Evaluation of treatment effects and more general estimands is typically achieved via parametric modelling, which is unsatisfactory since model misspecification is likely. Data-adaptive model building (e.g. statistical/machine learning) is commonly employed to reduce the risk of misspecification. Naïve use of such methods, however, delivers estimators whose bias may shrink too slowly with sample size for inferential methods to perform well, including those based on the bootstrap. Bias arises because standard data-adaptive methods are tuned towards minimal prediction error as opposed to e.g. minimal MSE in the estimator. This may cause excess variability that is difficult to acknowledge, due to the complexity of such strategies.Building on results from nonparametric statistics, targeted learning and debiased machine learning overcome these problems by constructing estimators using the estimand's efficient influence function under the nonparametric model. These increasingly popular methodologies typically assume that the efficient influence function is given, or that the reader is familiar with its derivation.In this paper, we focus on derivation of the efficient influence function and explain how it may be used to construct statistical/machine-learning-based estimators. We discuss the requisite conditions for these estimators to perform well and use diverse examples to convey the broad applicability of the theory.

Background Although it is well known that obesity is a risk factor for gastrointestinal (GI) cancer, it is not well established if obesity can cause earlier GI cancer onset. Methods A cross-sectional study examining the linked 2004–2008 California Cancer Registry Patient Discharge Database was performed to evaluate the association between obesity and onset age among four gastrointestinal cancers, including esophageal, gastric, pancreatic, and colorectal cancers. Regression models were constructed to adjust for other carcinogenic factors. Results The diagnosis of obesity (BMI > 30) was associated with a reduction in diagnosis age across all four cancer types: 3.25 ± 0.53 years for gastric cancer, 4.56 ± 0.18 years for colorectal cancer, 4.73 ± 0.73 years for esophageal cancer, and 5.35 ± 0.72 for pancreatic cancer. The diagnosis of morbid obesity (BMI > 40) was associated with a more pronounced reduction in the age of diagnosis: 5.48 ± 0.96 years for gastric cancer, 7.75 ± 0.30 years for colorectal cancer, 7.67 ± 1.26 years for esophageal cancer, and 8.19 ± 1.25 years for pancreatic cancer. Both morbid obesity and obesity remained strongly associated with earlier cancer diagnosis for all four cancer types even after adjusting for other available cancer risk factors. Conclusions The diagnosis of obesity, especially morbid obesity, was associated with a significantly earlier gastrointestinal cancer onset in California. Further research with prospective cohort data may be required to establish the causal relationship between obesity and cancer onset age.

The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization and repolarization, estimated by QRS duration and JT interval, respectively. QT interval abnormalities are associated with potentially fatal ventricular arrhythmia. Using genome-wide multi-ancestry analyses (>250,000 individuals) we identify 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identify associations with Mendelian disease genes. Enrichments are observed in established pathways for QT and JT, and previously unreported genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast for QRS, connective tissue components and processes for cell growth and extracellular matrix interactions are significantly enriched. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlight potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization.

Aims The rising prevalence of obesity and its associated comorbidities represent a growing public health issue; in particular, obesity is known to be a major risk factor for cardiovascular disease. Despite the evidence behind the efficacy of orlistat in achieving weight loss in patients with obesity, no study thus far has quantified its long-term effect on cardiovascular outcomes. The purpose of this study is to explore long-term cardiovascular outcomes after orlistat therapy. Methods and results A propensity-score matched cohort study was conducted on the nation-wide electronic primary and integrated secondary healthcare records of the Clinical Practice Research Datalink (CPRD). The 36 876 patients with obesity in the CPRD database who had completed a course of orlistat during follow-up were matched on a 1:1 basis with equal numbers of controls who had not taken orlistat. Patients were followed up for a median of 6 years for the occurrence of the primary composite endpoint of major adverse cardiovascular events (fatal or non-fatal myocardial infarction or ischaemic stroke), and a number of secondary endpoints including primary endpoint components individually, the occurrence of new-onset heart failure, coronary revascularization, new chronic kidney disease stage III+ (CKD3+), and all-cause mortality. During the median study follow-up of 6 years, the occurrence of major adverse cardiovascular events was lower in the orlistat cohort [hazard ratio (HR) 0.74; 95% confidence interval (CI) 0.66–0.83, P < 0.001]. Patients who took orlistat experienced lower rates of myocardial infarction (HR 0.77; 95% CI 0.66–0.88, P < 0.001) and ischaemic stroke (HR 0.68; 95% CI 0.56 to −0.84, P < 0.001) as well as new-onset heart failure (HR 0.79; 95% CI 0.67–0.94, P = 0.007). There was no differences in revascularization rates (HR 1.12; 95% CI 0.91–1.38, P = 0.27), but a lower rate of both CKD3+ development (HR 0.78; 95% CI 0.73–0.83, P < 0.001) and mortality (HR 0.39, 95% CI 0.36 to −0.41, P < 0.001) was observed. Conclusion In this nation-wide, propensity-score matched study, orlistat was associated with lower rates of overall major adverse cardiovascular events, new-onset heart failure, renal failure, and mortality. This study adds to current evidence on the known improvements in cardiovascular risk factor profiles of orlistat treatment by suggesting a potential role in primary prevention.