Last data update: 2014.03.03

R: Estimates survival and treatment effect using inverse...
 delta.iptw.km R Documentation

## Estimates survival and treatment effect using inverse probability of treatment weighted (IPTW) Kaplan-Meier estimation

### Description

Estimates the probability of survival past some specified time and the treatment effect, defined as the difference in survival at the specified time, using inverse probability of treatment weighted (IPTW) Kaplan-Meier estimation

### Usage

delta.iptw.km(tl, dl, treat, tt, var = FALSE, conf.int = FALSE, ps.weights = NULL,
weight.perturb = NULL, perturb.ps = FALSE, cov.for.ps = NULL)


### Arguments

 tl observed event time of primary outcome, equal to min(T, C) where T is the event time and C is the censoring time. dl event indicator, equal to I(T

### Details

Let T_{Li} denote the time of the primary event of interest for person i, C_i denote the censoring time, Z_{i} denote the vector of baseline (pretreatment) covariates, and G_i be the treatment group indicator such that G_i = 1 indicates treatment and G_i = 0 indicates control. Due to censoring, we observe X_{Li}= min(T_{Li}, C_{i}) and δ_{Li} = I(T_{Li}≤q C_{i}). This function estimates survival at time t within each treatment group, S_j(t) = P(T_{L} > t | G = j) for j = 1,0 and the treatment effect defined as Δ(t) = S_1(t) - S_0(t).

The inverse probability of treatment weighted (IPTW) Kaplan-Meier (KM) estimate of survival at time t for each treatment group is

hat{S}_{IPTW,KM, j}(t) = ∏ _{t_{kj} ≤q t} ≤ft [1-frac{d_{kj}^w}{y_{kj}^w} ight ] mbox{ if } t≥q t_{1j}, mbox{ or } 1 mbox{ if } t<t_{1j}

where t_{1j},...,t_{Dj} are the distinct observed event times of the primary outcome in treatment group j, d_{kj}^w = ∑_{i: X_{Li} = t_{kj}, δ_{Li} = 1} {hat{W}_j(Z_i)}^{-1}δ_{Li} I(G_i = j) and y_{kj}^w = ∑_{i: X_{Li} ≥q t_{kj}} {hat{W}_j(Z_i)}^{-1} I(G_i = j), W_j(Z_i) = {P(G_{i} = j | Z_i)}, and hat{W}_j(Z_i) is the estimated propensity score (see ps.wgt.fun for more information). The IPTW KM estimate of treatment effect at time t is hat{Δ}_{IPTW,KM}(t) = hat{S}_{IPTW,KM, 1}(t) - hat{S}_{IPTW,KM, 0}(t).

To obtain variance estimates and construct confidence intervals, we use a perturbation-resampling method. Specifically, let {V^{(b)}=(V_1^{(b)}, . . . ,V_n^{(b)})^{T}, b=1,...B} be n\times B independent copies of a positive random variable U from a known distribution with unit mean and unit variance such as an Exp(1) distribution. To estimate the variance of our estimates, we appropriately weight the estimates using these perturbation weights to obtain perturbed values: hat{S}_{IPTW,KM,0} (t)^{(b)}, hat{S}_{IPTW,KM,1} (t)^{(b)}, and hat{Δ}_{IPTW,KM} (t)^{(b)}, b=1,...B. We then estimate the variance of each estimate as the empirical variance of the perturbed quantities. To construct confidence intervals, one can either use the empirical percentiles of the perturbed samples or a normal approximation.

### Value

A list is returned:

 S.estimate.1 the estimate of survival at the time of interest for treatment group 1, hat{S}_1(t) = P(T>t | G=1) S.estimate.0 the estimate of survival at the time of interest for treatment group 0, hat{S}_0(t) = P(T>t | G=0) delta.estimate the estimate of treatment effect at the time of interest S.var.1  the variance estimate of hat{S}_1(t); if var = TRUE or conf.int = TRUE S.var.0  the variance estimate of hat{S}_0(t); if var = TRUE or conf.int = TRUE delta.var the variance estimate of hat{Δ}(t); if var = TRUE or conf.int = TRUE p.value the p-value from testing Δ(t) = 0; if var = TRUE or conf.int = TRUE conf.int.normal.S.1 a vector of size 2; the 95% confidence interval for hat{S}_1(t) based on a normal approximation; if conf.int = TRUE conf.int.normal.S.0 a vector of size 2; the 95% confidence interval for hat{S}_0(t) based on a normal approximation; if conf.int = TRUE conf.int.normal.delta a vector of size 2; the 95% confidence interval for hat{Δ}(t) based on a normal approximation; if conf.int = TRUE conf.int.quantile.S.1 a vector of size 2; the 95% confidence interval for hat{S}_1(t) based on sample quantiles of the perturbed values, described above; if conf.int = TRUE conf.int.quantile.S.0 a vector of size 2; the 95% confidence interval for hat{S}_0(t) based on sample quantiles of the perturbed values, described above; if conf.int = TRUE conf.int.quantile.delta a vector of size 2; the 95% confidence interval for hat{Δ}(t) based on sample quantiles of the perturbed values, described above; if conf.int = TRUE

Layla Parast

### References

Xie, J., & Liu, C. (2005). Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data. Statistics in Medicine, 24(20), 3089-3110.

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.

Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 516-524.

### Examples

data(example_obs)
W.weight = ps.wgt.fun(treat = example_obs$treat, cov.for.ps = as.matrix(example_obs$Z))
delta.iptw.km(tl=example_obs$TL, dl = example_obs$DL, treat = example_obs$treat, tt=2, ps.weights = W.weight) delta.iptw.km(tl=example_obs$TL, dl = example_obs$DL, treat = example_obs$treat, tt=2,
cov.for.ps = as.matrix(example_obs\$Z))


### Results