Markov Model
A Markov model is a state-transition model in which a cohort or individuals move among mutually exclusive health states over repeated cycles, with future transitions determined by the current state under the model’s memory assumptions.
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Martingale Residual
A residual used to diagnose the fit of a Cox model, checking the functional form of continuous covariates and identifying influential observations.
Maximin Criterion
A social welfare decision rule judging an allocation by the wellbeing of the worst-off group, seeking to maximise that minimum level.
Maximum Likelihood Estimation
A method estimating a model's parameters by finding the values that make the observed data most probable.
Maximum Likelihood Fitting
The process of applying maximum likelihood estimation to determine the parameter values of a chosen model that best fit observed data.
Mean
A central tendency measure calculated as the sum of a set of values divided by the number of values.
Mean Absolute Error
A measure of average prediction error magnitude between predicted and actual values, calculated without regard to whether errors are positive or negative.
Mean Squared Error
A measure of the average squared difference between predicted and actual values, penalising larger errors more heavily due to squaring.
Mechanism Design
A branch of economic theory concerned with designing rules, such as auctions or payment systems, that produce good outcomes despite strategic, privately informed participants.
Median
A central tendency measure representing the middle value of an ordered dataset, dividing it into two equal halves.
Mediation Analysis
A statistical approach examining whether an exposure's effect on an outcome operates through an intermediate variable, called a mediator, rather than directly.
Memorylessness
The defining property of a Markov process that future transition probabilities depend only on the present state, with no influence from the path taken.
Meta-Regression Transfer
A form of benefit transfer in which a regression model estimated across many original studies predicts a value for a new context based on its characteristics.
Method of Moments
A technique estimating a distribution's parameters by equating sample moments, such as mean and variance, to their theoretical population equivalents.
Metropolis-Hastings
A Markov chain Monte Carlo algorithm generating samples by proposing candidate values and accepting or rejecting them by a rule ensuring convergence.
Micro-Costing
A detailed bottom-up costing method that identifies and values every individual resource consumed, such as each minute of staff time.
Microsimulation
A simulation technique modelling individual entities separately through time based on their own characteristics and randomly sampled events, with results emerging from the total.
Missing at Random
A missing data classification indicating the probability of a value being missing depends on other observed variables, not the missing value itself.
Missing Completely at Random
A missing data classification indicating the probability of missingness is entirely unrelated to any observed or unobserved variables, the strongest assumption.
Missing Not at Random
A missing data classification indicating the probability of missingness depends on the unobserved value itself, even after accounting for other variables.