Smokers lose at least one decade of life expectancy, as compared with those who have never smoked. Cessation before the age of 40 years reduces the risk of death associated with continued smoking by about 90%.

in the invited group (49 in the invited group vs 95 in the control group). After a normal first scan, 59 ruptured AAAs occurred, and 80% of these were fatal. Ruptured AAA after a normal first scan had a marked increase after 8 years of follow-up. Among the measured baseline aortic diameters, about half of those initially screened as normal and subsequently having ruptured AAA were in the initial screening range of aortic diameters of 2.5 to 2.9 cm.Comment: There are at least two findings that should be emphasized here. The first is that screening for AAA reduces all-cause mortality. The second is that screening is not perfect, and some patients will still develop aneurysms and die from them after an initial so-called negative screening study. The MASS trial measured AAAs with inner-to-inner wall measurements of diameter by ultrasound imaging. Theoretically, because this results in a smaller diameter measurement, use of the Society for Vascular Surgery guidelines for measuring abdominal aortas from outer-to-outer wall could potentially save some ruptures without rescreening of "normals" or an increase in the number of initial screening studies.

No abstract

In observational studies the assignment of units to treatments is not under control. Consequently, the estimation and comparison of treatment effects based on the empirical distribution of the responses can be biased since the units exposed to the various treatments could differ in important unknown pretreatment characteristics, which are related to the response. An important example studied in this article is the question of whether private schools offer better quality of education than public schools. In order to address this question we use data collected in the year 2000 by OECD for the Programme for International Student Assessment (PISA). Focusing for illustration on scores in mathematics of 15-years old pupils in Ireland, we find that the raw average score of pupils in private schools is higher than of pupils in public schools. However, application of a newly proposed method for observational studies suggests that the less able pupils tend to enroll in public schools, such that their lower scores is not necessarily an indication of bad quality of the public schools. Indeed, when comparing the average score in the two types of schools after adjusting for the enrollment effects, we find quite surprisingly that public schools perform better on average. This outcome is supported by the methods of instrumental variables and latent variables, commonly used by econometricians for analyzing and evaluating social programs.

Analysis of population-based case-control studies with complex sampling designs is challenging because the sample selection probabilities (and, therefore, the sample weights) depend on the response variable and covariates. Commonly, the design-consistent (weighted) estimators of the parameters of the population regression model are obtained by solving (sample) weighted estimating equations. Weighted estimators, however, are known to be inefficient when the weights are highly variable as is typical for case-control designs. In this paper, we propose two alternative estimators that have higher efficiency and smaller finite sample bias compared with the weighted estimator. Both methods incorporate the information included in the sample weights by modeling the sample expectation of the weights conditional on design variables. We discuss benefits and limitations of each of the two proposed estimators emphasizing efficiency and robustness. We compare the finite sample properties of the two new estimators and traditionally used weighted estimators with the use of simulated data under various sampling scenarios. We apply the methods to the U.S. Kidney Cancer Case-Control Study to identify risk factors. Published 2012. This article is a US Government work and is in the public domain in the USA.

Background: Blinding aims to minimize biases from what participants and investigators know or believe. Randomized controlled trials, despite being the gold standard to evaluate treatment effect, do not generally assess the success of blinding. We investigated the extent of blinding in back pain trials and the associations between participant guesses and treatment effects. Methods: We did a review with PubMed/OvidMedline, 2000–2019. Eligibility criteria were back pain trials with data available on treatment effect and participants’ guess of treatment. For blinding, blinding index was used as chance-corrected measure of excessive correct guess (0 for random guess). For treatment effects, within- or between-arm effect sizes were used. Analyses of investigators’ guess/blinding or by treatment modality were performed exploratorily. Results: Forty trials (3899 participants) were included. Active and sham treatment groups had mean blinding index of 0.26 (95% confidence interval: 0.12, 0.41) and 0.01 (−0.11, 0.14), respectively, meaning 26% of participants in active treatment believed they received active treatment, whereas only 1% in sham believed they received sham treatment, beyond chance, that is, random guess. A greater belief of receiving active treatment was associated with a larger within-arm effect size in both arms, and ideal blinding (namely, “random guess,” and “wishful thinking” that signifies both groups believing they received active treatment) showed smaller effect sizes, with correlation of effect size and summary blinding indexes of 0.35 ( p = 0.028) for between-arm comparison. We observed uniformly large sham treatment effects for all modalities, and larger correlation for investigator’s (un)blinding, 0.53 ( p = 0.046). Conclusion: Participants in active treatments in back pain trials guessed treatment identity more correctly, while those in sham treatments tended to display successful blinding. Excessive correct guesses (that could reflect weaker blinding and/or noticeable effects) by participants and investigators demonstrated larger effect sizes. Blinding and sham treatment effects on back pain need due consideration in individual trials and meta-analyses.

The current study demonstrates that 76% of sampled women experienced pregnancy-related back pain and the prevalence of site-specific pain (LBP, PGP, and Combo Pain) increases with increased gestation. Risk factors include advanced GA and experiencing both types of pain prior to pregnancy (Prior Both). Furthermore, it is suggested that a standard definition of pain by location should be developed and employed so that future studies can elucidate appropriate prevention strategies and treatment options for each.

Blinding is a critical component in randomized clinical trials along with treatment effect estimation and comparisons between the treatments. Various methods have been proposed for the statistical analyses of blinding-related data, but there is little guidance for determining the sample size for this type of data, especially if blinding assessment is done in pilot studies. In this paper, we try to fill this gap and provide simple methods to address sample size calculations for a "new" study with different research questions and scenarios. The proposed methods are framed in terms of estimation/precision or statistical testing to allow investigators to choose the best suited method for their goals. We illustrate the methods using worked examples with real data.