On the aggregation of published prognostic scores for causal inference in observational studies

Abstract

As real world evidence on drug efficacy involves nonrandomized studies, statistical methods adjusting for confounding are needed. In this context, prognostic score (PGS) analysis has recently been proposed as a method for causal inference. It aims to restore balance across the different treatment groups by identifying subjects with a similar prognosis for a given reference exposure (“control”). This requires the development of a multivariable prognostic model in the control arm of the study sample, which is then extrapolated to the different treatment arms. Unfortunately, large cohorts for developing prognostic models are not always available. Prognostic models are therefore subject to a dilemma between overfitting and parsimony; the latter being prone to a violation of the assumption of no unmeasured confounders when important covariates are ignored. Although it is possible to limit overfitting by using penalization strategies, an alternative approach is to adopt evidence synthesis. Aggregating previously published prognostic models may improve the generalizability of PGS, while taking account of a large set of covariates—even when limited individual participant data are available. In this article, we extend a method for prediction model aggregation to PGS analysis in nonrandomized studies. We conduct extensive simulations to assess the validity of model aggregation, compared with other methods of PGS analysis for estimating marginal treatment effects. We show that aggregating existing PGS into a “meta‐score” is robust to misspecification, even when elementary scores wrongfully omit confounders or focus on different outcomes. We illustrate our methods in a setting of treatments for asthma.

Establishment of a Nomogram-Based Prognostic Model (LASSO-COX Regression) for Predicting Progression-Free Survival of Primary Non-Small Cell Lung Cancer Patients Treated with Adjuvant Chinese Herbal Medicines Therapy: A Retrospective Study of Case Series

“…And Song et al team applied LASSO to establishing prognostic model for predicting personalized PFS of PNSCLC patients with EGFR tyrosine kinase inhibitors therapy (33). Whereas, a sequence of restrictions hindering LASSO from more frequently precise modeling may not be ignored: (1) with achieving parsimony towards vital variables' coefficients, the result of LASSO regression is undoubtedly biased estimates due to constraint parameter entered (34,35); (2) without more prior knowledge about their structural sparsity, it seems reasonable that every variable's coefficient has equal chance of exact shrinkage of all to zero, but a variable with an accurate zero is unlikely to occur in actually most cases (36); (3) despite achieving parsimony, seriously speaking, LASSO is not good at addressing variables with multilabel classification and multi-collinearity, whose coexisting or unexclusive property of interaction for prediction is outside the scope of its typical features' selection (37). Interestingly, some researches focused on sIL-2R and CA724 that could provide several clues to our further study: ( Our study is in need of a serious and an objective interpretation because of a couple of limitations and strengths.…”

EA3: A softmax algorithm for evidence appraisal aggregation

“…However, many researchers have expressed concerns related to data quality, validity, reliability and sensitivity to capture the exposure, adverse effects and outcomes of interest when using RWE [18][19][20][21][22]. Using RWE for medical inference presents methodological challenges [23], though some efforts have been carried out to efficiently merge evidence coming from RCTs and observational studies [24][25][26], also for causal inference purposes [27,28]. Attempts to provide a framework for appraising the quality of evidence for medical inference have been going on since long before the current debate on uses of RWE began, e.g.…”

On the relevance of prognostic information for clinical trials: A theoretical quantification

“…In clinical practice, prognostic scores aim to provide a tool for risk stratification, for example for clinical behaviour of a disease (e.g., in prostate cancer, Kreuz et al 2020) or in the intensive care unit (e.g., the APACHE or FOUR scores, Knaus et al 1991;Wijdicks et al 2005). Statistical methods relying on prognostic scores (i.e., disease risk scores for binary outcomes), are widely employed for observational studies (Nguyen et al 2020;Aikens et al 2020;Wyss et al 2016;Ray 2011, 2009) and have since also found application in clinical trials, e.g., for stratification (Cellini et al 2019;Hurwitz et al 2018;Herrera et al 2020;Saffi et al 2014) or covariate adjustment (Schuler et al 2021;Branders et al 2021).…”