BACKGROUND Populations willing to participate in randomized trials may not correspond well to policy-relevant target populations. Evidence of effectiveness that is complementary to randomized trials may be obtained by combining the 'target trial' causal inference framework with whole-of-population linked administrative data. METHODS We demonstrate this approach in an evaluation of the South Australian Family Home Visiting Program, a nurse home visiting programme targeting socially disadvantaged families. Using de-identified data from 2004-10 in the ethics-approved Better Evidence Better Outcomes Linked Data (BEBOLD) platform, we characterized the policy-relevant population and emulated a trial evaluating effects on child developmental vulnerability at 5 years (n = 4160) and academic achievement at 9 years (n = 6370). Linkage to seven health, welfare and education data sources allowed adjustment for 29 confounders using Targeted Maximum Likelihood Estimation (TMLE) with SuperLearner. Sensitivity analyses assessed robustness to analytical choices. RESULTS We demonstrated how the target trial framework may be used with linked administrative data to generate evidence for an intervention as it is delivered in practice in the community in the policy-relevant target population, and considering effects on outcomes years down the track. The target trial lens also aided in understanding and limiting the increased measurement, confounding and selection bias risks arising with such data. Substantively, we did not find robust evidence of a meaningful beneficial intervention effect. CONCLUSIONS This approach could be a valuable avenue for generating high-quality, policy-relevant evidence that is complementary to trials, particularly when the target populations are multiply disadvantaged and less likely to participate in trials.

中文翻译：

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使用关联的管理数据模拟政策相关人群中密集护士家访的目标试验。


背景愿意参加随机试验的人群可能与政策相关的目标人群不太相符。通过将“目标试验”因果推理框架与整体人群相关的管理数据相结合，可以获得与随机试验互补的有效性证据。方法 我们在对南澳大利亚家庭家访计划的评估中展示了这种方法，该计划是一项针对社会弱势家庭的护士家访计划。我们使用道德批准的更好证据更好结果关联数据 (BEBOLD) 平台中 2004-10 年间的去识别化数据，描述了政策相关人群的特征，并模拟了一项评估 5 年儿童发育脆弱性影响的试验 (n = 4160)以及 9 岁时的学业成绩 (n = 6370)。通过与 7 个健康、福利和教育数据源的链接，可以使用 SuperLearner 的目标最大似然估计 (TMLE) 来调整 29 个混杂因素。敏感性分析评估了分析选择的稳健性。结果我们展示了如何将目标试验框架与相关的管理数据结合使用，以在政策相关目标人群的社区实践中生成干预措施的证据，并考虑对未来几年结果的影响。目标试验镜头还有助于理解和限制此类数据带来的测量、混淆和选择偏差风险的增加。实质上，我们没有发现有力的证据表明干预措施具有有意义的有益效果。 结论 这种方法可能是生成高质量、与政策相关的证据的宝贵途径，这些证据与试验相辅相成，特别是当目标人群处于多重不利地位且不太可能参与试验时。