A potential outcomes approach to defining and estimating gestational age-specific exposure effects during pregnancy

Many studies seek to evaluate the effects of potentially harmful pregnancy exposures during specific gestational periods. We consider an observational pregnancy cohort where pregnant individuals can initiate medication usage or become exposed to a drug at various times during their pregnancy. An important statistical challenge involves how to define and estimate exposure effects when pregnancy loss or delivery can occur over time. Without proper consideration, the results of standard analysis may be vulnerable to selection bias, immortal time-bias, and time-dependent confounding. In this study, we apply the “target trials” framework of Hernán and Robins in order to define effects based on the counterfactual approach often used in causal inference. This effect is defined relative to a hypothetical randomized trial of timed pregnancy exposures where delivery may precede and thus potentially interrupt exposure initiation. We describe specific implementations of inverse probability weighting, G-computation, and Targeted Maximum Likelihood Estimation to estimate the effects of interest. We demonstrate the performance of all estimators using simulated data and show that a standard implementation of inverse probability weighting is biased. We then apply our proposed methods to a pharmacoepidemiology study to evaluate the potentially time-dependent effect of exposure to inhaled corticosteroids on birthweight in pregnant people with mild asthma.

The impact of COVID-19 on tourists’ length of stay and daily expenditures

This study evaluates the effect of the COVID-19 pandemic on tourists’ length of stay and daily expenditures at a destination. The paper compares detailed microdata for visitors to a Northern Spanish region in the summer periods of 2019 (pre-pandemic) and 2020 (after the pandemic outbreak). We estimate the pandemic-induced impacts on the length of stay and expenditures per person for several categories using regression adjustment, inverse probability weighting regression and propensity score matching. We find clear evidence of a drop in the length of stay of around 1.26 nights, representing a 23.8% decline. We also show that, although total expenditures per person and day have remained constant, there has been a change in the allocations for categories in the tourism budget.

G-computation and doubly robust standardisation for continuous-time data: A comparison with inverse probability weighting

In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse-probability-weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination.

Using Inverse Probability Weighting to Address Post-Outcome Collider Bias

We consider the problem of bias arising from conditioning on a post-outcome collider. We illustrate this with reference to Elwert and Winship (2014) but we go beyond their study to investigate the extent to which inverse probability weighting might offer solutions. We use linear models to derive expressions for the bias arising in different kinds of post-outcome confounding, and we show the specific situations in which inverse probability weighting will allow us to obtain estimates that are consistent or, if not consistent, less biased than those obtained via ordinary least squares regression.

Radiotherapy Plus Chemotherapy Leads to Prolonged Survival in Patients With Anaplastic Thyroid Cancer Compared With Radiotherapy Alone Regardless of Surgical Resection and Distant Metastasis: A Retrospective Population Study

BackgroundWhether anaplastic thyroid cancer (ATC) patients benefit more from radiotherapy plus chemotherapy (RCT) than from radiotherapy alone (RT) was controversial. We aimed to investigate the effectiveness of RCT versus RT on ATC overall and within subgroups by surgical resection and distant metastasis in a large real-world cohort.MethodsPatients with ATC diagnosed between 2004 and 2015 were identified from the Surveillance, Epidemiology, and End Results Program database. Inverse probability weighting (IPW) was performed to balance variables between the two groups. Multivariate Cox proportional hazard model and Fine-Gray compete-risk model were carried out to investigate prognostic factors relating to overall survival (OS) and cancer-specific survival (CSS). Subgroup analysis was carried out, and a forest plot was graphed.ResultsOf the 491 ATC patients, 321 (65.4%) were in the RCT group and 170 (34.6%) were in the RT group. The median OS was 4 months [interquartile range (IQR) 2–7] and 2 months (IQR 1–4) for patients in the RCT and RT groups, respectively. As indicated by the inverse probability weighting multivariate regression, RCT was associated with significantly improved OS (adjusted HR = 0.69, 95% CI = 0.56–0.85, p < 0.001) and CSS (adjusted subdistribution HR = 0.77, 95% CI = 0.61–0.96, p = 0.018). The prominent effect of RCT versus RT alone remains significant within each subgroup stratified by surgical resection and distant metastasis. Older age, single marital status, surgical resection, distant metastasis, and tumor extension were significant prognostic factors of survival.ConclusionsRCT contributes to prolonged OS and CSS compared with RT alone in ATC patients, regardless of surgical resection and distant metastasis. RCT should be preferentially applied to ATC patients.

Unicompartmental compared with total knee replacement for patients with multimorbidities: a cohort study using propensity score stratification and inverse probability weighting

Background Although routine NHS data potentially include all patients, confounding limits their use for causal inference. Methods to minimise confounding in observational studies of implantable devices are required to enable the evaluation of patients with severe systemic morbidity who are excluded from many randomised controlled trials. Objectives Stage 1 – replicate the Total or Partial Knee Arthroplasty Trial (TOPKAT), a surgical randomised controlled trial comparing unicompartmental knee replacement with total knee replacement using propensity score and instrumental variable methods. Stage 2 – compare the risk benefits and cost-effectiveness of unicompartmental knee replacement with total knee replacement surgery in patients with severe systemic morbidity who would have been ineligible for TOPKAT using the validated methods from stage 1. Design This was a cohort study. Setting Data were obtained from the National Joint Registry database and linked to hospital inpatient (Hospital Episode Statistics) and patient-reported outcome data. Participants Stage 1 – people undergoing unicompartmental knee replacement surgery or total knee replacement surgery who met the TOPKAT eligibility criteria. Stage 2 – participants with an American Society of Anesthesiologists grade of ≥ 3. Intervention The patients were exposed to either unicompartmental knee replacement surgery or total knee replacement surgery. Main outcome measures The primary outcome measure was the postoperative Oxford Knee Score. The secondary outcome measures were 90-day postoperative complications (venous thromboembolism, myocardial infarction and prosthetic joint infection) and 5-year revision risk and mortality. The main outcome measures for the health economic analysis were health-related quality of life (EuroQol-5 Dimensions) and NHS hospital costs. Results In stage 1, propensity score stratification and inverse probability weighting replicated the results of TOPKAT. Propensity score adjustment, propensity score matching and instrumental variables did not. Stage 2 included 2256 unicompartmental knee replacement patients and 57,682 total knee replacement patients who had severe comorbidities, of whom 145 and 23,344 had linked Oxford Knee Scores, respectively. A statistically significant but clinically irrelevant difference favouring unicompartmental knee replacement was observed, with a mean postoperative Oxford Knee Score difference of < 2 points using propensity score stratification; no significant difference was observed using inverse probability weighting. Unicompartmental knee replacement more than halved the risk of venous thromboembolism [relative risk 0.33 (95% confidence interval 0.15 to 0.74) using propensity score stratification; relative risk 0.39 (95% confidence interval 0.16 to 0.96) using inverse probability weighting]. Unicompartmental knee replacement was not associated with myocardial infarction or prosthetic joint infection using either method. In the long term, unicompartmental knee replacement had double the revision risk of total knee replacement [hazard ratio 2.70 (95% confidence interval 2.15 to 3.38) using propensity score stratification; hazard ratio 2.60 (95% confidence interval 1.94 to 3.47) using inverse probability weighting], but half of the mortality [hazard ratio 0.52 (95% confidence interval 0.36 to 0.74) using propensity score stratification; insignificant effect using inverse probability weighting]. Unicompartmental knee replacement had lower costs and higher quality-adjusted life-year gains than total knee replacement for stage 2 participants. Limitations Although some propensity score methods successfully replicated TOPKAT, unresolved confounding may have affected stage 2. Missing Oxford Knee Scores may have led to information bias. Conclusions Propensity score stratification and inverse probability weighting successfully replicated TOPKAT, implying that some (but not all) propensity score methods can be used to evaluate surgical innovations and implantable medical devices using routine NHS data. Unicompartmental knee replacement was safer and more cost-effective than total knee replacement for patients with severe comorbidity and should be considered the first option for suitable patients. Future work Further research is required to understand the performance of propensity score methods for evaluating surgical innovations and implantable devices. Trial registration This trial is registered as EUPAS17435. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 66. See the NIHR Journals Library website for further project information.

Influence of Morphometry on Echocardiographic Measurements in Cavalier King Charles Spaniels: An Inverse Probability Weighting Analysis

The development and progression of myxomatous mitral valve disease (MMVD) in Cavalier King Charles Spaniels (CKCS) are difficult to predict. Thus, the identification of dogs with a morphotype associated with more severe mitral disease at a young age is desirable. The aims of this study were to: (1) describe the physical, morphometric, and echocardiographic features of class B1 MMVD-affected Cavalier King Charles Spaniels (CKCS) according to the American College of Veterinary Internal Medicine (ACVIM) guidelines; (2) evaluate the influence of morphometric physical measurements on murmur intensity, mitral valve prolapse (MVP), regurgitant jet size, and indexed mitral valve and annulus measurements. Fifty-two MMVD-affected CKCS were included in the ACVIM class B1. This is a prospective clinical cross-sectional study. Morphometric measurements, which included the body, thorax, and head sizes of each dog, were investigated to establish the association with heart murmur intensity, valvular and annular echocardiographic measurements, MVP, and regurgitant jet size, using inverse probability weighting (IPW) analyses to adjust for confounding. The IPW analyses showed that when the head length and nose length decreased, dogs had a more severe regurgitant jet size. Furthermore, subjects with a more pronounced head stop angle had thicker anterior mitral valve leaflets. A brachycephalic morphotype, as seen in dogs similar to the King Charles Spaniel breed in terms of cephalic morphology, is associated with a more severe regurgitant jet size and with valvular characteristics that are related to the most severe forms of MMVD.