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Actual to expected studies--Part 2. 2014

David Wesley, and Hugh Cox

In Part I of this two-part series, we used a synthetic dataset which we called the "Health Risk Appraisal" dataset. Using this dataset, we performed an actual/expected mortality study using one and only one health assessment per individual. This synthetic dataset is actually more realistic in that many persons were evaluated more than once. Our previous study assessed predicted mortality based on the "first" HRA evaluation. There are three other alternatives: "last", "every", and "weighted-every" aka, "fractional". In this Part 2, we will briefly describe the "last" and "every" scenarios" and compare their results with those from the "first". We will then describe the fractional method in detail because our experience has shown this method provides the most desirable results.

UI MeSH Term Description Entries
D007352 Insurance, Life Insurance providing for payment of a stipulated sum to a designated beneficiary upon death of the insured. Life Insurance,Insurances, Life,Life Insurances
D008297 Male Males
D009026 Mortality All deaths reported in a given population. CFR Case Fatality Rate,Crude Death Rate,Crude Mortality Rate,Death Rate,Age Specific Death Rate,Age-Specific Death Rate,Case Fatality Rate,Decline, Mortality,Determinants, Mortality,Differential Mortality,Excess Mortality,Mortality Decline,Mortality Determinants,Mortality Rate,Mortality, Differential,Mortality, Excess,Age-Specific Death Rates,Case Fatality Rates,Crude Death Rates,Crude Mortality Rates,Death Rate, Age-Specific,Death Rate, Crude,Death Rates,Determinant, Mortality,Differential Mortalities,Excess Mortalities,Mortalities,Mortality Declines,Mortality Determinant,Mortality Rate, Crude,Mortality Rates,Rate, Age-Specific Death,Rate, Case Fatality,Rate, Crude Death,Rate, Crude Mortality,Rate, Death,Rate, Mortality,Rates, Case Fatality
D003627 Data Interpretation, Statistical Application of statistical procedures to analyze specific observed or assumed facts from a particular study. Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D005260 Female Females
D006305 Health Status Indicators The measurement of the health status for a given population using a variety of indices, including morbidity, mortality, and available health resources. Health Risk Appraisal,Health Status Index,Health Status Indexes,Appraisal, Health Risk,Appraisals, Health Risk,Health Risk Appraisals,Health Status Indicator,Health Status Indices,Index, Health Status,Indexes, Health Status,Indicator, Health Status,Indicators, Health Status,Indices, Health Status,Risk Appraisal, Health,Risk Appraisals, Health
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000368 Aged A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available. Elderly
D000369 Aged, 80 and over Persons 80 years of age and older. Oldest Old
D012307 Risk Factors An aspect of personal behavior or lifestyle, environmental exposure, inborn or inherited characteristic, which, based on epidemiological evidence, is known to be associated with a health-related condition considered important to prevent. Health Correlates,Risk Factor Scores,Risk Scores,Social Risk Factors,Population at Risk,Populations at Risk,Correlates, Health,Factor, Risk,Factor, Social Risk,Factors, Social Risk,Risk Factor,Risk Factor Score,Risk Factor, Social,Risk Factors, Social,Risk Score,Score, Risk,Score, Risk Factor,Social Risk Factor

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