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To test the hypothesis that tolerating some subretinal fluid (SRF) in patients with neovascular agerelated macular degeneration (nAMD) treated with ranibizumab using a treat-and-extend (T&E) regimen can achieve similar visual acuity (VA) outcomes as treatment aimed at resolving all SRF.Design: Multicenter, randomized, 24-month, phase 4, single-masked, noninferiority clinical trial.Participants: Participants with treatment-naïve active subfoveal choroidal neovascularization (CNV). Methods: Participants were randomized to receive ranibizumab 0.5 mg monthly until either complete resolution of SRF and intraretinal fluid (IRF; intensive arm: SRF intolerant) or resolution of all IRF only (relaxed arm: SRF tolerant except for SRF >200 mm at the foveal center) before extending treatment intervals. A 5-letter noninferiority margin was applied to the primary outcome.Main Outcome Measures: Mean change in best-corrected VA (BCVA), and central subfield thickness and number of injections from baseline to month 24.Results: Of the 349 participants randomized (intensive arm, n ¼ 174; relaxed arm, n ¼ 175), 279 (79.9%) completed the month 24. The mean change in BCVA from baseline to month 24 was 3.0 letters (standard deviation, 16.3 letters) in the intensive group and 2.6 letters (standard deviation, 16.3 letters) in the relaxed group, demonstrating noninferiority of the relaxed compared with the intensive treatment (P ¼ 0.99). Similar proportions of both groups achieved 20/40 or better VA (53.5% and 56.6%, respectively; P ¼ 0.92) and 20/200 or worse VA (8.7% and 8.1%, respectively; P ¼ 0.52). Participants in the relaxed group received fewer ranibizumab injections over 24 months (mean, 15.8 [standard deviation, 5.9]) than those in the intensive group (mean, 17 [standard deviation, 6.5]; P ¼ 0.001). Significantly more participants in the intensive group never extended beyond 4-week treatment intervals (13.5%) than in the relaxed group (2.8%; P ¼ 0.003), and significantly more participants in the relaxed group extended to and maintained 12-week treatment intervals (29.6%) than the intensive group (15.0%; P ¼ 0.005).Conclusions: Patients treated with a ranibizumab T&E protocol who tolerated some SRF achieved VA that is comparable, with fewer injections, with that achieved when treatment aimed to resolve all SRF completely.

Stigma negatively impact quality of life of people with dementia and their family members. Yet the literature in stigma and dementia remains scant. This article systematically reviews manifestations of and associated factors with public-stigma and self-stigma in the context of dementia. After searching and screening on the three major databases of PubMed, Embase, and psycINFO, 26 articles, including 17 quantitative papers and nine qualitative papers, were selected for synthesis. Results show consistently limited knowledge, as well as stereotype, prejudice, and discrimination of public toward people with dementia and their family caregivers. Demographic characteristics of general public were found to be associated with the level of their stigma against dementia. People with dementia and their family caregivers also perceived negative stereotype, prejudice, and discrimination from general public and healthcare professionals. They reported negative feelings of themselves and tended to delay help-seeking. Psychological factors rather than sociodemographic factors shaped self-stigma of people with dementia and their families. This systematic review highlights the need of future studies in both public-stigma and self-stigma in dementia research in different contexts and cultures, as well as the development of evidence-based and culturally competent interventions and mass media campaigns to reconstruct public perception of dementia.

The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements—effect definitions with causal interpretation, clarification of assumptions required for effect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar with causal inference or unfamiliar with mediation. The goal of this article is to help ease the understanding and adoption of causal mediation analysis. It starts by highlighting a key difference between the causal inference and traditional approaches to mediation analysis and making a case for the need for explicit causal thinking and the causal inference approach in mediation analysis. It then explains in as-plain-as-possible language existing effect types, paying special attention to motivating these effects with different types of research questions, and using concrete examples for illustration. This presentation differentiates 2 perspectives (or purposes of analysis): the explanatory perspective (aiming to explain the total effect) and the interventional perspective (asking questions about hypothetical interventions on the exposure and mediator, or hypothetically modified exposures). For the latter perspective, the article proposes tapping into a general class of interventional effects that contains as special cases most of the usual effect types—interventional direct and indirect effects, controlled direct effects and also a generalized interventional direct effect type, as well as the total effect and overall effect. This general class allows flexible effect definitions which better match many research questions than the standard interventional direct and indirect effects.

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In the presence of treatment effect heterogeneity, the average treatment effect (ATE) in a randomized controlled trial (RCT) may differ from the average effect of the same treatment if applied to a target population of interest. If all treatment effect moderators are observed in the RCT and in a dataset representing the target population, we can obtain an estimate for the target population ATE by adjusting for the difference in the distribution of the moderators between the two samples. This paper considers sensitivity analyses for two situations: (1) where we cannot adjust for a specific moderator V observed in the RCT because we do not observe it in the target population; and (2) where we are concerned that the treatment effect may be moderated by factors not observed even in the RCT, which we represent as a composite moderator U . In both situations, the outcome is not observed in the target population. For situation (1), we offer three sensitivity analysis methods based on (i) an outcome model, (ii) full weighting adjustment, and (iii) partial weighting combined with an outcome model. For situation (2), we offer two sensitivity analyses based on (iv) a bias formula and (v) partial weighting combined with a bias formula. We apply methods (i) and (iii) to an example where the interest is to generalize from a smoking cessation RCT conducted with participants of alcohol/illicit drug use treatment programs to the target population of people who seek treatment for alcohol/illicit drug use in the US who are also cigarette smokers. In this case a treatment effect moderator is observed in the RCT but not in the target population dataset.

We investigate a method to estimate the combined effect of multiple continuous/ordinal mediators on a binary outcome: 1) fit a structural equation model with probit link for the outcome and identity/probit link for continuous/ordinal mediators, 2) predict potential outcome probabilities, and 3) compute natural direct and indirect effects. Step 2 involves rescaling the latent continuous variable underlying the outcome to address residual mediator variance/covariance. We evaluate the estimation of risk-difference- and risk-ratio-based effects (RDs, RRs) using the ML, WLSMV and Bayes estimators in Mplus. Across most variations in path-coefficient and mediator-residual-correlation signs and strengths, and confounding situations investigated, the method performs well with all estimators, but favors ML/WLSMV for RDs with continuous mediators, and Bayes for RRs with ordinal mediators. Bayes outperforms WLSMV/ML regardless of mediator type when estimating RRs with small potential outcome probabilities and in two other special cases. An adolescent alcohol prevention study is used for illustration.