Patent Document ID: 9811504
Application ID: 15607598
Patent Flag: 1

Claim One:
1. A method for including and distinguishing lifespan in multivariable binary regression analysis of one of mortality and survivorship, said method comprising: at least one of obtaining and creating data about N individuals i at respective J situations j, said data including variable Y ij and at least two variables X vij ; conducting a multivariable binary regression analysis of said data, analyzing the relationship between a dependent variable Y ij and independent variables X vij for one of Y ij =M ij and Y ij =S ij , said analyzing utilizing model n ij =β 0 +β v X vij +. .. +β W X Wij and binary link function B(n ij ); effectuating said multivariable binary regression analysis into yielding at least one result of said analysis; wherein at least one of the steps is carried out by a computer; and wherein: i=1:N, indicating that i are sequential positive integers 1 through N; i denotes an individual; N denotes the total number of individuals i in said data; j=1:J, indicating that j are sequential positive integers 1 through J; j denotes a situation of individual i in reference to said variables X vij and Y ij ; J denotes the total number of situations of an individual i in said data, allowing distinct J for distinct individuals i; M ij denotes the mortality status of individual i at situation j; S ij denotes the survivorship status of individual i at situation j; Y ij ε{0,1}, indicating that Y ij is a binary variable that adopts one of values 0 and 1 for one of Y ij =M ij and Y ij =S ij of individual i at situation j; v=1:W, indicating that v are sequential positive integers 1 through W; W≧2; v denotes an index of each of the following: variables X v , variables X vij of individual i at situation j, and coefficients β v ; W denotes the following: total number of variables X v , total number of variables X vij of individual i at situation j, and total number of coefficients β v ; X vij denotes a variable X v of individual i at situation j; r=1:R, indicating that r are sequential positive integers 1 through R; R≧2; W≧R; r denotes an index of each of the following: variables K r and variables K rij ; K r denotes a variable that directly denotes a distinct phenomenon, and R respective variables K r respectively directly denote R distinct phenomena; K rij denotes a variable K r of individual i at situation j; T denotes a transformation function, allowing identity transformation; q=1:Q, indicating that q are sequential positive integers 1 through Q; X v =T q (K r ), indicating that X v is a transformation T q of variable K r that directly denotes a specific phenomenon, further indicating that X v indirectly denotes said specific phenomenon; X vij =T q (K rij ), indicating that X vij is a transformation T q of K rij that directly denotes a specific phenomenon, further indicating that X vij indirectly denotes said specific phenomenon; Q denotes the total number of transformations T q (K r ) for a specific K r , and Q also denotes the the total number of transformations T q (K rij ) for a specific K rij of individual i at situation j, allowing distinct Q for distinct variables K r , and allowing distinct Q for distinct variables K rij of individual i at situation j; R denotes the following: the total number of variables K r , the total number of variables K rij of individual i at situation j, the total number of phenomena denoted by variables K r , and the total number of phenomena denoted by variables K rij of individual i at situation j; A directly denotes age; L directly denotes lifespan; K 1 =A and K 2 =L, indicating that one of at least two variables K r directly denotes age, and indicating that another of said at least two variables K r directly denotes lifespan; K 1ij =A ij and K 2ij =L ij , indicating that one of at least two variables K rij directly denotes the age of individual i at situation j, and indicating that another of said at least two variables K rij directly denotes the lifespan of individual i at situation j; β denotes a regression coefficient; and β 0 denotes the regression coefficient for the intercept, allowing β 0 to be one of the following: estimated, suppressed, and user-provided.