Caution about using survival data with left
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: PHRCAUT */
/* TITLE: Caution about using survival data with left */
/* truncation */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: product-limit estimator, left-truncated data */
/* PROCS: PHREG */
/* DATA: Channing data in Klein and Moeschberger (2003) */
/* SUPPORT: sasycs */
/* REF: SAS/STAT User's Guide, PROC PHREG Chapter */
/* MISC: */
/****************************************************************/
data Channing;
input Gender$ Age_entry Age_exit Death @@;
datalines;
Female 1042 1172 1 Female 921 1040 1 Female 885 1003 1
Female 901 1018 1 Female 808 932 1 Female 915 1004 1
Female 901 1023 1 Female 852 908 1 Female 828 868 1
Female 968 990 1 Female 936 1033 1 Female 977 1056 1
Female 929 999 1 Female 936 1064 1 Female 1016 1122 1
Female 910 1020 1 Female 1140 1200 1 Female 1015 1056 1
Female 850 940 1 Female 895 996 1 Female 854 969 1
Female 957 1089 1 Female 1013 1115 1 Female 1073 1192 1
Female 976 1085 1 Female 872 976 1 Female 1027 1142 1
Female 1071 1200 1 Female 919 1017 1 Female 894 1006 1
Female 885 1012 1 Female 889 1000 1 Female 887 1012 1
Female 920 1040 1 Female 1015 1024 1 Female 942 1070 1
Female 924 1014 1 Female 883 996 1 Female 930 944 1
Female 956 1085 1 Female 886 994 1 Female 987 1097 1
Female 883 966 1 Female 837 948 1 Female 958 1029 1
Female 850 963 1 Female 890 905 1 Female 847 970 1
Female 919 1015 1 Female 748 804 1 Female 934 1041 1
Female 895 991 1 Female 874 982 1 Female 877 989 1
Female 900 959 1 Female 957 1084 1 Female 1013 1131 1
Female 967 1068 1 Female 904 919 0 Female 829 848 0
Female 842 979 0 Female 802 876 0 Female 840 938 0
Female 792 929 0 Female 837 848 0 Female 941 1006 0
Female 746 804 0 Female 834 932 0 Female 865 932 0
Female 828 965 0 Female 894 1011 0 Female 874 1011 0
Female 917 1054 0 Female 993 1028 0 Female 918 1055 0
Female 818 955 0 Female 984 1019 0 Female 1002 1010 0
Female 857 994 0 Female 827 836 0 Female 883 1020 0
Female 1008 1042 0 Female 954 1091 0 Female 905 1042 0
Female 838 975 0 Female 934 946 0 Female 872 940 0
Female 918 922 0 Female 844 981 0 Female 805 928 0
Female 922 1059 0 Female 821 958 0 Female 838 975 0
Female 934 961 0 Female 886 1023 0 Female 934 1023 0
Female 878 1010 0 Female 935 1072 0 Female 799 825 0
Female 849 952 0 Female 920 953 0 Female 948 998 1
Female 968 1105 0 Female 908 996 0 Female 828 965 0
Female 897 1034 0 Female 823 938 0 Female 950 1008 0
Female 1049 1186 0 Female 878 1015 0 Female 854 991 0
Female 877 1014 0 Female 820 955 0 Female 899 1036 0
Female 855 893 0 Female 827 964 0 Female 925 960 0
Female 900 1037 0 Female 935 948 0 Female 1005 1053 0
Female 855 992 0 Female 920 992 0 Female 810 895 0
Female 792 857 0 Female 882 1019 0 Female 934 1071 0
Female 910 1047 0 Female 865 973 0 Female 899 1036 0
Female 982 1119 0 Female 856 993 0 Female 961 963 0
Female 893 1030 0 Female 861 998 0 Female 829 932 0
Female 882 1019 0 Female 875 1012 0 Female 833 970 0
Female 972 1013 0 Female 807 944 0 Female 924 959 0
Female 845 982 0 Female 840 977 0 Female 867 1004 0
Female 881 913 0 Female 901 917 0 Female 944 947 0
Female 821 824 0 Female 811 898 0 Female 1007 1014 0
Female 912 1049 0 Female 802 939 0 Female 928 1065 0
Female 911 938 0 Female 847 899 0 Female 1035 1172 0
Female 893 973 0 Female 922 971 0 Female 977 985 0
Female 941 944 0 Female 869 1006 0 Female 885 955 0
Female 859 996 0 Female 948 1085 0 Female 890 1005 0
Female 887 891 0 Female 968 1105 0 Female 927 989 0
Female 997 1134 0 Female 846 983 0 Female 831 861 0
Female 842 979 0 Female 768 905 0 Female 896 1033 0
Female 894 1014 0 Female 885 1022 0 Female 822 959 0
Female 927 954 0 Female 897 904 0 Female 848 985 0
Female 912 1001 0 Female 863 1000 0 Female 813 950 0
Female 802 939 0 Female 956 1061 0 Female 822 945 0
Female 934 993 0 Female 1026 1054 0 Female 981 1114 0
Female 934 1071 0 Female 836 927 0 Female 760 897 0
Female 820 957 0 Female 907 1044 0 Female 979 1016 0
Female 894 1031 0 Female 852 989 0 Female 948 971 0
Female 813 950 0 Female 902 1024 0 Female 913 1050 0
Female 810 812 0 Female 841 978 0 Female 875 1012 0
Female 841 927 0 Female 948 1012 0 Female 859 995 1
Female 820 957 0 Female 860 997 0 Female 917 948 0
Female 936 1073 0 Female 950 986 0 Female 1013 1031 0
Female 847 984 0 Female 777 914 0 Female 960 988 0
Female 920 1057 0 Female 935 1051 0 Female 933 979 0
Female 933 985 0 Female 797 934 0 Female 733 870 0
Female 866 953 0 Female 870 930 0 Female 795 875 0
Female 905 1005 1 Female 796 891 0 Female 965 1102 0
Female 775 912 0 Female 942 977 0 Female 895 926 0
Female 981 1038 0 Female 991 1006 0 Female 894 918 0
Female 886 943 0 Female 871 924 0 Female 839 976 0
Female 839 976 0 Female 858 995 0 Female 830 967 0
Female 868 1005 0 Female 831 925 0 Female 783 888 0
Female 925 1062 0 Female 898 1035 0 Female 910 1009 0
Female 958 1008 0 Female 866 1003 0 Female 851 988 0
Female 906 1043 0 Female 882 1019 0 Female 815 952 0
Female 972 1083 1 Female 973 985 0 Female 957 957 0
Female 1010 1147 0 Female 1070 1207 0 Female 895 1032 0
Female 818 860 0 Female 864 1001 0 Female 857 994 0
Female 1028 1063 0 Female 892 1029 0 Female 769 906 0
Female 883 1020 0 Female 972 1109 0 Female 965 1088 0
Female 925 961 0 Female 814 872 0 Female 805 942 0
Female 992 1010 0 Female 943 1080 0 Female 951 958 0
Female 926 987 0 Female 954 962 0 Female 944 944 0
Female 935 935 0 Female 900 912 0 Female 762 854 0
Female 823 882 0 Female 978 1010 1 Female 966 1027 1
Female 912 916 0 Female 823 829 0 Female 909 933 0
Female 967 1041 1 Female 851 905 1 Female 843 861 1
Female 963 1021 1 Female 888 919 1 Female 794 798 0
Female 905 928 1 Female 1039 1086 1 Female 901 923 1
Female 823 830 1 Female 809 822 1 Female 887 905 0
Female 859 926 1 Female 1004 1015 0 Female 919 931 1
Female 958 1041 1 Female 1003 1093 1 Female 871 944 1
Female 864 873 1 Female 996 1073 1 Female 1034 1068 1
Female 873 897 1 Female 984 1047 1 Female 943 1011 1
Female 1007 1019 1 Female 935 1006 1 Female 929 996 1
Female 939 978 1 Female 772 840 1 Female 871 912 0
Female 873 954 1 Female 981 1018 1 Female 894 927 0
Female 994 1040 1 Female 976 995 1 Female 847 883 1
Female 859 941 1 Female 933 990 1 Female 861 934 1
Female 886 908 1 Female 943 986 1 Female 931 969 1
Female 948 1019 1 Female 955 992 1 Female 1004 1023 1
Female 828 895 1 Female 835 845 1 Female 868 870 0
Female 988 1074 1 Female 861 930 1 Female 959 976 0
Female 959 912 1 Female 871 874 0 Female 847 892 0
Female 874 885 1 Female 992 1044 1 Female 1027 1072 1
Female 857 901 1 Female 994 1013 1 Female 1041 1043 1
Female 900 926 0 Female 1096 1152 1 Female 930 936 1
Female 943 994 1 Female 1024 1063 1 Female 802 821 0
Female 811 819 0 Female 927 1001 1 Female 967 975 1
Female 943 982 1 Female 840 905 0 Female 979 1040 1
Female 921 926 0 Female 986 1030 1 Female 1039 1132 1
Female 968 990 1 Female 955 990 1 Female 837 911 1
Female 861 915 1 Female 967 983 1 Male 782 909 1
Male 1020 1128 1 Male 856 969 1 Male 915 957 1
Male 863 983 1 Male 906 1012 1 Male 955 1055 1
Male 943 1025 1 Male 943 1043 1 Male 837 945 1
Male 966 1009 1 Male 936 971 1 Male 919 1033 1
Male 852 869 1 Male 1073 1139 1 Male 925 1036 1
Male 967 1085 1 Male 806 943 0 Male 969 1001 0
Male 923 1060 0 Male 865 1002 0 Male 953 1031 0
Male 871 945 0 Male 982 1006 0 Male 883 959 0
Male 817 843 0 Male 875 1012 0 Male 821 956 0
Male 936 1073 0 Male 971 1107 0 Male 830 940 0
Male 885 911 0 Male 894 1031 0 Male 893 996 0
Male 866 895 0 Male 878 1015 0 Male 820 957 0
Male 1007 1043 0 Male 879 1016 0 Male 956 1093 0
Male 854 989 1 Male 890 1027 0 Male 1041 1044 0
Male 978 1005 0 Male 836 973 0 Male 938 1064 0
Male 886 1023 0 Male 876 1013 0 Male 955 977 0
Male 823 960 0 Male 960 1047 0 Male 843 943 0
Male 856 951 0 Male 847 984 0 Male 1027 1058 0
Male 988 1045 0 Male 953 953 0 Male 978 1018 0
Male 981 1118 0 Male 926 970 0 Male 1036 1070 0
Male 1016 1153 0 Male 969 1106 0 Male 900 936 0
Male 898 906 0 Male 846 866 0 Male 964 1029 1
Male 984 1053 1 Male 1046 1080 1 Male 871 872 1
Male 847 893 1 Male 962 966 0 Male 853 894 1
Male 967 985 1 Male 1063 1094 1 Male 856 927 1
Male 865 948 1 Male 1051 1059 1 Male 1010 1012 1
Male 878 911 1 Male 1021 1094 1 Male 959 972 0
Male 921 993 1 Male 836 876 1 Male 919 993 1
Male 751 777 1 Male 906 966 1 Male 835 907 1
Male 946 1031 1 Male 759 781 1 Male 909 914 0
Male 962 998 1 Male 984 1022 1 Male 891 932 1
Male 835 898 1 Male 1039 1060 1 Male 1010 1044 1
;
ods graphics on;
proc phreg data=Channing plots(overlay=row)=survival atrisk;
model Age_exit*Death(0)= /entrytime=Age_entry;
strata Gender;
baseline out=Outs survival=Probability / method=pl;
ods output RiskSetInfo=Atrisk;
run;
data Outs;
set Outs;
if Gender="Female" then StratumNumber=1;
else StratumNumber=2;
run;
data Outs;
merge atrisk outs;
by StratumNumber Age_exit;
run;
proc print data=Outs;
id Gender;
var Age_exit Atrisk Event Probability;
run;
