Replication data for: On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension (doi:10.7910/DVN/I5O6OS)

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Replication data for: On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension

doi:10.7910/DVN/I5O6OS

Harvard Dataverse

2007-11-28

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Kosuke Imai; Samir Soneji, 2007, "Replication data for: On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension", https://doi.org/10.7910/DVN/I5O6OS, Harvard Dataverse, V1

Replication data for: On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension

doi:10.7910/DVN/I5O6OS

Kosuke Imai (Princeton University)

Samir Soneji (Princeton University)

2007

Harvard Dataverse

Murray Research Archive

2007

2007

https://doi.org/10.7910/DVN/I5O6OS

Social Sciences, Aggregate Data, Demography, Life Tables, Morbidity, Mortality, Stationarity

Contributed

A rapidly aging population, such as the United States today, is characterized by the increased prevalence of chronic impairment. Robust estimation of disability-free life expectancy (DFLE) is essential for examining whether additional years of life are spent in good health and whether life expectancy is increasing faster than the decline of disability rates. Over thirty years since its publication, Sullivan's method remains the most widely used method to estimate DFLE. Therefore, it is surprising to note that Sullivan did not provide any formal justification of his method. Debates in the literature have centered around the properties of Sullivan's method and have yielded conflicting results regarding the assumptions required for Sullivan's method. In this paper, we establish a statistical foundation of Sullivan's method. We prove that under stationarity assumptions, Sullivan's estimator is unbiased and consistent. This resolves the debate in the literature which has generally concluded that additional assumptions are necessary. We also show that the standard variance estimator is consistent and approximately unbiased. Finally, we demonstrate that Sullivan's method can be extended to estimate DFLE without stationarity assumptions. Such an extension is possible whenever a cohort life table and either consecutive cross-sectional disability surveys or a longitudinal survey are available. Our empirical analysis of the 1907 and 1912 U.S. birth cohorts suggests that while mortality rates remain approximately stationary, disability rates decline during this time period.

1907-1912

<div style="padding-left: 30px;"> <ul style="list-style-type: decimal;" ><li> The Murray Archive (the Distributor) has granted me a revocable license to use this dataset solely for the purposes of conducting research, and the Distributor may terminate this license at any time and for any reason. </li> <li> I will use the dataset solely for statistical analysis and reporting of aggregated information, and not for investigation of specific individuals or organizations, except when identification is authorized in writing by the Distributor. </li> <li>I will produce no links among the Distributor’s datasets or among the Distributor’s data and other datasets that could identify individuals or organizations. </li> <li>I represent that neither I, nor anyone I know, has any prior knowledge of the possible identities of any study participants in any dataset that I am being licensed to use. </li> <li> I will not knowingly divulge any information that could be used to identify individual participants in the study, nor will I attempt to identify or contact any study participant, and I agree to use any precautions necessary to prevent such identification. </li> <li> I will make no use of the identity of any person or establishment discovered inadvertently. If I suspect that I might recognize or know a study participant, I will immediately inform the Distributor, and I will not use or retain a copy of data regarding that study participant. If these measures to resolve an identity disclosure are not sufficient, the Distributor may terminate my use of the dataset. </li> <li> I will not reproduce the dataset except as is necessary for my scholarly purposes. I will destroy the dataset upon the completion of my scholarly work with it. </li> <li> I will not share data from the dataset (in any form or by any means) with any third party, including other members of my research team, as I understand that all users of data must obtain the data directly from the Distributor. </li> <li> I will make appropriate acknowledgement of the contributor of the dataset as well as the Distributor in any manuscript or presentation (published or unpublished) using the citation standard documented here: <a href="http://thedata.org/citation"> http://thedata.org/citation</a> </li> <li> THE DISTRIBUTOR MAKES NO WARRANTIES, EXPRESS OR IMPLIED, BY OPERATION OF LAW OR OTHERWISE, REGARDING OR RELATING TO THE DATASET. </li> </ul> </div>

Imai, Kosuke; Soneji, Samir, 2007, "On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension," JASA jasa v.2004/12/09: http://imai.princeton.edu/research/files/life.pdf <a href= "http://imai.princeton.edu/research/files/life.pdf" target= "_new">(article available here)</a>

Imai, Kosuke; Soneji, Samir, 2007, "On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension," JASA jasa v.2004/12/09: http://imai.princeton.edu/research/files/life.pdf <a href= "http://imai.princeton.edu/research/files/life.pdf" target= "_new">(article available here)</a>

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cohort

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File that calculates cohort life expectancy and cohort disability-free life expectancy

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cohort_graphs

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Graphical display of cohort and period life expectancy and disability-free life expectancy

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dis

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Program file to process disability data

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Program file to process mortality data

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period

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File that calculates period life expectancy and disability-free life expectancy

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toplevel.pdf

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Top level document that describes source code and data, pdf format

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