Some principles of cost-benefit analysis of family planning services in developing countries

A number of controversial issues are discussed, relating to the assessment of both costs and benefits of family planning services. All costs and benefits, whether accruing to society or the parents of the child whose birth is averted, and the child itself, should be included in the analysis, even if they cannot easily be measured or appear as externalities. Different rates of interest to be used in discounting to obtain present values apply to various items; these rates may vary between the commercial rates of interest and negative values. It is also shown that the costs of running a service include, in addition to current expenditure, not only basic investments, but also that the benefit accruing to society by averting births comprises a kind of investment, namely the amount spent on the subsistence of the child whose birth is averted before he would have started producing. Marginal values are to be preferred to average values for the calculation of costs and benefits. Thus, the costs of a family planning service should be expressed as the amount of money needed to avert one additional birth and the benefits as the amount saved by one additional averted birth. The latter cannot be measured by comparing the GNP per head when the birth is averted, with the situation when it is not. This can only be done by computing the excess of the child’s life-time consumption over his life-time production.     

论过失犯中结果避免可能性的判断

在过失犯中采纳结果避免可能性理论,既有实定法依据,也能够解决实务中践行传统过失审查模式所导致的问题,保证归责范围的妥当性。要使结果避免可能性理论研究的教义学成果在实务中落地生根,必须结合实务案例确定结果避免可能性理论适用的具体操作指南。首先,合义务替代行为是最低限度的符合全部注意义务的行为。其次,应当区分风险,在假定因果流程中需要考虑的是与行为人所创设风险之实现有关的“关联风险”,而要避免的“结果”必须结合具体的时空关系进行判断。最后,应当判断是否确定能够避免结果发生,在不能确定能否避免结果发生时,应采取风险升高理论;但在避免结果发生的可能性较小时,可对行为人酌情从轻处罚。     

Toward Best Practices in Analyzing Datasets with Missing Data: Comparisons and Recommendations

Although several methods have been developed to allow for the analysis of data in the presence of missing values, no clear guide exists to help family researchers in choosing among the many options and procedures available. We delineate these options and examine the sensitivity of the findings in a regression model estimated in three random samples from the National Survey of Families and Households (n = 250–2,000). These results, combined with findings from simulation studies, are used to guide answers to a set of 10 common questions asked by researchers when selecting a missing data approach. Modern missing data techniques were found to perform better than traditional ones, but differences between the types of modern approaches had minor effects on the estimates and substantive conclusions. Our findings suggest that the researcher has considerable flexibility in selecting among modern options for handling missing data.     

基于数据缺失率和缺失模式的多重插补误差研究

文章通过多重插补方法对不同缺失率和缺失模式的多变量缺失样本进行插补,研究了多重插补误差与缺失率和缺失模式的依赖关系。结果表明,当缺失率为0~15%时,多重插补误差与缺失率呈线性关系;当缺失率大于15%时,两者呈偏离线性关系。多重插补误差与缺失模式的方差均值比呈正相关性,当方差均值比越大时,误差也越大。     

A new approach for disclosure control in the IAB establishment panel—multiple imputation for a better data access

For micro-datasets considered for release as scientific or public use files, statistical agencies have to face the dilemma of guaranteeing the confidentiality of survey respondents on the one hand and offering sufficiently detailed data on the other hand. For that reason, a variety of methods to guarantee disclosure control is discussed in the literature. In this paper, we present an application of Rubin’s (J. Off. Stat. 9, 462–468, 1993) idea to generate synthetic datasets from existing confidential survey data for public release.We use a set of variables from the 1997 wave of the German IAB Establishment Panel and evaluate the quality of the approach by comparing results from an analysis by Zwick (Ger. Econ. Rev. 6(2), 155–184, 2005) with the original data with the results we achieve for the same analysis run on the dataset after the imputation procedure. The comparison shows that valid inferences can be obtained using the synthetic datasets in this context, while confidentiality is guaranteed for the survey participants.     

基于随机森林模型的分类数据缺失值插补

缺失数据是影响调查问卷数据质量的重要因素,对调查问卷中的缺失值进行插补可以显著提高调查数据的质量。调查问卷的数据类型多以分类型数据为主,数据挖掘技术中的分类算法是处理属性分类问题的常用方法,随机森林模型是众多分类算法中精度较高的方法之一。将随机森林模型引入调查问卷缺失数据的插补研究中,提出了基于随机森林模型的分类数据缺失值插补方法,并根据不同的缺失模式探讨了相应的插补步骤。通过与其它方法的实证模拟比较,表明随机森林插补法得到的插补值准确度更优、可信度更高。     

Developments in sample survey theory: An appraisal

Recent developments in sample survey theory include the following topics: foundational aspects of inference, resampling methods for variance and confidence interval estimation, imputation for nonresponse and analysis of complex survey data. An overview and appraisal of some of these developments are presented.     

The attributable estimand: A new approach to account for intercurrent events

The term “intercurrent events” has recently been used to describe events in clinical trials that may complicate the definition and calculation of the treatment effect estimand. This paper focuses on the use of an attributable estimand to address intercurrent events. Those events that are considered to be adversely related to randomized treatment (eg, discontinuation due to adverse events or lack of efficacy) are considered attributable and handled with a composite estimand strategy, while a hypothetical estimand strategy is used for intercurrent events not considered to be related to randomized treatment (eg, unrelated adverse events). We explore several options for how to implement this approach and compare them to hypothetical “efficacy” and treatment policy estimand strategies through a series of simulation studies whose design is inspired by recent trials in chronic obstructive pulmonary disease (COPD), and we illustrate through an analysis of a recently completed COPD trial.     

Goodness of fit tests with misclassified data

The most popular goodness of fit test for a multinomial distribution is the chi-square test. But this test is generally biased if observations are subject to misclassification, In this paper we shall discuss how to define a new test procedure when we have double sample data obtained from the true and fallible devices. An adjusted chi-square test based on the imputation method and the likelihood ratio test are considered, Asymptotically, these two procedures are equivalent. However, an example and simulation results show that the former procedure is not only computationally simpler but also more powerful under finite sample situations.     

Imputation of repeatedly observed multinomial variables in longitudinal surveys

It is now a standard practice to replace missing data in longitudinal surveys with imputed values, but there is still much uncertainty about the best approach to adopt. Using data from a real survey, we compared different strategies combining multiple imputation and the chained equations method, the two main objectives being (1) to explore the impact of the explanatory variables in the chained regression equations and (2) to study the effect of imputation on causality between successive waves of the survey. Results were very stable from one simulation to another, and no systematic bias did appear. The critical points of the method lied in the proper choice of covariates and in the respect of the temporal relation between variables.