Phosphate-Binding Agents in Adults With CKD: A Network Meta-analysis of Randomized Trials

Background Guidelines preferentially recommend noncalcium phosphate binders in adults with chronic kidney disease (CKD). We compare and rank phosphate-binder strategies for CKD. Study Design Network meta-analysis. Setting & Population Adults with CKD. Selection Criteria for Studies Randomized trials with allocation to phosphate binders. Interventions Sevelamer, lanthanum, iron, calcium, colestilan, bixalomer, nicotinic acid, and magnesium. Outcomes The primary outcome was all-cause mortality. Additional outcomes were cardiovascular mortality, myocardial infarction, stroke, adverse events, serum phosphorus and calcium levels, and coronary artery calcification. Results 77 trials (12,562 participants) were included. Most (62 trials in 11,009 patients) studies were performed in a dialysis population. Trials were generally of short duration (median, 6 months) and had high risks of bias. All-cause mortality was ascertained in 20 studies during 86,744 patient-months of follow-up. There was no evidence that any drug class lowered mortality or cardiovascular events when compared to placebo. Compared to calcium, sevelamer reduced all-cause mortality (OR, 0.39; 95% CI, 0.21-0.74), whereas treatment effects of lanthanum, iron, and colestilan were not significant (ORs of 0.78 [95% CI, 0.16-3.72], 0.37 [95% CI, 0.09-1.60], and 0.55 [95% CI, 0.07-4.43], respectively). Lanthanum caused nausea, whereas sevelamer posed the highest risk for constipation and iron caused diarrhea. All phosphate binders lowered serum phosphorus levels to a greater extent than placebo, with iron ranked as the best treatment. Sevelamer and lanthanum posed substantially lower risks for hypercalcemia than calcium. Limitations Limited testing of consistency; short follow-up. Conclusions There is currently no evidence that phosphate-binder treatment reduces mortality compared to placebo in adults with CKD. It is not clear whether the higher mortality with calcium versus sevelamer reflects whether there is net harm associated with calcium, net benefit with sevelamer, both, or neither. Iron-based binders show evidence of greater phosphate lowering that warrants further examination in randomized trials

Can subjective well-being predict unemployment length ?

This paper uses 16 waves of panel data from the British Household Panel Survey to evaluate the role of subjective well-being in determining labor market transitions. It confirms a previous finding in the literature: individuals report a fall in their happiness when they lose a job, but they report a smaller fall when they are surrounded by unemployed peers, an effect called the"social norm". The main results of interest are that job search effort and unemployment duration areaffected by the utility differential between having a job and being unemployed. Since this differential is also affected by the social norm, it implies that when unemployment increases, the unemployed are happier and they reduce their search effort. These results indicate that unemployment hysteresis has labor supply causes.Labor Markets,Labor Policies,Population Policies,Youth and Governance,Economic Theory&Research

The binding constraint on firms'growth in developing countries

Firms in developing countries face numerous and serious constraints on their growth, ranging from corruption to lack of infrastructure to inability to access finance. Countries lack the resources to remove all the constraints at once and so would be better off removing the most binding one first. This paper uses data from World Bank Enterprise Surveys in 2006-10 to identify the most binding constraints on firm operations in developing countries. While each country faces a different set of constraints, these constraints also vary by firm characteristics, especially firm size. Across all countries, access to finance is among the most binding constraints; other obstacles appear to matter much less. This result is robust for all regions. Smaller firms must rely more on their own funds to invest and would grow significantly faster if they had greater access to external funds. As a result, a low level of financial development skews the firm size distribution by increasing the relative share of small firms. The results suggest that financing constraints play a significant part in explaining the"missing middle"-- the failure of small firms in developing countries to grow into medium-size or large firms.Access to Finance,Environmental Economics&Policies,Microfinance,Debt Markets,Banks&Banking Reform

Estimating the sample size of sham-controlled randomized controlled trials using existing evidence [version 2; peer review: 2 approved].

Background: In randomized controlled trials (RCTs), the power is often 'reverse engineered' based on the number of participants that can realistically be achieved. An attractive alternative is planning a new trial conditional on the available evidence; a design of particular interest in RCTs that use a sham control arm (sham-RCTs). Methods: We explore the design of sham-RCTs, the role of sequential meta-analysis and  conditional planning in a systematic review of renal sympathetic denervation for patients with arterial hypertension. The main efficacy endpoint was mean change in 24-hour systolic blood pressure. We performed sequential meta-analysis to identify the time point where the null hypothesis would be rejected in a prospective scenario. Evidence-based conditional sample size calculations were performed based on fixed-effect meta-analysis. Results: In total, six sham-RCTs (981 participants) were identified. The first RCT was considerably larger (535 participants) than those subsequently published (median sample size of 80). All trial sample sizes were calculated assuming an unrealistically large intervention effect which resulted in low power when each study is considered as a stand-alone experiment. Sequential meta-analysis provided firm evidence against the null hypothesis with the synthesis of the first four trials (755 patients, cumulative mean difference -2.75 (95%CI -4.93 to -0.58) favoring the active intervention)). Conditional planning resulted in much larger sample sizes compared to those in the original trials, due to overoptimistic expected effects made by the investigators in individual trials, and potentially a time-effect association. Conclusions: Sequential meta-analysis of sham-RCTs can reach conclusive findings earlier and hence avoid exposing patients to sham-related risks. Conditional planning of new sham-RCTs poses important challenges as many surgical/minimally invasive procedures improve over time, the intervention effect is expected to increase in new studies and this violates the underlying assumptions. Unless this is accounted for, conditional planning will not improve the design of sham-RCTs

A forward search algorithm for detecting extreme study effects in network meta-analysis

In a quantitative synthesis of studies via meta-analysis, it is possible that some studies provide a markedly different relative treatment effect or have a large impact on the summary estimate and/or heterogeneity. Extreme study effects (outliers) can be detected visually with forest/funnel plots and by using statistical outlying detection methods. A forward search (FS) algorithm is a common outlying diagnostic tool recently extended to meta-analysis. FS starts by fitting the assumed model to a subset of the data which is gradually incremented by adding the remaining studies according to their closeness to the postulated data-generating model. At each step of the algorithm, parameter estimates, measures of fit (residuals, likelihood contributions), and test statistics are being monitored and their sharp changes are used as an indication for outliers. In this article, we extend the FS algorithm to network meta-analysis (NMA). In NMA, visualization of outliers is more challenging due to the multivariate nature of the data and the fact that studies contribute both directly and indirectly to the network estimates. Outliers are expected to contribute not only to heterogeneity but also to inconsistency, compromising the NMA results. The FS algorithm was applied to real and artificial networks of interventions that include outliers. We developed an R package (NMAoutlier) to allow replication and dissemination of the proposed method. We conclude that the FS algorithm is a visual diagnostic tool that helps to identify studies that are a potential source of heterogeneity and inconsistency

Introducing the Treatment Hierarchy Question in Network Meta-Analysis

Comparative effectiveness research using network meta-analysis can present a hierarchy of competing treatments, from the most to the least preferable option. However, in published reviews, the research question associated with the hierarchy of multiple interventions is typically not clearly defined. Here we introduce the novel notion of a treatment hierarchy question that describes the criterion for choosing a specific treatment over one or more competing alternatives. For example, stakeholders might ask which treatment is most likely to improve mean survival by at least 2 years, or which treatment is associated with the longest mean survival. We discuss the most commonly used ranking metrics (quantities that compare the estimated treatment-specific effects), how the ranking metrics produce a treatment hierarchy, and the type of treatment hierarchy question that each ranking metric can answer. We show that the ranking metrics encompass the uncertainty in the estimation of the treatment effects in different ways, which results in different treatment hierarchies. When using network meta-analyses that aim to rank treatments, investigators should state the treatment hierarchy question they aim to address and employ the appropriate ranking metric to answer it. Following this new proposal will avoid some controversies that have arisen in comparative effectiveness research

Comparative Effectiveness of Brief Alcohol Interventions for College Students: Results from a Network Meta-Analysis

Background Late adolescence is a time of increased drinking, and alcohol plays a predominant role in college social experiences. Colleges seeking to prevent students’ hazardous drinking may elect to implement brief alcohol interventions (BAIs). However, numerous manualized BAIs exist, so an important question remains regarding the comparative effectiveness of these different types of BAIs for college students. Aim This study uses network meta-analyses (NMA) to compare seven manualized BAIs for reducing problematic alcohol use among college students. Methods We systematically searched multiple sources for literature, and we screened studies and extracted data in duplicate. For the quantitative synthesis, we employed a random-effects frequentist NMA to determine the effectiveness of different BAIs compared to controls, and estimated the relative effectiveness ranking of each BAI. Results A systematic literature search resulted in 52 included studies: on average, 58% of participants were male, 75% were binge drinkers, and 20% were fraternity/sorority-affiliated students. Consistency models demonstrated that BASICS was consistently effective in reducing students’ problematic alcohol use (ES range: g=−0.23, 95%CI [−0.36,−0.16] to g=−0.36, 95% CI [−0.55,−0.18]), but AlcoholEDU (g=−0.13, 95%CI [−0.22,−0.04]), e-CHUG (g=−0.35, 95%CI [−0.45,−0.05]), and THRIVE (g=−0.47, 95%CI [−0.60,−0.33]) were also effective for some outcomes. Intervention rankings indicated that BASICS, THRIVE, and AlcoholEDU hold the most promise for future trials. Conclusions Several BAIs appear effective for college students. BASICS was the most effective but is resource intensive and may be better suited for higher risk students; THRIVE and e-CHUG are less resource intensive and show promise for universal prevention efforts

The host with the most? The effects of the Olympic Games on happiness

We show that hosting the Olympic Games in 2012 had a positive impact on the life satisfaction and happiness of Londoners during the Games, compared to residents of Paris and Berlin. Notwithstanding issues of causal inference, the magnitude of the effects is equivalent to moving from the bottom to the fourth income decile. But they do not last very long: the effects are gone within a year. These conclusions are based on a novel panel survey of 26,000 individuals who were interviewed during the summers of 2011, 2012, and 2013, i.e. before, during, and after the event. The results are robust to selection into the survey and to the number of medals won

Bayesian models for aggregate and individual patient data component network meta-analysis.

Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics