Cranial Anatomical Integration and Disparity Among Bones Discriminate Between Primates and Non-primate Mammals

The primate skull hosts a unique combination of anatomical features among mammals, such as a short face, wide orbits, and big braincase. Together with a trend to fuse bones in late development, these features define the anatomical organization of the skull of primates—which bones articulate to each other and the pattern this creates. Here, I quantified the anatomical organization of the skull of 17 primates and 15 non-primate mammals using anatomical network analysis to assess how the skulls of primates have diverged from those of other mammals, and whether their anatomical differences coevolved with brain size. Results show that primates have a greater anatomical integration of their skulls and a greater disparity among bones than other non-primate mammals. Brain size seems to contribute in part to this difference, but its true effect could not be conclusively proven. This supports the hypothesis that primates have a distinct anatomical organization of the skull, but whether this is related to their larger brains remains an open question.

Addendum to the Acknowledgements: Stepwise-Hierarchical Pooled Analysis for Synergistic Interpretation of Meta-analyses Involving Randomized and Observational Studies: Methodology Development (Preprint)

UNSTRUCTURED The necessity of including observational studies in meta-analyses has been discussed in the literature, but a synergistic analysis method for combining randomized and observational studies has not been reported. Observational studies differ in validity depending on the degree of the confounders’ influence. Combining interpretations may be challenging, especially if the statistical directions are similar but the magnitude of the pooled results are different between randomized and observational studies (the ”gray zone”). To overcome these hindrances, in this study, we aim to introduce a logical method for clinical interpretation of randomized and observational studies. We designed a stepwise-hierarchical pooled analysis method to analyze both distribution trends and individual pooled results by dividing the included studies into at least three stages (eg, all studies, balanced studies, and randomized studies). According to the model, the validity of a hypothesis is mostly based on the pooled results of randomized studies (the highest stage). Ascending patterns in which effect size and statistical significance increase gradually with stage strengthen the validity of the hypothesis; in this case, the effect size of the observational studies is lower than that of the true effect (eg, because of the uncontrolled effect of negative confounders). Descending patterns in which decreasing effect size and statistical significance gradually weaken the validity of the hypothesis suggest that the effect size and statistical significance of the observational studies is larger than the true effect (eg, because of researchers’ bias). We recommend using the stepwise-hierarchical pooled analysis approach for meta-analyses involving randomized and observational studies.

The required size of cluster randomized trials of non-pharmaceutical interventions in epidemic settings

To control the SARS-CoV-2 pandemic and future pathogen outbreaks requires an understanding of which non-pharmaceutical interventions are effective at reducing transmission. Observational studies, however, are subject to biases, even when there is no true effect. Cluster randomized trials provide a means to conduct valid hypothesis tests of the effect of interventions on community transmission. While they may only require a short duration, they often require large sample sizes to achieve adequate power. However, the sample sizes required for such tests in an outbreak setting are largely undeveloped and the question of whether these designs are practical remains unanswered. We develop approximate sample size formulae and simulation-based sample size methods for cluster randomized trials in infectious disease outbreaks. We highlight key relationships between characteristics of transmission and the enrolled communities and the required sample sizes, describe settings where cluster randomized trials powered to detect a meaningful true effect size may be feasible, and provide recommendations for investigators in planning such trials. The approximate formulae and simulation banks may be used by investigators to quickly assess the feasibility of a trial, and then more detailed methods may be used to more precisely size the trial. For example, we show that community-scale trials requiring 220 clusters with 100 tested individuals per cluster are powered to identify interventions that reduce transmission by 40% in one generation interval, using parameters identified for SARS-CoV-2 transmission. For more modest treatment effects, or settings with extreme overdispersion of transmission, however, much larger sample sizes are required.

Infants’ Social Evaluation of Helpers and Hinderers: A Large-Scale, Multi-Lab, Coordinated Replication Study

Evaluating others’ actions as praiseworthy or blameworthy is a fundamental aspect of human nature. A seminal study published in 2007 suggested that the ability to form social evaluations based on third-party interactions emerges within the first year of life, considerably earlier than previously thought (Hamlin, Wynn, & Bloom, 2007). In this study, infants demonstrated a preference for a character (i.e., a shape with eyes) who helped, over one who hindered, another character who tried but failed to climb a hill. This study sparked a new line of inquiry into infants’ social evaluations; however, numerous attempts to replicate the original findings yielded mixed results, with some reporting effects not reliably different from chance. These failed replications point to at least two possibilities: (1) the original study may have overestimated the true effect size of infants’ preference for helpers, or (2) key methodological or contextual differences from the original study may have compromised the replication attempts. Here we present a pre-registered, closely coordinated, multi-laboratory, standardized study aimed at replicating the helping/hindering finding using a well-controlled video version of the hill show. We intended to (1) provide a precise estimate of the true effect size of infants’ preference for helpers over hinderers, and (2) determine the degree to which infants’ preferences are based on social features of the Helper/Hinderer scenarios. XYZ labs participated in the study yielding a total sample size of XYZ infants between the ages of 5.5 and 10.5 months. Brief summary of results will be added after data collection.

Efficacy of Hypochlorite in Disinfesting Non-fungal Plant Pathogens in Agricultural and Horticultural Plant Production: A Meta-analysis

Bleach products containing hypochlorite are commonly used as disinfestants to eliminate non-fungal plant pathogens from production surfaces, tools, plant surfaces, irrigation water, and produce dump tanks. While bleach products are useful, their effectiveness has been reported to vary under specific settings. A meta-analysis was conducted using 86 studies to assess the overall efficacy of hypochlorite against plant pathogenic bacteria, oomycetes and viruses and to identify factors that explain differences in product efficacy. Hypochlorite resulted in a significant (P < 0.0001) reduction in either disease intensity or propagule viability with a mean Hedges’ g standardized mean difference (g+ ) of 3.01, indicating that overall, hypochlorite treatments are highly effective. However, heterogeneity in g was significant (P < 0.0001) among studies, wherein 69.8% of the variance observed in g was attributed to true effects. Further, an estimate of between-study variability was moderate (τ2 = 1.46). Random effects (RE) meta-regression showed limited effects of moderator variables dose, contact time, targeted material of treatment and organism type on product efficacy when all organism types were considered together. Since subgroup was significantly higher (P = 0.0070) for oomycetes (g+ = 3.30) than for bacteria ( g+ = 2.19), subsequent meta-regressions were performed by organism type. For oomycetes, five RE meta-regression models, each containing two moderators and their interaction, resulted in significant (P = 0.05) effects, where models with dose and time, dose and genus, time and genus, dose and target and time and target accounted for up to 50%, 71%, 57%, 48% and 47%, respectively, of the variance in true effect sizes (R2) associated with g+. For viruses, only the RE meta-regression model containing time and target and their interaction resulted in significant (P = 0.0435) effects accounting for 38% of the variance in true effect sizes associated with g+. None of the RE meta-regression models for bacteria were significant, although they still accounted for up to 28% of the variance in true effect sizes associated with g+. These results show that although the current recommended rates for dose and contact time for commercial bleach products are generally expected to result in effective disinfestation, the efficacy against non-fungal plant pathogens is expected to be influenced by the organism type and target being treated with hypochlorite.

When applying GRADE, how do we decide the target of certainty of evidence rating?

The Grades of Recommendation, Assessment, Development and Evaluation’ (GRADE) offers a widely adopted, transparent and structured process for developing and presenting summaries of evidence, including the certainty of evidence, for systematic reviews and recommendations in healthcare. GRADE defined certainty of evidence as ‘the extent of our confidence that the estimates of the effect are correct (in the context of systematic review), or are adequate to support a particular decision or recommendation (in the context of guideline)’. Realising the incoherence in the conceptualisation, the GRADE working group re-clarified the certainty of evidence as ‘the certainty that a true effect lies on one side of a specified threshold, or within a chosen range’. Following the new concept, in the context of both systematic reviews and health technology assessments, it is desirable for GRADE users to specify the thresholds and clarify of which effect they are certain. To help GRADE users apply GRADE in accordance with the new conceptualisation, GRADE defines three levels of contextualisation: minimally, partially and fully contextualised approaches, and provides possible thresholds for each level of contextualisation. In this article, we will use a hypothetic systematic review to illustrate the application of the minimally and partially contextualised approaches, and discuss the application of a fully contextualised approach in deciding how we are rating our certainty (i.e.target of the rating of certainty of evidence).

Vankeusaikaisten kuntoutusohjelmien vaikutusarvioinnit ja arvioitavuus

Suomalaisissa vankiloissa on tehty systemaattista ohjelmatoimintaa noin 20 vuoden ajan. Ohjelmatoiminnan tärkeimpänä tavoitteena on edistää rikoksetonta elämäntapaa, ja siten vähentää vapautumisen jälkeistä uusintarikollisuutta. Uusintarikollisuusvaikutusten todentaminen edellyttää vaikutustutkimuksiin perustuvaa tutkimusnäyttöä, jota on vähitellen alkanut kertymään myös Suomesta. Tässä artikkelissa käydään läpi yksittäisten ohjelmien uusintarikollisuusvaikutuksia koskevien kotimaisten arviointitutkimusten keskeiset tulokset, sekä pohditaan ohjelmatoiminnan vaikutusten arvioinnin mahdollisuuksia tilastollisten tutkimusmenetelmien, tutkimusaineistojen ja ohjelmatoiminnan volyymin näkökulmista. Ohjelmatoiminnan vaikutuksista uusintarikollisuuteen Suomessa ei ole toistaiseksi saatu selvää näyttöä. Vaikka ohjelmien vaikutusten tutkimiseen tarvittavat aineistot ovatkin parantuneet 2000-luvulla selvästi, uskottavaa arviointia vaikeuttavat edelleen hyvien tutkimusasetelmien puute sekä yksittäisten ohjelmien pienet osallistujamäärät. Vaikutusarviointien menetelmällisessä laadussa on parantamisen varaa. Koska yksittäisen ohjelman vaikutus uusintarikollisuuteen on saatavilla olevan tutkimustiedon valossa luultavasti aika maltillinen, kovin pienillä tutkimusaineistoilla vaikutuksia ei kannata jatkossa yrittää tilastollisesti tutkia. Pienet vaikutukset eivät kuitenkaan tarkoita sitä, että ohjelmat eivät voisi olla kannattavia ja kustannustehokkaita. Merkittävä osa nykyisestä ohjelmatoiminnasta on osallistujamääriltään niin pienimuotoista, ettei niiden toimivuudesta tai toimimattomuudesta ole helppo saada luotettavaa tietoa. Tämä tilanne haastaa tutkimustietoon perustuvan ohjelmatoiminnan kehittämistä. Sasu Tyni and Mikko Aaltonen: Evaluation research on rehabilitation programs in prison. Rehabilitation programs have been used systematically in Finnish prisons now for two decades. The main aim of these programs is to promote desistance and reduce recidivism after release. Several Finnish evaluations of prison programs have been published during the recent years. In this article, we start by reviewing the key results of these studies and consider the possibilities and limitations of quasi-experimental program evaluation in the light of available register data and the scale of program uptake. So far none of the Finnish evaluations have shown evidence of programs reducing recidivism. Even though the datasets needed for evaluation have improved clearly in the 21st century, credible evaluation of programs’ causal effects is still hampered by lack of strong research designs and low participant rates in most programs. The methodological quality of evaluations should be improved. Given that the available evidence suggests that true effect sizes of programs are likely to be relatively small, large samples are needed to attain enough statistical power to detect effects or their absence. At the same time, even if true effect sizes are small, the programs can still be cost-effective. The low numbers of participants in most prison programs present a challenge to evidence-based program development. Keywords: prison rehabilitation – recidivism – program evaluation – statistical power – causal inference

Stepwise-hierarchical pooled analysis: a synergistic interpretation of meta-analysis including randomized and observational studies: A methodologic study (Preprint)

BACKGROUND The necessity of meta-analyses including observational studies has been discussed in the literature, but a synergistic analysis method combining randomised and observational studies has not been reported. OBJECTIVE This study introduces a logical method for clinical interpretation. METHODS Observational studies differ in validity depending on the degree of the confounders’ influence. Combining interpretations might be challenging, especially if the statistical directions are similar but the magnitude of the pooled results are different, between randomised and observational studies (grey zone). We designed a stepwise-hierarchical pooled analysis, a method of analysing distribution trends as well as individual pooled results by dividing included studies into at least three stages (e.g. all studies, balanced studies, and randomised studies), to overcome such hindrances. RESULTS According to the model, the validity of hypothesis are mostly based on the pooled results of randomised studies (the highest stage). In addition, ascending patterns where effect size and statistical significance increase gradually with stage, strengthen the validity of the hypothesis; in this case, the effect size of observational studies is lower than that of the true effect (e.g. because of uncontrolled effect of negative confounders). Descending patterns where decreasing effect size and statistical significance gradually weaken the validity of the hypothesis suggest that the effect size and statistical significance of observational studies is larger than the true effect (e.g. because of researchers’ bias). These are described in more detail in the main text as four descriptive patterns. CONCLUSIONS We recommend using the stepwise-hierarchical pooled analysis for meta-analyses involving randomised and observational studies. CLINICALTRIAL NA