Correction: A simple method for analyzing data from a randomized trial with a missing binary outcome

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Comparing breast cancer mortality rates before-and-after a change in availability of screening in different regions: Extension of the paired availability design

BACKGROUND: In recent years there has been increased interest in evaluating breast cancer screening using data from before-and-after studies in multiple geographic regions. One approach, not previously mentioned, is the paired availability design. The paired availability design was developed to evaluate the effect of medical interventions by comparing changes in outcomes before and after a change in the availability of an intervention in various locations. A simple potential outcomes model yields estimates of efficacy, the effect of receiving the intervention, as opposed to effectiveness, the effect of changing the availability of the intervention. By combining estimates of efficacy rather than effectiveness, the paired availability design avoids confounding due to different fractions of subjects receiving the interventions at different locations. The original formulation involved short-term outcomes; the challenge here is accommodating long-term outcomes. METHODS: The outcome is incident breast cancer deaths in a time period, which are breast cancer deaths that were diagnosed in the same time period. We considered the plausibility of the basic five assumptions of the paired availability design and propose a novel analysis to accommodate likely violations of the assumption of stable screening effects. RESULTS: We applied the paired availability design to data on breast cancer screening from six counties in Sweden. The estimated yearly change in incident breast cancer deaths per 100,000 persons ages 40–69 (in most counties) due to receipt of screening (among the relevant type of subject in the potential outcomes model) was -9 with 95% confidence interval (-14, -4) or (-14, -5), depending on the sensitivity analysis. CONCLUSION: In a realistic application, the extended paired availability design yielded reasonably precise confidence intervals for the effect of receiving screening on the rate of incident breast cancer death. Although the assumption of stable preferences may be questionable, its impact will be small if there is little screening in the first time period. However, estimates may be substantially confounded by improvements in systemic therapy over time. Therefore the results should be interpreted with care

Randomized trials, generalizability, and meta-analysis: Graphical insights for binary outcomes

BACKGROUND: Randomized trials stochastically answer the question. "What would be the effect of treatment on outcome if one turned back the clock and switched treatments in the given population?" Generalizations to other subjects are reliable only if the particular trial is performed on a random sample of the target population. By considering an unobserved binary variable, we graphically investigate how randomized trials can also stochastically answer the question, "What would be the effect of treatment on outcome in a population with a possibly different distribution of an unobserved binary baseline variable that does not interact with treatment in its effect on outcome?" METHOD: For three different outcome measures, absolute difference (DIF), relative risk (RR), and odds ratio (OR), we constructed a modified BK-Plot under the assumption that treatment has the same effect on outcome if either all or no subjects had a given level of the unobserved binary variable. (A BK-Plot shows the effect of an unobserved binary covariate on a binary outcome in two treatment groups; it was originally developed to explain Simpsons's paradox.) RESULTS: For DIF and RR, but not OR, the BK-Plot shows that the estimated treatment effect is invariant to the fraction of subjects with an unobserved binary variable at a given level. CONCLUSION: The BK-Plot provides a simple method to understand generalizability in randomized trials. Meta-analyses of randomized trials with a binary outcome that are based on DIF or RR, but not OR, will avoid bias from an unobserved covariate that does not interact with treatment in its effect on outcome

Infection control and the significance of sputum and other respiratory secretions from adult patients with cystic fibrosis

BACKGROUND: There is limited data available on the environmental and public health impact of the microbiological hazards associated with sputa from patients with cystic fibrosis [CF]. Pseudomonas aeruginosa, Burkholderia cenocepacia (formerly Burkholderia cepacia genomovar III), Staphylococcus aureus and Stenotrophomonas maltophilia are bacterial pathogens which are commonly found in the sputum of CF patients. A study was performed to ascertain the amount of sputum produced relating to microbial loading, as well as the diversity of bacteria present in a population of adult patients, with particular attention to pathogenic organisms. METHODS: Sputum from adult [>18 years old] CF patients [n = 20], chosen randomly from a population of 138 CF patients, was collected over a 24 h period on admission to the in-patient CF unit and enumerated quantitatively, as well as the sputa from 138 adult CF patients was examined qualitatively for the presence of infecting microflora. In addition, all different phenotypes from the sputum of each patient were identified phenotypically employing a combination of conventional identification methods [e.g. oxidase], as well as the API Identification schemes [API 20 NE, API 20 E]. RESULTS: This study demonstrated that patients with cystic fibrosis generate large numbers of bacteria in their sputum, approximating to 10(9 )organisms per patient per day. Although these organisms are introduced to the environment from the respiratory tract mainly via sputum, relatively few represent true bacterial pathogens and therefore are not clinically important to the general public who are immunocompotent. The greatest risk of such environmental microbial loading is to other patients with CF and therefore CF patients should be made aware of the hazards of acquiring such organisms from the environment, as well as socializing with other CF patients with certain transmissible types, such as Pseudomonas aeruginosa and Burkholderia cenocepacia. CONCLUSIONS: Environmental health professionals should therefore be aware that CF patients are a greater risk to their peer grouping rather than to the general public or health care workers and that good personal hygiene practices with CF patients should be encouraged to minimize environmental contamination and potential acquistion

The Paired Availability Design for Historical Controls

BACKGROUND: Although a randomized trial represents the most rigorous method of evaluating a medical intervention, some interventions would be extremely difficult to evaluate using this study design. One alternative, an observational cohort study, can give biased results if it is not possible to adjust for all relevant risk factors. METHODS: A recently developed and less well-known alternative is the paired availability design for historical controls. The paired availability design requires at least 10 hospitals or medical centers in which there is a change in the availability of the medical intervention. The statistical analysis involves a weighted average of a simple "before" versus "after" comparison from each hospital or medical center that adjusts for the change in availability. RESULTS: We expanded requirements for the paired availability design to yield valid inference. (1) The hospitals or medical centers serve a stable population. (2) Other aspects of patient management remain constant over time. (3) Criteria for outcome evaluation are constant over time. (4) Patient preferences for the medical intervention are constant over time. (5) For hospitals where the intervention was available in the "before" group, a change in availability in the "after group" does not change the effect of the intervention on outcome. CONCLUSION: The paired availability design has promise for evaluating medical versus surgical interventions, in which it is difficult to recruit patients to a randomized trial

Design Principles for Plasmonic Nanoparticle Devices

For all applications of plasmonics to technology it is required to tailor the resonance to the optical system in question. This chapter gives an understanding of the design considerations for nanoparticles needed to tune the resonance. First the basic concepts of plasmonics are reviewed with a focus on the physics of nanoparticles. An introduction to the finite element method is given with emphasis on the suitability of the method to nanoplasmonic device simulation. The effects of nanoparticle shape on the spectral position and lineshape of the plasmonic resonance are discussed including retardation and surface curvature effects. The most technologically important plasmonic materials are assessed for device applicability and the importance of substrates in light scattering is explained. Finally the application of plasmonic nanoparticles to photovoltaic devices is discussed.Comment: 29 pages, 15 figures, part of an edited book: "Linear and Non-Linear Nanoplasmonics

Dwarf Galaxy Formation Was Suppressed By Cosmic Reionization

A large number of faint galaxies, born less than a billion years after the big bang, have recently been discovered. The fluctuations in the distribution of these galaxies contributed to a scatter in the ionization fraction of cosmic hydrogen on scales of tens of Mpc, as observed along the lines of sight to the earliest known quasars. Theoretical simulations predict that the formation of dwarf galaxies should have been suppressed after cosmic hydrogen was reionized, leading to a drop in the cosmic star formation rate. Here we present evidence for this suppression. We show that the post-reionization galaxies which produced most of the ionizing radiation at a redshift z~5.5, must have had a mass in excess of ~10^{10.6+/-0.4} solar masses or else the aforementioned scatter would have been smaller than observed. This limiting mass is two orders of magnitude larger than the galaxy mass that is thought to have dominated the reionization of cosmic hydrogen (~10^8 solar masses). We predict that future surveys with space-based infrared telescopes will detect a population of smaller galaxies that reionized the Universe at an earlier time, prior to the epoch of dwarf galaxy suppression.Comment: 19 pages, 3 figures. Accepted for publication in Nature; press embargo until publishe

The fallacy of enrolling only high-risk subjects in cancer prevention trials: Is there a "free lunch"?

BACKGROUND: There is a common belief that most cancer prevention trials should be restricted to high-risk subjects in order to increase statistical power. This strategy is appropriate if the ultimate target population is subjects at the same high-risk. However if the target population is the general population, three assumptions may underlie the decision to enroll high-risk subject instead of average-risk subjects from the general population: higher statistical power for the same sample size, lower costs for the same power and type I error, and a correct ratio of benefits to harms. We critically investigate the plausibility of these assumptions. METHODS: We considered each assumption in the context of a simple example. We investigated statistical power for fixed sample size when the investigators assume that relative risk is invariant over risk group, but when, in reality, risk difference is invariant over risk groups. We investigated possible costs when a trial of high-risk subjects has the same power and type I error as a larger trial of average-risk subjects from the general population. We investigated the ratios of benefit to harms when extrapolating from high-risk to average-risk subjects. RESULTS: Appearances here are misleading. First, the increase in statistical power with a trial of high-risk subjects rather than the same number of average-risk subjects from the general population assumes that the relative risk is the same for high-risk and average-risk subjects. However, if the absolute risk difference rather than the relative risk were the same, the power can be less with the high-risk subjects. In the analysis of data from a cancer prevention trial, we found that invariance of absolute risk difference over risk groups was nearly as plausible as invariance of relative risk over risk groups. Therefore a priori assumptions of constant relative risk across risk groups are not robust, limiting extrapolation of estimates of benefit to the general population. Second, a trial of high-risk subjects may cost more than a larger trial of average risk subjects with the same power and type I error because of additional recruitment and diagnostic testing to identify high-risk subjects. Third, the ratio of benefits to harms may be more favorable in high-risk persons than in average-risk persons in the general population, which means that extrapolating this ratio to the general population would be misleading. Thus there is no free lunch when using a trial of high-risk subjects to extrapolate results to the general population. CONCLUSION: Unless the intervention is targeted to only high-risk subjects, cancer prevention trials should be implemented in the general population