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Estimate the association between an exposure and a binary outcome

logistic_model.Rd

This function is used to estimate the (adjusted) association (log odds ratio) between an exposure variable and a binary outcome variable using a logistic regression model. It is a wrapper for the glm function with family = "binomial" for convenience and provides results from some additional analysis steps if requested.

Usage

logistic_model(
  data,
  exposure,
  outcome,
  adjust_for = NULL,
  exclude = NULL,
  exclude_controls = NULL,
  exposure_term = exposure,
  additional = "",
  return = c("model", "reduced-model", "coefs", "exposure"),
  return_fn = identity,
  ...
)

Arguments

data

A data frame.

exposure

Name of column containing exposure variable.

outcome

Name of the column containing binary outcome variable.

adjust_for

Character vector of terms to include in the model formula.

exclude

Names of columns that identify observations that are to be excluded.

exclude_controls

Names of columns that identify observations that are to be exluded when outcome is equal to zero.

exposure_term

The term(s) to include in the model for the exposure. This can be a character vector or a function that take the exposure name as the argument and returns a character vector. (Default: exposure)

additional

Extra results to include. See "Additional results" for details.

return

What should the function return:

  • "model" (default): The model object (object of class "glm").

  • "reduced-model": The model object with some large components removed.

  • "coefs": A data frame of all estimated coefficients and other key model statistics.

  • "exposure": A data frame of estimated coefficient(s) for the exposure and other key model statistics.

return_fn

If return="model", name of function to apply to model before it's returned. (Default: identity)

...

Arguments passed to stats::glm()

Value

Either a model object, reduced model object or data frame of coefficients.

Additional results

The additional argument is used to add results from additional analyses. It must be a character vector containing one or more of:

  • "lrt": Likehood ratio test comparing the full model to one without the exposure. Output will have additional columns "Chisq" (chi-square test statistics), "Df" (degrees of freedom), "p_Chisq" (p-value).

  • "float": Standard errors for the exposure calculated by float or by ftrend if the model has no intercept term. Output will have additional columns "float_estimate", "float_se" and "limits".

  • "qvcalc": Standard errors for the exposure calculated by qvcalc. Output will have additional columns "qvcalc_estimate", "quasi_se" and "worstErrors".

  • "" (default): Nothing.

These will only be included when relevant e.g. standard errors from float when the exposure has more than two levels. This only has an effect if return is "coefs" or "exposure".

See float and qvcalc for details about these results. Note that "limits" and "worstErrors" do not correspond to particular groups but accuracy other all contrasts.

Examples

logistic_model(ckbtools_participant_data,
               exposure = "bmi_calc",
               outcome = "has_diabetes",
               adjust_for = c("age_at_study_date", "sex"),
               return = "coefs")
#> # A tibble: 4 × 9
#>   term     estimate std.error statistic   p.value     n ncases ncontrols formula
#>   <chr>       <dbl>     <dbl>     <dbl>     <dbl> <int>  <int>     <int> <chr>  
#> 1 bmi_calc   0.109    0.00738     14.8  2.60e- 49 25000   1893     23107 has_di…
#> 2 (Interc…  -8.60     0.233      -36.9  1.20e-298    NA     NA        NA NA     
#> 3 age_at_…   0.0623   0.00232     26.9  2.14e-159    NA     NA        NA NA     
#> 4 sexFema…   0.205    0.0505       4.06 4.88e-  5    NA     NA        NA NA     

logistic_model(ckbtools_participant_data,
               exposure = "bmi_grp",
               outcome = "has_diabetes",
               adjust_for = c("age_at_study_date", "sex"),
               return = "coefs")
#> # A tibble: 7 × 12
#>   term    estimate std.error statistic    p.value     n ncases ncontrols n_group
#>   <chr>      <dbl>     <dbl>     <dbl>      <dbl> <int>  <int>     <int>   <dbl>
#> 1 bmi_gr…  NA       NA           NA    NA         25000   1893     23107    2154
#> 2 bmi_gr…   0.629    0.113        5.54  2.94e-  8    NA     NA        NA   15903
#> 3 bmi_gr…   1.09     0.117        9.32  1.15e- 20    NA     NA        NA    6513
#> 4 bmi_gr…   1.74     0.171       10.2   2.10e- 24    NA     NA        NA     430
#> 5 (Inter…  -6.77     0.179      -37.8   2.87e-312    NA     NA        NA      NA
#> 6 age_at…   0.0616   0.00231     26.6   2.58e-156    NA     NA        NA      NA
#> 7 sexFem…   0.215    0.0504       4.27  1.95e-  5    NA     NA        NA      NA
#> # ℹ 3 more variables: ncases_group <dbl>, ncontrols_group <dbl>, formula <chr>