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This paper examines the approaches accounting researchers adopt to draw causal inferences using observational (or nonexperimental) data. The vast majority of accounting research papers draw causal inferences notwithstanding the well-known difficulties in doing so. While some recent papers seek to use quasi-experimental methods to improve causal inferences, these methods also make strong assumptions that are not always fully appreciated. We believe that accounting research would benefit from more in-depth descriptive research, including a greater focus on the study of causal mechanisms (or causal pathways) and increased emphasis on the structural modeling of the phenomena of interest. We argue these changes offer a practical path forward for rigorous accounting research.

Abstract Objectives Regression discontinuity (RD) designs allow for rigorous causal inference when patients receive a treatment based on scoring above or below a cutoff point on a continuously measured variable. We provide an introduction to the theory of RD and a systematic review and assessment of the RD literature in medicine, epidemiology, and public health. Study Design and Setting We review the necessary conditions for valid RD results, provide a practical guide to RD implementation, compare RD to other methodologies, and conduct a systematic review of the RD literature in PubMed. Results We describe five key elements of analysis all RD studies should report, including tests of validity conditions and robustness checks. Thirty two empirical RD studies in PubMed met our selection criteria. Most of the 32 RD articles analyzed the effectiveness of social policies or mental health interventions, with only two evaluating clinical interventions to improve physical health. Seven out of the 32 studies reported on all the five key elements. Conclusion Increased use of RD provides an exciting opportunity for obtaining unbiased causal effect estimates when experiments are not feasible or when we want to evaluate programs under “real-life” conditions. Although treatment eligibility in medicine, epidemiology, and public health is commonly determined by threshold rules, use of RD in these fields has been very limited until now.

ABSTRACT Background Randomized controlled trials (RCTs) are uncommon in precision oncology. We provide an introduction and illustrative example of matching methods for evaluating precision oncology in the absence of RCTs. We focus on British Columbia's Personalized OncoGenomics (POG) program, which applies whole‐genome and transcriptome analysis (WGTA) to inform advanced cancer care. Methods Our cohort comprises 230 POG patients enrolled between 2014 and 2015 and matched POG‐naive controls. We generated our matched cohort using 1:1 propensity score matching (PSM) and genetic matching prior to exploring survival differences. Results We find that genetic matching outperformed PSM when balancing covariates. In all cohorts, overall survival did not significantly differ across POG and POG‐naive patients (p > 0.05). Stratification by WGTA‐informed treatment indicated unmatched survival differences. Patients whose WGTA information led to treatment change were at a reduced hazard of death compared to POG‐naive controls in all cohorts, with estimated hazard ratios ranging from 0.33 (95% CI: 0.13, 0.81) to 0.41 (95% CI: 0.17, 0.98). Conclusion These results signal that clinical effectiveness of precision oncology approaches will depend on rates of genomics‐informed treatment change. Our study will guide future evaluations of precision oncology and support reliable effect estimation when RCT data are unavailable.

Background Single‐arm trials are common in precision oncology. Owing to the lack of randomized counterfactual, resultant data are not amenable to comparative outcomes analyses. Difference‐in‐difference (DID) methods present an opportunity to generate causal estimates of time‐varying treatment outcomes. Using DID, our study estimates within‐cohort effects of genomics‐informed treatment versus standard care on clinical and cost outcomes. Methods We focus on adults with advanced cancers enrolled in the single‐arm BC Cancer Personalized OncoGenomics program between 2012 and 2017. All individuals had a minimum of 1‐year follow up. Logistic regression explored baseline differences across patients who received a genomics‐informed treatment versus a standard care treatment after genomic sequencing. DID estimated the incremental effects of genomics‐informed treatment on time to treatment discontinuation (TTD), time to next treatment (TTNT), and costs. TTD and TTNT correlate with improved response and survival. Results Our study cohort included 346 patients, of whom 140 (40%) received genomics‐informed treatment after sequencing and 206 (60%) received standard care treatment. No significant differences in baseline characteristics were detected across treatment groups. DID estimated that the incremental effect of genomics‐informed versus standard care treatment was 102 days (95% CI: 35, 167) on TTD, 91 days (95% CI: −9, 175) on TTNT, and CAD$91,098 (95% CI: $46,848, $176,598) on costs. Effects were most pronounced in gastrointestinal cancer patients. Conclusions Genomics‐informed treatment had a statistically significant effect on TTD compared to standard care treatment, but at increased treatment costs. Within‐cohort evidence generated through this single‐arm study informs the early‐stage comparative effectiveness of precision oncology. Difference‐in‐difference analysis is used to address confounding when analyzing real‐world data from a single‐arm precision oncology trial. Enrolled patients receiving genomics‐informed treatment are treated longer, suggesting improved efficacy, but at an increased cost compared to those receiving standard care.

Background. The objective of this quasi-experimental study was to determine whether bolus vitamin D supplementation taken either regularly over the preceding year or after the diagnosis of COVID-19 was effective in improving survival among hospitalized frail elderly COVID-19 patients. Methods. Seventy-seven patients consecutively hospitalized for COVID-19 in a geriatric unit were included. Intervention groups were participants regularly supplemented with vitamin D over the preceding year (Group 1), and those supplemented with vitamin D after COVID-19 diagnosis (Group 2). The comparator group involved participants having received no vitamin D supplements (Group 3). Outcomes were 14-day mortality and highest (worst) score on the ordinal scale for clinical improvement (OSCI) measured during COVID-19 acute phase. Potential confounders were age, gender, functional abilities, undernutrition, cancer, hypertension, cardiomyopathy, glycated hemoglobin, number of acute health issues at admission, hospital use of antibiotics, corticosteroids, and pharmacological treatments of respiratory disorders. Results. The three groups (n = 77; mean ± SD, 88 ± 5 years; 49% women) were similar at baseline (except for woman proportion, p = 0.02), as were the treatments used for COVID-19. In Group 1 (n = 29), 93.1% of COVID-19 participants survived at day 14, compared to 81.2% survivors in Group 2 (n = 16) (p = 0.33) and 68.7% survivors in Group 3 (n = 32) (p = 0.02). While considering Group 3 as reference (hazard ratio (HR) = 1), the fully-adjusted HR for 14-day mortality was HR = 0.07 (p = 0.017) for Group 1 and HR = 0.37 (p = 0.28) for Group 2. Group 1 had longer survival time than Group 3 (log-rank p = 0.015), although there was no difference between Groups 2 and 3 (log-rank p = 0.32). Group 1, but not Group 2 (p = 0.40), was associated with lower risk of OSCI score ≥5 compared to Group 3 (odds ratio = 0.08, p = 0.03). Conclusions. Regular bolus vitamin D supplementation was associated with less severe COVID-19 and better survival in frail elderly.

The aim of the study was to extend a previously published checklist of study design features to include study designs often used by health systems researchers and economists. Our intention is to help review authors in any field to set eligibility criteria for studies to include in a systematic review that relate directly to the intrinsic strength of the studies in inferring causality. We also seek to clarify key equivalences and differences in terminology used by different research communities. Expert consensus meeting. The checklist comprises seven questions, each with a list of response items, addressing: clustering of an intervention as an aspect of allocation or due to the intrinsic nature of the delivery of the intervention; for whom, and when, outcome data are available; how the intervention effect was estimated; the principle underlying control for confounding; how groups were formed; the features of a study carried out after it was designed; and the variables measured before intervention. The checklist clarifies the basis of credible quasi-experimental studies, reconciling different terminology used in different fields of investigation and facilitating communications across research communities. By applying the checklist, review authors' attention is also directed to the assumptions underpinning the methods for inferring causality.

Many major corporations and countries have made commitments to purchase or produce only “sustainable” palm oil, a commodity responsible for substantial tropical forest loss. Sustainability certification is the tool most used to fulfill these procurement policies, and around 20% of global palm oil production was certified by the Roundtable on Sustainable Palm Oil (RSPO) in 2017. However, the effect of certification on deforestation in oil palm plantations remains unclear. Here, we use a comprehensive dataset of RSPO-certified and noncertified oil palm plantations (∼188,000 km²) in Indonesia, the leading producer of palm oil, as well as annual remotely sensed metrics of tree cover loss and fire occurrence, to evaluate the impact of certification on deforestation and fire from 2001 to 2015. While forest loss and fire continued after RSPO certification, certified palm oil was associated with reduced deforestation. Certification lowered deforestation by 33% from a counterfactual of 9.8 to 6.6% y−1. Nevertheless, most plantations contained little residual forest when they received certification. As a result, by 2015, certified areas held less than 1% of forests remaining within Indonesian oil palm plantations. Moreover, certification had no causal impact on forest loss in peatlands or active fire detection rates. Broader adoption of certification in forested regions, strict requirements to avoid all peat, and routine monitoring of clearly defined forest cover loss in certified and RSPO member-held plantations appear necessary if the RSPO is to yield conservation and climate benefits from reductions in tropical deforestation.

Police kill more than 300 black Americans—at least a quarter of them unarmed—each year in the USA. These events might have spillover effects on the mental health of people not directly affected. In this population-based, quasi-experimental study, we combined novel data on police killings with individual-level data from the nationally representative 2013–15 US Behavioral Risk Factor Surveillance System (BRFSS) to estimate the causal impact of police killings of unarmed black Americans on self-reported mental health of other black American adults in the US general population. The primary exposure was the number of police killings of unarmed black Americans occurring in the 3 months prior to the BRFSS interview within the same state. The primary outcome was the number of days in the previous month in which the respondent's mental health was reported as “not good”. We estimated difference-in-differences regression models—adjusting for state-month, month-year, and interview-day fixed effects, as well as age, sex, and educational attainment. We additionally assessed the timing of effects, the specificity of the effects to black Americans, and the robustness of our findings. 38 993 (weighted sample share 49%) of 103 710 black American respondents were exposed to one or more police killings of unarmed black Americans in their state of residence in the 3 months prior to the survey. Each additional police killing of an unarmed black American was associated with 0·14 additional poor mental health days (95% CI 0·07–0·22; p=0·00047) among black American respondents. The largest effects on mental health occurred in the 1–2 months after exposure, with no significant effects estimated for respondents interviewed before police killings (falsification test). Mental health impacts were not observed among white respondents and resulted only from police killings of unarmed black Americans (not unarmed white Americans or armed black Americans). Police killings of unarmed black Americans have adverse effects on mental health among black American adults in the general population. Programmes should be implemented to decrease the frequency of police killings and to mitigate adverse mental health effects within communities when such killings do occur. Robert Wood Johnson Foundation and National Institutes of Health.

BackgroundDisruptive behaviour disorders are a common set of diagnoses in childhood and adolescence, with global estimates of 5.7%. The most investigated risk factor for disruptive behaviour disorders is parenting practices. By conducting a systematic review and meta-analysis of studies using quasi-experimental methods which enable stronger causal inference, we aimed to identify the most stringent evidence on the relationship between both positive and negative parenting practices and disruptive behaviour.MethodsWe included publications that used genetically informed family-based designs, natural experiments, propensity score methods or within-person fixed-effects analyses. Two researchers independently screened articles for eligibility and, if deemed eligible, extracted their data. Multi-level random-effects meta-analyses were used to pool the results and assess evidence of heterogeneity. Potential subgroups, including participant characteristics and study features, were selected a-priori.ResultsWe identified 41 studies which used data from 27 distinct cohorts (n = 36,661) and implemented 8 different quasi-experimental methods. There was no support for an association between positive parenting and offspring disruptive behaviour (pooled standardised regression coefficient r = -0.064; 95% CI = -0.154, 0.026; I2 = 22.03%). There was evidence of a moderate association between negative parenting on offspring disruptive behaviour (pooled r = 0.142; 95% CI = 0.104, 0.180;I2 = 44.52%) and analyses of subgroups indicated that this association was consistent across offspring sex and type of disruptive behaviour outcome but varied by offspring age, type of quasi-experimental design, raters for exposure and outcome and study quality.DiscussionThe current study provides stringent evidence of a small, causal effect of negative parenting on offspring disruptive behaviour. The clinical implications of these results are that even the most effective parenting interventions may be expected to produce small effects on offspring disruptive behaviours.