Describe the population a study enrolled

Written the way a baseline demographics table reads. Each covariate takes whichever summary the paper printed — mean/sd, median/iqr, a cv as a percent, or a proportion for a binary one — and correlations are given for the PAIRS that were reported, everything else being independent.

Usage

admPopulation(..., cor = NULL, dist = c("lnorm", "normal"), data = NULL)

Arguments

...

Named covariates. A continuous one takes a named vector, e.g. WT = c(mean = 75, sd = 16) or CRCL = c(median = 92, iqr = c(62, 118)). A binary one takes a single named proportion, e.g. SEX = c(male = 0.55), which becomes levels 0/1 with that probability on 1.

cor

Correlations between covariate PAIRS, named A.B, e.g. cor = c(WT.CRCL = 0.45). Pairs not named are independent, so a partial table needs no identity padding. A full matrix is accepted too.

dist

"lnorm" (default) or "normal", for the continuous margins. Lognormal is the usual choice for a positive covariate — a normal margin wide enough to matter puts mass at or below zero, which is NaN inside any power or log term.

data

A data frame of individual covariates to derive the table FROM, instead of typing it out — a digitised baseline listing, or the cohort itself in a simulation study. Each numeric column becomes a margin (a 0/1 column becomes a proportion, everything else a continuous margin matching that column's mean and SD), and every continuous PAIR gets its correlation — taken on the LATENT scale, so on the logs for a lognormal margin, which is the conversion easiest to get wrong by hand. Anything named in ... or cor overrides what the data would have given, so a column you would rather state yourself simply gets stated.

Value

A covariate specification, as covDist() returns.

See also

admStudy(), which takes one; covDraw() to inspect it.