trong một quy định thời gian của thời gian. Để cung cấp một biện pháp chính xác hơn mối quan hệ tỷ lệ sống sót đã quan sát và dự kiến , Cutler et al. Đề nghị tính toán tỷ lệ cho mỗicó thể được sử dụng để phù hợp với các mô hình được đưa ra vào cuối những ví dụ. | PROBABILITY PLOTTING 203 observations have the same value the sample cumulative distribution function is plotted against only the t with the largest i value. Step 3. Plot t or a function of it versus the estimated sample cumulative distribution or a function of it. Step 4. Fit a straight line through the points by eye. The position of the straight line should be chosen to provide a fit to the bulk of the data and may ignore outliers or data points of doubtful validity. Figure gives a normal probability poot of the WBC versus h 1 F where Th is the inverse of the standard normal distribution function. The values of h F WBC i are shown in Table . The plot is reasonably iínear. Tdec straight line fitted byeey in a probabilityplot can be used to estimate percentiles and proportions within given limits in the same manner as for the sample cumulative distribution curve. In addition a probability pkt provides estimates of the parameters of the theoretical distribution chosen. The mean or median WBC estimated from the normal probability pkt m Figuee is 56 000 at I F 0 F and WBC 56 000 . At o 1 F 1 WBC 91 000 which corresponds to the mean plus 1 standard deviation. Thus the standard deviation is estimated as 35 000. We now discuss probability ptoss ff hhe oppone ta Wiibull nom and log-logistic distributions. Figure Normal probability plot ff the WBC dala én I .xamplc 8T 204 GRAPHICAL METHODS FOR SURVIVAL DISTRIBUTION FITTING Exponential Distribution The exponential cumulative distribution function is F t 1 - exp - At t 0 The probability plot for the exponential disrribution ừ based on the relationship between t and F t from t 1loSl A 1 F t This relationship is linear between t and the function log 1 1 F t . Thus an exponential probability pOot ĨS made by plotting the ith ordered observed survival time trn versus log 1 1 F G d where F c d is an estimate of F tm for example i n for i 1 . n. From at log 1 1 F t 1 t 1 Ấ. This fact .
