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Epidemiologic screening programs often make use of tests with small, but nonzero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of individuals from the population. In this setting, we propose an enhanced inferential approach for use in conjunction with sampling-based bias-corrected prevalence estimation. While ignoring the finite nature of the population can yield markedly conservative estimates, direct application of a standard finite population correction (FPC) conversely leads to underestimation of variance. We uncover a way to leverage the typical FPC indirectly toward valid statistical inference. In particular, we derive a readily estimable extra variance component induced by misclassification in this specific but arguably common diagnostic testing scenario. Our approach yields a standard error estimate that properly captures the sampling variability of the usual bias-corrected maximum likelihood estimator of disease prevalence. Finally, we develop an adapted Bayesian credible interval for the true prevalence that offers improved frequentist properties (i.e., coverage and width) relative to a Wald-type confidence interval. We report the simulation results to demonstrate the enhanced performance of the proposed inferential methods. [ABSTRACT FROM AUTHOR]

Classical randomized experiments, equipped with randomization-based inference, provide assumption-free inference for treatment effects. They have been the gold standard for drawing causal inference and provide excellent internal validity. However, they have also been criticized for questionable external validity, in the sense that the conclusion may not generalize well to a larger population. The randomized survey experiment is a design tool that can help mitigate this concern, by randomly selecting the experimental units from the target population of interest. However, as pointed out by Morgan and Rubin, chance imbalances often exist in covariate distributions between different treatment groups even under completely randomized experiments. Not surprisingly, such covariate imbalances also occur in randomized survey experiments. Furthermore, the covariate imbalances happen not only between different treatment groups, but also between the sampled experimental units and the overall population of interest. In this article, we propose a two-stage rerandomization design that can actively avoid undesirable covariate imbalances at both the sampling and treatment assignment stages. We further develop asymptotic theory for rerandomized survey experiments, demonstrating that rerandomization provides better covariate balance, more precise treatment effect estimators, and shorter large-sample confidence intervals. We also propose covariate adjustment to deal with remaining covariate imbalances after rerandomization, showing that it can further improve both the sampling and estimated precision. Our work allows general relationship among covariates at the sampling, treatment assignment and analysis stages, and generalizes both rerandomization in classical randomized experiments and rejective sampling in survey sampling. [ABSTRACT FROM AUTHOR]

Evaluating the importance and the strength of empirical evidence requires asking three questions: First, what are the practical implications of the findings? Second, how precise are the estimates? Confidence intervals provide an intuitive way to communicate precision. Although nontechnical audiences often misinterpret confidence intervals (CIs), I argue that the result is less dangerous than the misunderstandings that arise from hypothesis tests. Third, is the model correctly specified? The validity of point estimates and CIs depends on the soundness of the underlying model. [ABSTRACT FROM AUTHOR]

The authors express their views on an article by Ian McKeague and Min Qian on statistical research that is published in this issue. Topics discussed include the cumulative distribution function (c.d.f.) of the estimation error; estimation error when testing a null hypothesis; and confidence interval mentioned in the article that is obtained through the limit distribution from Theorem 1.

The authors express their views on an article on bootstrap-based methods for statistical estimation, that is published in this issue. Topics discussed include correlation coefficients in a linear regression model; estimation of confidence intervals for adaptive bootstrap methods; and normalized estimation error in regression analysis.

Globally, alcohol use significantly contributes to the disease burden. Alcohol consumption in Uganda is related to several health consequences among young people, including university students. Social media is commonly used by students to share academic information and create social networks. Among young people in high-income countries, previous studies have also shown that social media use can have negative health outcomes related to alcohol use, and associated problems. To date, similar studies conducted in low- and middle-income countries are largely missing. To assess the prevalence of and associations between social media use and alcohol consumption among university students in Uganda. This was a cross-sectional study among 996 undergraduate students at Makerere University. Data were collected using a questionnaire. Alcohol use in the previous 12 months was the dependent variable. The independent variable was social media use categorised as general use, alcohol-related use, and social media lurking/passive participation. Multinomial logistic regression was used to assess associations. Crude and adjusted odds ratios were reported. Nearly all students (97%) used social media and 39% reported alcohol use. Regular alcohol use was significantly associated with moderate (OR = 2.22, CI: 1.35–3.66) and high level general social media use (OR = 2.45, CI: 1.43–4.20). Regular alcohol use was also associated with alcohol-related social media (OR = 6.46, CI: 4.04–10.30), and alcohol-related lurking (OR = 4.59, CI: 2.84–7.39). Similar, although weaker associations were identified for occasional alcohol use. Approximately four in ten students reported alcohol use in the past year, and almost all students used social media. Alcohol-related social media use was associated with occasional and regular alcohol use, with stronger associations for regular use. These findings may guide further research and present an opportunity for potential alcohol control interventions to improve health among young populations in low- and middle-income countries. [ABSTRACT FROM AUTHOR]