PHARMACOECONOMIC EVALUATIONS & METHODS MARKOV MODELING IN DECISION ANALYSIS FROM THE PHARMACOECONOMICS ON THE INTERNET ®SERIES ©Paul C Langley 2004 Maimon Research LLC

OBJECTIVES To describe the place of Markov models in pharmacoeconomic analysis To provide an introduction to Matrix methods To illustrate the steps required to set up a Markov model To consider the limitations of Markov models

THE PLACE OF MARKOV MODELS Markov models represent a variant of decision analysis for pharmacoeconomic evaluations where the treatment pathways and options may be both complex and repetitive Markov models can also be used in situations where prevalence as well as incidence based impact assessments are required.

TYPES OF MARKOV MODEL In representing complex decision processes in simple and convenient mathematical form, we can use two types of Markov model: –regular Markov chain models –absorbing Markov chain models

ABSORBING MARKOV CHAINS These are the most widely used form of Markov model in pharmacoeconomics They are used to estimate time spent by a patient group in a particular disease state when all patients eventually leave that disease state by recovery or death They can be used to estimate both time spent in a disease state or time spent in a budget period

MATRIX ALGEBRA The principal obstacles to utilizing Markov models are the lack of familiarity with the rules of matrix manipulation and their application to Markov processes These rules are summarized in the downloadable text which includes a brief introduction to Markov processes and the mathematics of absorbing Markov chains

WHAT DO I NEED TO KNOW? What are matrices and vectors? What are the rules of matrix manipulation? What is an identity matrix? What is matrix inversion? What is a transition matrix? What is a fundamental matrix?

MATRICES AND VECTORS Matrices and vectors are arrays or ordered collections of real numbers Vectors, which can be row vectors or column vectors, are designated by lower bold case (i.e., u = [ 5 6 7] is a row vector with three real numbers as components) Matrices are rectangular or square arrays of real numbers designated by upper case bold letters (i.e., N or I or Q)

MATRIX NOTATION When we describe a matrix it is in terms of the number of rows (the i th row) and the number of columns (the j th column) We can describe matrices in terms of their size by using i and j (i.e., size i x j), where a square matrix is a special case of i = j Row vectors would be written (1 x j) and column vectors (i x 1)

IDENTITY MATRIX In order to manipulate matrices and apply Markov models we require a special type of square matrix which we call an identity matrix By construction an identity matrix has ones in its leading diagonal and zeros everywhere else

MATRIX ADDITION AND SUBTRACTION In standard arithmetic we can add and subtract one number from another In matrix manipulation, where we are dealing with arrays of numbers, we can similarly add or subtract one matrix from another by operating on the corresponding components However, to do addition and subtraction matrices must be of the same size

MATRIX MULTIPLICATION The process of matrix multiplication is somewhat more complex Note also that we don’t require matrices to be of the same size or order but they must follow the rule where we have two matrices (m x n) and (n x k) we can only multiply if n is common, i.e., (m x n)(n x k) = (m x k) which is a product matrix of size (m x k) The mechanics of multiplication involves combining row and column components

OPERATIONS NOTATION Addition: A + B = C Subtraction: A - B = D Multiplication: AB = E but note that multiplication is not commutative but depends upon the order of multiplication (as a result of the rules of matrix multiplication Hence we use the terms pre-multiplication and post-multiplication so that BA  E

MATRIX DIVISION There is no division as such in matrix algebra, we cannot divide the components in one matrix by the components in another Rather, we talk about multiplying by a matrix which is the inverse of that matrix We talk about an operation analogous to division: if we have two matrices A and B and we are told B is the product of A and come unknown matrix X, where AX = B, then we solve for X

SOLUTION To perform our operation and solve for X we pre-multiply by the inverse of A to give A -1 AX = A -1 B since A -1 A = I (by construction) and IX = X then we have X = A -1 B (providing A has an inverse)

TRANSITION MATRIX The only other element of matrix algebra we require is to define a transition matrix This is a square matrix where the components are non-negative real numbers expressed as probabilities and the sum of each row is equal to unity Usually denoted by P

MARKOV CHAINS There are two types of Markov chain which are of interest in pharmacoeconomic modeling and which will now consider: regular Markov chains absorbing Markov chains

REGULAR MARKOV CHAIN Regular Markov chains illustrate an interesting property of the behavior of transition matrices If we multiply the transition matrix P by itself we get the probability of being in a particular state after 2 periods (P x P = P 2 ) Eventually P n converges to a fixed point matrix solution (a W matrix) where each of the rows w is an identical probability vector

ABSORBING MARKOV CHAINS Our principal interest is in absorbing Markov chains There are three steps in applying absorbing Markov chains to a health care decision problem –identifying mutually exclusive treatment states –specifying a transition matrix for these states –solving for the fundamental matrix of the absorbing Markov chain

TREATMENT STATES Defining mutually exclusive and exhaustive disease states through which a patient might move relies upon the clinical knowledge of the analyst These are most easily thought of as treatment stages which eventually result in the patient leaving the system via death or recovery, although the patient does not have to move through each of them and some transitions may be barred

TRANSITION PROBABILITIES Each row of the transition matrix summarizes the probability of persons moving between treatment states Transition probabilities are defined for a fixed time interval (e.g., month, quarter) The rows sum to one including the probability of moving to the absorbing state Matrix manipulation focuses on the square transition matrix hence everyone eventually leaves the system

FUNDAMENTAL MATRIX The part of the transition matrix that is manipulated to derive the fundamental matrix (denoted N) is called the Q matrix N is solved as the solution to an infinite series I + Q + Q 2 + Q 3 + or as N = (I - Q) -1 Each element of the N matrix is the mean number of times that the chain is in state a j given it started in state a i

APPLICATION (I) This final property of the fundamental matrix means that if we add up all of the elements in any row of the matrix, multiplying this by the fixed time interval, we get the survival time of patients who entered the system in this row state Hence the fundamental matrix generates survival times and time spent in each treatment state

APPLICATION (II) Rather than solve for the fundamental matrix we can take any number of the elements of the geometric sequence and generate time spent in treatment states over a given time period This means that the absorbing Markov process can generate both incidence and prevalence based estimates

APPLICATION (III) In order to apply the absorbing Markov chain to the impact of new therapies all we need to do is to vary the transition probabilities (while maintaining consistency in the probabilities) Hence we can compare new drug impacts on time spent in treatment states

APPLICATION (IV) Estimates of time spent in treatment states are the basis for costs and outcomes impact assessments If we estimate resources and costs used to support fixed interval treatment times in the various treatment states we can estimate treatment costs for the time spent in each If we have outcome measures (e.g., QALYs) for each state we can estimate outcomes in QALY terms

OVERVIEW Markov models are potentially a major contribution to decision analysis Even so, they still embody assumptions of constant cost and complete therapy switching They are very demanding of data and are difficult to populate