tvma: Time Varying Mediation Function: Continuous Outcome and Two...

In tvmediation: Time Varying Mediation Analysis

Time Varying Mediation Function: Continuous Outcome and Two Treatment Groups

Description

Function to estimate the time-varying mediation effect and bootstrap standard errors for two treatment groups and a continuous outcome.

Usage

tvma(
  treatment,
  t.seq,
  mediator,
  outcome,
  t.est = t.seq,
  plot = FALSE,
  CI = "boot",
  replicates = 1000,
  verbose = FALSE
)

Arguments

treatment	a vector indicating treatment group
t.seq	a vector of time points for each observation
mediator	matrix of mediator values in wide format
outcome	matrix of outcome values in wide format
t.est	a vector of time points at which to estimate. Default = t.seq (OPTIONAL ARGUMENT)
plot	TRUE or FALSE for producing plots. Default = "FALSE" (OPTIONAL ARGUMENT)
CI	"none" or "boot" method of deriving confidence intervals. Default = "boot" (OPTIONAL ARGUMENT)
replicates	number of replicates for bootstrapping confidence intervals. Default = 1000 (OPTIONAL ARGUMENT)
verbose	TRUE or FALSE for printing results to screen. Default = "FALSE" (OPTIONAL ARGUMENT)

Value

hat.alpha	estimated time-varying treatment effect on mediator
CI.lower.alpha	CI lower limit for estimated coefficient hat.alpha
CI.upper.alpha	CI upper limit for estimated coefficient hat.alpha
hat.gamma	estimated time-varying treatment effect on outcome (direct effect)
CI.lower.gamma	CI lower limit for estimated coefficient hat.gamma
CI.upper.gamma	CI upper limit for estimated coefficient hat.gamma
hat.beta	estimated time-varying effect of the mediator on outcome
CI.lower.beta	CI lower limit for estimated coefficient hat.beta
CI.upper.beta	CI upper limit for estimated coefficient hat.beta
hat.tau	estimated time-varying treatment effect on outcome (total effect)
CI.lower.tau	CI lower limit for estimated coefficient hat.tau
CI.upper.tau	CI upper limit for estimated coefficient hat.tau
est.M	time varying mediation effect
boot.se.m	estimated standard error for est.M
CI.lower	CI lower limit for est.M
CI.upper	CI upper limit for est.M

Plot Returns

1. Alpha_CI plot for hat.alpha with CIs over t.est

2. Gamma_CI plot for hat.gamma with CIs over t.est

3. Beta_CI plot for hat.beta with CIs over t.est

4. Tau_CI plot for hat.tau with CIs over t.est

5. MedEff plot for est.M over t.est

6. MedEff_CI plot for est.M with CIs over t.est

Note

1. ** IMPORTANT ** An alternate way of formatting the data and calling the function is documented in detail in the tutorial for the tvmb() function.

References

1. Fan, J. and Gijbels, I. Local polynomial modelling and its applications: Monographs on statistics and applied probability 66. CRC Press; 1996.

2. Fan J, Zhang W. Statistical Estimation in Varying Coefficient Models. The Annals of Statistics. 1999;27(5):1491-1518.

3. Fan J, Zhang JT. Two-step estimation of functional linear models with applications to longitudinal data. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2000;62(2):303-322.

4. Cai X, Coffman DL, Piper ME, Li R. Estimation and inference for the mediation effect in a time-varying mediation model. BMC Med Res Methodol. 2022;22(1):1-12.

5. Baker TB, Piper ME, Stein JH, et al. Effects of Nicotine Patch vs Varenicline vs Combination Nicotine Replacement Therapy on Smoking Cessation at 26 Weeks: A Randomized Clinical Trial. JAMA. 2016;315(4):371.

6. B. Efron, R. Tibshirani. Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Statistical Science. 1986;1(1):54-75.

Examples

## Not run: data(smoker)

# REDUCE DATA SET TO ONLY 2 TREATMENT CONDITIONS (EXCLUDING COMBINATION NRT)
smoker.sub <- smoker[smoker$treatment != 4, ]

# GENERATE WIDE FORMATTED MEDIATORS
mediator <- LongToWide(smoker.sub$SubjectID,
                       smoker.sub$timeseq,
                       smoker.sub$NegMoodLst15min)

# GENERATE WIDE FORMATTED OUTCOMES
outcome <- LongToWide(smoker.sub$SubjectID,
                      smoker.sub$timeseq,
                      smoker.sub$cessFatig)

# GENERATE A BINARY TREATMENT VARIABLE
trt <- as.numeric(unique(smoker.sub[,c("SubjectID","varenicline")])[,2])-1

# GENERATE A VECTOR OF UNIQUE TIME POINTS
t.seq <- sort(unique(smoker.sub$timeseq))

# COMPUTE TIME VARYING MEDIATION ANALYSIS USING BOOTSTRAPPED CONFIDENCE INTERVALS
results <- tvma(trt, t.seq, mediator, outcome)

# COMPUTE TIME VARYING MEDIATION ANALYSIS FOR SPECIFIED POINTS IN TIME USING 250 REPLICATES
results <- tvma(trt, t.seq, mediator, outcome,
                t.est = c(0.2, 0.4, 0.6, 0.8),
                replicates = 250)
## End(Not run)

tvmediation documentation built on May 25, 2022, 9:05 a.m.