help episens  
-------------------------------------------------------------------------------

Title

    episens - Basic sensitivity analysis of epidemiological results

Syntax

    Cohort studies and Case-control studies

        episens var_case var_exposed [var_time] [if] [in] [weight] [, 
              options]

        episensi #a #b #c #d [, options]

Options

    options                         Description
    -------------------------------------------------------------------------
    Options
      study(cc|cs|ir)                specify the type of study
      unconf(#pexp #punexp #rrcd)    analysis of unmeasured confounding
      miexp(#seca #spca #senc spnc)  analysis of misclassification of the
                                      exposure
      corder(#)                      corrections order
      format(%fmt)                   display format for numbers
    -------------------------------------------------------------------------
 
Description

    episens provides basic sensitivity analysis of the observed relative risk
    for unmeasured confounding and misclassification of the exposure, or
    both. episensi is the immediate form of episens; see immed.

Options

        +-------+
    ----+ Model +------------------------------------------------------------

    study(cc|cs|ir) specifies the type of study: cc (case-control data), cs
        (cumulative-incidence data), or ir (incidence-rate data). The default
        is cc. See epitab for more information.

    unconf(#pexp #punexp #rrcd) specifies the analysis of unmeasured
        confounding, where #pexp is the prevalence of the unmeasured
        confounder among the exposed subjects; #punexp is the prevalence of
        the unmeasured confounder among the unexposed subjects; and #rrcd is
        the relative risk between the unmeasured confounder and disease.

    miexp(#seca #spca #senc spnc) analysis of misclassification of the
        exposure, where #seca is the sensitivity among the cases; #spca is
        the specificity among the cases; #senc is the sensitivity among the
        non-cases; and #spnc is the specificity among the non-cases.

    corder(#) specifies the correction order: 1 means first correct for
        unmeasured confounding and then for misclassification of the
        exposure; 2 means first correct for misclassification of the exposure
        and then for unmeasured confounding.

    format(%fmt) display format for numbers; default is format(%3.2f).

Examples

// Cumulative incidence data

        webuse ugdp.dta
        cs case exposed [fweight=pop]
        episensi  30  21  174  184 , st(cs) unc(0.3 0.7 3)
        episens case exposed [fweight=pop], st(cs)
        episens case exposed [fweight=pop], st(cs) unc(0.3 0.7 1)
        episens case exposed [fweight=pop], st(cs) unc(0.3 0.7 3)
        episens case exposed [fweight=pop], st(cs) unc(0.3 0.7 3)  mie(0.9 0.8 
> 0.9 0.8)

// Incidence-rate data

        sysuse cancer.dta
        gen exp = drug == 2
        ir died exp studytime
        episens died exp studytime
        episens died exp studytime , st(ir) unc(0.5 0.2 2)
        episensi  6  25  209  535  , st(ir)

// Case-control data

        episensi 45 94 257 945, st(cc) unc(0.7 0.5 5)                        
        episensi 45 94 257 945, st(cc) mie(0.9 0.9 0.8 0.8)  
        episensi 45 94 257 945, st(cc) mie(0.9 0.9 0.8 0.8)   
        episensi 45 94 257 945, st(cc) mie(0.9 0.9 0.8 0.8)   
        episensi 45 94 257 945, st(cc) unc(0.7 0.5 5)  mie(0.9 0.9 0.8 0.8) co(
> 1) 
        episensi 45 94 257 945, st(cc) unc(0.7 0.5 5)  mie(0.9 0.9 0.8 0.8) co(
> 2) 
        episensi 45 94 257 945, st(cc) mie(0.8 0.8 0.8 0.8)  
        episensi 45 94 257 945, st(cc) unc(0.7 0.5 5)  mie(0.9 0.9 0.8 0.8)  
        episensi 45 94 257 945, st(cc) unc(0.7 0.5 5)  mie(1 1 1 1)          

References

    Greenland S., 1996, Basic methods for sensitivity analysis of biases. Int
        J Epidemiol, 5(6):1107-16.

Authors

    Nicola Orsini, Division of Nutritional Epidemiology, Institute of
        Environmental Medicine, Karolinska Institutet, Sweden

    Rino Bellocco, Department of Medical Epidemiology and Biostatistics,
        Karolinska Institutet, Sweden

    Sander Greenland, Department of Epidemiology, UCLA School of Public
        Health, USA

Support

    http://nicolaorsini.altervista.org
    nicola.orsini@ki.se

Also see

    Manual:  [ST] epitab

    Online:  epitab, episensrri (if installed)