Influence of human immunodeficiency virus infection on neurological impairment: an analysis of longitudinal binary data with informative drop-out

A study to investigate the human immunodeficiency virus (HIV) status on the course of neurological impairment, conducted by the HIV Center at Columbia University, followed a cohort of HIV positive and negative gay men for 5 years and assessed the presence or absence of neurological impairment every 6 months. Almost half of the subjects dropped out before the end of the study for reasons that might have been related to the missing neurological data. We propose likelihood-based methods for analysing such binary longitudinal data under informative and non-informative drop-out. A transition model is assumed for the binary response, and several models for the drop-out processes are considered which are functions of the response variable (neurological impairment). The likelihood ratio test is used to compare models with informative and non-informative drop-out mechanisms. Using simulations, we investigate the percentage bias and mean-squared error (MSE) of the parameter estimates in the transition model under various assumptions for the drop-out. We find evidence for informative drop-out in the study, and we illustrate that the bias and MSE for the parameters of the transition model are not directly related to the observed drop-out or missing data rates. The effect of HIV status on the neurological impairment is found to be statistically significant under each of the models considered for the drop-out, although the regression coefficient may be biased in certain cases. The presence and relative magnitude of the bias depend on factors such as the probability of drop-out conditional on the presence of neurological impairment and the prevalence of neurological impairment in the population under study.     

Monte-Carlo Sensitivity Analysis for Controlled Direct Effects Using Marginal Structural Models in the Presence of Confounded Mediators

In randomized trials, investigators are frequently interested in estimating the direct effect of a treatment on an outcome that is not relayed by intermediate variables, in addition to the usual intention-to-treat (ITT) effect. Even if the ITT effect is not confounded due to randomization, the direct effect is not identified when unmeasured variables affect the intermediate and outcome variables. Although the unmeasured variables cannot be adjusted for in the models, it is still important to evaluate the potential bias of these variables quantitatively. This article proposes a sensitivity analysis method for controlled direct effects using a marginal structural model that is an extension of the sensitivity analysis method of unmeasured confounding introduced in the context of observational studies. The proposed method is illustrated using a randomized trial of depression.     

Back to basics: explaining sample size in outcome trials,are statisticians doing a thorough job?

Time to event outcome trials in clinical research are typically large, expensive and high‐profile affairs. Such trials are commonplace in oncology and cardiovascular therapeutic areas but are also seen in other areas such as respiratory in indications like chronic obstructive pulmonary disease. Their progress is closely monitored and results are often eagerly awaited. Once available, the top line result is often big news, at least within the therapeutic area in which it was conducted, and the data are subsequently fully scrutinized in a series of high‐profile publications. In such circumstances, the statistician has a vital role to play in the design, conduct, analysis and reporting of the trial. In particular, in drug development it is incumbent on the statistician to ensure at the outset that the sizing of the trial is fully appreciated by their medical, and other non‐statistical, drug development team colleagues and that the risk of delivering a statistically significant but clinically unpersuasive result is minimized. The statistician also has a key role in advising the team when, early in the life of an outcomes trial, a lower than anticipated event rate appears to be emerging. This paper highlights some of the important features relating to outcome trial sample sizing and makes a number of simple recommendations aimed at ensuring a better, common understanding of the interplay between sample size and power and the final result required to provide a statistically positive and clinically persuasive outcome. Copyright © 2009 John Wiley & Sons, Ltd.     

A win ratio approach to comparing continuous non‐normal outcomes in clinical trials

Clinical trials are often designed to compare continuous non‐normal outcomes. The conventional statistical method for such a comparison is a non‐parametric Mann–Whitney test, which provides a P‐value for testing the hypothesis that the distributions of both treatment groups are identical, but does not provide a simple and straightforward estimate of treatment effect. For that, Hodges and Lehmann proposed estimating the shift parameter between two populations and its confidence interval (CI). However, such a shift parameter does not have a straightforward interpretation, and its CI contains zero in some cases when Mann–Whitney test produces a significant result. To overcome the aforementioned problems, we introduce the use of the win ratio for analysing such data. Patients in the new and control treatment are formed into all possible pairs. For each pair, the new treatment patient is labelled a ‘winner’ or a ‘loser’ if it is known who had the more favourable outcome. The win ratio is the total number of winners divided by the total numbers of losers. A 95% CI for the win ratio can be obtained using the bootstrap method. Statistical properties of the win ratio statistic are investigated using two real trial data sets and six simulation studies. Results show that the win ratio method has about the same power as the Mann–Whitney method. We recommend the use of the win ratio method for estimating the treatment effect (and CI) and the Mann–Whitney method for calculating the P‐value for comparing continuous non‐Normal outcomes when the amount of tied pairs is small. Copyright © 2016 John Wiley & Sons, Ltd.     

Pregnancy after loss during the COVID19 pandemic

BackgroundRapid changes to how maternity health care is delivered has occurred in many countries across the globe in response to the COVID-19 pandemic. Maternity care provisions have been challenged attempting to balance the needs and safety of pregnant women and their care providers. Women experiencing a pregnancy after loss (PAL) during these times face particularly difficult circumstances.AimIn this paper we highlight the situation in three high income countries (Australia, Ireland and USA) and point to the need to remember the unique and challenging circumstances of these PAL families. We suggest new practices may be deviating from established evidence-based guidelines and outline the potential ramifications of these changes.FindingsRecommendations for health care providers are suggested to bridge the gap between the necessary safety requirements due to the pandemic, the role of the health care provider, and the needs of families experiencing a pregnancy after loss.DiscussionChanges to practices i.e. limiting the number of antenatal appointments and access to a support person may have detrimental effects on both mother, baby, and their family. However, new guidelines in maternity care practices developed to account for the pandemic have not necessarily considered women experiencing pregnancy after loss.ConclusionBereaved mothers and their families experiencing a pregnancy after loss should continue to be supported during the COVID-19 pandemic to limit unintended consequences.     

The nutrition-related information seeking behaviours and attitudes of pregnant women with a high BMI: A qualitative study

BackgroundThe prevalence of high body mass index is increasing amongst women of child bearing age. High maternal body mass index has ramifications for both mother and baby including increased health risks from gestational diabetes mellitus, caesarean section and stillbirth. Despite the increasing prevalence of high maternal body mass index little is known of the experiences of these women regarding nutrition information access and use during the antenatal period.MethodsA qualitative study using individual interviews was undertaken at a tertiary hospital in south-eastern Australia. Twenty-Eight women with a body mass index ≥30 kg/m² participated. Interviews were audio recorded, transcribed, cross-checked for consistency and entered into a word processing document for further scrutiny. Data was analysed using interpretative phenomenological analysis (IPA). In any phenomenological study the researcher’s objective is to elicit the participant’s views on their lived experiences.FindingsThree major themes emerged: (1) Nutrition-related information attainment; (2) Nutrition-related information management; (3) Nutrition-related information needs and wants.ConclusionThe findings from this study may assist the future development and dissemination of nutrition-related information for pregnant women with a high body mass index. Women want more individualised support regarding nutritional requirements during pregnancy.     

Racial/Ethnic Minority Community College Students' Critical Consciousness and Social Cognitive Career Outcomes

A need exists to better understand how racial/ethnic minority students' critical consciousness development in response to marginalization may be involved in their educational and career development. We therefore examined the link between critical consciousness development and career decision self-efficacy and career outcome expectations among racial/ethnic minority community college students. Following social cognitive career theory's conceptual pillars, we developed a testable model integrating critical consciousness and social cognitive variables. This model was tested with 135 racially and ethnically diverse community college students. Data analysis included path analyses and tests of model fit using structural equation modeling. Results suggested that (a) higher critical agency is linked to higher career decision self-efficacy and outcome expectations and (b) critical action and reflection have a bidirectional link and predict higher critical agency. Implications for research and practice aiming to close educational and career gaps among racial/ethnic minorities are discussed.