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Background and Objectives Dementia-friendly training should be incorporated in neighborhood stores for people living with dementia to maintain engagement in social activities. However, there is a lack of evidence of dementia-friendly training in these workplaces, and existing trainings have time constraints. We developed a mobile microlearning program based on stigma theory and the bystander intervention model. This study aimed to evaluate the microlearning program's effectiveness. Research Design and Methods Convenience store workers in Tokyo were recruited for a randomized, waiting-list, and controlled trial. The intervention group completed a 50-min online course. The primary outcome was an attitude toward people living with dementia. The secondary outcomes were knowledge of dementia and helping behavior toward customers suspected of having dementia. Data were collected at baseline, after 1 month, and 4 months following the randomization. Results Process evaluations confirmed satisfaction and high completion rates of the program. In total, 150 participants were included in the analysis. The intervention group showed significantly greater improvements in attitude (Hedge's g = 0.70) and knowledge (g = 0.59) after 1 month, compared to the control group. Helping behavior increased in the intervention group, although it did not differ significantly between the groups. All outcomes remained significantly improved after 4 months. Discussion and Implications The findings provide evidence that dementia-friendly training reduces the general public's stigma and increases helping behavior in stores. Mitigation of time constraints through mobile microlearning is expected to contribute to the dissemination and help people living with dementia maintain their social participation in the communities. Clinical Trials Registration Number: UMIN000043623 [ABSTRACT FROM AUTHOR]

Subsequent treatments can result in a difficulty in interpretation of the overall survival results in confirmatory oncology clinical trials. To complement the intention‐to‐treat (ITT) analysis affected by subsequent treatment patterns unintentional in the trial protocol, several causal methods targeting the per‐protocol effect have been proposed. When two or more types of subsequent treatments are allowed in the trial protocol, however, these methods cannot answer clinical questions such as how sensitive the ITT analysis result is to higher or lower proportions of each subsequent treatment allowed in the trial protocol than observed, and to what extent ITT analysis result is generalizable to subsequent treatment patterns other than observed one. To answer these clinical questions, we propose a sensitivity analysis method for subsequent treatments using the inverse probability of treatment weighting method for stochastic dynamic treatment regimes (DTRs). We formulate oncology clinical trials with subsequent treatments as two‐stage designs in which initial treatments are randomized, but subsequent treatments are observational. In this formulation, we use stochastic DTRs to simulate specific proportions of each subsequent treatment and compare an initial experimental treatment with an initial control treatment under various proportions of each subsequent treatment. We applied our proposed method to a motivating randomized noninferiority trial for metastatic breast cancer. Simulation results are also reported to show the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

G‐estimation of structural nested models (SNMs) plays an important role in estimating the effects of time‐varying treatments with appropriate adjustment for time‐dependent confounding. As SNMs for a failure time outcome, structural nested accelerated failure time models (SNAFTMs) and structural nested cumulative failure time models have been developed. The latter models are included in the class of structural nested mean models (SNMMs) and are not involved in artificial censoring, which induces several difficulties in g‐estimation of SNAFTMs. Recently, restricted mean time lost (RMTL), which corresponds to the area under a distribution function up to a restriction time, is attracting attention in clinical trial communities as an appropriate summary measure of a failure time outcome. In this study, we propose another SNMM for a failure time outcome, which is called structural nested RMTL model (SNRMTLM) and describe randomized and observational g‐estimation procedures that use different assumptions for the treatment mechanism in a randomized trial setting. We also provide methods to estimate marginal RMTLs under static treatment regimes using estimated SNRMTLMs. A simulation study evaluates finite‐sample performances of the proposed methods compared with the conventional intention‐to‐treat and per‐protocol analyses. We illustrate the proposed methods using data from a randomized controlled trial for cardiovascular disease with treatment changes. G‐estimation of SNRMTLMs is a useful tool to estimate the effects of time‐varying treatments on a failure time outcome. [ABSTRACT FROM AUTHOR]

Risk difference is a relevant effect measure in epidemiologic research. Although it is well known that when there are few events per confounder, logistic regression is not suitable for confounding control, it is not clear how many events per confounder are required for valid estimation of risk difference using linear binomial models. Because the maximum likelihood method has a convergence problem, we investigated the number of events per confounder necessary to validly estimate risk difference using modified least-squares regression in a simulation. We simulated 864 scenarios, according to the number of confounders (2–20), the number of events per confounder (2–12), marginal risk (0.5%–40%), exposure proportion (20% and 40%), and 3 sizes of risk difference. Our simulation showed that modified least-squares regression provided unbiased risk difference—regardless of the number of events per confounder—and reliable confidence intervals when more than 5 events were expected in the exposed and in the unexposed, irrespective of the number of events per confounder. We illustrated the modified least-squares regression analysis using perinatal epidemiologic data. Modified least-squares regression is considered to be a useful analytical tool for rare binary outcomes relative to the number of confounders. [ABSTRACT FROM AUTHOR]