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Semiparametric modeling of grouped current duration data with preferential reporting.

ABSTRACT: Current duration data arise in cross-sectional studies from questions on the length of time from an initiating event to the time of interview. For example, in the National Survey on Family Growth, women who were considered at risk for pregnancy were asked (i) 'Are you currently attempting pregnancy?' and (ii) 'If yes, how many months have you been attempting to get pregnant?' The responses to (ii), referred to as the current durations, are length-biased because women with longer durations are more likely to answer yes to question (i) and therefore be included in the sample. Previous methods to analyze such data include continuous time nonparametric and parametric approaches. In this article, we propose a semiparametric Cox model and a piecewise constant baseline model (used to account for digit preference) to analyze grouped current duration data. We discuss and investigate through simulation studies, the robustness properties of the proposed methods when digit preference is present. Lastly, we present an analysis of the current duration data resulting from the 2002 National Survey on Family Growth. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

SUBMITTER: McLain AC

PROVIDER: S-EPMC4159422 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Semiparametric modeling of grouped current duration data with preferential reporting.

McLain Alexander C AC Sundaram Rajeshwari R Thoma Marie M Buck Louis Germaine M GM

Statistics in medicine 20140527 23

Current duration data arise in cross-sectional studies from questions on the length of time from an initiating event to the time of interview. For example, in the National Survey on Family Growth, women who were considered at risk for pregnancy were asked (i) 'Are you currently attempting pregnancy?' and (ii) 'If yes, how many months have you been attempting to get pregnant?' The responses to (ii), referred to as the current durations, are length-biased because women with longer durations are mo ...[more]

PMID: 24862959

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Project description:IntroductionSurveys are common research tools, and questionnaires revisions are a common occurrence in longitudinal studies. Revisions can, at times, introduce systematic shifts in measures of interest. We formulate that questionnaire revision are a stochastic process with transition matrices. Thus, revision shifts can be reduced by first estimating these transition matrices, which can be utilized in estimation of interested measures.Materials and methodAn ideal survey response model is defined by mapping between the true value of a participant's response to an interval in the grouped data type scale. A population completed surveys multiple times, as modeled with multiple stochastic process. This included stochastic processes related to true values and intervals. While multiple factors contribute to changes in survey responses, here, we explored the method that can mitigate the effects of questionnaire revision. We proposed the Version Alignment Method (VAM), a data preprocessing tool, which can separate the transitions according to revisions from all transitions via solving an optimization problem and using the revision-related transitions to remove the revision effect. To verify VAM, we used simulation data to study the estimation error and a real life MJ dataset containing large amounts of long-term questionnaire responses with several questionnaire revisions to study its feasibility.ResultWe compared the difference of the annual average between consecutive years. Without adjustment, the difference is 0.593 when the revision occurred, while VAM brought it down to 0.115, where difference between years without revision was in the 0.005, 0.125 range. Furthermore, our method rendered the responses to the same set of intervals, thus comparing the relative frequency of items before and after revisions became possible. The average estimation error in L infinity was 0.0044 which occupied the 95% CI which was constructed by bootstrap analysis.ConclusionQuestionnaire revisions can induce different response bias and information loss, thus causing inconsistencies in the estimated measures. Conventional methods can only partly remedy this issue. Our proposal, VAM, can estimate the aggregate difference of all revision-related systematic errors and can reduce the differences, thus reducing inconsistencies in the final estimations of longitudinal studies.

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Semiparametric modeling of grouped current duration data with preferential reporting.

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Semiparametric modeling of grouped current duration data with preferential reporting.

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