Range Of Plausible Assumptions Research Articles

Conducting power analysis for meta-analysis with dependent effect sizes: Common guidelines and an introduction to the POMADE R package.

Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work has provided power approximation formulas for meta-analyses involving studies with multiple, dependent effect size estimates, which are common in syntheses of social science research. Prior work focused on developing and validating the approximations but did not address the practice challenges encountered in applying them for purposes of planning a synthesis involving dependent effect sizes. We aim to facilitate the application of these recent developments by providing practical guidance on how to conduct power analysis for planning a meta-analysis of dependent effect sizes and by introducing a new R package, POMADE, designed for this purpose. We present a comprehensive overview of resources for finding information about the study design features and model parameters needed to conduct power analysis, along with detailed worked examples using the POMADE package. For presenting power analysis findings, we emphasize graphical tools that can depict power under a range of plausible assumptions and introduce a novel plot, the traffic light power plot, for conveying the degree of certainty in one's assumptions.

The influence of loss to follow-up in autism screening research: Taking stock and moving forward.

How best to improve the early detection of autism spectrum disorder (ASD) is the subject of significant controversy. Some argue that universal ASD screeners are highly accurate, whereas others argue that evidence for this claim is insufficient. Relatedly, there is no clear consensus as to the optimal role of screening for making referral decisions for evaluation and treatment. Published screening research can meaningfully inform these questions-but only through careful consideration of children who do not complete diagnostic follow-up. We developed two simulation models that re-analyze the results of a large-scale validation study of the M-CHAT-R/F by Robins etal. (2014, Pediatrics, 133, 37). Model #1 re-analyzes screener accuracy across six scenarios, each reflecting different assumptions regarding loss to follow-up. Model #2 builds on this by closely examining differential attrition at each point of the multi-step detection process. Estimates of sensitivity ranged from 40% to 94% across scenarios, demonstrating that estimates of accuracy depend on assumptions regarding the diagnostic status of children who were lost to follow-up. Across a range of plausible assumptions, data also suggest that children with undiagnosed ASD may be more likely to complete follow-up than children without ASD, highlighting the role of clinicians and caregivers in the detection process. Using simulation modeling as a quantitative method to examine potential bias in screening studies, analyses suggest that ASD screening tools may be less accurate than is often reported. Models also demonstrate the critical importance of every step in a detection process-including steps that determine whether children should complete an additional evaluation. We conclude that parent and clinician decision-making regarding follow-up may contribute more to detection than is widely assumed.

The societal economic value of COVID-19 vaccines in the United States

Aims The COVID-19 pandemic has claimed the lives of more than 800,000 people in the United States (US) and has been estimated to carry a societal cost of $16 trillion over the next decade. The availability of COVID-19 vaccines has had a profound effect on the trajectory of the pandemic, with wide-ranging benefits. We aimed to estimate the total societal economic value generated in the US from COVID-19 vaccines. Methods We developed a population-based economic model informed by existing data and literature to estimate the total societal value generated from COVID-19 vaccines by avoiding COVID-19 infections as well as resuming social and economic activity more quickly. To do this, we separately estimated the value generated from life years saved, healthcare costs avoided, quality of life gained, and US gross domestic product (GDP) gained under a range of plausible assumptions. Results Findings from our base case analysis suggest that from their launch in December 2020, COVID-19 vaccines were projected to generate $5.0 trillion in societal economic value for the US from avoided COVID-19 infections and resuming unrestricted social and economic activity more quickly. Our scenario analyses suggest that the value could range between $1.8 and $9.9 trillion. Our model indicates that the most substantial sources of value are derived from reduction in prevalence of depression ($1.9 trillion), gains to US GDP ($1.4 trillion), and lives saved from fewer COVID-19 infections ($1.0 trillion). Limitations Constructed as a projection from December 2020, our model does not account for the Delta or future variants, nor does it account for improvements in COVID-19 treatment. Conclusions The magnitude of economic benefit from vaccination highlights the need for coordinated policy decisions to support continued widespread vaccine uptake in the US.

A practical guide to pre-trial simulations for Bayesian adaptive trials using SAS and BUGS.

It is often unclear what specific adaptive trial design features lead to an efficient design which is also feasible to implement. Before deciding on a particular design, it is generally advisable to carry out a simulation study to characterise the properties of candidate designs under a range of plausible assumptions. The implementation of such pre‐trial simulation studies presents many challenges and requires considerable statistical programming effort and time. Despite the scale and complexity, there is little existing literature to guide the implementation of such projects using commonly available software. This Teacher's Corner article provides a practical step‐by‐step guide to implementing such simulation studies including how to specify and fit a Bayesian model in WinBUGS or OpenBUGS using SAS, and how results from the Bayesian analysis may be pulled back into SAS and used for adaptation of allocation probabilities before simulating subsequent stages of the trial. The interface between the two software platforms is described in detail along with useful tips and tricks. A key strength of our approach is that the entire exercise can be defined and controlled from within a single SAS program.

Utility-free heuristic models of two-option choice can mimic predictions of utility-stage models under many conditions.

Economists often model choices as if decision-makers assign each option a scalar value variable, known as utility, and then select the option with the highest utility. It remains unclear whether as-if utility models describe real mental and neural steps in choice. Although choices alone cannot prove the existence of a utility stage, utility transformations are often taken to provide the most parsimonious or psychologically plausible explanation for choice data. Here, we show that it is possible to mathematically transform a large set of common utility-stage two-option choice models (specifically ones in which dimensions are can be decomposed into additive functions) into a heuristic model (specifically, a dimensional prioritization heuristic) that has no utility computation stage. We then show that under a range of plausible assumptions, both classes of model predict similar neural responses. These results highlight the difficulties in using neuroeconomic data to infer the existence of a value stage in choice.

Why so negative? Evidence aggregation and armchair philosophy

This paper aims to clarify a debate on philosophical method, and to give a probabilistic argument vindicating armchair philosophy under a wide range of plausible assumptions. The use of intuitions by so-called armchair philosophers has been criticized on empirical grounds. The debate between armchair philosophers and their empirical critics would benefit from greater clarity and precision in our understanding of what it takes for intuition-based approaches to philosophy to make sense. This paper discusses a set of rigorous, probability-based tools for determining what we can and cannot learn from intuitions in various conditions. These tools can tell us whether beliefs can be justified by armchair practices, and what empirical findings would have to show to undermine the use of intuitions in philosophy. Using these tools, the paper shows that armchair philosophy makes sense in a broad range of situations, and that it is quite plausible that we are in those situations at the moment.

From a single-level analysis to a multilevel analysis of single-case experimental designs

Multilevel modeling provides one approach to synthesizing single-case experimental design data. In this study, we present the multilevel model (the two-level and the three-level models) for summarizing single-case results over cases, over studies, or both. In addition to the basic multilevel models, we elaborate on several plausible alternative models. We apply the proposed models to real datasets and investigate to what extent the estimated treatment effect is dependent on the modeling specifications and the underlying assumptions. By considering a range of plausible models and assumptions, researchers can determine the degree to which the effect estimates and conclusions are sensitive to the specific assumptions made. If the same conclusions are reached across a range of plausible assumptions, confidence in the conclusions can be enhanced. We advise researchers not to focus on one model but conduct multiple plausible multilevel analyses and investigate whether the results depend on the modeling options.

184 Handling missing participant data in meta-analysis of dichotomous outcomes: Proposed guidelines for systematic reviews of randomised trials

ObjectivesSystematic reviewers including all randomised participants in their meta-analyses need to make assumptions about the outcomes of those with missing data.Our objective is to provide systematic review authors with guidance...

057 Handling Trial Participants with Missing Data in Meta-Analyses Of Dichotomous Outcomes: Guidance for Systematic Reviewers

BackgroundSystematic reviewers including all randomised participants in their meta-analyses need to make assumptions about the outcomes of those with missing data.Objectives To provide systematic review authors with guidance on dealing...

Sensitivity Analysis Using Elicited Expert Information for Inference With Coarsened Data: Illustration of Censored Discrete Event Times in the AIDS Link to Intravenous Experience (ALIVE) Study

In this paper, the authors use the rubric of "coarsened data," of which missing and censored data are special cases, to motivate the elicitation and use of expert information for performing sensitivity analyses of censored event-time data. Elicited information is important because observed data are insufficient to estimate how study participants with coarsened data compare with participants with uncoarsened data, and misspecifying this comparison may produce biased analysis results. In the presence of coarsening, performing a sensitivity analysis over a range of plausible assumptions is the best one can do. Here the authors illustrate an approach for eliciting expert information for use in sensitivity analyses to compare cumulative incidence functions of censored nonmortality outcomes. An example of such data is the AIDS Link to Intravenous Experience (ALIVE) Study, where the authors aim to estimate and compare cumulative incidence functions for human immunodeficiency virus between risk factor categories. The interval and right-censoring and censoring due to death found in the ALIVE data (1988-1998) are thought to be informative; thus, a sensitivity analysis is performed using information elicited from 2 ALIVE scientists and an expert in acquired immunodeficiency syndrome epidemiology about the relation between seroconversion and censoring.

Getting Big Too Fast: Strategic Dynamics with Increasing Returns and Bounded Rationality

Prior research on competitive strategy in the presence of increasing returns suggests that early entrants can achieve sustained competitive advantage by pursuing Get Big Fast (GBF) strategies: rapidly expanding capacity and cutting prices to gain market share advantage and exploit positive feedbacks faster than their rivals. Yet a growing literature in dynamics and behavioral economics, and the experience of firms during the 2000 crash, raise questions about the GBF prescription. Prior studies generally presume rational actors, perfect foresight and equilibrium. Here we consider the robustness of the GBF strategy in a dynamic model with boundedly rational agents. Agents are endowed with high local rationality but imperfect understanding of the feedback structure of the market; they use intendedly rational heuristics to forecast demand, acquire capacity, and set prices. These heuristics are grounded in empirical study and experimental test. Using a simulation of the duopoly case we show GBF strategies become suboptimal when market dynamics are rapid relative to capacity adjustment. Under a range of plausible assumptions, forecasting errors lead to excess capacity, overwhelming the cost advantage conferred by increasing returns. We explore the sensitivity of the results to assumptions about agent rationality and the feedback complexity of the market. The results highlight the risks of incorporating traditional neoclassical simplifications in strategic prescriptions and demonstrate how disequilibrium behavior and bounded rationality can be incorporated into strategic analysis to form a dynamic, behavioral game theory amenable to rigorous analysis.

The evolution of the water distribution in a viscous protoplanetary disk

Astronomical observations have shown that protoplanetary disks are dynamic objects through which mass is transported and accreted by the central star. This transport causes the disks to decrease in mass and cool over time, and such evolution is expected to have occurred in our own solar nebula. Age dating of meteorite constituents shows that their creation, evolution, and accumulation occupied several Myr, and over this time disk properties would evolve significantly. Moreover, on this timescale, solid particles decouple from the gas in the disk and their evolution follows a different path. It is in this context that we must understand how our own solar nebula evolved and what effects this evolution had on the primitive materials contained within it. Here we present a model which tracks how the distribution of water changes in an evolving disk as the water-bearing species experience condensation, accretion, transport, collisional destruction, and vaporization. Because solids are transported in a disk at different rates depending on their sizes, the motions will lead to water being concentrated in some regions of a disk and depleted in others. These enhancements and depletions are consistent with the conditions needed to explain some aspects of the chemistry of chondritic meteorites and formation of giant planets. The levels of concentration and depletion, as well as their locations, depend strongly on the combined effects of the gaseous disk evolution, the formation of rapidly migrating rubble, and the growth of immobile planetesimals. Understanding how these processes operate simultaneously is critical to developing our models for meteorite parent body formation in the Solar System and giant planet formation throughout the galaxy. We present examples of evolution under a range of plausible assumptions and demonstrate how the chemical evolution of the inner region of a protoplanetary disk is intimately connected to the physical processes which occur in the outer regions.

Pattern–Mixture and Selection Models for Analysing Longitudinal Data with Monotone Missing Patterns

Summary We examine three pattern–mixture models for making inference about parameters of the distribution of an outcome of interest Y that is to be measured at the end of a longitudinal study when this outcome is missing in some subjects. We show that these pattern–mixture models also have an interpretation as selection models. Because these models make unverifiable assumptions, we recommend that inference about the distribution of Y be repeated under a range of plausible assumptions. We argue that, of the three models considered, only one admits a parameterization that facilitates the examination of departures from the assumption of sequential ignorability. The three models are nonparametric in the sense that they do not impose restrictions on the class of observed data distributions. Owing to the curse of dimensionality, the assumptions that are encoded in these models are sufficient for identification but not for inference. We describe additional flexible and easily interpretable assumptions under which it is possible to construct estimators that are well behaved with moderate sample sizes. These assumptions define semiparametric models for the distribution of the observed data. We describe a class of estimators which, up to asymptotic equivalence, comprise all the consistent and asymptotically normal estimators of the parameters of interest under the postulated semiparametric models. We illustrate our methods with the analysis of data from a randomized clinical trial of contracepting women.

Carbon Taxes and Carbon Emissions Trading

This paper surveys the literature on, and examples of current implementation of, carbon taxes and carbon emission permits. It sets out the theoretical basis for these instruments, with special reference to the revenue‐recycling and tax interaction effects. This theoretical work concludes that instruments which raise revenue which can be recycled so as to reduce pre‐existing distortionary taxes are significantly less costly than those which do not. The paper then reviews the sizable literature on the distributional effects of these instruments, especially with regard to industrial competitiveness and regressive effects on low‐income groups, evaluating attempts to mitigate these where they are perceived as unacceptable. The paper concludes that such efforts at mitigation, while possible, can substantially reduce the efficiency benefits of the instruments. The projected costs of carbon taxes depend on a wide range of assumptions. This is still a contested area, but the paper concludes that, on a range of plausible assumptions, these costs need not be high. Finally the paper notes that early evaluations of the environmental effectiveness of carbon taxes have been generally positive. This suggests that, if concern about anthropogenic climate change continues to increase, more countries will introduce carbon taxes and emission permits, with the latter increasingly auctioned.

Cost Effectiveness of Fluticasone and Budesonide in Patients with Moderate Asthma

The objective of this study was to assess the relative cost effectiveness of fluticasone via metered dose inhaler and budesonide via Turbuhaler((R)) in corticosteroid-naive patients with moderate asthma from a third-party payer perspective (German Sickness Funds). A retrospective economic assessment of direct medication costs of treatment was performed on data from a prospective, randomised, parallel group, 6-week clinical trial. 457 corticosteroid-naive patients between the ages of 18 and 70 years with moderate asthma were included in the intention-to-treat analysis. The fluticasone group had a higher proportion of successfully treated patients (those with a peak expiratory flow rate improvement of >/=10%) [47 vs 42%], a higher average proportion of symptom-free days (40 vs 34%) and lower direct healthcare costs [1997 Deutschmarks (DM)] per day (DM4.23 vs DM5.19) than the budesonide group. Therefore, the daily costs per successfully treated patient (DM9.00 vs DM12.36) and the cost per symptom-free day (DM10.58 vs DM15.26) were both lower with fluticasone than with budesonide. Sensitivity analysis demonstrated that these results were relatively robust over a wide range of plausible assumptions. These results showed that from the perspective of a third-party payer, fluticasone was more cost effective than budesonide over the 6-week study period.

Optimal weighting scheme for affected sib-pair analysis of sibship data.

Application of the affected sib-pair method of linkage analysis to sibships with variable numbers of affected and unaffected members requires a scheme for weighting the contributions from the sibships in the calculation of an overall test statistic. Currently accepted weighting schemes are based on the concepts of independent pairs and Shannon information content. Here, we show that the weighting scheme with maximum power to detect linkage can be determined from the theoretical means and variances of the sibship contributions under the null and alternative hypotheses. We derive the theoretical means and variances of the contributions from different types of sibships under a generalized single locus model. We use these theoretical means and variances to obtain the optimal weights for a variety of single locus models, and compare the power of existing weighting schemes relative to the optimum. The results suggest that, under a range of plausible assumptions, optimal power is nearly obtained by weighting to all sibpairs equally, except those occurring in sibships with so many affected siblings (usually five or more) that the probability of parental homozygosity for the disease allele becomes substantial. A corollary is that, in affected sib-pair analysis, the 'informativeness' of a sibship is more nearly proportional to the number of affected sib-pairs than to the number of affected siblings, up to about five affected siblings.

Uncertainties in Climatological Tropical Humidity Profiles: Some Implications for Estimating the Greenhouse Effect

Abstract The vertical profile of water vapor, the principal infrared-absorbing gas in the atmosphere, is an important factor in determining the energy balance of the climate system. This study examines uncertainties in calculating a climatologicai humidity profile: specifically one derived from radiosonde data representative of the moist and highly convective region over the western tropical Pacific Ocean. Uncertainties in the humidity data are large in conditions of low temperature or low humidity in the mid- and upper troposphere. Results derived from a single United States station (Koror) and from an average of four United States--operated stations (all near the equator west of the date line) yield nearly identical results. No humidity measurements are reported in fully the upper third of the troposphere. The implications of these uncertainties for determining the climatological humidity profile are quantitatively assessed by bracketing the range of plausible assumptions for unreported humidity to prod...

Prevention of the Wernicke‐Korsakoff syndrome in Australia: a cost‐benefit analysis

The National Health and Medical Research Council (NHMRC) recently has recommended that thiamin be added to cask and flagon wines, fortified wines, beer and flour. This paper presents a cost-benefit analysis of the fortification of alcoholic beverages with thiamin. A total cost-saving in present-value terms of between $60 million and $600 million in 1985 Australian dollars has been calculated. Considerable methodological problems are associated with this type of analysis, and these are discussed. However, the cost-benefit ratio under a range of plausible assumptions remains extremely high--in the region of six to one to 30 to one, or even higher. The methods that are used are compared with those of a 1978 US study, which is appraised critically. The fortification programme that was recommended by the NHMRC is supported strongly on economic grounds.