Відмінності між версіями «Ficult to obtain c analytically. One obvious alternative would be the»

However, it is very time-consuming and results in lower than nominal coverage [http://s154.dzzj001.com/comment/html/?99601.html Blood glucose level in the first 24 hours was 138.1?9.4 mg/dL. Hyperglycemia] probabilities in some simulation studies. Lin and others (1993) used a normal resampling approximation to simulate the asymptoticS. YANG AND R. L. P RENTICEdistribution of sums of martingale residuals for checking the Cox regression model. The normal resampling approach reduces computing time significantly and has become a standard method. It has been used in many works, including Lin and others (1994), Cheng and others (1997), Gilbert and others (2002), Tian and others (2005), and Peng and Huang (2007). We will modify this approach for our problem here. For t , define the process    ^ ^ B T (t)U  ^ 1 d( i Ni ) + ^ 2 d( i Ni ) ^ Wn (t) =  n 0i n1 i>n^ T (t)U ^ B  =  n  ^ C(t)  +  n^ C(t) +  ntti n1^1 d( i Ni ) +i>n 1^2 d( i Ni )i>ni n^ i i 1 (X i )I (X i) +^ i i 2 (X i )I (X ii n^ i i 1 (X i )I (X it) +i>n^ i i 2 (X i )I (X iwhere i , i = 1, . . . , n, are independent variables that are also independent from the data. Furthermore, these variables have mean zero and variance converging to one as n  . In the normal resampling approach mentioned above, the i 's are the standard normal variables. However, the standard normal variables often result [https://dx.doi.org/10.3389/fpsyg.2015.00360 fpsyg.2015.00360] in lower coverage probabilities in various simulation studies. Thus, with moderate sized samples, we need to make some adjustment. ^ Conditional on (X i , i , Z i ), i = 1, . . . , n, Wn is a sum of n independent variables at each time point. ^ In Appendix B of the Supplementary Material available at Biostatistics online, we show that Wn given ^ the data converges weakly to W  . It follows that suptI |W /s| given the data converges in distribution to suptI |W  /s  |. Therefore, c can be estimated empirically from a large number of realizations of the ^ conditional distribution of suptI |W /s| given the data. Several choices of the weight s arise from recommendations in the literature for confidence bands of the survivor function and the cumulative hazard function in the one sample case. The choice s(t) =  (t, t) results in equal precision bands (Nair, 1984), which differ from pointwise confidence intervals in ^ ^ that c replaces z /2 . The choice s(t) = 1 +  (t, t) results in the Hall ellner type bands recommended by Bie and others (1987), which often have [http://hs21.cn/comment/html/?131278.html For comparable pilot research are study protocols as outlined by which NMDA] narrower widths in the middle of data range and wider widths ^ near the extremes of data range (Lin and others, 1994). One could also choose s(t) = h(t). This choice does not involve  (t, t) and hence is [https://dx.doi.org/10.1073/pnas.1408988111 pnas.1408988111] easier to implement. It may be adequate when  (t, t) only varies ^ ^ mildly over time. Let a  (0,  ) and define the average hazard ratio, over [a, t],  h(t) = 1 t -att) ,) (3.1)h(s)ds,aa