Cause Of Bias Research Articles

Randomized Adapters for Reducing Bias in Small RNA-Seq Libraries

Distinguishing closely-related small RNAs is difficult using microarray- and qPCR-based approaches, since imperfectly matched small RNAs may still be able to hybridize to PCR primers or immobilized probes. These considerations have led to the realization that next generation sequencing (NGS) is the most practical method for large-scale small RNA studies that aim to identify and enumerate small RNAs in various species and tissues. NGS offers advantages of sensitivity, specificity, and the ability to maximize data acquisition and minimize costs by using multiplex strategies to allow many samples to be sequenced simultaneously. NGS approaches for small RNA analysis are not without their own challenges. Small RNA libraries prepared for sRNA-Seq have been found to contain biases, resulting in libraries that inaccurately represent relative levels of the different small RNAs present in the starting RNA sample. Much effort has gone into identifying the cause of bias, and it is now generally accepted that bias in sRNA-seq libraries is primarily introduced by the T4-phage RNA ligases used during the ligation step of small RNA library preparation (2, 3, 4). NGS libraries are made by ligation of adapter oligonucleotides to the 5’ and 3’ ends of the target nucleic acids. The adapters comprise sequences needed to amplify the library by PCR using generic Forward and Reverse primers, as well as sequences needed to associate the target nucleic acids with the NGS sequencing instrument (e.g. the flowcell in Illumina sequencers) and optionally, sequences comprising barcodes to allow multiplexing. For sequencing small RNAs, adapters are added directly to the population of small RNAs using RNA ligases derived from T4 bacteriophage (Rnl1 for 5’ adapter ligation and Rnl2 for 3’ adapter ligation). Ideally, the RNA ligases would show no preference for attaching adapters to small RNAs of different sequences, but the reality is that RNA ligases show sequencespecific bias, resulting in preferential inclusion of some small RNAs in the sRNA-seq libraries, at the expense of others. Bioo Scientific has developed a novel solution to this problem, which allows meaningful results to be obtained in comparing small RNA profiles between samples.

Improving uncertainty estimation in urban hydrological modeling by statistically describing bias

Abstract. Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and relatively efficient methodology (i) to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii) to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall) or/and output (runoff) of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described with a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias, and measurement errors. In our case study, the best performing bias description is the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to address the causes of model discrepancies. Further research should focus on quantifying and reducing the causes of bias by improving the model structure and propagating input uncertainty.

Accuracy of micrometeorological techniques for detecting a change in methane emissions from a herd of cattle

Micrometeorological techniques are effective in measuring methane (CH4) emission rates at the herd scale, but their suitability as verification tools for emissions mitigation depends on the uncertainty with which they can detect a treatment difference. An experiment was designed to test for a range of techniques whether they could detect a change in weekly mean emission rate from a herd of cattle, in response to a controlled change in feed supply. The cattle were kept in an enclosure and fed pasture baleage, of amounts increasing from one week to the next. Methane emission rates were measured at the herd scale by the following techniques: (1) an external tracer-ratio technique, releasing nitrous oxide (N2O) from canisters on the animals’ necks and measuring line-averaged CH4 and N2O mole fractions with Fourier-transform infra-red (FTIR) spectrometers deployed upwind and downwind of the cattle, (2) a mass-budget technique using vertical profiles of wind speed and CH4 mole fraction, (3) a dispersion model, applied separately to CH4 mole fraction data from the FTIR spectrometers, the vertical profile, and a laser system measuring along four paths surrounding the enclosure. For reference, enteric CH4 emissions were also measured at the animal scale on a daily basis, using an enteric tracer-ratio technique (with SF6 as the tracer). The animal-scale technique showed that mean CH4 emissions increased less than linearly with increasing feed intake. The herd-scale techniques showed that the emission rates followed a diurnal pattern, with the maximum about 2h after the feed was offered. The herd-scale techniques could detect the weekly changes in emission levels, except that the two vertical-profile techniques (mass-budget technique and dispersion model applied to profile) failed to resolve the first step change. The weekly emission rates from the external tracer-ratio technique and the dispersion model, applied to data from either the two FTIR paths or the four laser paths, agreed within ±10% with the enteric tracer-ratio technique. By contrast, the two vertical-profile techniques gave 33–68% higher weekly emission rates. It is shown with a sensitivity study that systematically uneven animal distribution within the enclosure could explain some of this discrepancy. Another cause for bias was the data yield of the vertical-profile techniques being higher at day-time than at night-time, thus giving more weight to times of larger emission rates. The techniques using line-averaged mole fractions were less sensitive to the exact locations of emission sources and less prone to data loss from unsuitable wind directions; these advantages outweighed the lack of a method to calibrate CH4 mole fractions in situ.

Health Economic Controversy and Cost-Effectiveness of Proton Therapy

Owing to increasing healthcare costs, there is a need to examine whether the benefits of new technologies are worth the extra cost. In proton therapy, where the evidence in favor is limited, it is heavily debated whether the expected benefit justifies the higher capital and operating costs. The aim of this article was to explore the existing methodologies of economic evaluations (EEs) of particle therapy and recommend an approach for future data collection and analysis. We reviewed the published literature on health economics of proton therapy using accepted guidelines on performing EE. Different cost strategies were assessed and comparisons with other treatment modalities were made in terms of cost-effectiveness. Potential bias in the existing studies was identified and new methodologies proposed. The principal cause of bias in EEs of proton therapy is the lack of valid data on effects as well as costs. The introduction of proton therapy may be seriously hampered by the lack of outcome and cost data and the situation is likely to continue not only in terms of justifying the capital investment but also covering the operational costs. We identified an urgent need to collect appropriate data to allow for reimbursement of such novel technology. In the absence of level 1 evidence, well-performed modeling studies taking into account the available cost and outcome parameters, including the current uncertainties, can help to address the problem of limited outcome and health economic data. The approach of coverage with evidence development, in which evidence is collected in an ongoing manner in population-based registries along with dedicated financing, may allow technological advances with limited initial evidence of benefit and value, such as protons, to become available to patients in an early phase of their technology life cycle.

Benchmarking global land surface models against the observed mean annual runoff from 150 large basins

Summary Using the observed mean annual runoff for 1986–1995 from 150 large basins globally, we evaluate the performance of the 14 global land surface models (LSMs) and six Budyko-type hydrological models that are forced by the meteorological data from the second phase of the Global Soil Wetness Project (GSWP-2). The results show that both the 14 LSMs and six Budyko-type models can explain 55–70% of the spatial variations of mean annual runoff across the selected 150 basins. However, the 14 LSMs show larger biases in the simulated mean annual runoff than the Budyko-type models. The LSMs biases are caused by errors in forcing data, model structure and model parameterisation. The errors in the precipitation forcing data are found to be the main cause for biases in the simulated mean annual runoffs by the Budyko-types models, and most likely for biases in the 14 global land surface models too. The GSWP-2 precipitation is noticeably overestimated at Northern high-latitudes, which causes large positive biases for the LSMs in simulating mean annual runoff in these regions. The most LSMs show large biases in the regions with low mean annual precipitation. Underestimation of the GSWP-2 precipitation in Amazon and Orinoco basins results in significant underestimation in the simulated mean annual runoff by all LSMs and Budyko-type models for these regions. The LSMs with smaller biases generally show larger baseflow ratio in wet basins than in dry basins while the LSMs with larger biases generally show smaller baseflow ratio in wet basins than in dry basins. This indicates that errors in model structure can result in large biases in the simulated runoff. Several parameter sensitivity experiments for one LSM are carried out to investigate impacts on simulated mean runoff. The result indicates that ±20% changes in five key model parameters have relatively smaller impacts on the simulated mean annual runoff across the 150 basins, compared to errors in model structure.

Use of Epinephrine for Cardiac Arrest Prior to Hospital Arrival

To the Editor: Dr Hagihara and colleagues analyzed the use of prehospital epinephrine in patients with out-of-hospital cardiac arrest. We have several concerns about the study. The cohort of patients receiving epinephrine comprised a fraction of the overall population (15 030 of 417 188; 4%) for reasons not addressed by the authors and that could represent selection bias. Given that epinephrine was part of the treatment algorithm, insight into reasons for this would allow for better interpretation of the data. The authors reported the algorithm for epinephrine administration: “ . . . after checking the initial rhythm and using defibrillation when appropriate, along with CPR [cardiopulmonary resuscitation], emergency lifesaving technicians perform tracheal intubation and/or insert an intravenous line with approval from an online emergency physician. Then, after verifying the absence of impulse in the carotid artery, the emergency lifesaving technicians administer epinephrine with the approval of the emergency physician.” This algorithm provides 2 potential causes of bias. First, those with early defibrillation and return of spontaneous circulation (ROSC) would never receive epinephrine but clearly have a survival advantage. Second, the time of administration of epinephrine dictated by this protocol may be too late to allow for adequate survival. For example, airway placement is prioritized above epinephrine with only 1 clinician able to perform both duties, and a telephone call for permission to provide epinephrine would lead to further delay. This concern is supported by an investigation in 1 of the prefectures in Japan, in which the average time to defibrillation was a mean (SD) of 9 (3) minutes, whereas the time to epinephrine was a mean (SD) of 21.3 (6.9) minutes despite the predominate rhythm being nonshockable. Thus, timing and selection bias driven by the protocol may have led to worse outcomes, as opposed to the intrinsic properties of the medication. Theauthorsacknowledgeduncontrolleddifferences inemergency department care as a weakness of the study and tried to account for this by evaluating a subgroup of patients who achieved ROSC in the field. They reported a marked difference in 1-month mortality between the epinephrine (18%) and no epinephrine (46.8%) groups to argue against emergency department epinephrine as a confounder. We are concerned that such a marked difference in 30-day mortality from only 1 mg of epinephrine may reflect an unrecognized intrinsic difference in the patient population rather than an adverse effect of this relatively low total dosage. In addition, the acknowledgment that induced hypothermia was not routinely performed might further explain the lack of 30-day benefit despite increased ROSC.

Process‐evaluation of tropospheric humidity simulated by general circulation models using water vapor isotopic observations: 2. Using isotopic diagnostics to understand the mid and upper tropospheric moist bias in the tropics and subtropics

Evaluating the representation of processes controlling tropical and subtropical tropospheric relative humidity (RH) in atmospheric general circulation models (GCMs) is crucial to assess the credibility of predicted climate changes. GCMs have long exhibited a moist bias in the tropical and subtropical mid and upper troposphere, which could be due to the mis‐representation of cloud processes or of the large‐scale circulation, or to excessive diffusion during water vapor transport. The goal of this study is to use observations of the water vapor isotopic ratio to understand the cause of this bias. We compare the three‐dimensional distribution of the water vapor isotopic ratio measured from space and ground to that simulated by several versions of the isotopic GCM LMDZ. We show that the combined evaluation of RH and of the water vapor isotopic composition makes it possible to discriminate the most likely cause of RH biases. Models characterized either by an excessive vertical diffusion, an excessive convective detrainment or an underestimated in situ cloud condensation will all produce a moist bias in the free troposphere. However, only an excessive vertical diffusion can lead to a reversed seasonality of the free tropospheric isotopic composition in the subtropics compared to observations. Comparing seven isotopic GCMs suggests that the moist bias found in many GCMs in the mid and upper troposphere most frequently results from an excessive diffusion during vertical water vapor transport. This study demonstrates the added value of water vapor isotopic measurements for interpreting shortcomings in the simulation of RH by climate models.

The use of Barn Owl Tyto alba pellets to assess population change in small mammals

Capsule Barn Owl pellets can be used to assess population changes in small mammals though with certain important reservations.Aims To conduct a large-scale, long-term analysis of pellets regurgitated by Barn Owls collected from a defined area of countryside within the UK south Midlands and compare results with those of other studies that have used pellet analysis to investigate the diet of Barn Owls.Methods Over 61 000 Barn Owl pellets collected over 20 years in the south Midlands of England were analyzed.Results We recorded a steady increase in the proportion of Field Voles in pellets over time at the expense of Wood Mice, and a positive relationship between various rainfall measures and the proportion of Field Voles in pellets. We suggest that the conclusions of previous studies of the diet of Barn Owls have often been predicated on inconsistent or untenable assumptions.Conclusion We challenge some of the premises on which Barn Owl pellet analyses have traditionally been based. We do not think that either a shortage of Field Voles in pellets, or of pellets containing the remains of more, smaller prey items, are indicative of foraging difficulty. Barn Owl pellets can be used to assess population changes in small mammals, so long as possible causes of bias, which we discuss, are acknowledged.

Funding source and research report quality in nutrition practice-related research.

BackgroundThe source of funding is one of many possible causes of bias in scientific research. One method of detecting potential for bias is to evaluate the quality of research reports. Research exploring the relationship between funding source and nutrition-related research report quality is limited and in other disciplines the findings are mixed.ObjectiveThe purpose of this study is to determine whether types of funding sources of nutrition research are associated with differences in research report quality.DesignA retrospective study of research reporting quality, research design and funding source was conducted on 2539 peer reviewed research articles from the American Dietetic Association's Evidence Analysis Library® database.ResultsQuality rating frequency distributions indicate 43.3% of research reports were rated as positive, 50.1% neutral, and 6.6% as negative. Multinomial logistic regression results showed that while both funding source and type of research design are significant predictors of quality ratings (χ2 = 118.99, p<0.001), the model's usefulness in predicting overall research report quality is little better than chance. Compared to research reports with government funding, those not acknowledging any funding sources, followed by studies with University/hospital funding were more likely to receive neutral vs positive quality ratings, OR = 1.85, P <0.001 and OR = 1.54, P<0.001, respectively and those that did not report funding were more likely to receive negative quality ratings (OR = 4.97, P<0.001). After controlling for research design, industry funded research reports were no more likely to receive a neutral or negative quality rating than those funded by government sources.ConclusionResearch report quality cannot be accurately predicted from the funding source after controlling for research design. Continued vigilance to evaluate the quality of all research regardless of the funding source and to further understand other factors that affect quality ratings are warranted.

Conflicts of Interest in Medicines Safety and Regulation

Conflict of interest is a well known and growing controversy in many areas of healthcare, not least in medicines safety and regulation. The direction of these concerns seems to be both narrow and conservative: narrow in the sense of being dominated by financial interests, and conservative in excluding many experts from contribution to debate and decisions. A recent publication provoked my interest. It concerned the global donor agencies and, after the authors had delved into donors’ background dealings and their decision making, they came to the conclusion that sometimes donor investments, board member interests and lack of alignment with real public health needs represented conflicts that should be addressed. They also quoted some conflict of interest definitions. In US law, a conflict exists when there is ‘‘a real or seeming incompatibility between one’s private interests and one’s public or fiduciary duties’’. Wikipedia’s definition is also quoted as when ‘‘an individual or organisation is involved in multiple interests, one of which could possibly corrupt the motivation for an act in another’’. Furthermore, a WHO definition includes ‘‘y when a partner’s ability in one role is impaired by his or her obligations in another role or by the existence of competing interests. Such situations create a risk of a tendency (sic) towards bias in favour of one interest over another or that the individual would not fulfil his or her duties impartiallyy A conflict of interest may exist even if no unethical or improper act results from it. It can create an appearance of impropriety that can undermine confidence y’’ None of these definitions is confined to financial interest but the article by Stuckler et al. largely is! Money is perhaps the most tangible cause for biases, and the WHO definition captures a second – the organization that one represents. Certainly they are identifiable but I argue that they may not be the most important and will use pharmacovigilance as an example discipline. The late Professor Sir William Trethowan, a wise psychiatrist, taught us students that when first meeting any patient we should ask ourselves ‘do I like this person or not?’ He explained that our treatment of the patient may well be different depending on that insight. For those who may think this is trivial, I have seen many situations where doctors treating colleagues omit routines that are unpleasant to spare their friend discomfort, but to the ultimate detriment of that patient’s care. The opposite may also occur with patients one dislikes being treated perfunctorily. Such preconceptions and prejudices cannot always be discerned, but they are very powerful. They may cause us to leave out important references that oppose our work when writing a paper, or they may lead us to write a more negative review on a paper involving abortion, for example, if we are very much opposed to it. More in keeping with my introductory example, if we generally do not like an author, might not we, as reviewers or readers, be more negative to his/her work. The prejudice may only be amore general one, such as ‘nothing good was ever written in that university’, or indeed that ‘if it is from Yale or Oxford it must be good’ (that has certainly been the reasoning EDITORIAL Drug Saf 2011; 34 (8): 617-621 0114-5916/11/0008-0617/$49.95/0

P1-S4.04 Biases in the design of studies assessing the role of sexually transmitted infections as HIV risk factors

BackgroundFor over 20 yearss sexually transmitted infections (STIs) have been studied as risk factors for HIV infection. We show how the design of studies in this field has inherent biases...

Tree line identification from pollen data: beyond the limit?

Aim The boreal tree line is a prominent biogeographic feature, the position of which reflects climatic conditions. Pollen is the key sensor used to reconstruct past tree line patterns. Our aims in this study were to investigate pollen– vegetation relationships at the boreal tree line and to assess the success of a modified version of the biomization method that incorporates pollen productivity and dispersal in distinguishing the tree line. Location Northern Canada (307 sites) and Alaska (316 sites). Methods The REVEALS method for estimating regional vegetation composition from pollen data was simplified to provide correction factors to account for differential production and dispersal of pollen among taxa. The REVEALS-based correction factors were used to adapt the biomization method and applied as a set of experiments to pollen data from lake sediments and moss polsters from the boreal tree line. Proportions of forest and tundra predicted from modern pollen samples along two longitudinal transects were compared with those derived from a vegetation map by: (1) a tally of ‘correct’ versus ‘incorrect’ assignments using vegetation in the relevant map pixels, and (2) a comparison of the shape and position of north–south forest-cover curves generated from all transect pixels and from pollen data. Possible causes of bias in the misclassifications were assessed. Results Correcting for pollen productivity alone gave fewest misclassifications and the closest estimate of the modern mapped tree line position (Canada, + 300 km; Alaska, + 10 km). In Canada success rates were c. 40–70% and all experiments over-predicted forest cover. Most corrections improved results over uncorrected biomization; using only lakes improved success rates to c. 80%. In Alaska success rates were 70–80% and classification errors were more evenly distributed; there was little improvement over uncorrected biomization. Main conclusions Corrected biomization should improve broad-scale reconstructions of spatial patterns in forest/non-forest vegetation mosaics and across climate-sensitive ecotones. The Canadian example shows this is particularly the case in regions affected by taxa with extremely high pollen productivity (such as Pinus). Improved representation of actual vegetation distribution is most likely if pollen data from lake sediments are used because the REVEALS algorithm is based on the pollen dynamics of lake-based systems.

Diagnosing the causes of bias in climate models – why is it so hard?

The equations of climate are, in principle, known. Why then is it so hard to formulate a bias-free model of climate? Here, some ideas in nonlinear dynamics are explored to try to answer this question. Specifically it is suggested that the climatic response to physically different forcings shows a tendency to project onto structures corresponding to the systems natural internal modes of variability. This is shown using results from complex climate models and from the relatively simple Lorenz three-component model. It is suggested that this behaviour is consistent with what might be expected from the fluctuation–dissipation theorem. Based on this, it is easy to see how climate models can easily suffer from having errors in the representation of two or more different physical processes, whose responses compensate one another and hence make individual error diagnosis difficult. A proposal is made to try to overcome these problems and advance the science needed to develop a bias-free climate model. The proposal utilises powerful diagnostics from data assimilation. The key point here is that these diagnostics derive from short-range forecast tendencies, estimated long before the model has asymptotically settled down to its (biased) climate attractor. However, it is shown that these diagnostics will not identify all sources of model error, and a so-called “bias of the second kind” is discussed. This latter bias may be alleviated by recently developed stochastic parametrisations.

Significant phorophyte (substrate) bias is not explained by fitness benefits in three epiphytic orchid species

Epiphytes rely on their phorophyte (host substrate) for support; epiphytic orchids also rely on mycorrhizal fungi for germination. Previous studies have proposed a degree of specificity in both interactions. Epiphytic orchids therefore provide an interesting system in which to examine multispecies interactions and the evolution of specialization. We examined the potential and actual distributions of three co-occurring, related epiphytic orchid species: Sarcochilus hillii, Plectorrhiza tridentata, and Sarcochilus parviflorus on phorophytes in Australia's temperate dry rainforests. These three small epiphytic orchid species were all biased toward certain woody plant species, in particular, the tree Backhousia myrtifolia, though the extent of specificity varied. Biases toward the most common phorophyte species were not explained by increases in adult orchid fitness, nor did probability of flowering increase on B. myrtifolia. Indeed, individuals on this woody phorophyte tended to have fewer inflorescences than those on other woody phorophytes. Only S. hillii benefited from establishment on B. myrtifolia; it had more leaves on this phorophyte than on others. In many cases what appear to be simple interactions between two species may be mediated by more complex symbioses. For this system, we propose that the cause for bias in orchid distribution occurs much earlier in an orchid's life and may be due to a bias of their mycorrhizal fungi for the dominant orchid phorophytes.

Reverse Causation and Illness-related Weight Loss in Observational Studies of Body Weight and Mortality

In studies of weight and mortality, the construct of reverse causation has come to be used to imply that the exposure-outcome relation is biased by weight loss due to preexisting illness. Observed weight-mortality associations are sometimes thought to result from this bias. Evidence for the occurrence of such bias is weak and inconsistent, suggesting that either the analytical methods used have been inadequate or else illness-related weight loss is not an important source of bias. Deleting participants has been the most frequent approach to control possible bias. As implemented, this can lead to deletion of almost 90% of all deaths in a sample and to deletion of more overweight and obese participants than participants with normal or below normal weight. Because it has not been demonstrated that the procedures used to adjust for reverse causation increase validity or have large or systematic effects on relative risks, it is premature to consider reverse causation as an important cause of bias. Further research would be useful to elucidate the potential effects and importance of reverse causation or illness-related weight loss as a source of bias in the observed associations between weight and mortality in cohort studies.

Assessing the Reliability of the Single Circular-Array Method for Love-Wave Ambient-Noise Surveying

Abstract The single circular array (SCA) method is a spatial autocorrelation (SPAC)–like technique for ambient noise exploration. Its main feature is the possibility of calculation of Love-wave dispersion curves by using centerless circular arrays of 3-component seismometers, allowing independent processing of each circle. Situations in which Rayleigh-wave and Love-wave arrivals or waves coming from different azimuths are mutually correlated are also correctly dealt with in this method. An algorithm for practical calculation of the SCA coefficient B is described. The algorithm includes averaging over a set of time windows and minimizes the number of spectral ratios to be computed for the purposes of stability. Numerical tests show that SCA coefficients estimated in this way have quite a robust behavior. Bias due to use of a finite number of sensors, as well as to effects of nonpropagating incoherent noise, has been theoretically studied in both the deterministic and the stationary random-field formulations. Using a finite number of stations is a cause of bias even under isotropic illumination conditions. Nevertheless, its effect can be neglected for wavelengths-to-radius ratios above a threshold that depends on the number of evenly distributed sensors. By contrast, uncorrelated noise may affect the whole frequency band and is behind the limitations of the method at low frequencies. Finally, we present the first real data test of this method, consisting of a comparison between theoretical and experimental Love-wave dispersion curves for a site where the structure is known. In practice, the minimum wavelength for direct velocity retrieval for a pentagonal array with radius r was approximately , although this value depends on the signal-to-noise ratio. Experiments demonstrate that the usable range can be extended, mainly toward shorter wavelengths, if the effects of noise and of the finite number of sensors are included in the analysis.

Public subsidies to private schools do make a difference for achievement in mathematics: Longitudinal evidence from Canada

Selection into private schools is the principal cause of bias when estimating the effect of private schooling on academic achievement. By exploiting the generous public subsidizing of private high schools in the province of Québec, the second most populous province in Canada, we identify the causal impact of attendance in a private high school on achievement in mathematics. Because the supply of highly subsidized spaces is much higher at the high school level than at the grade school level, 94% of transitions from the public to the private sector occur at the end of primary school, we assume that these transitions are exogenous with respect to changes in transitory unobserved variables affecting math scores conditional on variables such as changes in income and child fixed effects. Using 7 cycles of data from Statistics Canada's National Longitudinal Survey on Children and Youth (NLSCY), we estimate the effect of attending a private high school on the percentile rank and a standardized math test score with different models (child fixed effects, random effects and a pooled OLS) and restricted samples to control for the degree of selection. The results, interpreted as a treatment on the treated effect show that the effect of changing schools, from a public grade school to a private high school, increases the percentile rank of the math score between 4 and 10 points and by between 0.12 and 0.36 of a standard deviation depending on the specification and samples.

Simulations of precipitation using NRCM and comparisons with satellite observations and CAM: annual cycle

The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36 km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000–2005) from within the Tropics (30°S–30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36 km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations.

Should Steroids be Offered to Patients With Nonarteritic Anterior Ischemic Optic Neuropathy?

The treatment of nonarteritic anterior optic neuropathy remains very limited and disappointing. Recent publications have suggested that oral steroids as well as intravitreal injections of steroids might be helpful to accelerate resolution of disc edema and improve visual outcome. However, the use of steroids to treat acute NAION remains largely debated.

Observational studies: going beyond the boundaries of randomized controlled trials

The term observational study describes a wide range of study designs including prospective and retrospective cohort studies, case-control studies, and cross-sectional studies, a defining feature of which is that any intervention studied is determined by clinical practice and not the protocol. Data from large, prospective observational studies provide information about the safety and efficacy of medicines in daily clinical use. Such observational studies are generally carried out once a medicine has received approval from regulatory agencies. Observational trials have inherent limitations in terms of their susceptibility to bias and confounding, restricting their ability to define causality. However, their strengths include that they reflect daily clinical practice more closely than randomized controlled trials (RCTs), both in terms of the heterogeneous patient populations that are included, and the medical interventions that they receive. Therefore, observational trials can provide clinically relevant information that is not necessarily provided by RCTs. Given the limitations of an observational study approach, it is important to optimize their study design to maximize their validity, and thus, in particular, known causes of bias and confounding should be measured. Medical investigators, health authorities, and the pharmaceutical industry all have important roles to play in designing, approving, and performing observational studies.