Past exam of the mathematics course of the University of Cambridge / 2003 / iii / Paper 40 / 4

Use individual stratified randomization within each prison, with concealed centrally generated permuted-block randomization and varying block sizes. Prison can affect both baseline risk and intervention delivery, so balancing assignments within prison protects against imbalance in that important covariate. Varying block sizes and allocation concealment prevent staff from predicting the next allocation and selecting participants accordingly. Prespecify how incomplete final blocks are treated. To enforce the overall 4000:4000 allocation exactly, coordinate the stratum totals centrally, allocating odd final places through a concealed random rule; exact half allocation within every prison is impossible if some prison totals are odd.

Individual randomization stratified by prison is preferable to randomizing only twenty whole prisons for this design. A cluster-randomized trial would have few independent units and could lose substantial precision through intraclass correlation. Sharing or selling the intervention can cause treatment contamination, so record it and retain an intention-to-treat analysis; substantial spillover might justify a separately powered cluster design rather than being ignored.

Use a conventional two-sided test at significance level $0.05$. Let $n=4000$ per arm, $p_C=0.005$, $p_T=0.0025$ and $\Delta =p_C-p_T=0.0025$. Under the specified alternative, the normal approximation to the difference of independent binomial proportions has standard deviationFor planning the pooled null standard error, use $\bar{p}=(p_C+p_T)/2=0.00375$, giving ${\sigma }_0=\sqrt{2\bar{p}(1-\bar{p})/n}=0.00136674$. The rejection threshold is approximately $1.96{\sigma }_0=0.00267880$, exceeding the mean alternative difference. With $\Phi$ the standard normal distribution function, the approximate statistical power isNo: the usual two-sided 5% calculation gives about 45% power, below 50%. The expected death counts are only 20 and 10, so an exact discrete design calculation could refine this approximation.

The question does not specify the tail convention. If a beneficial direction is prespecified and a one-sided test at 5% is justified, replace $1.96$ by approximately $1.645$; the resulting statistical power is about $0.573$, and the answer is then yes. This change of testing convention must be declared.

Assume individuals who immediately sell the intervention retain the untreated risk $0.005$, while those who keep it have the posited halved risk $0.0025$. The law of total expectation gives the expected count per thousand assigned to treatment:The assignment risk is therefore $0.003$. This is an intention-to-treat mixture under the stated adherence-specific risks, not an assertion that receipt or sale is randomized. It also assumes that selling does not itself change the seller's baseline risk and that effects on recipients outside this group do not alter this calculation.

For equal allocation, let $n$ be the required sample size per arm, $p_C=0.0045$, $p_T=0.003$ and $\Delta =0.0015$. In the standard sample size for comparing two proportions calculation, a two-sided test at 5% with 80% statistical power requiresUsing $z_{0.975}=1.95996$ and $z_{0.8}=0.84162$ gives $n\simeq 26063.64$, which is rounded upward for actual recruitment. ThusSince the risks are small, dropping the $(1-p)$ corrections and using $1.96+0.84$ gives $N_{\mathrm{total}}\simeq 2(2.8{)}^2(0.0075)/(0.0015{)}^2=52266.7$, conventionally about 52.3 thousand. These are slightly different approximations to the same planning requirement. Add an allowance if outcome ascertainment is incomplete or the allocation is clustered. A prespecified one-sided test would need a different, smaller sample size; the calculation here uses the conventional two-sided interpretation consistently with part (b).

Two useful questions separate intervention retention from actual opportunities to use it.

Responses can explain implementation, but cannot replace the randomized intention-to-treat analysis. Reincarcerated survivors are a selected subgroup, and recall errors and missing follow-up may affect their reports.