rema: A permutation-based approach for heterogeneous meta-analyses...
In rema: Rare Event Meta Analysis

Description Usage Arguments Details Value References See Also Examples

View source: R/rema.R

rema (rare event meta-analysis) performs a permutation-based meta-analysis for heterogeneous, rare event data.

rema(
  trt.events = NULL,
  trt.total = NULL,
  ctrl.events = NULL,
  ctrl.total = NULL,
  rema.obj,
  mid.p = TRUE,
  distr = TRUE,
  one.sided.p = FALSE,
  alpha = 0.05
)

`trt.events`	Numeric vector containing the number of observed events in the treatment group from each independent study.
`trt.total`	Numeric vector containing the total number of observations (events plus non-events) in the treatment group from each independent study.
`ctrl.events`	Numeric vector containing the number of observed events in the control group from each independent study.
`ctrl.total`	Numeric vector containing the total number of observations (events plus non-events) in the control group from each independent study.
`rema.obj`	An object of class `rema` that contains the permutational distribution of the test statistic. If this argument is supplied, the trt.events, trt.total, ctrl.events, and ctrl.total vectors are not required.
`mid.p`	A logical indicating if the p-values and confidence interval are adjusted using the mid-p correction to reduce conservatism. If `TRUE` (default), the mid-p p-values and mid-p confidence interval will be reported.
`distr`	A logical indicating if the permutational distribution of the test statistic is reported. If `TRUE` (default), the distribution is returned.
`one.sided.p`	A logical indicating if the one-sided p-value (in the direction of the treatment effect) is computed and returned. If `FALSE` (default), the one-sided p-value is not provided.
`alpha`	A number between 0 and 1 to create a (1 - alpha)% confidence interval for the treatment effect.

Conventional meta-analysis approaches tend to perform poorly for heterogeneous, rare event data. rema implements a permutation-based approach for binary meta-analyses of 2x2 tables, founded on conditional logistic regression, that provides more reliable statistical tests when heterogeneity is observed in rare event data \insertCiteZabriskie2021rema. To adjust for the effect of heterogeneity, this method conditions on the sufficient statistic of a proxy for the heterogeneity effect as opposed to estimating the heterogeneity variance. While this results in the model not strictly falling under the random-effects framework, it is akin to a random-effects approach in that it assumes differences in variability due to treatment. Further, this method does not rely on large-sample approximations or continuity corrections for rare event data.

This method uses the permutational distribution of the test statistic instead of asymptotic approximations for inference. The number of observed events drives the computation complexity for creating this permutational distribution. Accordingly, for this method to be computationally feasible, it should only be applied to meta-analyses with a relatively low number of observed events. To create this permutational distribution, a network algorithm, based on the work of \insertCiteMehta1992;textualrema and \insertCiteCorcoran2001;textualrema, is employed using C++ and integrated into the package.

\insertNoCite

Normand1999rema

An object of class "rema" with corresponding print, summary, and plot (if distr == TRUE) functions. The object is a list containing the following elements:

`trt.events`	As defined above.
`trt.total`	As defined above.
`ctrl.events`	As defined above.
`ctrl.total`	As defined above.
`mid.p`	As defined above.
`alpha`	As defined above.
`arguments`	A string of the arguments passed into `rema`.
`TE`	The estimated overall treatment effect (odds ratio).
`CI`	A vector containing the estimated lower and upper bounds of a (1 - alpha)%
	confidence interval of the overall treatment effect (odds ratio).
`method`	A string specifying the method used to compute the odds ratio and its associated
	confidence interval. Either the conditional maximum likelihood estimate (CMLE)
	or the median unbiased estimate (MUE) will be used.
`pval`	The two-sided p-value for the overall treatment effect.
`pval.one.sided`	The one-sided p-value (in the direction of the treatment effect) for the overall
	treatment effect (if `one.sided.p == TRUE`).
`dist`	A data frame containing the permutational distribution of the test statistic
	(if `distr == TRUE`).
`tstat`	The observed value of the test statistic (if `distr == TRUE`).

\insertAllCited

metabin and metafor for more traditional meta-analysis methods for combining odds ratios.

# Lidocaine data set from Normand (1999)
rema(trt.events = c(2, 4, 6, 7, 7, 11),
     trt.total = c(39, 44, 107, 103, 110, 154),
     ctrl.events = c(1, 4, 4, 5, 3, 4),
     ctrl.total = c(43, 44, 110, 100, 106, 146),
     mid.p = FALSE,
     distr = FALSE,
     one.sided.p = TRUE)

# Example using a rema object as the input once the permutational
# distribution is obtained
my.rema.object <- rema(trt.events = c(1, 2, 0, 2, 0, 0, 1, 0, 1, 0),
                       trt.total = c(30, 14, 30, 49, 38, 11, 31, 13, 49, 23),
                       ctrl.events = c(4, 3, 3, 0, 4, 4, 3, 2, 3, 4),
                       ctrl.total = c(15, 26, 42, 24, 40, 26, 47, 24, 27, 26),
                       mid.p = FALSE,
                       distr = TRUE,
                       one.sided.p = FALSE)
rema(rema.obj = my.rema.object,
     mid.p = TRUE)

## Not run: 
# Vectors of non-whole numbers (such as after applying a continuity correction)
rema(trt.events = c(0.5, 0.5, 1, 3),
     trt.total = c(2, 5, 4, 12),
     ctrl.events = c(2.5, 4.5, 6, 7),
     ctrl.total = c(7, 9, 11, 12))

# Vectors with greater observed events than total observations
rema(trt.events = c(11, 13, 7, 10),
     trt.total = c(10, 12, 5, 7),
     ctrl.events = c(22, 25, 32, 26),
     ctrl.total = c(20, 20, 30, 25))

## End(Not run)