Past exam of the mathematics course of the University of Cambridge / 2016 / iii / Paper 207 / 2

Use three states of a continuous-time Markov chain: disease-free $1$, pre-clinical $2$, and clinical $3$. The transition intensities are $\lambda =q_{12}>0$ for onset and $\nu =q_{23}>0$ for progression. Clinical disease is an absorbing state. The mandatory pre-clinical phase excludes a direct $1\to 3$ jump, and this untreated progression model has no reverse transitions.

With row-vector probabilities and state order $(1,2,3)$, the generator matrix isIts rows sum to zero, so only the two off-diagonal transition intensities are unknown. This is a continuous-time multi-state model with sequential irreversible progression.

Starting in state $1$, the onset waiting time has the exponential distribution of rate $\lambda$. Thus the probability of onset before the next screen is $1-e^{-\lambda t}$. This counts individuals who have already progressed to clinical disease as well as those still pre-clinical.

Conditional on onset at time $u<t$, the Markov property makes the remaining progression time exponential with rate $\nu$. Thus the probability of clinical progression before the screen, conditional on that onset time, is $1-e^{-\nu (t-u)}$.

Integrating over the onset density gives the unconditional clinical probabilityThe probability of being pre-clinical at the next screen is insteadTogether with $P_{11}(t)=e^{-\lambda t}$ and the exponential distribution for a patient initially in state $2$, the transition matrix isThis equals the matrix exponential $e^{Qt}$. The assumed unequal intensities avoid a zero denominator; at $\lambda =\nu$ the continuous limit is $P_{12}(t)=\lambda t{e}^{-\lambda t}$.

Assume the 1000 individuals are initially disease-free. The event being counted is onset during the ten-year period, not occupancy of the pre-clinical state at its end. Using the estimated transition intensity as a constant onset rate and linearity of expectation, the expected number of onsets isThe first-order approximation $1000\times 0.005\times 10=50$ is close, but does not account for depletion of the disease-free group.

Let $A_i(s)$ be age in years at follow-up time $s$ and let $G_i\in \{0,1\}$ indicate the genetic risk factor. An age-dependent log-linear transition intensity model isHere $e^{{\theta }_0}$ is the onset baseline hazard for a 50-year-old without the factor; $e^{{\theta }_G}$ is the genetic hazard ratio, and $e^{{\theta }_A}$ is the hazard ratio per additional year of age. A smooth function of age could replace the linear age term if a constant age hazard ratio were unsuitable.

Under the stated constant multiplicative hazard ratios, the requested extrapolation is therefore the baseline rate times both multipliers:

Age changes during follow-up, so the generator matrix $Q(s)$ changes with time. The transition matrix must solve ${\partial }_{t_2}P(t_1,t_2)=P(t_1,t_2)Q(t_2)$, with $P(t_1,t_1)=I$. It is generally not the matrix exponential of one constant matrix; even $\exp (\int Q(s)ds)$ is not generally valid because the matrices at different times need not commute.

For example, write the age-dependent onset rate as $\lambda (s)$ and keep progression rate $\nu$ constant. ThenEven when the integrated onset rate is elementary, this remaining integral need not have an elementary closed form. Special cases can have closed forms; time dependence does not by itself prove universal impossibility.

For a tractable likelihood function, freeze age at its left-endpoint value, or a midpoint value, on each observation interval $[t_j,t_{j+1})$. The genetic covariate is already constant. This piecewise-constant covariate approximation gives a constant matrix $Q_j$ on each interval and a product of closed-form interval probabilities:Here $x_j$ is the observed state at $t_j$, and the likelihood function is conditional on the initial state. The approximation improves when intervals are short relative to the variation in the age effect.

Let $ϵ$ be the false positive rate for a genuinely cancer-free person, encompassing the harmless-tumour misclassification, and assume screening errors are conditionally independent given the latent states. A pre-clinical case tests positive with probability one; a clinical case is separately recognized. Thus a negative screen identifies latent state $1$, whereas a positive pre-clinical screen can arise from state $1$ or $2$. This is a Hidden Markov model with a screening misclassification model.

Conditional on the negative baseline screen, the state at year $0$ is $1$. The negative screen at year $2$ requires staying in state $1$ for two years and then testing negative. From that state, the positive screen at year $4$ can arise either with probability $ϵ$ while still disease-free or through pre-clinical disease. Consequently the conditional likelihood isIf the study instead specifies an initially disease-free patient and includes the baseline screening result in the likelihood function, its probability contributes a further factor $1-ϵ$, givingThe formulas use the positive emission for a pre-clinical screen, with no clinical diagnosis in this record. A persistent harmless-tumour subtype producing correlated screening errors would need an additional latent-state or emission specification; its likelihood is not determined by the single parameter $ϵ$.