Package 'RefBasedMI'

Sample data: acupuncture trial

Description

A data set containing results of a randomised, double-blind, parallel-group comparing active treatment with placebo The primary outcome is head, measured at time 3 and 12

Usage

acupuncture

Format

A data frame with 802 rows and 11 columns

id

time

age

sex

migraine

chronicity

practice_id

treat

head_base

covariate

head

outcome variable

withdrawal_reason

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Sample data: antidepressant trial

Description

A data set containing antidepressant trial data as described in paper by White,Royes,Best (2019) The primary outcome is HAMD17.TOTAL measured at visit number 4,5,6,7.

Usage

antidepressant

Format

dataframe containing 688 rows and 14 columns

PATIENT.NUMBER

HAMA.TOTAL

PGI_IMPROVEMENT

VISIT...VISIT.3.DATE

VISIT.NUMBER

TREATMENT.NAME

PATIENT.SEX

POOLED.INVESTIGATOR

basval

HAMD17.TOTAL

outcome variable

change

miss_flag

methodcol

individual-specific method

referencecol

individual-specific reference arm

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Sample data: asthma trial

Description

A data set containing asthma trial data as used in the Stata mimix help file The primary outcome variable is fev, measured at 2,4,8,12 weeks

Usage

asthma

Format

A data frame containing 732 rows and 5 columns

id

patient identifier

time

treat

base

covariate

fev

outcome variable

---

Reference-based multiple imputation of longitudinal data

Description

Performs reference-based multiple imputation of longitudinal data where data are missing after treatment discontinuation. Methods available are missing at random, jump to reference, copy reference, copy increments in reference, last mean carried forward, the causal model, and delta-adjustment.

Usage

RefBasedMI(
  data,
  covar = NULL,
  depvar,
  treatvar,
  idvar,
  timevar,
  method = NULL,
  reference = NULL,
  methodvar = NULL,
  referencevar = NULL,
  K0 = NULL,
  K1 = NULL,
  delta = NULL,
  dlag = NULL,
  M = 1,
  seed = 101,
  prior = "jeffreys",
  burnin = 1000,
  bbetween = NULL,
  mle = FALSE
)

Arguments

data	Dataset in long format
covar	Baseline covariate(s): must be complete (no missing values)
depvar	Outcome variable
treatvar	Treatment group variable: can be numeric or character
idvar	Participant identifiervariable
timevar	Variable indicating time point for repeated measures
method	Reference-based imputation method: must be "J2R", "CR", "CIR", "MAR", "Causal" or "LMCF"
reference	Reference group for "J2R", "CIR", "CR" methods, or control group for causal method: can be numeric or string
methodvar	Variable in dataset specifying individual method
referencevar	Variable in dataset specifying reference group for individual method
K0	Causal constant for use with Causal method
K1	Exponential decaying causal constant for use with Causal method
delta	Optional vector of delta values to add onto imputed values (non-mandatory) (a's in Five_Macros user guide), length equal to number of time points
dlag	Optional vector of delta values to add onto imputed values (non-mandatory) (b's in Five_Macros user guide), length equal to number of time points
M	Number of imputations to be created
seed	Seed value: specify this so that a new run of the command will give the same imputed values
prior	Prior when fitting multivariate normal distributions: can be one of "jeffreys" (default), "uniform" or "ridge"
burnin	Number of burn-in iterations when fitting multivariate normal distributions
bbetween	Number of iterations between imputed data sets when fitting multivariate normal distributions
mle	Use with extreme caution: do improper imputation by drawing from the model using the maximum likelihood estimates. This does not allow for uncertainty in the MLEs and invalidates interval estimates from Rubin's rules.

Details

The program works through the following steps:

1. Set up a summary table based on treatment arm and missing data pattern (i.e. which timepoints are unobserved)

2. Fit a multivariate normal distribution to each treatment arm using MCMC methods in package norm2

3. Impute all interim missing values under a MAR assumption, looping over treatments and patterns

4. Impute all post-discontinuation missing values under the user-specified assumption, looping over treatments and patterns (and over methodvar and referencevar if specified)

5. Perform delta-adjustment if specified

6. Repeat steps 2-5 M times and form into a single data frame

The baseline value of the outcome could be handed as an outcome, but this would allow a treatment effect at baseline. We instead recommend handling it as a covariate.

The program is based on Suzie Cro's Stata program mimix

The user can use the as.mids() function in the mice package to convert the output data to mids data type and then perform analysis using Rubin's rules.

Value

A data frame containing the original data stacked above the M imputed data sets. The original ID variable (idvar) is renamed as .id. A new variable .imp indicates the original data (.imp=0) or the imputed data sets (.imp=1,...,M).

Examples

# Perform jump to reference imputation on asthma trial data, with reference arm 1 
asthmaJ2R <- RefBasedMI(data=asthma, depvar=fev, treatvar=treat, 
 idvar=id, timevar=time, method="J2R", reference=1, M=5, seed=54321)
# Fit regression model to each imputed data set by treating output data frame as mids object
library(mice)
fit <- with(data = as.mids(asthmaJ2R), lm(fev ~ factor(treat), subset=(time==12)))
# Find pooled treatment effects using Rubin's rules 
summary(pool(fit))

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