2.5.2.2 Algorithm: Nonparametric Distribution Analysis (Arbitrary Censoring)

The Uncensor/Arbitrary Censor data are represented as time intervals (tl_i, tr_i):

  • tl_i: lower bound (time of last inspection or last known survival)
  • tr_i: upper bound (time when failure was first detected)
  • If tr_i = \infty, it represents right-censoring.
  • If tl_i = tr_i, it represents exact failure (uncensored).

Contents

Turnbull estimation method

Time intervals and the probabilities of each interval are calculated by the Turbull estimation[1]. Turnbull developed an iterative algorithm to obtain the nonparametric maximum likelihood estimate (NPMLE) of the cumulative distribution function for censored data. This approach is applicable to more general cases, including situations where the observation intervals overlap.

The covariance of the constrained MLE is computed via the Observed Fisher information matrix in the reduced parameter space[2] with the constraint \sum p_j=1.

Actuarial estimation method

First, a clinical life table is constructed, with each interval [t_i,t_{i+1}) representing the range into which survival times and times to loss or withdrawal are distributed. Each interval spans from t_i up to, but not including t_{i+1} for i=1,2,...,s. The final interval extends to infinity. These intervals are considered fixed. Using the input censoring information, we can tabulate the basic data required for the calculation.

Actuarial Table

Conditional Probability of Failure

\hat{q}_{i}=d_i/n_i

Varaince of Conditional Probability of Failure

Var[\hat{q}_{i}]=\frac{\hat{q}_{i}(1-\hat{q}_{i})}{n_i}

Survival Probabilities

Cumulative Survival Probabilities

\hat{S}(t_i)= \begin{cases} 1 & \text{if } i = 1, \\ \hat{p}_{i-1}\hat{S}(t_{i-1}) & \text{if } i = 2,...,k \end{cases}

Variance of Cumulative Survival Probabilities

Var[\hat{S}(t_i)] = [\hat{S}(t_i)]^2\sum_{j=1}^{i-1}\frac{\hat{q}_j}{n_j\hat{p}_j}

Hazards and Densities

Hazard Estimates

\hat{h}(t_{mi})=\frac{2\hat{q}_i}{b_i(1+\hat{p}_i)}

Variance of Hazards

Var[\hat{h}(t_{mi})] = \frac{(\hat{h}(t_{mi}))^2}{n_i\hat{q}_i}(1-(\frac{1}{2}\hat{h}(t_{mi})b_i)^2)

Probability Density Estimates

\hat{f}(t_{mi})=\frac{\hat{S}(t_i)\hat{q}_i}{b_i}

Variance of Probability Densities

Var[\hat{f}(t_{mi})] = \frac{(\hat{S}(t_i)\hat{q}_i)^2}{b_i}\sum_{j=1}^{i-1}(\frac{\hat{q}_j}{n_j\hat{p}_j} + \frac{\hat{p}_j}{n_j\hat{q}_j})

Characteristics of Variable

Median

\hat{t}_{m}=t_j + \frac{\hat{S}(t_j) - 0.5}{\hat{f}(t_{mj})}

Variance of Median

Var[\hat{t}_{m}] = \frac{\hat{S}(t_0)^2}{4n_0\hat{f}(t_{mj})}

Additional Time from Time T until Half of Units Fail

First find time interval (t_j, t_{j+1}) so that \hat{S}(t_j)\le\frac{1}{2}\hat{S}(t_i) and \hat{S}(t_{j+1}) > \frac{1}{2}\hat{S}(t_i). Then

\hat{t}_{mr}(i) = (t_j-t_i)+\frac{b_j(\hat{S}(t_j)-1/2\hat{S}(t_i))}{\hat{S}(t_j)-\hat{S}(t_{j+1})}

Variance of \hat{t}_{mr}(i)

Var[\hat{t}_{mr}(i)] = \frac{(\hat{S}(t_i))^2}{4n_i(\hat{f}(t_{mj}))^2}

Confidence Interval

The confidence intervals are calculated using a normal approximation:

  • Two-sided 100(1-\alpha)\% confidence interval
    x_{L} = \hat{x} - z_{1-\alpha/2}\times SE
    x_{U} = \hat{x} + z_{1-\alpha/2}\times SE
  • One-sided 100(1-\alpha)\% lower confidence bound
    x_{L} = \hat{x} - z_{1-\alpha}\times SE
  • One-sided 100(1-\alpha)\% upper confidence bound
    x_{U} = \hat{x} + z_{1-\alpha}\times SE

Reference

  1. B.W. Turnbull (1976). "The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data". Journal of the Royal Statistical Society, 38: pp. 290-295.
  2. A. P. Dawid (1979). "Conditional independence in statistical theory." Journal of the Royal Statistical Society, Series B 41(1):1–31.