2,497 publications from this institution
Research funding systems fundamentally influence how science operates. This paper aims to analyze the allocation of competitive research funding from different perspectives: How reliable are decision processes for funding? What are the economic costs of competitive funding? How does competition for funds affect doing risky research? How do competitive funding environments affect scientists themselves, and which ethical issues must be considered? We attempt to identify gaps in our knowledge of research funding systems; we propose recommendations for policymakers and funding agencies, including empirical experiments of decision processes and the collection of data on these processes. With our recommendations, we hope to contribute to developing improved ways of organizing research funding.
Most diseases are likely to result largely from the interplay of lifestyle and genetic factors. However, both observational studies and randomized trials have faced major limitations in trying to address the impact of lifestyle on health. As large cohorts and biobanks are being developed, we need to find novel, efficient ways to address the effects of lifestyle interventions. We propose that this could be done using multiple lifestyle factorial experimental designs that combine characteristics of randomized trials and epidemiologic studies. Randomized trials of simple lifestyle interventions can be nested within large cohorts linked to reliable registries of outcomes. Participants can choose from a long list of simple lifestyle randomization options and many interventions may be tested concurrently with factorial randomization. Participants can tailor their own personal trial choosing several items among long laundry lists of randomization options. Participants are citizen-scientists rather than passive subjects and this may be attractive in modern societies of health-conscious people. These trials can use the existing machinery of the cohort for data collection and outcome linkage at no or minimal additional cost. We discuss a number of issues on the implementation of multiple lifestyle factorial experimental designs, as compared with the usual observational studies and randomized trials. These include participation, the number of allowed randomizations per participant, compliance/adherence, power, false-negatives, false-positives, composite lifestyle effects, selection of outcomes, follow-up and monitoring, masking and allocation concealment, age of participants, confounding, and cost. The aim should be to combine carefully the strengths of both observational epidemiology and randomized research without compounding their limitations.
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
A series of simulations that explore the interactions between various organizational decisions and program execution time are presented. The tradeoffs between cache size and CPU/cache cycle-time, set associativity and cycle time, and block size and main-memory speed, are investigated. The results indicate that neither cycle time nor cache size dominates the other across the entire design space. For common implementation technologies, performance is maximized when the size is increased to the size is increased to the 32-kB to 128-kB range with modest penalties to the cycle time. If set associativity impacts the cycle time by more than a few nanoseconds, it increases overall execution time. Since the block size and memory-transfer rate combine to affect the cache miss penalty, the optimum block size is substantially smaller than that which minimizes the miss rate. The interdependence between optimal cache configuration and the main memory speed necessitates multilevel cache hierarchies for high-performance uniprocessors.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Clinical practice guidelines are provided by the minority of professional societies and organizations. Available guidelines tend to recommend chemotherapy even for diseases where the effect of chemotherapy is controversial and recommendations are based on scant evidence.
The E-value was recently introduced on the basis of earlier work as "the minimum strength of association…that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates." E-values have been proposed for wide application in observational studies evaluating causality. However, they have limitations and are prone to misinterpretation. E-values have a monotonic, almost linear relationship with effect estimates and thus offer no additional information beyond what effect estimates can convey. Whereas effect estimates are based on real data, E-values may make unrealistic assumptions. No general rule can exist about what is a "small enough" E-value, and users of the biomedical literature are not familiar with how to interpret a range of E-values. Problems arise for any measure dependent on effect estimates and their CIs-for example, bias due to selective reporting and dependence on choice of exposure contrast and level of confidence. The automation of E-values may give an excuse not to think seriously about confounding. Moreover, biases other than confounding may still undermine results. Instead of misused or misinterpreted E-values, the authors recommend judicious use of existing methods for sensitivity analyses with careful assumptions; systematic assessments of whether and how known confounders have been handled, along with consideration of their prevalence and magnitude; thorough discussion of the potential for unknown confounders considering the study design and field of application; and explicit caution in making causal claims from observational studies.
