Adjustment for ``super'' or ``prognostic'' composite covariates has become more popular in randomized trials recently. These prognostic covariates are often constructed from historical data by fitting a predictive model of the outcome on the raw covariates. A natural question that we have been asked by applied researchers is whether this can be done without the historical data: can the prognostic covariate be constructed or derived from the trial data itself, possibly using different folds of the data, before adjusting for it? Here we clarify that such ``within-trial'' prognostic adjustment is nothing more than a form of targeted maximum likelihood estimation (TMLE), a well-studied procedure for optimal inference. We demonstrate the equivalence with a simulation study and discuss the pros and cons of within-trial prognostic adjustment (standard efficient estimation) relative to standard TMLE and standard prognostic adjustment with historical data.
翻译:近年来,在随机试验中对“超级”或“预后”复合协变量进行调整的做法日益普遍。这些预后协变量通常通过基于历史数据拟合原始协变量对结局的预测模型来构建。应用研究者常提出的一个自然问题是:是否可以在没有历史数据的情况下完成此操作?能否直接从试验数据本身(可能使用数据的不同折迭)构建或推导预后协变量,再对其进行调整?本文阐明,此类“试验内”预后调整本质上仅是目标最大似然估计(TMLE)的一种形式,而TMLE是一种经过充分研究的最优推断方法。我们通过模拟研究证明了其等价性,并讨论了试验内预后调整(标准有效估计)相对于标准TMLE及使用历史数据的标准预后调整的优缺点。