Supplemental information.

Ben Cooper

Joshua Schiffer

eLife Assessment
This paper presents important new findings about the impact of the TAK-003 vaccine against dengue based on a convincing reanalysis of trial data. The results corroborate those of the original trial analyses, but with reduced uncertainty about the estimates of the impact of the vaccine. The findings will be of interest to clinicians, infectious disease epidemiologists, trial statisticians and policymakers seeking to understand the vaccine's efficacy profile and associated uncertainties.

anonymous

Reviewer #1 (Public review):
Summary:
The authors reduce uncertainties in TAK-003 vaccine efficacy estimates by applying a multi-level model to all published Phase III clinical trial case data and sharing parameters across strata consistent with the data generation process. In line with our current understanding of the vaccine, they show that its efficacy depends on the serostatus and infecting serotype.
Strengths:
The methodology is well-described and technically sound, with clear explanations of how the authors reduce uncertainty through the model structure. The comparison of model estimates with and without independence parameter assumptions is particularly valuable. The data come from the Phase III RCT conducted over 4.5 years in 8 countries, and the study is the first to model efficacy using available country-specific data. The analysis is timely and addresses important public health questions regarding TAK-003 efficacy.
Weaknesses:
It is unclear whether the simulation study used to validate the model sampled from the priors (as stated in the methods) or the posterior distributions. Supplementary figures 19-28 show that sampled parameters often derive from narrower distributions than the priors, with sampled areas varying by subgroup. Sampling from posterior distributions makes the validation somewhat circular. As many parameters are estimated stratified by multiple subgroups, identifiability issues may arise. Model variations with fewer parameter dependencies could impact the resulting estimates.
Assessment of aims and conclusions:
The authors achieve their aims of reducing uncertainty in efficacy estimates and show that efficacy varies by serostatus and serotype. The conclusions are well-justified, although they could be strengthened by clarifying the model validation, as discussed above.
Impact and utility:
This work contributes valuable evidence demonstrating TAK-003's serostatus and serotype-specific efficacy and highlights remaining uncertainties in the protection or risk against DENV3/4 in seronegative individuals. The methods are well-described and would be useful to other modellers, and could be applied to additional dengue vaccines like the Butantan-DV vaccine currently under development.
Additional context:
Several factors may influence the estimates but cannot be addressed using public data, including the role of subclinical infections, flavivirus cross-immunity, and the imperfect use of hospitalisation as a proxy for severe disease.

Reviewer #2 (Public review):
Summary:
In this paper, the authors used a multi-level modelling approach to reanalyse trial data from Takeda's Phase III randomised control trial investigating the efficacy of the TAK-003 vaccine against dengue. The aim of the paper is to refine uncertainty by incorporating all the available data into the model and pooling across stratifications that are correlated. A major challenge in constructing a likelihood that allows for data available at differing levels of aggregation by group and outcome, and at different time intervals. This is done by first supposing that the data is available without aggregation for all groups, outcomes and time points, and then marginalising over the aggregated levels. The model is validated using simulations and then applied to trial data from Takeda. Results appear to corroborate those of Takeda with reduced uncertainty in the estimates.
Strengths:
The main strength of the paper is the multi-level modelling approach. It is a particularly natural one for this setting. One reason for this, as discussed in the paper, is that correlations across stratifications can arise when there are similarities in their underlying causal structure. It is more realistic to model this nested data structure hierarchically. Another reason, also well discussed in the paper, is the reduction in uncertainty you get when you pool estimates across related groups. Multi-level modelling is also beneficial when group sizes are different. For example, there were too few cases of DENV-4 from seronegatives, which resulted in hospitalised disease for the original analysis to produce estimates, but by using multi-level modelling, this paper can produce estimates. The modelling framework developed in this paper will be simple to extend to further trial data collected in the future.
Another strength is that it is reanalysing existing trial data, which is both cost-effective and beneficial for scientific reproducibility. This approach also helps to assess the robustness of conclusions about the efficacy of the TAK-003 vaccine to use of different analytical methods.
The paper is well-written. The tables and figures presented in this paper are particularly informative. Protection conferred by the vaccine varies depending upon which variant a person is exposed to, their serostatus, and time since vaccination. The analysis presented supports the discussed conclusions. Comparisons between the results of this paper and the results of the original trial analysis are also shown and demonstrate a reduction in the uncertainty of parameter estimates, as desired.
Weaknesses:
The weakness of the paper is that it reports per-exposure protection instead of vaccine efficacy. This is methodologically sound, but it does limit the comparability of this study with the original trial analyses, which reported vaccine efficacy. It is therefore unclear whether the reduction in uncertainty observed is due solely to the multi-level modelling approach or whether it may be due in part to the parameters of interest being slightly different.

Reviewer #3 (Public review):
Summary:
The authors provide estimates of the efficacy of the dengue vaccine, which is notoriously complex given the different serotypes and complex immunity. Through their method using publicly available data, the estimates have less uncertainty and are of use to the field in understanding the future possible impact of the vaccine.
Strengths:
This is an elegant analysis addressing an important question. The pooling of common factors for estimation is nice and adds strength to the analysis. It is an important analysis for the field and our understanding of the vaccine, and for the analysis of future multi-site trials for the dengue vaccine.
Weaknesses:
It would be useful to have more understanding of how the way the vaccine efficacy is defined here is related to the previous estimates and a greater understanding of how the estimated impact changes over time.

Refining uncertainty about the TAK-003 dengue vaccine with a multi-level model of clinical efficacy trial data

Figures and data

Data availability by outcome, group, and time intervals.

Per-exposure protection against non-hospitalized disease over time, denoted as PEP_{o_d,s,e}(t), for each of the four serotypes (panels) by baseline serostatus (colors).

Per-exposure protection against hospitalized disease over time, PEP_{o_h,s,e}(t), for each of the four serotypes (panels) by baseline serostatus (colors).

Estimates of protection conferred by the vaccine as per-exposure protection estimated by the full model (circle), efficacy reported by the trial (triangle), and efficacy estimated with the simplified independent-parameter model (square).

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Figures and data

Data availability by outcome, group, and time intervals.

Per-exposure protection against non-hospitalized disease over time, denoted as PEPo_d,s,e(t), for each of the four serotypes (panels) by baseline serostatus (colors).

Per-exposure protection against hospitalized disease over time, PEPo_h,s,e(t), for each of the four serotypes (panels) by baseline serostatus (colors).

Estimates of protection conferred by the vaccine as per-exposure protection estimated by the full model (circle), efficacy reported by the trial (triangle), and efficacy estimated with the simplified independent-parameter model (square).

Per-exposure protection against non-hospitalized disease over time, denoted as PEP_{o_d,s,e}(t), for each of the four serotypes (panels) by baseline serostatus (colors).

Per-exposure protection against hospitalized disease over time, PEP_{o_h,s,e}(t), for each of the four serotypes (panels) by baseline serostatus (colors).