Random effects models for estimation of the probability and time to progression of a continuous biomarker

Biomarkers play a key role in the monitoring of disease progression. The time taken for an individual to reach a biomarker exceeding or lower than a meaningful threshold is often of interest. Due to the inherent variability of biomarkers, persistence criteria are sometimes included in the definitions of progression, such that only two consecutive measurements above or below the relevant threshold signal that “true” progression has occurred. In previous work, a novel approach was developed, which allowed estimation of the time to threshold using the parameters from a linear mixed model where the residual variance was assumed to be pure measurement error. In this paper, we extend this methodology so that serial correlation can be accommodated. Assuming that the Markov property holds and applying the chain rule of probabilities, we found that the probability of progression at each timepoint can be expressed simply as the product of conditional probabilities. The methodology is applied to a cohort of HIV positive individuals, where the time to reach a CD4 count threshold is estimated. The second application we present is based on a study on abdominal aortic aneurysms, where the time taken for an individual to reach a diameter exceeding 55 mm is studied. We observed that erroneously ignoring the residual correlation when it is strong may result in substantial overestimation of the time to threshold. The estimated probability of the biomarker reaching a threshold of interest, expected time to threshold, and confidence intervals are presented for selected patients in both applications.     

What's an Oscar worth?

This article examines the impact of an Academy Award nomination and award for best picture, best actor/actress, and best supporting actor/actress on a film's (i) market share of theaters, (ii) average revenue per screen, and (iii) its probability of survival. The model is estimated using weekly box-office data for a matched sample of nominated and non-nominated films. The results indicate substantial financial benefits for a nomination and award for best picture and best actor/actress. The structure of rewards is consistent with that found in two-stage, single-elimination tournaments.     

College women's perceived risk to experience sexual victimization: a prospective analysis

This study implemented a prospective design to explore college women's perceived risk to experience sexual victimization over a 2-month interim (N = 143). Compared to women without such histories, women with a history of unwanted sexual contact via arguments/ pressure, or a history of unwanted sexual intercourse via administration of alcohol/drugs reported higher perceived risk to subsequently experience these forms of victimization. Compared to women who were not victimized, women who subsequently experienced unwanted sexual intercourse via administration of alcohol/drugs or arguments/pressure reported higher levels of risk to experience these forms of victimization. Controlling for victimization history, higher levels of risk to experience sexual intercourse over the interim via arguments predicted this form of victimization over the follow-up. Implications are discussed.     

Optimal stratification in stratified designs using weibull-distributed auxiliary information

Sampling has evolved into a universally accepted approach for gathering information and data mining as it is widely accepted that a reasonably modest-sized sample can sufficiently characterize a much larger population. In stratified sampling designs, the whole population is divided into homogeneous strata in order to achieve higher precision in the estimation. This paper proposes an efficient method of constructing optimum stratum boundaries (OSB) and determining optimum sample size (OSS) for the survey variable. The survey variable may not be available in practice since the variable of interest is unavailable prior to conducting the survey. Thus, the method is based on the auxiliary variable which is usually readily available from past surveys. To illustrate the application as an example using a real data, the auxiliary variable considered for this problem follows Weibull distribution. The stratification problem is formulated as a Mathematical Programming Problem (MPP) that seeks minimization of the variance of the estimated population parameter under Neyman allocation. The solution procedure employs the dynamic programming technique, which results in substantial gains in the precision of the estimates of the population characteristics.     

Measuring Inequality of Opportunity for the Backward Communities: Regional Evidence from the Indian Labour Market

The affirmative action policy for socially and economically backward communities in employment has been a debated issue in India. In this context, this paper aims to analyze the level of inequality by distinguishing between ‘circumstance’ and ‘effort’ factors in the Roemer’s framework on equality of opportunity. We measure inequality of opportunities due to two circumstances: caste and religion. Our empirical analysis, at state-level, utilizes a recent household survey data, which provides information related to efforts as well as circumstances of workers. The paper estimated inequality in the labour market and then decomposed it to know the circumstances that cause income inequality. Our estimates indicated that inequality and inequality of opportunity is substantially higher in India. Specifically, the outcome of our analysis evidently indicated that the socially backward communities do have economically disadvantageous position in some of the Indian states. However, the degree of circumstances based on inequality varies to a great extent among the states. Therefore, we suggest that the country does not need a nation-level affirmative action policy instead a state-level policy could be more appropriate as the intensity of the problem differ significantly among the Indian states.     

Effects of method on participants and disclosure rates in research on sensitive topics

This study replicates and extends the research of Rosenbaum, Rabenhorst, Reddy, Fleming, and Howells, which also appears in this special issue. Responses from 398 randomly assigned participants regarding differentially sensitive topics were collected via four methods of data collection: written questionnaires, face-to-face interviews, telephone interviews, and an automated telephonic data collection system (ATDC). Several significant differences in data collection methodology and topic area were found, including greater disclosure of sensitive information via the ATDC system than via face-to-face and paper-and-pencil conditions. Participants who were assigned to the ATDC condition felt significantly more comfortable answering questions compared to those in the face-to-face interview condition. Participants in the telephone interview condition reported answering significantly more carefully than participants answering via written questionnaire. Taken together, the results of this study and the previous one it replicates suggest that the ATDC produces disclosure rates that are at least equivalent to, if not greater than, those generated using traditional methods for collecting sensitive data.     

Global Development Goals: The Folly of Technocratic Pretensions

This article argues that, although effective strategic choices for achieving global development goals need to be based on assessments of the costs and benefits of alternative approaches, existing methods of arriving at such assessments are highly unreliable, in particular deriving from implausible and restrictive assumptions and often depending on data of poor quality, and on the pretence that the future can be adequately known. Such weaknesses can be mitigated, but not easily overcome, without abandoning deeply held technocratic presumptions.     

DECENTRALIZATION AND PARTICIPATORY SERVICE DELIVERY

User participation has been promoted to improve the quality, responsiveness, and outreach of public services in India. This qualitative study evaluates new grassroots institutions—Village Education Committees and Mothers’ Committees—in Andhra Pradesh, which promoting parents’ participation in the management and monitoring of local schools and maternal/child health services. Although the committees were found to have increased participation in public affairs by the poor and brought about some improvements in service quality, inclusive and meaningful participation has often been hindered by illiteracy and lack of information, as well as caste, gender, and socioeconomic inequalities. The article suggests that there are considerable tensions between inclusive participation and committee effectiveness as members need skills, status, and resources in order to have a meaningful impact on service provision. In addition, research findings point to the need for more structured opportunities for committee members to interact with authorities and service providers, as well as for children to voice their concerns and ideas. Lastly, in order to tackle childhood poverty more holistically, the focus of these committees should be revised to give greater emphasis to improving service quality improvements and cross-sectoral synergies, rather than the current move narrow focus on infrastructural development and singlesector initiatives.     

Kernel density estimation on grouped data: the case of poverty assessment

Grouped data have been widely used to analyze the global income distribution because individual records from nationally representative household surveys are often unavailable. In this paper we evaluate the performance of nonparametric density smoothing techniques, in particular kernel density estimation, in estimating poverty from grouped data. Using Monte Carlo simulations, we show that kernel density estimation gives rise to nontrivial biases in estimated poverty levels that depend on the bandwidth, kernel, poverty indicator, size of the dataset, and data generating process. Furthermore, the empirical bias in the poverty headcount ratio critically depends on the poverty line. We also undertake a sensitivity analysis of global poverty estimates to changes in the bandwidth and show that they vary widely with it. A comparison of kernel density estimation with parametric estimation of the Lorenz curve, also applied to grouped data, suggests that the latter fares better and should be the preferred approach.