Calculate effect sizes

This is a function to calculate effect sizes of meta-analysis data prepared in the Metapsy data format.

Usage

calculateEffectSizes(data,
                     funcs.g = list(g.m.sd = g.m.sd,
                                    g.change.m.sd = g.change.m.sd,
                                    g.binary = g.binary,
                                    g.precalc = g.precalc),
                     funcs.rr = list(rr.binary = rr.binary,
                                     rr.precalc = rr.precalc),
                     include.switched.arms = FALSE,
                     change.sign = NULL,
                     vars.for.id = c("study", "outcome_type",
                                     "instrument", "time",
                                     "time_weeks",
                                     "rating"),
                     .condition = "condition",
                     .condition.specification = "multi",
                     .groups.column.indicator = c("_arm1", "_arm2"),
                     .trt.indicator = "arm")

Arguments

data: Meta-analysis data set formatted using the Metapsy guidelines.
funcs.g: list of functions. These functions will be used to calculate the effect sizes (Hedges' g) based on the raw data (see Details).
funcs.rr: list of functions. These functions will be used to calculate risk ratios based on the raw event data (see Details).
include.switched.arms: logical. Should all unique arm comparisons (in lieu of unique arm combinations) be calculated? Default is FALSE.
change.sign: character. Name of a logical column in data, encoding if the sign of a calculated effect size should be reversed (TRUE) or not (FALSE). Set to NULL (default) if no changes should be made.
vars.for.id: character vector, containing column names of all variables used to construct unique comparison IDs.
.condition: character. The prefix of the two variables in data in which the conditions (e.g. "guided iCBT", "waitlist") of the trial arm comparison are stored.
.condition.specification: character. The prefix of the two variables in the dataset which provide a "specification" of the trial arm condition in multiarm trials.
.groups.column.indicator: character. A character vector with two elements, representing the suffix used to differentiate between the first and second arm in a comparison.
.trt.indicator: character. A character specifying the name used to indicate the treatment arm.

Value

calculateEffectSizes returns the meta-analysis data set as class data.frame in wide format (if results are saved to a variable). It also generates the following columns, wich are added to the data:

.id: Unique identifier for a trial arm comparison/row.
.g: Calculated effect size (Hedges' g).
.g_se: Standard error of Hedges' g.
.log_rr: Calculated effect size (logRR).
.log_rr_se: Standard error of logRR.
.event_arm1: Number of events (responders, remission, deterioration cases) in the first trial arm.
.event_arm2: Number of events (responders, remission, deterioration cases) in the second trial arm.
.totaln_arm1: Total sample size in the first trial arm.
.totaln_arm2: Total sample size in the second trial arm.

Details

By default, calculateEffectSizes calculates the small-sample bias corrected standardized mean difference (Hedges' g) and log-risk ratios, as well their respective standard errors, if adequate raw effect size data is available for a comparison.

It is essential that the data set in data contains a few required columns for this to work. An overview of the required data format is provided on the "Get Started" page of the metapsyTools documentation.

Standardized mean differences (Hedges' g) can be calculated from the following column types:

(1) Continuous Outcome Data
- mean_arm1: Mean of the outcome in the first arm at the measured time point.
- mean_arm2: Mean of the outcome in the second arm at the measured time point.
- sd_arm1: Standard deviation of the outcome in the first arm at the measured time point.
- sd_arm2: Standard deviation of the outcome in the second arm at the measured time point.
- n_arm1: Sample size in the first trial arm.
- n_arm2: Sample size in the second trial arm.
(2) Change Score Data
- mean_change_arm1: Mean score change between baseline and the measured time point in the first arm.
- mean_change_arm2: Mean score change between baseline and the measured time point in the second arm.
- sd_change_arm1: Standard deviation of the mean change in the first arm.
- sd_change_arm2: Standard deviation of the mean change in the second arm.
- n_change_arm1: Sample size in the first trial arm.
- n_change_arm2: Sample size in the second trial arm.
(3) Dichotomous Outcome Data
- event_arm1: Number of events (responders, remission, deterioration cases) in the first trial arm.
- event_arm2: Number of events (responders, remission, deterioration cases) in the second trial arm.
- totaln_arm1: Sample size in the first trial arm.
- totaln_arm2: Sample size in the second trial arm.
(4) Pre-calculated Hedges' g
- precalc_g: The pre-calculated value of Hedges' g (small-sample bias corrected standardized mean difference; Hedges, 1981).
- precalc_g_se: Standard error of g.

The log-risk ratio and its standard error can be calculated from the followin column types:

(1) Dichotomous Outcome Data
- event_arm1: Number of events (responders, remission, deterioration cases) in the first trial arm.
- event_arm2: Number of events (responders, remission, deterioration cases) in the second trial arm.
- totaln_arm1: Sample size in the first trial arm.
- totaln_arm2: Sample size in the second trial arm.
(2) Pre-calculated log-risk ratio
- precalc_log_rr: The pre-calculated value of the log-risk ratio logRR, comparing events in the first arm to events in the second arm.
- precalc_log_rr_se: The standard error of the log-risk ratio logRR, comparing events in the first arm to events in the second arm.

Other functions can be added to the list provided to funcs.g and funcs.rr. However, results of the function must result in a data.frame that contains the following columns:

.id: Unique identifier for a trial arm comparison/row.
.g: Calculated effect size (Hedges' g).
.g_se: Standard error of Hedges' g.
.log_rr: Calculated effect size (logRR).
.log_rr_se: Standard error of logRR.
.event_arm1: Number of events (responders, remission, deterioration cases) in the first trial arm.
.event_arm2: Number of events (responders, remission, deterioration cases) in the second trial arm.
.totaln_arm1: Total sample size in the first trial arm.
.totaln_arm2: Total sample size in the second trial arm.

It is possible to set one or several of these column entries to NA; but the columns themselves must be included.

For more details see the Get Started vignette.

Author

Mathias Harrer mathias.h.harrer@gmail.com, Paula Kuper paula.r.kuper@gmail.com, Pim Cuijpers p.cuijpers@vu.nl

Examples