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  • 1. The effect of record linkage errors on risk estimates in cohort mortality studies Archived
    Articles and reports: 12-001-X20050018083
    Description:

    The advent of computerized record linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. This, however, introduces linkage errors due to mismatching an individual from one database with a different individual from the other database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk such as standardized mortality ratios and relative risk regression model parameters is explored. It is shown that the observed and expected number of deaths are affected in opposite direction and, as a result, these indicators can be subject to bias and additional variability in the presence of linkage errors.

    Release date: 2005-07-21
  • 2. A case study in record linkage Archived
    Articles and reports: 12-001-X20050018085
    Description:

    Record linkage is a process of pairing records from two files and trying to select the pairs that belong to the same entity. The basic framework uses a match weight to measure the likelihood of a correct match and a decision rule to assign record pairs as "true" or "false" match pairs. Weight thresholds for selecting a record pair as matched or unmatched depend on the desired control over linkage errors. Current methods to determine the selection thresholds and estimate linkage errors can provide divergent results, depending on the type of linkage error and the approach to linkage. This paper presents a case study that uses existing linkage methods to link record pairs but a new simulation approach (SimRate) to help determine selection thresholds and estimate linkage errors. SimRate uses the observed distribution of data in matched and unmatched pairs to generate a large simulated set of record pairs, assigns a match weight to each pair based on specified match rules, and uses the weight curves of the simulated pairs for error estimation.

    Release date: 2005-07-21
  • 3. Combining information from multiple surveys through the empirical likelihood method Archived
    Articles and reports: 11-522-X20030017711
    Description:

    This article uses the recently developed pseudo-empirical likelihood method to construct estimators that not only meet the consistency and efficiency requirements but have more attractive features.

    Release date: 2005-01-26
  • 4. Implementing new statistical techniques Archived
    Articles and reports: 11-522-X20030017712
    Description:

    This paper discusse variance estimation in the presence of imputations with an application to price index estimation, multiphase sampling, and the use of graphics in publications.

    Release date: 2005-01-26
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  • 1. The effect of record linkage errors on risk estimates in cohort mortality studies Archived
    Articles and reports: 12-001-X20050018083
    Description:

    The advent of computerized record linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. This, however, introduces linkage errors due to mismatching an individual from one database with a different individual from the other database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk such as standardized mortality ratios and relative risk regression model parameters is explored. It is shown that the observed and expected number of deaths are affected in opposite direction and, as a result, these indicators can be subject to bias and additional variability in the presence of linkage errors.

    Release date: 2005-07-21
  • 2. A case study in record linkage Archived
    Articles and reports: 12-001-X20050018085
    Description:

    Record linkage is a process of pairing records from two files and trying to select the pairs that belong to the same entity. The basic framework uses a match weight to measure the likelihood of a correct match and a decision rule to assign record pairs as "true" or "false" match pairs. Weight thresholds for selecting a record pair as matched or unmatched depend on the desired control over linkage errors. Current methods to determine the selection thresholds and estimate linkage errors can provide divergent results, depending on the type of linkage error and the approach to linkage. This paper presents a case study that uses existing linkage methods to link record pairs but a new simulation approach (SimRate) to help determine selection thresholds and estimate linkage errors. SimRate uses the observed distribution of data in matched and unmatched pairs to generate a large simulated set of record pairs, assigns a match weight to each pair based on specified match rules, and uses the weight curves of the simulated pairs for error estimation.

    Release date: 2005-07-21
  • 3. Combining information from multiple surveys through the empirical likelihood method Archived
    Articles and reports: 11-522-X20030017711
    Description:

    This article uses the recently developed pseudo-empirical likelihood method to construct estimators that not only meet the consistency and efficiency requirements but have more attractive features.

    Release date: 2005-01-26
  • 4. Implementing new statistical techniques Archived
    Articles and reports: 11-522-X20030017712
    Description:

    This paper discusse variance estimation in the presence of imputations with an application to price index estimation, multiphase sampling, and the use of graphics in publications.

    Release date: 2005-01-26
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