Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

Addressing missing data in the estimation of time-varying treatments in comparative effectiveness research

Comparative effectiveness research is often concerned with evaluating treatment strategies sustained over time, that is, time-varying treatments. Inverse probability weighting (IPW) is often used to address the time-varying confounding by re-weighting the sample according to the probability of treatment receipt at each time point. IPW can also be used to address any missing data by re-weighting individuals according to the probability of observing the data. The combination of these two distinct sets of weights may lead to inefficient estimates of treatment effects due to potentially highly variable total weights. Alternatively, multiple imputation (MI) can be used to address the missing data by replacing each missing observation with a set of plausible values drawn from the posterior predictive distribution of the missing data given the observed data. Recent studies have compared IPW and MI for addressing the missing data in the evaluation of time-varying treatments, but they focused on missing confounders and monotone missing data patterns. This article assesses the relative advantages of MI and IPW to address missing data in both outcomes and confounders measured over time, and across monotone and non-monotone missing data settings. Through a comprehensive simulation study, we find that MI consistently provided low bias and more precise estimates compared to IPW across a wide range of scenarios. We illustrate the implications of method choice in an evaluation of biologic drugs for patients with severe rheumatoid arthritis, using the US National Databank for Rheumatic Diseases, in which 25% of participants had missing health outcomes or time-varying confounders.

The Future of Causal Inference

The past several decades have seen exponential growth in causal inference approaches and their applications. In this commentary, we provide our top-10 list of emerging and exciting areas of research in causal inference. These include methods for high-dimensional data and precision medicine, causal machine learning, causal discovery, and others. These methods are not meant to be an exhaustive list; instead, we hope that this list will serve as a springboard for stimulating the development of new research.

A scoping review of studies using observational data to optimise dynamic treatment regimens

BACKGROUND

Dynamic treatment regimens (DTRs) formalise the multi-stage and dynamic decision problems that clinicians often face when treating chronic or progressive medical conditions. Compared to randomised controlled trials, using observational data to optimise DTRs may allow a wider range of treatments to be evaluated at a lower cost. This review aimed to provide an overview of how DTRs are optimised with observational data in practice.

METHODS

Using the PubMed database, a scoping review of studies in which DTRs were optimised using observational data was performed in October 2020. Data extracted from eligible articles included target medical condition, source and type of data, statistical methods, and translational relevance of the included studies.

RESULTS

From 209 PubMed abstracts, 37 full-text articles were identified, and a further 26 were screened from the reference lists, totalling 63 articles for inclusion in a narrative data synthesis. Observational DTR models are a recent development and their application has been concentrated in a few medical areas, primarily HIV/AIDS (27, 43%), followed by cancer (8, 13%), and diabetes (6, 10%). There was substantial variation in the scope, intent, complexity, and quality between the included studies. Statistical methods that were used included inverse-probability weighting (26, 41%), the parametric G-formula (16, 25%), Q-learning (10, 16%), G-estimation (4, 6%), targeted maximum likelihood/minimum loss-based estimation (4, 6%), regret regression (3, 5%), and other less common approaches (10, 16%). Notably, studies that were primarily intended to address real-world clinical questions (18, 29%) tended to use inverse-probability weighting and the parametric G-formula, relatively well-established methods, along with a large amount of data. Studies focused on methodological developments (45, 71%) tended to be more complicated and included a demonstrative real-world application only.

CONCLUSIONS

As chronic and progressive conditions become more common, the need will grow for personalised treatments and methods to estimate the effects of DTRs. Observational DTR studies will be necessary, but so far their use to inform clinical practice has been limited. Focusing on simple DTRs, collecting large and rich clinical datasets, and fostering tight partnerships between content experts and data analysts may result in more clinically relevant observational DTR studies.

Bisphosphonate Treatment Beyond 5 Years and Hip Fracture Risk in Older Women

IMPORTANCE

Clinical trials have demonstrated the antifracture efficacy of bisphosphonate drugs for the first 3 to 5 years of therapy. However, the efficacy of continuing bisphosphonate for as long as 10 years is uncertain.

OBJECTIVE

To examine the association of discontinuing bisphosphonate at study entry, discontinuing at 2 years, and continuing for 5 additional years with the risk of hip fracture among women who had completed 5 years of bisphosphonate treatment at study entry.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included women who were members of Kaiser Permanente Northern and Southern California, 2 integrated health care delivery systems, and who had initiated oral bisphosphonate and completed 5 years of treatment by January 1, 2002, to September 30, 2014. Data analysis was conducted from January 2018 to August 2020.

EXPOSURE

Discontinuation of bisphosphonate at study entry (within a 6-month grace period), discontinuation at 2 years (within a 6-month grace period), and continuation for 5 additional years.

MAIN OUTCOMES AND MEASURES

The outcome was hip fracture determined by principal hospital discharge diagnoses. Demographic, clinical, and pharmacological data were ascertained from electronic health records.

RESULTS

Among 29 685 women (median [interquartile range] age, 71 [64-77] years; 17 778 [60%] non-Hispanic White individuals), 507 incident hip fractures were identified. Compared with bisphosphonate discontinuation at study entry, there were no differences in the cumulative incidence (ie, risk) of hip fracture if women remained on therapy for 2 additional years (5-year risk difference [RD], -2.2 per 1000 individuals; 95% CI, -20.3 to 15.9 per 1000 individuals) or if women continued therapy for 5 additional years (5-year RD, 3.8 per 1000 individuals; 95% CI, -7.4 to 15.0 per 1000 individuals). While 5-year differences in hip fracture risk comparing continuation for 5 additional years with discontinuation at 2 additional years were not statistically significant (5-year RD, 6.0 per 1000 individuals; 95% CI, -9.9 to 22.0 per 1000 individuals), interim hip fracture risk appeared lower if women discontinued after 2 additional years (3-year RD, 2.8 per 1000 individuals; 95% CI, 1.3 to 4.3 per 1000 individuals; 4-year RD, 9.3 per 1000 individuals; 95% CI, 6.3 to 12.3 per 1000 individuals) but not without a 6-month grace period to define discontinuation.

CONCLUSIONS AND RELEVANCE

In this study of women treated with bisphosphonate for 5 years, hip fracture risk did not differ if they discontinued treatment compared with continuing treatment for 5 additional years. If women continued for 2 additional years and then discontinued, their risk appeared lower than continuing for 5 additional years. Discontinuation at other times and fracture rates during intervening years should be further studied.