Assessing heterogeneous effects and their determinants via estimation of potential outcomes

Sociodemographic differences in the response to changes in COVID-19 testing guidelines

During the coronavirus disease 2019 (COVID-19) pandemic, Sweden emphasized voluntary guidelines over mandates. We exploited a rapid change and reversal of the Public Health Agency of Sweden's COVID-19 testing guidelines for vaccinated and recently infected individuals as a quasi-experiment to examine sociodemographic differences in the response to changes in pandemic guidelines. We analyzed daily polymerase chain reaction tests from 1 October 2021 to 15 December 2021, for vaccinated or recently infected adults (≥20 years; n = 1 596 321) from three Swedish regions (Stockholm, Örebro, and Dalarna). Using interrupted time series analysis, we estimated abrupt changes in testing rates at the two dates when the guidelines were changed (1 November and 22 November). Stratified analysis and meta-regression were employed to explore sociodemographic differences in the strength of the response to the guideline changes. Testing rates declined substantially when guideline against testing of vaccinated and recently infected individuals came into effect on 1 November [testing rate ratio: 0.50 (95% confidence interval, CI 0.41, 0.61)], and increased again from these lowered levels by a similar amount upon its reversal on 22 November [testing rate ratio: 2.19 (95% CI: 1.69, 2.85)]. Being Sweden-born, having higher household income, or higher education, were all associated with a stronger adherent response to the guideline changes. Adjusting for stratum-specific baseline testing rates and test-positivity did not influence the results. Our findings suggest that the population was responsive to the rapid changes in testing guidelines, but with clear sociodemographic differences in the strength of the response.

Jointly pooling aggregated effect sizes and their standard errors from studies with continuous clinical outcomes

The DerSimonian-Laird (DL) weighted average method for aggregated data meta-analysis has been widely used for the estimation of overall effect sizes. It is criticized for its underestimation of the standard error of the overall effect size in the presence of heterogeneous effect sizes. Due to this negative property, many alternative estimation approaches have been proposed in the literature. One of the earliest alternative approaches was developed by Hardy and Thompson (HT), who implemented a profile likelihood instead of the moment-based approach of DL. Others have further extended this likelihood approach and proposed higher-order likelihood inferences (e.g., Bartlett-type corrections). In addition, corrections factors for the estimated DL standard error, like the Hartung-Knapp-Sidik-Jonkman (HKSJ) adjustment, and the restricted maximum likelihood (REML) estimation have been suggested too. Although these improvements address the uncertainty in estimating the between-study variance better than the DL method, they all assume that the true within-study standard errors are known and equal to the observed standard errors of the effect sizes. Here, we will treat the observed standard errors as estimators for the within-study variability and we propose a bivariate likelihood approach that jointly estimates the overall effect size, the between-study variance, and the potentially heteroskedastic within-study variances. We study the performance of the proposed method by means of simulation, and compare it to DL (with and without HKSJ), HT, their higher-order likelihood methods, and REML. Our proposed approach seems to have better or similar coverages compared to the other approaches and it appears to be less biased in the case of heteroskedastic within-study variances when this heteroskedasticty is correlated with the effect size.

A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial

Personalizing treatment recommendations or guidelines requires evidence about the heterogeneity of treatment effects (HTE). Machine-learning (ML) approaches can explore HTE by considering many covariates, including complex interactions between them. Causal ML approaches can avoid overfitting, which arises when the same dataset is used to select covariate by treatment interaction terms as to make inferences and reduce reliance on the correct specification of fixed parametric models. We investigate causal forests (CF), a ML method based on modified decision trees that can estimate subgroup- and individual-level treatment effects, without requiring correct prespecification of the effect model. We consider CF alongside parametric approaches for estimating HTE, within the 65 Trial, which evaluates the effect of a permissive hypotension strategy versus usual care on 90-d mortality for critically ill patients aged 65 y or older with vasodilatory hypotension. Here, the CF approach provides similar estimates of treatment effectiveness for prespecified and post hoc subgroups to the parametric approach, and the results of a test for overall HTE show weak evidence of heterogeneity. The CF estimates of individual-level treatment effects, the expected effects of treatment for individuals in subpopulations defined by their covariates, suggest that the permissive hypotension strategy is expected to reduce 90-d mortality for 98.7% of patients but with 95% confidence intervals that include zero for 71.6% of patients. A ML approach is then used to assess the patient characteristics associated with these individual-level effects, and to help target future research that can identify those patient subgroups for whom the intervention is most effective.

Heterogeneous treatment effect analysis based on machine-learning methodology

Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE‐informed understanding can critically guide physicians to individualize the medical treatment for a certain disease. However, HTE analysis has not been widely recognized and used, even given the explosive increase of data availability attributed to the arrival of the Big Data era. Part of the reason behind its underuse is that data are often of high dimension and high complexity, which pose significant challenges for applying conventional HTE analysis methods. To meet these challenges, a newly developed causal forest HTE method has been derived from the random forest machine‐learning algorithm. We conducted a systematic performance evaluation for the causal forest method against the conventional two‐step method by simulating scenarios with different levels of complexity for the analysis. Our results show that causal forest outperforms the conventional HTE method in assessing treatment effect, especially when data are complex (e.g., nonlinear) and high dimensional, suggesting that causal forest is a promising tool for real‐world applications of HTE analysis.

Using causal forests to assess heterogeneity in cost-effectiveness analysis

We develop a method for data-driven estimation and analysis of heterogeneity in cost-effectiveness analyses (CEA) with experimental or observational individual-level data. Our implementation uses causal forests and cross-fitted augmented inverse probability weighted learning to estimate heterogeneity in incremental outcomes, costs and net monetary benefits, as well as other parameters relevant to CEA. We also show how the results can be visualized in relevant ways for the analysis of heterogeneity in CEA, such as using individual-level cost effectiveness planes. Using a simulated dataset and an R package implementing our methods, we show how the approach can be used to estimate the average cost-effectiveness in the entire sample or in subpopulations, explore and analyze the heterogeneity in incremental outcomes, costs and net monetary benefits (and their determinants), and learn policy rules from the data.

Cardiovascular Risk Factors and Social Development Index

Cardiovascular diseases (CVD) are the leading causes of morbidity and mortality worldwide. The complex etiology of CVD is known to be significantly affected by environmental and social factors. There is, however, a lag in our understanding of how population level components may be related to the onset and severity of CVD, and how some indicators of unsatisfied basic needs might be related to known risk factors. Here, we present a cross-sectional study aimed to analyze the association between cardiovascular risk factors (CVRF) and Social Development Index (SDI) in adult individuals within a metropolitan urban environment. The six components of SDI as well as socioeconomic, anthropometric, clinical, biochemical, and risk behavior parameters were explored within the study population. As a result, several CVRF (waist circumference, waist-to-height ratio, body mass index, systolic blood pressure, glucose, lower high-density lipoprotein cholesterol, triglycerides, and sodium) were found in a higher proportion in the low or very low levels of the SDI, and this pattern occurs more in women than in men. Canonical analysis indicates a correlation between other socioeconomic features and anthropometric, clinical, and biochemical factors (canonical coefficient = 0.8030). Further studies along these lines are needed to fully establish how to insert such associations into the design of health policy and interventions with a view to lessen the burden of cardiovascular diseases, particularly in metropolitan urban environments.

Objectives, design and main findings until 2020 from the Rotterdam Study

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.