Suppose a person can belong to any of the RACEs with the probability of PROB_RACE. And marginal probability of dying in years 1 to 3 is given(marginal survival will be 1-PROB_DYING): RACE PROB_RACE PROB_DYING_YR1 PROB_DYING_YR2 PROB_DYING_YR3 A 0.26 0.1 0.2 0.3 B 0.35 0.16 0.25 0.45 C 0.23 0.23 0.34 0.17 D 0.16 0.18 0.17 0.14   So the person belonging to RACE A will have a probability of survival after 3 years, call it SURV3_A = (1-0.1)*(1-0.2)*(1-0.3). Similarly, SURV3_B=(1-0.16)*(1-0.25)*(1-0.45) and so on... And a random person will have a probability of survival after 3 years as : PROB_A*SURV3_A+PROB_B*SURV3_B+PROB_C*SURV3_C+PROB_D*SURV3_D. I was thinking of a generalization to calculate probability of survival of any random person after 'T' years when there can be 'N' possible RACEs.   I can think of taking one row at a time,using CUPROD, then summing across columns which seem inefficient, so wondering if any easy way? ... View more

I need to find the most efficient and fastest way to compute : {sum over t} ({product over j} (X[j-1])*Y[t]) For a high enough j and t and with a couple of more variables in the equation this needs a lot of variables(columns) to be created which seem inefficient. I was thinking that I can define array with macro variables for X(j), Y(t) and such vars, but if it's fastest or is there a better way with PROC IML or any other trick. Any suggestions? Thanks a lot in advance.   P.S.-X, Y are numerical variables between (0,1). Also 't' varies from 1 to n and 'j' varies from 1 to 't'. ... View more