Optimal Time Allocation between Idle and Active Time

Idle time is an essential and valuable factor in the production of any service. While idle time is necessary and helpful for efficient and effective utilization of time, it also has a negative effect that managers try to minimize. This paper illustrates either analytically or numerically the different effects of idle time on total net revenue. It first presents the case in which idle time is determined arbitrarily, and then shows idle time that has no direct negative effect on total revenue but a positive effect on the efficient use of active time

Thirteen Questions about Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!)

Machine learning is gaining prominence in the health sciences, where much of its use has focused on datadriven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspecification. Using a question-and-answer format, we provide an introduction and orientation for epidemiologists interested in using machine learning but concerned about potential bias or loss of rigor due to use of "black box"models. We conclude with sample software code that may lower the barrier to entry to using these techniques

Exploring the subtleties of inverse probability weighting and marginal structural models

Since being introduced to epidemiology in 2000, marginal structural models have become a commonly used method for causal inference in a wide range of epidemiologic settings. In this brief report, we aim to explore three subtleties of marginal structural models. First, we distinguish marginal structural models from the inverse probability weighting estimator, and we emphasize that marginal structural models are not only for longitudinal exposures. Second, we explore the meaning of the word “marginal” in “marginal structural model.” Finally, we show that the specification of a marginal structural model can have important implications for the interpretation of its parameters. Each of these concepts have important implications for the use and understanding of marginal structural models, and thus providing detailed explanations of them may lead to better practices for the field of epidemiology

Comment on Williamson et al. (OpenSAFELY): The Table 2 Fallacy in a Study of COVID-19 Mortality Risk Factors

To the Editor: We write with respect to the recently published work by Williamson et al. “OpenSAFELY: factors associated with COVID-19 death in 17 million patients.” We have serious concerns about both the way these results are presented, and how they are likely to be interpreted. Our specific concerns revolve around whether the work is intended by the authors to estimate causal effects, or not—and how, regardless of their intent, it seems likely to us that their work will be interpreted as causal

Impact of three empirical anti-tuberculosis treatment strategies for people initiating antiretroviral therapy

Early mortality in people initiating antiretroviral treatment (ART) in Africa remains high. Empiric TB treatment strategies aim to reduce early mortality by initiating TB treatment in individuals without clinical suspicion of TB who are at high-risk of death from undiagnosed TB

Using Bounds to Compare the Strength of Exchangeability Assumptions for Internal and External Validity

In the absence of strong assumptions (e.g., exchangeability), only bounds for causal effects can be identified. Here we describe bounds for the risk difference for an effect of a binary exposure on a binary outcome in 4 common study settings: observational studies and randomized studies, each with and without simple random selection from the target population. Through these scenarios, we introduce randomizations for selection and treatment, and the widths of the bounds are narrowed from 2 (the width of the range of the risk difference) to 0 (point identification). We then assess the strength of the assumptions of exchangeability for internal and external validity by comparing their contributions to the widths of the bounds in the setting of an observational study without random selection from the target population. We find that when less than two-thirds of the target population is selected into the study, the assumption of exchangeability for external validity of the risk difference is stronger than that for internal validity. The relative strength of these assumptions should be considered when designing, analyzing, and interpreting observational studies and will aid in determining the best methods for estimating the causal effects of interest

Male circumcision and HIV prevention: ethical, medical and public health tradeoffs in low-income countries

Ethical challenges surrounding the implementation of male circumcision as an HIV prevention strateg

Can the new CKD-EPI BTP-B2M formula be applied in children?

Although measuring creatinine to determine kidney function is currently the clinical standard, new markers such as beta-trace protein (BTP) and beta-2-microglobulin (B2M) are being investigated in an effort to measure glomerular filtration rate more accurately. In their recent publication, Inker et al. (Am J Kidney Dis 2015; 67:40–48) explored the use of these two relatively new markers in combination with some commonly available clinical characteristics in a large cohort of adults with chronic kidney disease. Their research led them to develop three formulae using BTP, B2M, and a combination of the two. The combined formula is particularly attractive as it removes all gender bias, which applies to both serum creatinine and cystatin C. Using data from a cohort of 127 pediatric patients from our center, we sought to determine whether these formulae would be equally as effective in children as in adults. Unfortunately, we found that the formulae cannot be applied to the pediatric population

How subgroup analyses can miss the trees for the forest plots: A simulation study

Objectives: Subgroup analyses of clinical trial data can be an important tool for understanding when treatment effects differ across populations. That said, even effect estimates from prespecified subgroups in well-conducted trials may not apply to corresponding subgroups in the source population. While this divergence may simply reflect statistical imprecision, there has been less discussion of systematic or structural sources of misleading subgroup estimates. Study Design and Setting: We use directed acyclic graphs to show how selection bias caused by associations between effect measure modifiers and trial selection, whether explicit (e.g., eligibility criteria) or implicit (e.g., self-selection based on race), can result in subgroup estimates that do not correspond to subgroup effects in the source population. To demonstrate this point, we provide a hypothetical example illustrating the sorts of erroneous conclusions that can result, as well as their potential consequences. We also provide a tool for readers to explore additional cases. Conclusion: Treating subgroups within a trial essentially as random samples of the corresponding subgroups in the wider population can be misleading, even when analyses are conducted rigorously and all findings are internally valid. Researchers should carefully examine associations between (and consider adjusting for) variables when attempting to identify heterogeneous treatment effects

An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects

Traditional epidemiologic approaches allow us to compare counterfactual outcomes under 2 exposure distributions, usually 100% exposed and 100% unexposed. However, to estimate the population health effect of a proposed intervention, one may wish to compare factual outcomes under the observed exposure distribution to counterfactual outcomes under the exposure distribution produced by an intervention. Here, we used inverse probability weights to compare the 5-year mortality risk under observed antiretroviral therapy treatment plans to the 5-year mortality risk that would had been observed under an intervention in which all patients initiated therapy immediately upon entry into care among patients positive for human immunodeficiency virus in the US Centers for AIDS Research Network of Integrated Clinical Systems multisite cohort study between 1998 and 2013. Therapy-naïve patients (n = 14,700) were followed from entry into care until death, loss to follow-up, or censoring at 5 years or on December 31, 2013. The 5-year cumulative incidence of mortality was 11.65% under observed treatment plans and 10.10% under the intervention, yielding a risk difference of −1.57% (95% confidence interval: −3.08, −0.06). Comparing outcomes under the intervention with outcomes under observed treatment plans provides meaningful information about the potential consequences of new US guidelines to treat all patients with human immunodeficiency virus regardless of CD4 cell count under actual clinical conditions