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  • 1.  SIR interpretation

    Posted 12-08-2017 14:18

    Dear All:

    The indirect standardization in epidemiology is defined as the ratio between the observed number cases (O) over the expected number cases (E) of any disease or death, which is denote as SIR (SMR)=O/E. Typically, the E is computed based on the age-specific rates (incidence or mortality of a standard population) times the age distribution of the study group.  The indirect standardized (incidence or mortality) rate is computed as the following product: SIR*Crude rate. My question is how the SIR can be best interpreted. I have used relative excess with respect to the standard population. I appreciate any suggestion.  


    Erick Suárez, PhD

    Department of BIostatistics and Epidemiology
    School of Public Health, University of Puerto Rico


  • 2.  RE: SIR interpretation

    Posted 12-11-2017 08:44
    "... indirect standardization in epidemiology is defined as the ratio
    between the observed number cases (O) over the expected number cases (E)
    of any disease or death, which is denote as SIR (SMR)=O/E. Typically,
    the E is computed based on the age-specific rates (incidence or
    mortality of a standard population) times the age distribution of the
    study group.?? The indirect standardized (incidence or mortality) rate is
    computed as the following product: SIR*Crude rate. My question is how
    the SIR can be best interpreted. I have used relative excess with
    respect to the standard population. I appreciate any suggestion. "

    The SIR or SMR is simply the proportionate incidence or mortality excess
    (or if?? under 1, the deficit) observed in a target (study) group
    relative to what would be expected based on its age distribution and the
    age-specific rates from the reference (standard) population - see for
    example Modern Epidemiology, 3rd ed. (2008) Ch. 4. Without further
    assumptions it has no further interpretation. The traditional last step
    of multiplying by the crude rate obscures the proportionate relationship
    and is more often than not omitted in the literature I see.
    Despite being pointed at least as far back as 1934 by Yates, sometimes
    overlooked is that the ratio of SMRs or indirectly standardized rates
    from two different targets will generally not equal the proportionate
    excess of one target relative to the other after categorical age
    adjustment; this is so even if the two SMRs are computed using the same
    reference standard, unless the population and age effects on the rates
    are multiplicative
    - see Breslow et al. JASA 1983 for a classic and still useful article on
    methods and pitfalls of comparing and modeling SIRs and SMRs:
    Breslow NE, Lubin JH, Marek P, Langholz, B. Multiplicative models and
    cohort analysis. J Am Stat Assoc 1983;78: 1???12.
    More details of SMR extensions, analysis and modeling can be found in
    Breslow NE, Day NE. Statistical methods in cancer research. Vol II: the
    design and analysis of cohort studies. Lyon: IARC, 1987.