naive_se function

Naive standard error estimates

Naive standard error estimates

Function to obtain naive standard error estimates for the parameter estimates of the get_estimates function, under the GLM or AFT setting for the analysis of a normally-distributed or censored time-to-event primary outcome.

naive_se(setting = "GLM", Y = NULL, X = NULL, K = NULL, L = NULL,
  C = NULL)

Arguments

  • setting: String with value "GLM" or "AFT" indicating whether standard error estimates are obtained for a normally-distributed ("GLM") or censored time-to-event ("AFT") primary outcome Y.
  • Y: Numeric input vector for the primary outcome.
  • X: Numeric input vector for the exposure variable.
  • K: Numeric input vector for the intermediate outcome.
  • L: Numeric input vector for the observed confounding factor.
  • C: Numeric input vector for the censoring indicator under the AFT setting (must be coded 0 = censored, 1 = uncensored).

Returns

Returns a vector with the naive standard error estimates of the parameter estimates.

Details

Under the GLM setting for the analysis of a normally-distributed primary outcome Y, naive standard error estimates are obtained for the estimates of the parameters α0,α1,α2,α3,α4,\alphaXY\alpha0, \alpha1, \alpha2, \alpha3, \alpha4, \alphaXY

in the models

Y=α0+α1⋅K+α2⋅X+α3⋅L+ϵ1,ϵ1∼N(0,σ12)Y=α0+α1∗K+α2∗X+α3∗L+ϵ1,ϵ1 N(0,σ12) Y = \alpha_0 + \alpha_1 \cdot K + \alpha_2 \cdot X + \alpha_3 \cdot L + \epsilon_1, \epsilon_1 \sim N(0,\sigma_1^2)Y = \alpha0 + \alpha1*K + \alpha2*X + \alpha3*L + \epsilon1, \epsilon1 ~ N(0,\sigma1^2) Y∗=Y−Y‾−α1⋅(K−K‾)Y∗=Y−mean(Y)−α1∗(K−mean(K)) Y^* = Y - \overline{Y} - \alpha_1 \cdot (K-\overline{K})Y* = Y - mean(Y) - \alpha1*(K-mean(K)) Y∗=α0+αXY⋅X+ϵ2,ϵ2∼N(0,σ22),Y∗=α0+\alphaXY∗X+ϵ2,ϵ2 N(0,σ22), Y^* = \alpha_0 + \alpha_{XY} \cdot X + \epsilon_2, \epsilon_2 \sim N(0,\sigma_2^2),Y* = \alpha0 + \alphaXY*X + \epsilon2, \epsilon2 ~ N(0,\sigma2^2),

using the lm function, without accounting for the additional variability due to the 2-stage approach.

Under the AFT setting for the analysis of a censored time-to-event primary outcome, bootstrap standard error estimates are similarly obtained of the parameter estimates of α0,α1,α2,α3,α4,\alphaXY\alpha0, \alpha1, \alpha2, \alpha3, \alpha4, \alphaXY

from the output of the survreg and lm functions.

Examples

dat <- generate_data(setting = "GLM")
naive_se(setting = "GLM", Y = dat$Y, X = dat$X, K = dat$K, L = dat$L)
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  • Maintainer: Stefan Konigorski
  • License: GPL-2
  • Last published: 2018-03-19

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