Dataset Information

Methods for Handling Missing Secondary Respondent Data.

ABSTRACT: Secondary respondent data are underutilized because researchers avoid using these data in the presence of substantial missing data. We reviewed, critically evaluated, and tested potential solutions to this problem. Five strategies of dealing with missing partner data are reviewed: complete case analysis, inverse probability weighting, correction with a Heckman selection model, maximum likelihood estimation, and multiple imputation. Two approaches were used to evaluate the performance of these methods. First, we used data from the National Survey of Fertility Barriers (N = 1,666) to estimate a model predicting marital quality based on characteristics of women and their husbands. Second, we conducted a simulation based on these data testing the five methods and compared the results to estimates where the true value was known. We found that the maximum likelihood and multiple imputation methods were advantageous because they allow researchers to utilize all of the available information as well as produce less biased and more efficient estimates.

SUBMITTER: Young R

PROVIDER: S-EPMC4477957 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Methods for Handling Missing Secondary Respondent Data.

Young Rebekah R Johnson David R DR

Journal of marriage and the family 20130201 1

Secondary respondent data are underutilized because researchers avoid using these data in the presence of substantial missing data. We reviewed, critically evaluated, and tested potential solutions to this problem. Five strategies of dealing with missing partner data are reviewed: complete case analysis, inverse probability weighting, correction with a Heckman selection model, maximum likelihood estimation, and multiple imputation. Two approaches were used to evaluate the performance of these me ...[more]

PMID: 26113747

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Project description:BackgroundTissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer.Patients and methodsWe pooled data from over 11,000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data - complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI-) and multiple imputation with inclusion of the outcome (MI+). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared.ResultsOver half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI+ were least biased and most accurate, whereas estimates for CCA were most biased and least accurate.ConclusionIn this study, empirical results from analyses using CCA, MS, MI- and MI+ were similar, although results from CCA were less precise. The results from simulations suggest that in general MI+ is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI+ and CCA should be compared in any multi-variate analysis where missing data are a problem.

Dataset Information

Methods for Handling Missing Secondary Respondent Data.

Publications

Methods for Handling Missing Secondary Respondent Data.

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