Immortal person-time in studies of cancer outcomes

Immortal person-time arises in an observational study when follow-up time is included in person-time at risk for the study outcome, even though that time precedes the last event required for entry into the study population or satisfaction of an exposure definition.1,2 Immune person-time is similar, but it pertains to outcomes other than death. If a study patient were to have incurred the outcome or been censored during immortal or immune person-time, then the patient would not have satisfied the requirements for inclusion in the study or exposure category. A study or exposure category that includes immortal or immune person-time yields a downwardly biased outcome rate and an upwardly biased survival curve. This bias occurs because the accumulated person-time exceeds person-time actually at risk. When comparing rates or survival curves among exposure categories, the net effect of immortal or immune person-time bias may be in any direction

Plagiochila rutilans (Hepaticae): A poorly known species from tropical America

The neotropical liverwort, Plagiochila rutilans Lindenb., is conspecific with P. remotifolia Hampe and Gottsche, P. farlowii Steph., P. harrisana Steph, and P. organensis Herzog. Plagiochila standleyi Carl is reduced to a variety of P. rutilans. Plagiochila gymnocalycina (Lehm. and Lindenb.) Mont. and P. portoricensis Hampe and Gottsche (= P. simplex (Sw.) Lindenb.) are excluded from the synonymy of P. rutilans. Plagiochila rutilans var. liebmanniana Gottsche is a synonym of P. crispabilis Lindenb.; P. rutilans var. laxa Lindenb. and var. angustifolia Herzog are conspecific with P. gymnocalycina. Sporophytes of P. rutilans are described for the first time. Fresh material of P. rutilans exhibits a distinct odor of peppermint caused by the presence of several menthane monoterpenoids, principally pulegone. NMR (nuclear magnetic resonance) fingerprints and GC-MS data indicate that the lipophilic secondary metabolite profiles are distinct for the two varieties accepted in this study

Nondogmatism

The following is based on my remarks on receipt of the 2015 ACE award for outstanding contributions to epidemiology

Assessing Exposure-Response Trends Using the Disease Risk Score

Standardization by a disease risk score (DRS) may be preferable to weighting on the exposure propensity score if the exposure is difficult to model (1), relatively novel (i.e., newly emerging or rapidly-evolving), or extremely rare (2, 3). For exposures with more than two levels, methods are lacking for a DRS-based approach. We present an approach to estimate trends in standardized risk ratios (RRs) based on a regression model that uses a DRS

Nonparametric Bounds for the Risk Function

Nonparametric bounds for the risk difference are straightforward to calculate and make no untestable assumptions about unmeasured confounding or selection bias due to missing data (e.g., dropout). These bounds are often wide and communicate uncertainty due to possible systemic errors. An illustrative example is provided

US Black Women and Human Immunodeficiency Virus Prevention: Time for New Approaches to Clinical Trials

Black women bear the highest burden of human immunodeficiency virus (HIV) infection among US women. Tenofovir/emtricitabine HIV prevention trials among women in Africa have yielded varying results. Ideally, a randomized controlled trial (RCT) among US women would provide data for guidelines for US women's HIV preexposure prophylaxis use. However, even among US black women at high risk for HIV infection, sample size requirements for an RCT with HIV incidence as its outcome are prohibitively high. We propose to circumvent this large sample size requirement by evaluating relationships between HIV incidence and drug concentrations measured among participants in traditional phase 3 trials in high-incidence settings and then applying these observations to drug concentrations measured among at-risk individuals in lower-incidence settings, such as US black women. This strategy could strengthen the evidence base to enable black women to fully benefit from prevention research advances and decrease racial disparities in HIV rates

A Fundamental Equivalence between Randomized Experiments and Observational Studies

A fundamental probabilistic equivalence between randomized experiments and observational studies is presented. Given a detailed scenario, the reader is asked to consider which of two possible study designs provides more information regarding the expected difference in an outcome due to a time-fixed treatment. A general solution is described, and a particular worked example is also provided. A mathematical proof is given in the appendix. The demonstrated equivalence helps to clarify common ground between randomized experiments and observational studies, and to provide a foundation for considering both the design and interpretation of studies

TWISTER PLOTS FOR TIME-TO-EVENT STUDIES

Results of randomized trials and observational studies can be difficult to communicate. Results are often presented as risk or survival functions stratified by the treatment or exposure. However, a contrast between the stratified risk functions is often of primary interest. Here we propose a “twister” plot to visualize contrasts in risk over the duration of a study. The twister plot is a −90-degree rotation of a typical contrast measure plot (e.g., Figure 3 in Cole et al.), whereby the contrast measure is instead on the abscissa (x-axis) and time on the ordinate (y-axis). Pointwise confidence intervals are similarly added as a shaded region that typically widens as follow-up duration increases, giving twister plots their characteristic shape that resembles their namesake. To ease application, we provide SAS, R, and Python code on GitHub

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

Surprise!

Measures of information and surprise, such as the Shannon information value (S value), quantify the signal present in a stream of noisy data. We illustrate the use of such information measures in the context of interpreting P values as compatibility indices. S values help communicate the limited information supplied by conventional statistics and cast a critical light on cutoffs used to judge and construct those statistics. Misinterpretations of statistics may be reduced by interpreting P values and interval estimates using compatibility concepts and S values instead of "significance"and "confidence.