Minimum information about a protein affinity reagent (MIAPAR)

This is a proposal developed within the community as an important first step in formalizing standards in reporting the production and properties of protein binding reagents, such as antibodies, developed and sold for the identification and detection of specific proteins present in biological samples. It defines a checklist of required information, intended for use by producers of affinity reagents, qualitycontrol laboratories, users and databases. We envision that both commercial and freely available affinity reagents, as well as published studies using these reagents, could include a MIAPAR-compliant document describing the product’s properties with every available binding partner. This would enable the user or reader to make a fully informed evaluation of the validity of conclusions drawn using this reagent

Global, regional, and national levels and causes of maternal mortality during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

The fifth Millennium Development Goal (MDG 5) established the goal of a 75% reduction in the maternal mortality ratio (MMR; number of maternal deaths per 100 000 livebirths) between 1990 and 2015. We aimed to measure levels and track trends in maternal mortality, the key causes contributing to maternal death, and timing of maternal death with respect to delivery

Trends in future health financing and coverage: future health spending and universal health coverage in 188 countries, 2016–40

Background: Achieving universal health coverage (UHC) requires health financing systems that provide prepaid pooled resources for key health services without placing undue financial stress on households. Understanding current and future trajectories of health financing is vital for progress towards UHC. We used historical health financing data for 188 countries from 1995 to 2015 to estimate future scenarios of health spending and pooled health spending through to 2040. Methods: We extracted historical data on gross domestic product (GDP) and health spending for 188 countries from 1995 to 2015, and projected annual GDP, development assistance for health, and government, out-of-pocket, and prepaid private health spending from 2015 through to 2040 as a reference scenario. These estimates were generated using an ensemble of models that varied key demographic and socioeconomic determinants. We generated better and worse alternative future scenarios based on the global distribution of historic health spending growth rates. Last, we used stochastic frontier analysis to investigate the association between pooled health resources and UHC index, a measure of a country's UHC service coverage. Finally, we estimated future UHC performance and the number of people covered under the three future scenarios. Findings: In the reference scenario, global health spending was projected to increase from US$10 trillion (95$20 trillion (18 trillion to 22 trillion) in 2040. Per capita health spending was projected to increase fastest in upper-middle-income countries, at 4·2% (3·4–5·1) per year, followed by lower-middle-income countries (4·0%, 3·6–4·5) and low-income countries (2·2%, 1·7–2·8). Despite global growth, per capita health spending was projected to range from only $40 (24–65) to$413 (263–668) in 2040 in low-income countries, and from $140 (90–200) to$1699 (711–3423) in lower-middle-income countries. Globally, the share of health spending covered by pooled resources would range widely, from 19·8% (10·3–38·6) in Nigeria to 97·9% (96·4–98·5) in Seychelles. Historical performance on the UHC index was significantly associated with pooled resources per capita. Across the alternative scenarios, we estimate UHC reaching between 5·1 billion (4·9 billion to 5·3 billion) and 5·6 billion (5·3 billion to 5·8 billion) lives in 2030. Interpretation: We chart future scenarios for health spending and its relationship with UHC. Ensuring that all countries have sustainable pooled health resources is crucial to the achievement of UHC. Funding: The Bill & Melinda Gates Foundation

Correction: Random-Effects, Fixed-Effects and the within-between Specification for Clustered Data in Observational Health Studies: A Simulation Study.

[This corrects the article DOI: 10.1371/journal.pone.0110257.]

Random-Effects, Fixed-Effects and the within-between Specification for Clustered Data in Observational Health Studies: A Simulation Study

<div><p>Background</p><p>When unaccounted-for group-level characteristics affect an outcome variable, traditional linear regression is inefficient and can be biased. The random- and fixed-effects estimators (RE and FE, respectively) are two competing methods that address these problems. While each estimator controls for otherwise unaccounted-for effects, the two estimators require different assumptions. Health researchers tend to favor RE estimation, while researchers from some other disciplines tend to favor FE estimation. In addition to RE and FE, an alternative method called within-between (WB) was suggested by Mundlak in 1978, although is utilized infrequently.</p><p>Methods</p><p>We conduct a simulation study to compare RE, FE, and WB estimation across 16,200 scenarios. The scenarios vary in the number of groups, the size of the groups, within-group variation, goodness-of-fit of the model, and the degree to which the model is correctly specified. Estimator preference is determined by lowest mean squared error of the estimated marginal effect and root mean squared error of fitted values.</p><p>Results</p><p>Although there are scenarios when each estimator is most appropriate, the cases in which traditional RE estimation is preferred are less common. In finite samples, the WB approach outperforms both traditional estimators. The Hausman test guides the practitioner to the estimator with the smallest absolute error only 61% of the time, and in many sample sizes simply applying the WB approach produces smaller absolute errors than following the suggestion of the test.</p><p>Conclusions</p><p>Specification and estimation should be carefully considered and ultimately guided by the objective of the analysis and characteristics of the data. The WB approach has been underutilized, particularly for inference on marginal effects in small samples. Blindly applying any estimator can lead to bias, inefficiency, and flawed inference.</p></div

MSE of marginal effect estimates at baseline.

<p>The red line shows the MSE from the errors in the marginal effect estimates from RE estimation. The blue and green lines shows the same for FE estimation and WB approach, respectively. All simulation inputs are baseline.</p

Well fit model that explains a significant portion of the outcome variable's variance.

<p>Row 1 (interpreted like <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110257#pone-0110257-g003" target="_blank">Figure 3</a>) shows the distribution of the errors in marginal effects estimates from the RE estimation (red), FE estimation (blue), and WB approach (green). Row 2 (interpreted like <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110257#pone-0110257-g004" target="_blank">Figure 4</a>) shows MSE associated with the RE estimation (red), FE estimation (blue), and WB approach (green) errors. Row 3 (interpreted like <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110257#pone-0110257-g006" target="_blank">Figure 6</a>) shows the distribution of the RMSE from the fitted values estimated using RE estimation (red), FE estimation (blue), and WB approach (green). The variance of the residual is set such that it explains only 10% of the variation of the outcome variable. All other simulation input parameters are set to baseline.</p

Dimensions adjusted for simulation.

<p>Dimensions adjusted for simulation.</p

Significant within-group variation relative to between-group variation.

<p>Row 1 (interpreted like <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110257#pone-0110257-g003" target="_blank">Figure 3</a>) shows the distribution of the errors in marginal effects estimates from the RE estimation (red), FE estimation (blue), and WB approach (green). Row 2 (interpreted like <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110257#pone-0110257-g004" target="_blank">Figure 4</a>) shows MSE associated with the RE estimation (red), FE estimation (blue), and WB approach (green) errors. Row 3 (interpreted like <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110257#pone-0110257-g006" target="_blank">Figure 6</a>) shows the distribution of the RMSE from the fitted values estimated using RE estimation (red), FE estimation (blue), and WB approach (green). The within-group variation is set to 0.75, while the between-group variation is 0.25. All other simulation input parameters are set to baseline.</p

Prevalence of random- and fixed-effects in health, economics, and political science literature.

<p>Each archive was searched for the terms “random effects” or “random effect” and “fixed effects” or “fixed effect” present in abstracts. Papers that also used the term “meta” in the abstract were not included in to avoid including meta-analyses which is a very specific use of RE and FE estimation. PubMed is a database that archives life science and biomedical abstracts and references, primarily drawn from the MEDLINE database. EconLit is also an archiving database, published by the American Economic Association, which focuses on economics literature. PAIS is the Public Affairs Information Service International database, which archives references focusing on public affairs.</p