# Wrapper for the `odds_ratio_function()`to perform several similar analyses in one go The function is intended to make it easy to get OR's for several similar models in one go, where either the same analysis is performed except for one variable or the same analysis is performed but by each variable (each level of the variable is analysed separately). ```r odds_ratio_function_repeated( normaldata, outcomevar, expvars, adjustment_fixed = NULL, by_var = NULL, number_decimals = 2, alpha = 0.05, regtype = c("logistic", "log-linear"), matchgroup = NULL, matchtiemethod = c("exact", "approximate", "efron", "breslow"), values_to_remove = NULL, weightvar = NULL, surveydata = FALSE, textvar = NULL, model_object = FALSE ) ``` ## Arguments - `normaldata`: A data frame or data frame extension (e.g. a tibble). - `outcomevar`: A character vector naming factor variables in normaldata to use as outcomes in separate models. - `expvars`: A character vector naming exposure variables (either numeric or factors) to use in separate models. - `adjustment_fixed`: A character vector naming adjustment variables to include in all models. NULL is the default resulting in no fixed adjustment. - `by_var`: A character vector specifying a factor on which to run the analyses completely separate for all levels. It only works with one variable (default is NULL). NOTE: NA and "" levels will not be used but all other levels will have separate models. - `number_decimals`: An integer giving the number of decimals to show in the standardized output (default is two decimals). - `alpha`: A scalar, between 0 and 1 specifying the desired significance level of the confidence intervals (default is 0.05 which will yield the usual 95% confidence interval). - `regtype`: A character string specifying the analysis method. Can either be "logistic" for logistic regression (the default) or "log-linear" for log-linear regression. Log-linear regression can only be used with binomial, unconditional analysis. - `matchgroup`: Character string specifying a variable in normaldata to condition the analysis on. Can only be used in binomial logistic regression models (default is NULL). - `matchtiemethod`: Character string specifying the method for ties when using a matched/conditional analysis. The default options is "exact", however this option does not take weights into account for the analysis, so if weights (other than 1) are used, another option should be selected. Other options are "approximate", "efron", and "breslow" - for further explanations, see documentation for clogit . - `values_to_remove`: Character vector specifying values to remove from ALL variables used in the regression before the analysis (default is NULL). This is useful if some value(s) are used consistently to encode missing/irrelevant in the data (e.g. c("888", "987") - normal missing (NA) don't need to be specified as it will be removed automatically. Do NOT remove the reference values as this will lead to unexpected results! - `weightvar`: A character string specifying a numeric variable in normaldata with pre-calculated weights for observations in the analysis. The default value NULL corresponds to weight 1 for all observations. - `surveydata`: A Boolean specifying whether the data comes from a survey (default is FALSE). - `textvar`: A character string with text (like a note) to be added to the output. The default value NULL corresponds to no added note. - `model_object`: A Boolean. If TRUE, returns the raw output object from the analysis instead of the standard output. This might be useful to see information not included in the standardized output (default is FALSE). ## Returns A standardized analysis object with results from multiple models. ## Details It's possible to have same variable in `expvars` and `adjustment_fixed`. When a model results in an error, the function will not stop - it continues with other models until done BUT in the output the error text can be seen. ## Examples ```r # Data to use data("infert", package = "datasets") infert2 <- infert |> dplyr::mutate( Age_grp = relevel(as.factor(dplyr::case_when( age < 25 ~ "<25", 25 <= age & age < 35 ~ "25-<35", age >= 35 ~ "35+" )), ref="25-<35"), Parity_grp = relevel(as.factor(dplyr::case_when( parity == 1 ~ "1", parity >= 2 & parity <= 3 ~ "2-3", parity > 3 ~ "4+" )), ref="2-3"), induced = relevel(as.factor(induced), ref="0"), case = relevel(as.factor(case), ref="0"), spontaneous = relevel(as.factor(spontaneous), ref="0") ) # Two outcomes (Parity_grp, case) with their own set of models, three # variables included in separate models (spontaneous,induced and education) # and one variable that is included in all models (Age_grp) test <- odds_ratio_function_repeated( normaldata = infert2, outcomevar = c("Parity_grp","case"), expvars = c("spontaneous","induced","education"), adjustment_fixed = c("Age_grp") ) # One outcome (case), two variables included in separate models # (spontaneous and induced), one variable included in all models (Age_grp) # and all analyses made for each level of another variable (Parity_grp) test2 <- odds_ratio_function_repeated( normaldata = infert2, outcomevar = c("case"), expvars = c("spontaneous","induced"), adjustment_fixed = c("Age_grp"), by_var = "Parity_grp" ) ``` ## See Also `odds_ratio_function()` to perform a single logistic or log-linear regression giving a standardized output table. ## Author(s) ASO

odds_ratio_function_repeated function