Statistical Methods for Survival Data Analysis Third Edition phần 7 docx

28 8.2 0 0 1 0 0 1 27.21 131 2.50 4.68 57.7 0 0 29 3.8 0 0 1 0 1 0 36.97 141 4.60 4.25 58.7 1 0 30 6.9 0 0 1 0 1 0 29.44 115 2.89 4.26 53.6 0 1 31 6.1 0 0 1 0 1 0 33.85 154 3.48 4.48 51.2 1 0 32 7.2 0 0 1 0 1 0 32.13 122 2.92 4.48 55.2 0 0 33 8.4 0 0 1 0 1 1 27.52 135 2.39 4.42 53.7 0 0 34 5.0 0 0 1 0 1 1 30.64 114 1.39 4.45 54.9 1 0 35 6.5 0 0 1 0 1 1 29.94 120 2.96 4.49 50.7 0 0 36 6.4 0 0 1 0 1 1 29.89 115 1.68 4.52 51.3 0 0 37 2.6 1 1 0 0 0 0 30.88 189 5.38 4.72 73.9 1 1 38 2.7 1 1 0 0 0 1 25.05 200 3.37 4.86 77.2 1 1 39 2.7 1 1 0 0 1 0 26.80 130 2.31 5.10 73.5 0 0 40 3.3 1 1 0 0 1 1 21.67 111 3.53 4.18 71.1 0 0 41 2.9 1 1 0 1 0 0 36.83 114 2.64 4.52 68.2 0 0 42 0.2 1 1 0 1 0 1 21.49 125 4.61 4.69 67.3 0 0 43 2.1 1 1 0 1 1 0 31.05 131 1.38 4.48 69.1 0 0 44 6.8 1 1 0 1 1 1 26.78 134 4.36 4.90 61.0 1 0 45 5.7 1 1 1 0 0 0 35.78 132 9.93 5.11 52.5 0 1 46 1.1 1 1 1 0 0 1 28.44 134 3.54 4.32 55.7 0 0 47 6.6 1 1 1 0 1 0 24.38 124 4.16 4.00 51.8 0 1 48 1.3 1 1 1 0 1 1 34.13 126 5.87 3.95 53.1 0 1 49 4.6 1 2 0 0 0 0 43.23 128 5.08 5.25 72.2 0 1 50 6.3 1 2 0 0 0 1 38.67 126 5.16 4.50 76.8 1 1 51 2.0 1 2 0 0 1 0 34.49 130 2.69 3.95 76.7 1 1 52 4.2 1 2 0 0 1 1 20.78 127 4.40 4.54 73.1 0 0 53 3.6 1 2 0 1 0 0 28.40 118 5.43 4.66 69.3 1 1 54 3.2 1 2 0 1 0 1 28.73 154 1.94 5.24 68.9 1 1 55 4.5 1 2 0 1 1 0 44.25 97 2.01 4.40 68.6 0 1 56 4.5 1 2 0 1 1 1 32.46 141 0.74 4.39 63.5 1 0 57 6.1 1 2 1 0 0 0 39.72 118 2.39 3.93 52.6 0 1 (Continued overleaf ) 311 Table 12.4 Continued ID T CENS DG AGEA AGEB SEX SMOKE BMI SBP LACR LTG AGE HTN DM 58 3.0 1 2 1 0 0 1 27.90 117 7.45 5.61 56.0 0 1 59 2.1 1 2 1 0 1 0 27.77 119 7.03 4.71 54.3 0 1 60 1.3 1 2 1 0 1 1 31.03 151 3.94 4.43 59.2 1 1 61 4.9 1 3 0 0 0 0 25.22 129 6.69 3.90 75.4 1 0 62 2.5 1 3 0 0 0 1 45.29 130 2.46 4.40 75.7 0 1 63 3.8 1 3 0 0 1 0 25.03 188 6.25 5.63 71.7 1 1 64 5.0 1 3 0 1 1 0 46.76 96 3.93 4.12 65.6 1 0 65 1.5 1 3 0 1 1 1 28.53 126 3.09 4.65 68.6 0 1 66 4.1 1 3 1 0 0 0 23.63 144 8.24 4.82 59.4 1 1 67 0.5 1 3 1 0 1 0 31.39 134 6.96 4.11 54.2 1 0 68 2.7 1 3 1 0 1 1 30.29 115 4.70 4.98 59.1 1 1 ? ID, participant id number; T, CVD event time (CVD-free time);CENS: 0 if censored, and :1 if uncensored; DG : 0ifnon-CVDat the end of the study or non-CVD death, :1ifstroke,:2 if coronary heart disease (CHD),and:3 if the other CVDs; AGEA : 1if aged 50—59 and :0otherwise;AGEB: 1ifaged60—69 and :0otherwise;SEX: 1ifmaleand:0 if female; SMOKE: 1ifcurrent smoker and 0 otherwise; BMI, body mass index; SBP, systolic blood pressure; LACR, logarithm of the ratio of urinary albumin and creatinine; LTG, logarithm of triglycerides; HTN : 1ifSBP.140 mmHg or DBP (diastolic blood pressure).90mmHg and :0 otherwise; DM : 1 if fasting glucose.126 mg/dL and :0otherwise. 312 Table 12.5 Results from Fitting a Cox Proportional Hazards Model Based on Different Methods for Ties on the CVD Data Regression Coefficient Variable Breslow Discrete Efron Exact Exponential Weibull AGEA 91.3478 91.3662 91.3558 91.3560 91.2550 91.0436 AGEB 90.7709 90.7828 90.7753 90.7755 90.7107 90.5966 SEX 0.7134 0.7233 0.7187 0.7189 0.6862 0.5659 SMOKE 0.3762 0.3810 0.3776 0.3776 0.3440 0.2855 BMI 0.0253 0.0256 0.0255 0.0255 0.0233 0.0194 LACR 0.1735 0.1759 0.1739 0.1740 0.1658 0.1357 likelihood function. The estimated regression coefficients based on the two parametric models, particularly the exponential regression model, are also close to those based on the Cox hazards model. From the signs of the coefficients, we see that men, current smokers, and persons with high BMI and albumin—creatinine ratios have a higher hazard (risk) of CVD and shorter CVD-free time. The coefficients of the two age variables are both negative, indicating that persons in the younger age groups have a lower hazard (risk) of CVD. Suppose that ‘‘C:!EX12d2d1.DAT’’ contains eight successive columns, for T, CENS, AGEA, AGEB, SEX, SMOKE, BMI, and LACR, and that the numbers in each row are space-separated. The following code for the SAS PHREG and LIFEREG procedures can be used to obtain the results in Table 12.5. data w1; infile ‘c:!ex12d2d1.dat’ missover; input t cens agea ageb sex smoke bmi lacr; run; proc phreg; model t*cens(0) : agea ageb sex smoke bmi lacr / ties : breslow; run; proc phreg; model t*cens(0) : agea ageb sex smoke bmi lacr / ties : discrete; run; proc phreg; model t*cens(0) : agea ageb sex smoke bmi lacr / ties : efron; run; proc phreg; model t*cens(0) : agea ageb sex smoke bmi lacr / ties : exact; run; proc lifereg; Model a: model t*cens(0) : agea ageb sex smoke bmi lacr / d : exponential; Model b: model t*cens(0) : agea ageb sex smoke bmi lacr / d : weibull; run;       313 12.2 IDENTIFICATION OF SIGNIFICANT COVARIATES As noted earlier, one principal interest is to identify significant prognostic factors or covariates. This involves hypothesis testing and covariate selection procedures, similar to those discussed in Chapter 11 for parametric methods. The differences are that the Cox proportional hazard model has a partial likelihood function in which the only parameters are the coefficients associated with the covariates. However, statistical inference based on the partial likelihood function has asymptotic properties similar to those based on the usual likelihood. Therefore, the estimation procedure (discussed in Section 12.1) is similar to those in Section 7.1, and the hypothesis-testing procedures are similar to those in Sections 9.1 and 11.2. For example, the Wald statistic in (9.1.4) can be used to test if any one of the covariates has no effect on the hazard, that is, to test H  : b G : 0. By replacing the log-likelihood function with the log partial likelihood function, the log-likelihood ratio statistic, the Wald statistic, and the score statistic in (9.1.10), (9.1.11), and (9.1.12) can be used to test the null hypothesis that all the coefficients are equal to zero, that is, to test H  : b  : 0, b  : 0, , b N : 0 or H  : b : 0 in (9.1.9). Similarly the forward, backward, and stepwise selection procedures discussed in Section 11.9.1 are applicable to the Cox proportional hazard model. The following example, using the SAS PHREG procedure, illustrates these procedures. Example 12.4 We use the entire CVD data set in Example 12.3 to demonstrate how to identify the most important risk factors among all the covariates. Suppose that the effects of age, gender, and current smoking status on CVD risk are of fundamental interest and we wish to include these variables in the model. In epidemiology this is often referred to as adjusting for these variables. Thus, AGEA, AGEB, SEX, and SMOKE are forced into the model and we are to select the most important variables from the remaining covariates (BMI, SBP, LACR, LTG, HTN, and DM), adjusting for age, gender, and current smoking status. The SAS procedure PHREG is used with Breslow’s approximation for ties (default procedure) and three variable selection methods (forward, backward, and stepwise). Two covariates, BMI and LACR, are selected at the 0.05 significance level by all three selection methods. The final model, in the form of (12.1.5), including only the four covariates that we purposefully included and the two most significant ones identified by the selection method, is 314         Table 12.6 Asymptotic Partial Likelihood Inference on the CVD Data from the Final Cox Proportional Hazards Model? 95% Confidence Interval Regression Standard Wald Relative Variable Coefficient Error Statistic p Hazards Lower Upper Final Model for the Cohort CV D Data AGEA 91.3558 0.2712 24.9910 0.0001 0.258 0.151 0.439 AGEB 90.7753 0.2618 8.7709 0.0031 0.461 0.276 0.769 SEX 0.7187 0.2193 10.7457 0.0010 2.052 1.335 3.153 SMOKE 0.3776 0.2208 2.9235 0.0873 1.459 0.946 2.249 BMI 0.0255 0.0124 4.2113 0.0402 1.026 1.001 1.051 LACR 0.1739 0.0446 15.2112 0.0001 1.190 1.090 1.299 b  9 b  90.580 4.9443 0.0262 0.560 b  ; b  1.096 11.5409 0.0007 2.993 b  9 b  0.341 1.3001 0.2542 1.407 Hypothesis Testing Results (H  : all b G : 0) Log-partial-likelihood ratio statistic 42.1130 0.0001 Score statistic 43.1750 0.0001 Wald statistic 41.3830 0.0001 ? The covariates, except AGEA, AGEB, SEX, and SMOKE, in the final model are selected among BMI, SBP, LACR, LTG, HTN, and DM. log h(t G ) h  (t G ) : b  AGEA G ; b  AGEB G ; b  SEX G ; b  SMOKE G ; b  BMI G ; b  LACR G :91.3558AGEA G 9 07753AGEB G ; 0.7187SEX G ; 0.3776SMOKE G ; 0.0255BMI G ; 0.1739LACR G (12.2.1) The regression coefficients, their standard errors, the Wald test statistics, p values, and relative hazards (relative risks as they are termed by many epidemiologists) are given in Table 12.6. The estimated regression coefficients b G , i : 1, 2, . . . , 6, are solutions of (12.1.9) using the Newton—Raphson iterated procedure (Section 7.1). The estimated variances of b G , i : 1, 2, , 6, are the respective diagonal elements of the estimated covariance matrix defined in (12.1.13). The square roots of these estimated variances are the standard errors in the table. The Wald statistics are for testing the null hypothesis that the covariate is not related to the risk of CVD or H  : b G : 0, i : 1, , 6, respect- ively. For example, the Wald statistic equals 10.7457 for gender with a p value     315 of 0.0010 and b : 0.7187. It indicates that after adjusting for all the variables in the model (12.2.1), gender is a significant predictor for the development of CVD, with men having a higher risk than women. The relative hazard (or risk) is exp(b  ), and for the covariate gender, it is exp(0.7187) : 2.052, which implies that men aged 50—79 years have about twice the risk of developing CVD in 10 years. The 95% confidence interval for the relative risk is (1.335, 3.153), which is calculated according to (7.1.8). For a continuous variable, exp(b G ) represents the increase in risk corresponding to a 1-unit increase in the variable. For example, for BMI, exp (0.0255) : 1.026; that is, for every unit increase in BMI, the risk for CVD increases 2.6%. To compare hazards among different age groups, between genders, or between smokers and nonsmokers, let h %# (t), h %# (t), h %#! (t), h +* (t), h $#+ (t), h 1+ (t), and h ,1+ (t) denote hazard functions for participants that are 50—59, 60—69, 70—79 years old, male, female, current smoker, and not current smoker, respectively. The log hazard ratio of a person in the 50 to 59-year age group to a person in the 70 to 79-year group assuming the two people are of same gender and the same current smoking status, BMI and LACR, is log[h %# (t)/h %#! (t)] : b  ; similarly, log[h %# (t)/h %#! (t)] : b  and log[h %# (t)/h %# (t)] : b  9 b  . Assuming that the two people are in the same age group and have the same BMI and LACR, the log hazard ratio of male to females is log h +* (t) h $#+ (t) : b  Similarly, assuming that the two people are in the same age group, of the same gender, and have the same BMI and LACR, the hazard ratio of a smoker to a nonsmoker is log h 1+ (t) h ,1+ (t) : b  Thus, testing whether risk of CVD are the same among different age groups is equivalent to testing H  : b  : 0, H  : b  : 0, and H  : b  9 b  : 0. Similarly, to test if the risk of CVD is the same between males and females or between smokers and nonsmokers is equivalent to tasting the null hypothesis H  : b  : 0 or H  : b  : 0, respectively. To consider more than one covariate, we also can formulate the null hypothesis by using (12.2.1). For example, if we wish to compare male nonsmokers to female smokers, from (12.2.1), log h +*\,1+ h $#+\1+ : b  9 b  316         assuming that they are in the same age group and have the same BMI and LACR. Thus to test if these two groups of people have the same risk of CVD, we test the null hypothesis H  : b  9 b  : 0. Similarly, to compare male smokers to female nonsmokers, we can test the null hypothesis H  : b  ; b  : 0. These null hypotheses are in the form of linear combinations of the coefficients. Using the notations in Section 11.2, the hypotheses H  : b  9 b  : 0 and H  : b  ; b  : 0 are the hypotheses in (11.2.13) with c : 0, L : (1 910000), and L : (001100), respectively. The Wald statistics in Table 12.6 are calculated according to (11.2.14). By assuming that the patients have the same BMI and LACR, we can construct hypotheses to compare subgroups defined by age groups, gender, and current smoking status. The last part of Table 12.6 shows the results of testing the null hypothesis that none of these covariates have any effect on the development of CVD. The log partial likelihood ratio, Wald, and score statistics, X * , X 5 , and X 1 are calculated according to (9.1.10), (9.1.11), and (9.1.12), respectively. Table 12.6 indicates that the hypotheses, H  : b  : 0, H  : b  : 0, H  : b  9 b  : 0, H  : b  : 0, H  : b  : 0, H  : b  : 0, and H  : b  ; b  : 0 are rejected at a signifi- cance level of p : 0.05. However, the hypotheses H  : b  : 0 and H  : b  9 b  : 0 are not rejected at a 0.05 level. The null hypothesis H  : all b G : 0, i : 1, , 6, is rejected with p : 0.0001 by using any of these tests. Assuming that the other covariates are the same, based on the relative hazards shown in the table, we conclude that (1) participants aged 50—59 and 60—69 have, respectively, about 25% and 50% lower CVD risk than those aged 70—79 (H  : b  : 0 and H  : b  : 0 are rejected); (2) participants aged 50—59 have 50% lower CVD risk than those aged 60—69 (H  : b  9 b  : 0 is rejected); (3) men’s CVD risk is twice as high as that of women (H  : b  : 0 is rejected); (4) BMI and LACR have a significant effect on CVD risk (H  : b  : 0 and H  : b  : 0 are rejected) and the risk increases about 3% and 19%, respectively, for every 1-unit increase in BMI and LACR, respectively; (5) male smokers have a CVD risk three times higher than that of female nonsmokers (H  : b  ; b  : 0 is rejected); (6) male nonsmokers have CVD risk similar to that of female smokers (H  : b  9 b  : 0 is not rejected); (7) consider- ing current smoking status alone, smokers had similar CVD risk as non- smokers (H  : b  : 0 is not rejected). This example is solely for the purpose of illustrating the use of the proportional hazards model and the interpretation of its results. Other hypotheses of interest can be constructed in a similar manner. The construction of null hypotheses for comparisons among sub- groups defined by AGEGROUP*SEX*SMOKE are left to the reader as exercises. Suppose that ‘‘C:!EX12d4d1.DAT’’ is a text data file that contains 12 successive columns for T, CENS, AGEA, AGEB, SEX, SMOKE, BMI, LACR, SBP, LTG, HTN, and DM. The following SAS code is used to obtained the results in Table 12.6.     317 data w1; infile ‘c:!ex12d4d1.dat’ missover; input t cens agea ageb sex smoke bmi lacr sbp ltg htn dm; run; proc phreg data : w1; model t*cens(0) : agea ageb sex smoke bmi lacr sbp ltg htn dm / include : 4 selection : f; run; proc phreg data : w1; model t*cens(0) : agea ageb sex smoke bmi lacr sbp ltg htn dm / include : 4 selection : b; run; proc phreg data : w1 outest: wcov covout; model t*cens(0) : agea ageb sex smoke bmi lacr sbp ltg htn dm / include : 4 selection : s; run; proc phreg data : w1; model t*cens(0) : agea ageb sex smoke bmi lacr sbp ltg htn dm / include : 4 selection : score best : 3; run; data wcov; set wcov; if - type - : ‘cov’; keep agea ageb sex smoke bmi lacr sbp ltg htn dm; run; title ‘The estimated covariance of the estimated coefficients’; proc print data : wcov; run; The following SPSS code can be used to select an optimal subset of covariates among all covariates by the forward and backward selection methods defined in Section 11.9.1 and to obtain the estimated coefficients and the other results in Table 12.6. data list file : ‘c:!ex12d4d1.dat’ free / t cens agea ageb sex smoke bmi lacr sbp ltg htn dm. coxreg t with agea ageb sex smoke bmi lacr sbp ltg htn dm /status : cens event (1) /method : fstep bmi lacr sbp ltg htn dm /criteria pin (0.05) pout (0.05) /print : all. coxreg t with agea ageb sex smoke bmi lacr sbp ltg htn dm /status : cens event (1) /method : bstep bmi lacr sbp ltg htn dm /criteria pin (0.05) pout (0.05) /print : all. 318         If BMDP 2L is used, the following code is applicable when selecting an optimal subset of covariates among all covariates by the stepwise selection method defined in Section 11.9.1 and to obtain the results in Table 12.6. /input file : ‘c:!ex12d4d1.dat’ . variables : 12. format : free. /print cova. /variable names : t,cens, agea, ageb, sex, smoke, bmi, lacr, sbp, ltg, htn, dm. /form time : t. status : cens. response : 1. /regress covariates : agea, ageb, sex, smoke, bmi, lacr, sbp, ltg, htn, dm. Step : phh. Example 12.5 If we do not force age, gender, and current smoking status on the model and are not interested in the three age groups, we can fit the proportional hazard model with age as a continuous variable and the other covariates: SEX, SMOKE, BMI, SBP, LACR, LTG, HTN, and DM. Using Breslow’s method for ties, the stepwise selection method, and the SAS pro- cedure PHREG, the final model with significant (p : 0.05) covariates is log h(t) h  (t) : 0.697AGE; 0.7528SEX ; 0.1111LACR; 0.3987LTG (12.2.2) The details are given in Table 12.7; all four covariates in the model have positive coefficients, indicating that the risk of developing CVD increases with age, gender, albumin/creatinine ratio, and triglyceride values. The relative hazards represent the increase in risk of CVD per unit increase in the covariates. For example, for every 1-unit increase in log(albumin/creatinine), the risk of developing CVD increases 12% after adjusting for age, gender, and log triglyceride. Men have more than twice the risk of CVD as women. The global null hypothesis that all four coefficients equal zero (H  : all b G : 0) is rejected by all three tests, as given in the lower part of Table 12.7. 12.3 ESTIMATION OF THE SURVIVORSHIP FUNCTION WITH COVARIATES When parametric regression models (Chapter 11) are used, we can estimate the survivorship function simply by replacing the parameters and coefficients in the survival function with their estimates. This is not the case when the Cox        319 Table 12.7 Asymptotic Partial Likelihood Inference on the CVD Data from the Final Cox Proportional Hazards Model Selected by the Stepwise Model Selection Method? 95% Confidence Interval for Relative Hazards Regression Standard Chi-Square Relative Variable Coefficient Error Statistic p Hazards Lower Upper AGE 0.0697 0.0136 26.1393 0.0001 1.07 1.04 1.10 SEX 0.7528 0.2192 11.7893 0.0006 2.12 1.38 3.26 LACR 0.1111 0.0459 5.8602 0.0155 1.12 1.02 1.22 LTG 0.3987 0.1976 4.0722 0.0436 1.49 1.01 2.20 H  : All coefficients equal zero Log-partial-likelihood ratio statistic 44.002 0.0001 Score statistic 44.278 0.0001 Wald statistic 42.527 0.0001 ? The covariates in the final model are selected among AGE, SEX, SMOKE, BMI, LACR, LTG, HTN, and DM using the stepwise selection method. proportional hazards model is used since we do not know the exact form of the baseline hazard function or the survival function. In this section we introduce briefly two estimators of the survival function, one proposed by Breslow (1974) and the other by Kalbfleisch and Prentice (1980). These estimates are available in commercial software packages. Readers interested in details are referred to the corresponding publications. As indicated earlier, under the Cox model, the survivorship function with covariates x H ’s is S(t, x) : [S  (t)] exp(N H b H x H ) (12.3.1) Once the regression coefficients, the b H ’s, are estimated, we need only estimate the underlying survivorship function, S  (t). From the estimated survivorship function, we can easily estimate the probability of surviving longer than a given time for a patient with a given set of covariates x  , , x N . By assuming that the baseline hazard function is constant between each pair of successive observed failure times, Breslow has proposed the following estimator of the baseline cumulative hazard function: H  (t) :  t G -t m G  l +R(t G ) exp(x  J b ) (12.3.2) 320         [...]... 49 .77 56.05 0.00 13.50 0.00 0.00 35.98 0.00 (Continued overleaf )         344 Table 13.1 Continued ID 53 53 53 53 54 5 5 5 5 56 57 58 58 59 60 60 61 61 61 61 LEX AGEB M1B M2B AGET M1T M2T CS TR TL 275 275 275 275 57 57 57 57 57 57 57 57 57 57 0 0 0 0 0 0 74 .28 74 .28 74 .28 74 .28 39.52 76 .22 76 .22 76 .22 76 .22 62.41 67. 64 80.61 80.61 67. 78 47. 35 47. 35... 0 75 .34 77 .28 77 .70 78 .26 43.50 79 .23 79 .64 80.21 81.24 65.83 71 .06 84.03 85.14 67. 68 47. 35 49.84 42.13 43.59 44.62 46.55 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 .75 36.04 41. 07 47. 80 47. 80 36. 07 41.10 47. 84 60.29 41.10 41.10 41.10 54. 37 72.12 13.83 43 .70 13.83 31.38 43 .70 66.92 0.00 12 .75 36.04 41. 07 0.00 0.00 36. 07. .. 0 1 24.61 14.65 42. 97 42.94 67. 06 14 .78 41.03 41.00 30.82 36.40 41.26 48.33 54.83 60 .71 66. 17 73. 07 41.00 41.03 41.20 41. 17 41.43 41.43 54. 97 36.50 41.43 48.46 60.85 41.56 54.93 41.43 41.03 41.23 12.32 41.10 41.13 48.16 31. 47 36. 17 4. 67 36. 07 44.19 49.68 61.44 14.13 26.25 13 .73 31.18 35.91 42. 97 0.00 0.00 0.00 0.00 42.94 0.00 0.00 0.00 0.00 30.82 36.40 41.26 48.33 54.83 60 .71 66. 17 0.00 0.00 0.00 0.00... 36.01 43. 27 56.02 22.14 21.95 25.43 42. 97 36.14 49.68 67. 88 36.11 25 .76 36.04 43. 07 56.05 36.40 14.16 26.02 31. 57 43.20 49. 87 36.14 36. 17 36.14 35.94 36. 17 36.14 13.90 31.41 36.01 43.10 36.01 13.83 49 .71 13.90 31.41 36.01 49 .77 56.05 67. 98 13.50 35.61 49.81 35.98 56.12 35.91 0.00 0.00 26.02 31.54 36.01 43. 27 0.00 0.00 0.00 0.00 0.00 36.14 49.68 0.00 0.00 25 .76 36.04 43. 07 0.00 0.00 14.16 26.02 31. 57 43.20... 15 15 15 15 15 164 15 144 144 192 192 54 264 264 264 264 40 40 265 265 265 265 58.64 40.99 57. 14 47. 82 47. 82 34.85 64.24 60 .72 58. 97 58. 97 58. 97 58. 97 58. 97 58. 97 58. 97 58. 97 49.95 69.19 48.98 65.52 47. 86 47. 82 47. 82 43.49 43.49 43.49 43.49 41.28 41.28 49.09 46.03 64.41 52.52 61.51 64.59 64.59 62.26 62.26 57. 56 60.03 60.03 60.03 60.03 44.30 44.30 52.84 52.84 52.84 52.84 0 0 0 1 1 1 0 0 0 0 0 0 0 0... 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0 0 0 69.31 64.39 64.85 65.22 65.82 66.89 70 .12 64. 07 65.88 74 .40 63.54 64. 67 66.18 66.00 65.15 66.01 66.60 67. 68 66.89 62.33 63.32 63 .78 64 .75 65.30 64.03 61.08 52.51 52.81 52.10 50.08 64.84 66.30 66.69 67. 28 58 .77 61.99 64.98 51.24 52 .70 53.09 54.23 54 .76 55 .75 63.53 65.38 78 ,03 47. 68 49.36 65.66 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0... 0.0001 0.0008 0. 471 4 0.0 478 0. 070 6 1. 07 2.10 0.92 1.48 1.15 1.05 1.36 0 .72 1 0.99 1.1 3.23 1.16 2.19 1.35 (b) AGE SEX LACR LTG LACR* log(t;1) 0. 071 0 .74 1 90.0 87 0.395 0.143 0.014 0.220 0.120 0.199 0. 079 26.635 11.3 27 0.519 3.9 17 3.269 (c) AGE SEX LACR LTG AGE*log(t ;1) 0.038 0 .76 4 0.111 0.4 17 0.033 0.220 0.046 0.1 97 1.330 12.020 5.888 4.469 0.2488 0.0005 0.0152 0.0345 1.04 2.15 1.12 1.52 0. 97 1.39 1.02... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 1 1 60 .70 42.21 60 .72 51.39 53.40; 36.08 67. 66 64.14 61.54 62.01 62.41 63.00 63.54 64.03 64.49 65.06 49.95 72 .61 52.41 68.95 47. 86 51.28 52.41 46.53 46.94 47. 53 48.56 44 .74 45.86 52.54 49.45 67. 85 53.54 64.94 68.01 68.60 64.88 65. 27 57. 95 73 .03 73 .71 64. 17 65.15 45.48 46.49 53.98 55.43 55.83 56.42 1 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0... statistical methods for the identification and  3 37 Table 12.9 Goodness-of-Fit Tests Based on Asymptotic Likelihood Inference in Fitting the CVD Data? Model Generalized gamma Log-logistic Lognormal Weibull Exponential Exponential LL LLR p BIC AIC 9198.842 9203.322 9206.0 17 9199.494 9203.061 9203.061 — — 14.3505 1.3046 8.4385@ 7. 1339A — — 0.0002 0.2534 0.01 47 0.0 076 92 17. 113 9218.983 9221. 678 ... 345 0 0 0 1 1 0 180 69 36 36 36 15 1 2 3 4 5 6 58.64 40.99 57. 14 47. 82 34.85 64.24 LEX AGEB M1B 0 0 0 0 0 0 60 .70 42.21 60 .72 51.39 36.08 67. 66 1 1 0 0 0 0 M2B AGET M1T t : 14.65  (observed from individual with ID : 2) ID Ordered Event time 0 0 0 0 0 1 M2T 58.64 57. 14 47. 82 64.24 180 36 36 15 0 0 1 0 LEX AGEB M1B 0 0 0 0 67. 66 60 .72 51.39 60 .70 0 0 0 1 M2B AGET M1T t : 24.61  (observed from individual . 90 .77 53 90 .77 55 90 .71 07 90.5966 SEX 0 .71 34 0 .72 33 0 .71 87 0 .71 89 0.6862 0.5659 SMOKE 0. 376 2 0.3810 0. 377 6 0. 377 6 0.3440 0.2855 BMI 0.0253 0.0256 0.0255 0.0255 0.0233 0.0194 LACR 0. 173 5 0. 175 9 0. 173 9. Cohort CV D Data AGEA 91.3558 0. 271 2 24.9910 0.0001 0.258 0.151 0.439 AGEB 90 .77 53 0.2618 8 .77 09 0.0031 0.461 0. 276 0 .76 9 SEX 0 .71 87 0.2193 10 .74 57 0.0010 2.052 1.335 3.153 SMOKE 0. 377 6 0.2208. 0 0 0 30.88 189 5.38 4 .72 73 .9 1 1 38 2 .7 1 1 0 0 0 1 25.05 200 3. 37 4.86 77 .2 1 1 39 2 .7 1 1 0 0 1 0 26.80 130 2.31 5.10 73 .5 0 0 40 3.3 1 1 0 0 1 1 21. 67 111 3.53 4.18 71 .1 0 0 41 2.9 1 1 0