Approach to Analysis of Self-Selected Interval Data

Quantile regression with interval-censored data in questionnaire-based studies

“…Interval-censored data may also occur in questionnaire-based studies when the respondent is requested to give an answer in the form of an interval without having a list of ranges to choose from. This type of data is referred to as self-selected interval data (Belyaev and Kriström 2010, 2012, 2015. Similar question formats have been explored by Tanur (2004a, 2004b), Håkansson (2008), and Mahieu et al (2017).…”

Maximum likelihood estimation for survey data with informative interval censoring

“…They employed Bayesian methods for estimating the parameters of the underlying distribution. Similar format, in which the respondent is free to answer with any interval containing his/her true value, was considered by Belyaev and Kriström (2010). They use the term self-selected interval (SSI).…”

“…These estimators rely on the assumption of noninformative censoring, i.e., the observation process that generates the censoring is independent of the variable of interest (see, e.g., Sun 2006, p. 244). In the sampling schemes considered by Belyaev and Kriström (2010, 2015 and Angelov and Ekström (2017) this is not a reasonable assumption as it is the respondent who chooses the interval; thus, the standard methods are not appropriate. The existing methods for data with informative interval censoring (see Finkelstein et al 2002;Shardell et al 2007) are specific for time-to-event data and are not directly applicable in the context that we are discussing.…”

Nonparametric estimation for self-selected interval data collected through a two-stage approach

“…Related to the respondent-generated intervals approach is the self-selected interval (SSI) approach suggested by Belyaev and Kriström (2010), where the respondent is free to provide any interval containing his/her true value. They proposed a maximum likelihood estimator of the underlying distribution based on SSI data.…”

“…All these methods rely on the assumption of noninformative censoring, which implies that the joint distribution of L and R contains no parameters that are involved in the distribution function of X and therefore does not contribute to the likelihood function (see, e.g., Sun 2006). In the sampling schemes considered by Belyaev and Kriström (2010, 2012, 2015 this is not a reasonable assumption, thus the standard methods are not appropriate. The existing methods for analysis of time-to-event data in the presence of informative interval censoring require modeling the censoring process and estimating nuisance parameters (see Finkelstein et al 2002) or making additional assumptions about the censoring process (see Shardell et al 2007).…”