special topic: logistic regression for binary outcomes

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Special Topic: Logistic Regression for Binary outcomes. The dependent variable is often binary such as whether a person litters or not, used a condom or not, dead or alive, diseased or not, intercourse or not, or divorced or not. - PowerPoint PPT Presentation

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  • Special Topic: Logistic Regression for Binary outcomesThe dependent variable is often binary such as whether a person litters or not, used a condom or not, dead or alive, diseased or not, intercourse or not, or divorced or not.In this case, logistic or probit regression is the method of choice because of violation of assumptions if ordinary least squares regression is used. Estimates of the mediated effect using logistic and probit regression can be distorted using conventional procedures.Here we examine binary or continuous X, continuous M, and binary Y.MacKinnon et al., under review in Clinical Trials and MacKinnon et al., under review Psychological Methods.

  • Logistic Regression Model for Equations 1 and 2Standard logistic regression model, where Y depends on X, 1 is the intercept and codes the relation between X and Y.logit Pr{Y=1|X} =1 + X (1)Standard logistic regression model, where Y depends on X and M, 2 is the intercept, codes the relation between X and Y adjusted for M and codes the relation between M and Y, adjusted for X.logit Pr{Y=1|X,M} = 2 + X + M (2)

  • Logistic Regression Model for latent variable Y*Y* = 1 + X + 1 (1)Y* = 2 + X + M + 2 (2)The unobserved latent variable Y* is linearly related to X and then to both X and M, 1 and 2 represent residual variability and have a standard logistic distribution. The dichotomous Y is derived from Y* through the relation Y = 1 if and only if Y* > 0. The same model applies for the probit with the errors having a standard normal distribution rather than a standard logistic distribution.

  • Equation 3M = 3 + X + 3 (3)M is a continuous variable so ordinary least squares regression is used to estimate this model where 3 is the intercept, represents the relation between X and Y, and 3 is residual variability.

  • Logistic Regression Model for latent variable Y* - Difference in coefficients. The coefficients are from separate logistic regression equations. Product of coefficients. The coefficient is from a logistic regression model and is from an ordinary least squares regression model. As will be shown, the difference in coefficient method can give distorted values for the mediated effect because of differences in the scale of separate logistic regression models. For both Equations 1 and 2, residual variability is fixed at 2/3 and fixed at 1 for probit regression.

  • What is the in the next plot?Expected logistic regression coefficients based on Haggstrom (1983) are used to compute - and .All possible combinations of , and values for small (2% variance explained), medium (13%), large (26%), and very large (40%) effects (4 X 4 X 4 = 64)Y-axis is the expected value for - and X-axis is the true value of the b coefficient in the continuous variable mediation model. It is indicated by C

  • Plot of true values of and - as a function of true mediated effect and true value of C.

    Chart1

    0.11987433370.1105121968

    0.33864277840.2568982182

    0.51694608480.3294832979

    0.86802549560.4039127943

    -

    c

    Mediated Effect

    TRUEBMEANS

    HB Standardization

    _TYPE__FREQ_c-//(+)1-(/)-/1-(/)

    1400000.140.11987433370.11051219680.33331692730.32927920390.3213190460.11680623090.33078088110.3265775869

    1400000.390.33864277840.25689821820.49310513970.45264680360.40525619180.31488833240.46522228090.4392280106

    1400000.590.51694608480.32948329790.61248960410.51503909180.42589516260.46977456650.53944086470.4945893575

    14000010.86802549560.40391279430.86334060180.60.42776648670.78548548250.63916387170.5759176539

    TRUEBMEANS

    00

    00

    00

    00

    -

    c

    Mediated Effect

    Fig2: True values of mediated effect estimates: Logistic regression

    BMEANS

    000

    000

    000

    000

    /

    /(+)

    1-(/)

    c

    Proportion Mediated

    Fig3: True values of proportion mediated: Logistic regression

    fig8

    00

    00

    00

    00

    HB t-t'

    c

    Mediated Effect

    Fig4: True values of mediated effect estimates: Logistic regression with HB standardization of t

    000

    000

    000

    000

    HB ab/t

    /(+)

    HB 1-(t'/t)

    c

    Proportion Mediated

    Fig5: True values of proportion mediated: Logistic regression with HB standardization of t

    HB Standardization

    _TYPE__FREQ_c-//(+)1-(/)-/1-(/)

    1400000.140.12467492240.10787716810.2044677448-425340040927.3720.18385117920.11927830390.20030663750.1932706962

    1400000.390.3535059810.25346487390.4518775498-15396197751.52860.3471532650.32285228190.41931624550.3877639116

    1400000.590.54530845730.32094263250.61043416132.21759028910.39088144920.48278129750.52727644310.4704341142

    14000010.95543571610.35905596610.92574261640.61802433830.38602575660.82063248530.65174093070.5627688948

    &A

    Page &P

    00

    00

    00

    00

    -

    c

    Mediated Effect

    Fig6: Mediated effect estimates: Logistic regression

    000

    000

    000

    000

    /

    /(+)

    1-(/)

    c

    Proportion Mediated

    Proportion Mediated: Logistic Regression

    000

    000

    000

    000

    /

    /(+)

    1-(/)

    Bc

    Proportion Mediated

    Proportion Mediated: Logistic Regression with HB standardization

    000

    000

    000

    000

    HB ab/t

    /(+)

    HB 1-(t't)

    Bc

    Proportion Mediated

    Proportion Mediated: Logistic Regression with HB standardization of t

    00

    00

    00

    00

    -

    c

    Mediated Effect

    Fig7: Mediated effect estimates: Logistic regression with HB standardization

    betataup

    =.14=.39=.59=1

    0.140.02938289740.02088001770.0119097836-0.0009841766

    0.390.06330254250.0185011509-0.0091454227-0.0730105895

    0.590.07503924150.0000320097-0.0635571444-0.1809276938

    10.0770957014-0.0785074187-0.2004613181-0.4457443602

    0.02938289740.02088001770.0119097836-0.0009841766

    0.06330254250.0185011509-0.0091454227-0.0730105895

    0.07503924150.0000320097-0.0635571444-0.1809276938

    0.0770957014-0.0785074187-0.2004613181-0.4457443602

    =.14

    =.39

    =.59

    =1

    c

    Mediated Effect

    Fig8: Estimated mediated effect t-t' as function of bc and t'c: Logistic regression

  • Plot of true proportion mediated as a function of true value of C.

    Chart2

    0.33331692730.32927920390.321319046

    0.49310513970.45264680360.4052561918

    0.61248960410.51503909180.4258951626

    0.86334060180.60.4277664867

    /

    /(+)

    1-(/)

    c

    Proportion Mediated

    TRUEBMEANS

    HB Standardization

    _TYPE__FREQ_c-//(+)1-(/)-/1-(/)

    1400000.140.11987433370.11051219680.33331692730.32927920390.3213190460.11680623090.33078088110.3265775869

    1400000.390.33864277840.25689821820.49310513970.45264680360.40525619180.31488833240.46522228090.4392280106

    1400000.590.51694608480.32948329790.61248960410.51503909180.42589516260.46977456650.53944086470.4945893575

    14000010.86802549560.40391279430.86334060180.60.42776648670.78548548250.63916387170.5759176539

    TRUEBMEANS

    00

    00

    00

    00

    -

    c

    Mediated Effect

    Fig2: True values of mediated effect estimates: Logistic regression

    BMEANS

    000

    000

    000

    000

    /

    /(+)

    1-(/)

    c

    Proportion Mediated

    Fig3: True values of proportion mediated: Logistic regression

    fig8

    00

    00

    00

    00

    HB t-t'

    c

    Mediated Effect

    Fig4: True values of mediated effect estimates: Logistic regression with HB standardization of t

    000

    000

    000

    000

    HB ab/t

    /(+)

    HB 1-(t'/t)

    c

    Proportion Mediated

    Fig5: True values of proportion mediated: Logistic regression with HB standardization of t

    HB Standardization

    _TYPE__FREQ_c-//(+)1-(/)-/1-(/)

    1400000.140.12467492240.10787716810.2044677448-425340040927.3720.18385117920.11927830390.20030663750.1932706962

    1400000.390.3535059810.25346487390.4518775498-15396197751.52860.3471532650.32285228190.41931624550.3877639116

    1400000.590.54530845730.32094263250.61043416132.21759028910.39088144920.48278129750.52727644310.4704341142

    14000010.95543571610.35905596610.92574261640.61802433830.38602575660.82063248530.65174093070.5627688948

    &A

    Page &P

    00

    00

    00

    00

    -

    c

    Mediated Effect

    Fig6: Mediated effect estimates: Logistic regression

    000

    000

    000

    000

    /

    /(+)

    1-(/)

    c

    Proportion Mediated

    Proportion Mediated: Logistic Regression

    000

    000

    000

    000

    /

    /(+)

    1-(/)

    Bc

    Proportion Mediated

    Proportion Mediated: Logistic Regression with HB standardization

    000

    000

    000

    000

    HB ab/t

    /(+)

    HB 1-(t't)

    Bc

    Proportion Mediated

    Proportion Mediated: Logistic Regression with HB standardization of t

    00

    00

    00

    00

    -

    c

    Mediated Effect

    Fig7: Mediated effect estimates: Logistic regression with HB standardization

    betataup

    =.14=.39=.59=1

    0.140.02938289740.02088001770.0119097836-0.0009841766

    0.390.06330254250.0185011509-0.0091454227-0.0730105895

    0.590.07503924150.0000320097-0.0635571444-0.1809276938

    10.0770957014-0.0785074187-0.2004613181-0.4457443602

    0.02938289740.02088001770.0119097836-0.0009841766

    0.06330254250.0185011509-0.0091454227-0.0730105895

    0.07503924150.0000320097-0.0635571444-0.1809276938

    0.0770957014-0.0785074187-0.2004613181-0.4457443602

    =.14

    =.39

    =.59

    =1

    c