Conditional estimation using prior information in 2‐stage group sequential designs assuming asymptotic normality when the trial terminated early

Two‐stage designs are widely used to determine whether a clinical trial should be terminated early. In such trials, a maximum likelihood estimate is often adopted to describe the difference in efficacy between the experimental and reference treatments; however, this method is known to display conditional bias. To reduce such bias, a conditional mean‐adjusted estimator (CMAE) has been proposed, although the remaining bias may be nonnegligible when a trial is stopped for efficacy at the interim analysis. We propose a new estimator for adjusting the conditional bias of the treatment effect by extending the idea of the CMAE. This estimator is calculated by weighting the maximum likelihood estimate obtained at the interim analysis and the effect size prespecified when calculating the sample size. We evaluate the performance of the proposed estimator through analytical and simulation studies in various settings in which a trial is stopped for efficacy or futility at the interim analysis. We find that the conditional bias of the proposed estimator is smaller than that of the CMAE when the information time at the interim analysis is small. In addition, the mean‐squared error of the proposed estimator is also smaller than that of the CMAE. In conclusion, we recommend the use of the proposed estimator for trials that are terminated early for efficacy or futility.     

Deliberative Democracy and the Paradox of Participation

Citizen involvement and participatory governance have been adopted as essential components of urban development policies in many countries. For instance, participatory budgeting in Porto Alegre was successful in mobilizing and empowering poor people. However, in many cases, participation is merely a buzzword which often leads to co‐optation. In this article, we propose an explanatory model of participatory governance for making comparative analyses and compare participatory budgeting in the 1990s in Brazil with the Japanese community policy of the 1970s. Based on this, our analysis identifies the paradox of participation and the importance of gatekeeping functions. As a consequence of enhanced citizen participation, the power of the bureaucratic administration becomes dominant unless politicians and the legislature retain their autonomy in decision‐making. If the gatekeeping functions of involvement and decision‐making are monopolized by the administrative body urban development is de‐politicized, which, in turn, leads to co‐optation by government and exclusion. It is important to retain the function of politics to deepen democracy through citizen participation in urban development.     

Effect of heteroscedasticity between treatment groups on mixed‐effects models for repeated measures

Mixed‐effects models for repeated measures (MMRM) analyses using the Kenward‐Roger method for adjusting standard errors and degrees of freedom in an “unstructured” (UN) covariance structure are increasingly becoming common in primary analyses for group comparisons in longitudinal clinical trials. We evaluate the performance of an MMRM‐UN analysis using the Kenward‐Roger method when the variance of outcome between treatment groups is unequal. In addition, we provide alternative approaches for valid inferences in the MMRM analysis framework. Two simulations are conducted in cases with (1) unequal variance but equal correlation between the treatment groups and (2) unequal variance and unequal correlation between the groups. Our results in the first simulation indicate that MMRM‐UN analysis using the Kenward‐Roger method based on a common covariance matrix for the groups yields notably poor coverage probability (CP) with confidence intervals for the treatment effect when both the variance and the sample size between the groups are disparate. In addition, even when the randomization ratio is 1:1, the CP will fall seriously below the nominal confidence level if a treatment group with a large dropout proportion has a larger variance. Mixed‐effects models for repeated measures analysis with the Mancl and DeRouen covariance estimator shows relatively better performance than the traditional MMRM‐UN analysis method. In the second simulation, the traditional MMRM‐UN analysis leads to bias of the treatment effect and yields notably poor CP. Mixed‐effects models for repeated measures analysis fitting separate UN covariance structures for each group provides an unbiased estimate of the treatment effect and an acceptable CP. We do not recommend MMRM‐UN analysis using the Kenward‐Roger method based on a common covariance matrix for treatment groups, although it is frequently seen in applications, when heteroscedasticity between the groups is apparent in incomplete longitudinal data.     

An application of the mixed-effects model and pattern mixture model to treatment groups with differential missingness suspected not-missing-at-random

Likelihood-based, mixed-effects models for repeated measures (MMRMs) are occasionally used in primary analyses for group comparisons of incomplete continuous longitudinal data. Although MMRM analysis is generally valid under missing-at-random assumptions, it is invalid under not-missing-at-random (NMAR) assumptions. We consider the possibility of bias of estimated treatment effect using standard MMRM analysis in a motivational case, and propose simple and easily implementable pattern mixture models within the framework of mixed-effects modeling, to handle the NMAR data with differential missingness between treatment groups. The proposed models are a new form of pattern mixture model that employ a categorical time variable when modeling the outcome and a continuous time variable when modeling the missingness-data patterns. The models can directly provide an overall estimate of the treatment effect of interest using the average of the distribution of the missingness indicator and a categorical time variable in the same manner as MMRM analysis. Our simulation results indicate that the bias of the treatment effect for MMRM analysis was considerably larger than that for the pattern mixture model analysis under NMAR assumptions. In the case study, it would be dangerous to interpret only the results of the MMRM analysis, and the proposed pattern mixture model would be useful as a sensitivity analysis for treatment effect evaluation.     

Social Survey Research1 in Japanese Sociology

Abstract: In this paper, we look at the history of social survey development in Japanese sociology. First, the history of social research in Japan before World War II is explored. Second, the introduction of survey research to Japan during the American occupation after World War II is examined, and third, the present state and roles of social survey research in Japanese sociology is discussed. Social research was introduced as an administrative tool for the government. Sociology and social research were developed under British empiricism and American pragmatism, but Japanese academia has been based on a metaphysical approach. Social research introduced as a practical tool long had difficulty in being accepted by Japanese academia. For this reason, most sociologists in universities did not use social survey research for practical purposes, but pursued qualitative methodologies for analyzing data to gain academic prestige even after Social Stratification and Mobility (SSM) and Sabro Yasuda's research projects spread social survey methods in the field of Japanese sociology. Such academics did not think that findings acquired through qualitative case studies had to be confirmed through quantitative data to serve a practical purpose, nor did they believe that quantitative data could be better understood when examined along side qualitative data. Social survey methods have been opposed by those who have favored case‐study analysis methods in Japanese sociology. Needless to say, this opposition is fruitless. I propose that professional sociologists in Japanese universities should use social survey research for practical problems more frequently. This is the best way to establish sociology and social research as a science in Japanese society.