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With ${\alpha }_1=0$, the marginal moment model givesThus $e^{{\alpha }_2}$ and $e^{{\alpha }_3}$ are ratios of expected visits in Lent and Easter to expected visits in Michaelmas, respectively, at the same baseline covariates. These are population mean ratios, including students with no propensity to attend; they are not odds ratios or probabilities of any attendance.

In the hierarchical model, for a fixed positive student effect $b_i$, the conditional mean ratio is also $e^{{\alpha }_j}$. It describes a multiplicative within-student change in expected visits holding the student effect fixed. For the structural-zero component all three means are zero, so no nonzero ratio is defined there. Since the distribution of $b_i$ and the mixture weight do not vary by term, integrating out $b_i$ preserves this same marginal mean ratio. This is mean-ratio preservation under a multiplicative random effect: the conditional and marginal term coefficients coincide for this multiplicative log-link model. They need not coincide in a general nonlinear mixed model, such as a logistic random-effect model. A log-mean difference ${\alpha }_j$ is not a difference of expected logarithms of counts, which can be undefined at zero.

Within the displayed sequential response selection model, $\theta =0$ makes every attempt's nonresponse probability independent of $Y$, and hence gives the outcome-independence needed for complete-case analysis. The estimate $\hat{\theta }=0.38$ suggests that larger outcomes are associated with greater odds of an unsuccessful attempt. Per one-unit outcome increase, the fitted odds ratio is $e^{0.38}\approx 1.46$, with the reported interval transformed to $(e^{-0.06},e^{0.82})\approx (0.94,2.27)$.

The confidence interval for $\theta$ includes zero, so there is no rejection of outcome-independent missingness at the five-percent level within this model. This does not establish that the missing at random assumption is true: the interval also allows appreciable outcome dependence, and inference depends on the chosen selection model. The intercepts describe response levels, not independence from the outcome. Nor can the respondents' mean and standard deviation establish the missing outcomes' distribution. There are $300-211=89$ unobserved outcomes, about $29.7\%$, so conclusions about the full trial depend materially on the missingness assumptions.

The two independent binomial distributions give fitted event probabilities $24/99$ and $12/100$. Their odds ratio and log odds ratio areUsing the log odds ratio variance from a two-by-two table,The stated normal approximation and quantile approximately two give the confidence intervalExponentiation gives the corresponding odds ratio interval $(1.0824,5.0876)$. Its lower endpoint exceeds one.

The additional estimate points in the same direction as the pooled effect: its odds ratio is about $1.90$, compared with the original pooled value about $2.34$. Its standard error is relatively large, and the approximate two-standard-error interval for its log odds ratio is $0.64\pm 1.14=(-0.50,1.78)$, or about $(0.61,5.93)$ on the odds-ratio scale. This interval includes both no effect and the existing pooled effect. Thus it supplies compatible but imprecise evidence and would tend to pull the pooled estimate somewhat toward one while adding information. A change in the main conclusion is unlikely.

For scale, a conventional inverse-variance common-effect fit to the six rounded tabulated estimates can be updated by ${\hat{\delta }}_{\mathrm{new}}=(W\hat{\delta }+wy)/(W+w)$, with new weight $w=0.57^{-2}$. This gives the new study roughly eleven percent of the combined weight, a modest effect on the estimate. An exact update of the supplied Mantel–Haenszel pooled odds ratio needs the new two-by-two table, which the reported log ratio and standard error do not uniquely specify. The qualitative compatibility conclusion does not depend on pretending that the supplied plot used inverse-variance weights.

The preferred cumulative-response logistic model estimatesIts logit link describes conditional smoking-free probability given baseline predictors and prior successful weeks. Holding the other predictors fixed, males have $e^{-0.364092}=0.695$ times the female success odds; the reported p-value is $0.0152$, and the approximate 95% odds ratio confidence interval is $(0.518,0.932)$. Age has estimated odds ratio $e^{0.001224}=1.0012$ per additional year, with p-value $0.918$, giving little evidence for an age association in this fit.

The combined treatment has conditional success odds ratio $e^{0.327118}=1.387$ versus the reference treatment, with approximate 95% confidence interval $(1.035,1.859)$ and p-value $0.0285$. The fitted conditional odds are about 39% higher for the combined treatment. The trial randomization supports treatment comparisons, but conditioning on accumulated post-treatment outcomes means this coefficient is not directly the marginal total treatment effect.

Each previous successful week multiplies current success odds by $e^{0.399296}=1.491$, with approximate 95% confidence interval $(1.329,1.673)$ and very small p-value $1.06\times 10^{-11}$. This is strong fitted persistence. It can reflect true state dependence, unobserved heterogeneity, or an omitted calendar-time trend; this fit alone cannot distinguish them. The baseline intercept implies success probability ${\mathrm{logit}}^{-1}(-1.417630)\simeq 0.195$ for a reference-treatment female aged zero with no previous success. That age is outside the study's useful interpretation range, so the intercept chiefly anchors the regression. The Bernoulli distribution dispersion parameter is fixed at one, and the residual binomial deviance is 1122 on 995 statistical degrees of freedom. With individual binary outcomes, comparing that binomial deviance mechanically to a chi-squared distribution is not a reliable general goodness-of-fit test.