not have a single operative definition, a comparison among the existing alternatives is necessary. Previous studies have included illustrations of differences between trend-line fitting techniques using real data. In the current study, I carry out a simulation to study the degree to which different trend-line fitting techniques lead to different degrees of bias, mean square error, and statistical power for a variety of quantifications that entail trend lines. The simulation involves generating both continuous and count data, for several phase lengths, degrees of autocorrelation, and effect sizes (change in level and change in slope). The results suggest that, in general, ordinary least squares estimation performs well in terms of relative bias and mean square error. Especially, a quantification of slope change is associated with better statistical results than quantifying an average difference between conditions on the basis of a projected baseline trend. In contrast, the performance of the split-middle (bisplit) technique is less than optimal.

Bayesian sample size determination using commensurate priors to leverage preexperimental data.

This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant preexperimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions, or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on preexperimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pretrial information is available to be leveraged. Hypothetical data examples, motivated by a rare-disease trial with an elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.