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Estimates for a repeated-measures study with two measures of a continuous variable

Source: R/estimate_mdiff_paired.R
estimate_mdiff_paired.Rd

Returns object estimate_mdiff_paired is suitable for a simple paired design with a continuous outcome variable. It provides estimates and CIs for the population mean difference between the repeated measures, the standardized mean difference (SMD; Cohen's d) between the repeated measures, and the median difference between the repeated measures (raw data only). You can pass raw data or summary data.

Usage

estimate_mdiff_paired(
  data = NULL,
  comparison_measure = NULL,
  reference_measure = NULL,
  comparison_mean = NULL,
  comparison_sd = NULL,
  reference_mean = NULL,
  reference_sd = NULL,
  n = NULL,
  correlation = NULL,
  comparison_measure_name = "Comparison measure",
  reference_measure_name = "Reference measure",
  conf_level = 0.95,
  save_raw_data = TRUE
)

Arguments

data

For raw data - a data frame or tibble

comparison_measure

For raw data - The column name of comparison measure of the outcome variable, or a vector of numeric data

reference_measure

For raw data - The column name of the reference measure of the outcome variable, or a vector of numeric data

comparison_mean

For summary data, a numeric

comparison_sd

For summary data, numeric > 0

reference_mean

For summary data, a numeric

reference_sd

For summary data, numeric > 0

n

For summary data, a numeric integer > 0

correlation

For summary data, correlation between measures, a numeric that is > -1 and < 1

comparison_measure_name

For summary data - An optional character label for the comparison measure. Defaults to 'Comparison measure'

reference_measure_name

For summary data - An optional character label for the reference measure. Defaults to 'Reference measure'

conf_level

The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95.

save_raw_data

For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object

Value

Returns object of class esci_estimate

  • overview

    • outcome_variable_name -

    • mean -

    • mean_LL -

    • mean_UL -

    • median -

    • median_LL -

    • median_UL -

    • sd -

    • min -

    • max -

    • q1 -

    • q3 -

    • n -

    • missing -

    • df -

    • mean_SE -

    • median_SE -

  • es_mean_difference

    • type -

    • comparison_measure_name -

    • reference_measure_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • SE -

    • df -

    • ta_LL -

    • ta_UL -

  • es_smd

    • comparison_measure_name -

    • reference_measure_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • numerator -

    • denominator -

    • SE -

    • d_biased -

    • df -

  • es_r

    • x_variable_name -

    • y_variable_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • SE -

    • n -

    • df -

    • ta_LL -

    • ta_UL -

  • es_median_difference

    • type -

    • comparison_measure_name -

    • reference_measure_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • SE -

    • ta_LL -

    • ta_UL -

  • es_mean_ratio

    • comparison_measure_name -

    • reference_measure_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • comparison_mean -

    • reference_mean -

  • es_median_ratio

    • comparison_measure_name -

    • reference_measure_name -

    • effect -

    • effect_size -

    • LL -

    • UL -

    • comparison_median -

    • reference_median -

  • raw_data

    • comparison_measure -

    • reference_measure -

Details

Reach for this function in place of a paired-samples t-test.

Once you generate an estimate with this function, you can visualize it with plot_mdiff() and you can test hypotheses with test_mdiff().

The estimated mean differences are from statpsych::ci.mean.ps().

The estimated SMDs are from CI_smd_ind_contrast().

The estimated median differences are from statpsych::ci.median.ps().

Examples

# From raw data
data("data_thomason_1")

estimate_from_raw <- esci::estimate_mdiff_paired(
  data = esci::data_thomason_1,
  comparison_measure = Posttest,
  reference_measure = Pretest
)

# To visualize the estimated median difference (raw data only)
myplot_from_raw <- esci::plot_mdiff(estimate_from_raw, effect_size = "median")
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using alpha for a discrete variable is not advised.
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using alpha for a discrete variable is not advised.
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using alpha for a discrete variable is not advised.

# To conduct a hypothesis test
res_htest_from_raw <- esci::test_mdiff(
  estimate_from_raw,
  effect_size = "median",
  rope = c(-2, 2)
)


sd1 <- 4.28
sd2 <- 3.4
sdiff <- 2.13

cor <- (sd1^2 + sd2^2 - sdiff^2) / (2*sd1*sd2)

estimate_from_summary <- esci::estimate_mdiff_paired(
  comparison_mean = 14.25,
  comparison_sd = 4.28,
  reference_mean = 12.88,
  reference_sd = 3.4,
  n = 16,
  correlation = 0.87072223749,
  comparison_measure_name = "After",
  reference_measure_name = "Before"
)

# To visualize the estimated mean difference
myplot_from_summary <- esci::plot_mdiff(
  estimate_from_summary,
  effect_size = "mean"
)
#> Warning: Using size for a discrete variable is not advised.
#> Warning: Using size for a discrete variable is not advised.

# To conduct a hypothesis test
res_htest_from_summary <- esci::test_mdiff(
  estimate_from_summary,
  effect_size = "mean",
  rope = c(-2, 2)
)