Simultaneous alleviation of verification and reference standard biases in a community-based tuberculosis screening study using Bayesian latent class analysis.

BACKGROUND: Estimation of prevalence and diagnostic test accuracy in tuberculosis (TB) prevalence surveys suffer from reference standard and verification biases. The former is attributed to the imperfect reference test used to bacteriologically confirm TB disease. The latter occurs when only the participants screening positive for any TB-compatible symptom or chest X-ray abnormality are selected for bacteriological testing (verification). Bayesian latent class analysis (LCA) alleviates the reference standard bias but suffers verification bias in TB prevalence surveys. This work aims to identify best-practice approaches to simultaneously alleviate the reference standard and verification biases in the estimates of pulmonary TB prevalence and diagnostic test performance in TB prevalence surveys. METHODS: We performed a secondary analysis of 9869 participants aged ≥15 years from a community-based multimorbidity screening study in a rural district of KwaZulu-Natal, South Africa (Vukuzazi study). Participants were eligible for bacteriological testing using Xpert Ultra and culture if they reported any cardinal TB symptom or had an abnormal chest X-ray finding. We conducted Bayesian LCA in five ways to handle the unverified individuals: (i) complete-case analysis, (ii) analysis assuming the unverified individuals would be negative if bacteriologically tested, (iii) analysis of multiply-imputed datasets with imputation of the missing bacteriological test results for the unverified individuals using multivariate imputation via chained equations (MICE), and simultaneous imputation of the missing bacteriological test results in the analysis model assuming the missing bacteriological test results were (iv) missing at random (MAR), and (v) missing not at random (MNAR). We compared the results of (i)-(iii) to the analysis based on a composite reference standard (CRS) of Xpert Ultra and culture. Through simulation with an overall true prevalence of 2.0%, we evaluated the ability of the models to alleviate both biases simultaneously. RESULTS: Based on simulation, Bayesian LCA with simultaneous imputation of the missing bacteriological test results under the assumption that the missing data are MAR and MNAR alleviate the reference standard and verification biases. CRS-based analysis and Bayesian LCA assuming the unverified are negative for TB alleviate the biases only when the true overall prevalence is <3.0%. Complete-case analysis produced biased estimates. In the Vukuzazi study, Bayesian LCA with simultaneous imputation of the missing bacteriological test results under the MAR and MNAR assumptions produced overall PTB prevalence of 0.9% (95% Credible Interval (CrI): 0.6-1.9) and 0.7% (95% CrI: 0.5-1.1) respectively alongside realistic estimates of overall diagnostic test sensitivity and specificity with substantially overlapping 95% CrI. The CRS-based analysis and Bayesian LCA assuming the unverified were negative for TB produced 0.7% (95% CrI: 0.5-0.9) and 0.7% (95% CrI: 0.5-1.2) overall PTB prevalence respectively with realistic estimates of overall diagnostic test sensitivity and specificity. Unlike CRS-based analysis, Bayesian LCA of multiply-imputed data using MICE mitigates both biases. CONCLUSION: The findings demonstrate the efficacy of these advanced techniques in alleviating the reference standard and verification biases, enhancing the robustness of community-based screening programs. Imputing missing values as negative for bacteriological tests is plausible under realistic assumptions.

Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation.

There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g $$ g $$ -formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains 'unbalanced' even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.

PEACE V-Salvage Treatment of OligoRecurrent nodal prostate cancer Metastases (STORM): Acute Toxicity of a Randomized Phase 2 Trial.

BACKGROUND: Treatment recommendations for patients with limited nodal recurrences are lacking, and different locoregional treatment approaches are currently being used. OBJECTIVE: The aim of this trial is to compare metastasis-directed therapy (MDT) with or without elective nodal pelvic radiotherapy (ENRT). DESIGN, SETTING, AND PARTICIPANTS: PEACE V-Salvage Treatment of OligoRecurrent nodal prostate cancer Metastases (STORM) is an international, phase 2, open-label, randomized, superiority trial (ClinicalTrials.gov identifier: NCT03569241). Patients diagnosed with positron emission tomography-detected pelvic nodal oligorecurrence (five or fewer nodes) following radical local treatment for prostate cancer were randomized in a 1:1 ratio between arm A (MDT and 6 mo of androgen deprivation therapy [ADT]) and arm B (ENRT [25 × 1.8 Gy] with MDT and 6 mo of ADT). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We report the secondary endpoint acute toxicity, defined as worst grade ≥2 Common Terminology Criteria for Adverse Events v4.0 gastrointestinal (GI) or genitourinary (GU) toxicity within 3 mo of treatment. The chi-square test was used to compare toxicity between treatment arms. We also compare the quality of life (QoL) using the European Organisation for Research and Treatment of Cancer QLQ C30 and PR25 questionnaires. RESULTS AND LIMITATIONS: Between June 2018 and April 2021, 196 patients were assigned randomly to MDT or ENRT. Ninety-seven of 99 patients allocated to MDT and 93 of 97 allocated to ENRT received per-protocol treatment. Worst acute GI toxicity proportions were as follows: grade ≥2 events in three (3%) in the MDT group versus four (4%) in the ENRT group (p = 0.11). Worst acute GU toxicity proportions were as follows: grade ≥2 events in eight (8%) in the MDT group versus 12 (13%) in the ENRT group (p = 0.95). We observed no significant difference between the study groups in the proportion of patients with a clinically significant QoL reduction from baseline for any subdomain score area. CONCLUSIONS: No clinically meaningful differences were observed in worst grade ≥2 acute GI or GU toxicity or in QoL subdomains between MDT and ENRT. PATIENT SUMMARY: We found no evidence of differential acute bowel or urinary side effects using metastasis-directed therapy and elective nodal radiotherapy for the treatment of patients with a pelvic lymph node recurrence.

Checkpoint Inhibitors in Combination With Stereotactic Body Radiotherapy in Patients With Advanced Solid Tumors: The CHEERS Phase 2 Randomized Clinical Trial.

Importance: Although immune checkpoint inhibitors (ICIs) targeting programmed cell death 1 (PD-1) and PD-1 ligand 1 have improved the outcome for many cancer types, the majority of patients fails to respond to ICI monotherapy. Hypofractionated radiotherapy has the potential to improve the therapeutic ratio of ICIs. Objective: To assess the addition of radiotherapy to ICIs compared with ICI monotherapy in patients with advanced solid tumors. Design, Setting, and Participants: This open-label, multicenter, randomized phase 2 trial was conducted in 5 Belgian hospitals and enrolled participants between March 2018 and October 2020. Patients 18 years or older with locally advanced or metastatic melanoma, renal cell carcinoma, urothelial carcinoma, head and neck squamous cell carcinoma, or non-small cell lung carcinoma were eligible. A total of 99 patients were randomly assigned to either the control arm (n = 52) or the experimental arm (n = 47). Of those, 3 patients (1 in the control arm vs 2 in the experimental arm) withdrew consent and thus were not included in the analysis. Data analyses were performed between April 2022 and March 2023. Interventions: Patients were randomized (1:1) to receive anti-PD-1/PD-1 ligand 1 ICIs alone as per standard of care (control arm) or combined with stereotactic body radiotherapy 3 × 8 gray to a maximum of 3 lesions prior to the second or third ICI cycle, depending on the frequency of administration (experimental arm). Randomization was stratified according to tumor histologic findings and disease burden (3 and fewer or more than 3 cancer lesions). Main Outcomes and Measures: The primary end point was progression-free survival (PFS) as per immune Response Evaluation Criteria in Solid Tumors. Key secondary end points included overall survival (OS), objective response rate, local control rate, and toxic effects. Efficacy was assessed in the intention-to-treat population, while safety was evaluated in the as-treated population. Results: Among 96 patients included in the analysis (mean age, 66 years; 76 [79%] female), 72 (75%) had more than 3 tumor lesions and 65 (68%) had received at least 1 previous line of systemic treatment at time of inclusion. Seven patients allocated to the experimental arm did not complete the study-prescribed radiotherapy course due to early disease progression (n = 5) or intercurrent illness (n = 2). With a median (range) follow-up of 12.5 (0.7-46.2) months, median PFS was 2.8 months in the control arm compared with 4.4 months in the experimental arm (hazard ratio, 0.95; 95% CI, 0.58-1.53; P = .82). Between the control and experimental arms, no improvement in median OS was observed (11.0 vs 14.3 months; hazard ratio, 0.82; 95% CI, 0.48-1.41; P = .47), and objective response rate was not statistically significantly different (22% vs 27%; P = .56), despite a local control rate of 75% in irradiated patients. Acute treatment-related toxic effects of any grade and grade 3 or higher occurred in 79% and 18% of patients in the control arm vs 78% and 18% in the experimental arm, respectively. No grade 5 adverse events occurred. Conclusions and Relevance: This phase 2 randomized clinical trial demonstrated that while safe, adding subablative stereotactic radiotherapy of a limited number of metastatic lesions to ICI monotherapy failed to show improvement in PFS or OS. Trial Registration: ClinicalTrials.gov Identifier: NCT03511391.

Single-arm studies involving patient-reported outcome data in oncology: a literature review on current practice.

Patient-reported outcomes (PROs) are increasingly used in single-arm cancer studies. We reviewed 60 papers published between 2018 and 2021 of single-arm studies of cancer treatment with PRO data for current practice on design, analysis, reporting, and interpretation. We further examined the studies' handling of potential bias and how they informed decision making. Most studies (58; 97%) analysed PROs without stating a predefined research hypothesis. 13 (22%) of the 60 studies used a PRO as a primary or co-primary endpoint. Definitions of PRO objectives, study population, endpoints, and missing data strategies varied widely. 23 studies (38%) compared the PRO data with external information, most often by using a clinically important difference value; one study used a historical control group. Appropriateness of methods to handle missing data and intercurrent events (including death) were seldom discussed. Most studies (51; 85%) concluded that PRO results supported treatment. Conducting and reporting of PROs in cancer single-arm studies need standards and a critical discussion of statistical methods and possible biases. These findings will guide the Setting International Standards in Analysing Patient-Reported Outcomes and Quality of Life Data in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI) in developing recommendations for the use of PRO-measures in single-arm studies.

Evaluation of tuberculosis diagnostic test accuracy using Bayesian latent class analysis in the presence of conditional dependence between the diagnostic tests used in a community-based tuberculosis screening study.

Diagnostic accuracy studies in pulmonary tuberculosis (PTB) are complicated by the lack of a perfect reference standard. This limitation can be handled using latent class analysis (LCA), assuming independence between diagnostic test results conditional on the true unobserved PTB status. Test results could remain dependent, however, e.g. with diagnostic tests based on a similar biological basis. If ignored, this gives misleading inferences. Our secondary analysis of data collected during the first year (May 2018 -May 2019) of a community-based multi-morbidity screening program conducted in the rural uMkhanyakude district of KwaZulu Natal, South Africa, used Bayesian LCA. Residents of the catchment area, aged ≥15 years and eligible for microbiological testing, were analyzed. Probit regression methods for dependent binary data sequentially regressed each binary test outcome on other observed test results, measured covariates and the true unobserved PTB status. Unknown model parameters were assigned Gaussian priors to evaluate overall PTB prevalence and diagnostic accuracy of 6 tests used to screen for PTB: any TB symptom, radiologist conclusion, Computer Aided Detection for TB version 5 (CAD4TBv5≥53), CAD4TBv6≥53, Xpert Ultra (excluding trace) and culture. Before the application of our proposed model, we evaluated its performance using a previously published childhood pulmonary TB (CPTB) dataset. Standard LCA assuming conditional independence yielded an unrealistic prevalence estimate of 18.6% which was not resolved by accounting for conditional dependence among the true PTB cases only. Allowing, also, for conditional dependence among the true non-PTB cases produced a 1.1% plausible prevalence. After incorporating age, sex, and HIV status in the analysis, we obtained 0.9% (95% CrI: 0.6, 1.3) overall prevalence. Males had higher PTB prevalence compared to females (1.2% vs. 0.8%). Similarly, HIV+ had a higher PTB prevalence compared to HIV- (1.3% vs. 0.8%). The overall sensitivity for Xpert Ultra (excluding trace) and culture were 62.2% (95% CrI: 48.7, 74.4) and 75.9% (95% CrI: 61.9, 89.2), respectively. Any chest X-ray abnormality, CAD4TBv5≥53 and CAD4TBv6≥53 had similar overall sensitivity. Up to 73.3% (95% CrI: 61.4, 83.4) of all true PTB cases did not report TB symptoms. Our flexible modelling approach yields plausible, easy-to-interpret estimates of sensitivity, specificity and PTB prevalence under more realistic assumptions. Failure to fully account for diagnostic test dependence can yield misleading inferences.

Causal inference in survival analysis under deterministic missingness of confounders in register data.

Long-term register data offer unique opportunities to explore causal effects of treatments on time-to-event outcomes, in well-characterized populations with minimum loss of follow-up. However, the structure of the data may pose methodological challenges. Motivated by the Swedish Renal Registry and estimation of survival differences for renal replacement therapies, we focus on the particular case when an important confounder is not recorded in the early period of the register, so that the entry date to the register deterministically predicts confounder missingness. In addition, an evolving composition of the treatment arms populations, and suspected improved survival outcomes in later periods lead to informative administrative censoring, unless the entry date is appropriately accounted for. We investigate different consequences of these issues on causal effect estimation following multiple imputation of the missing covariate data. We analyse the performance of different combinations of imputation models and estimation methods for the population average survival. We further evaluate the sensitivity of our results to the nature of censoring and misspecification of fitted models. We find that an imputation model including the cumulative baseline hazard, event indicator, covariates and interactions between the cumulative baseline hazard and covariates, followed by regression standardization, leads to the best estimation results overall, in simulations. Standardization has two advantages over inverse probability of treatment weighting here: it can directly account for the informative censoring by including the entry date as a covariate in the outcome model, and allows for straightforward variance computation using readily available software.

Implications of covariate induced test dependence on the diagnostic accuracy of latent class analysis in pulmonary tuberculosis.

Background: In application studies of latent class analysis (LCA) evaluating imperfect diagnostic tests, residual dependence among the diagnostic tests still remain even after conditioning on the true disease status due to measured variables known to affect prevalence and/or alter diagnostic test accuracy. Presence of severe comorbidities such as HIV in pulmonary tuberculosis (PTB) diagnosis alter the prevalence of PTB and affect the diagnostic performance of the available imperfect tests in use. This violates two key assumptions of LCA: (1) that the diagnostic tests are independent conditional on the true disease status (2) that the sensitivity and specificity remain constant across subpopulations. This leads to incorrect inferences. Methods: Through simulation we examined implications of likely model violations on estimation of prevalence, sensitivity and specificity among passive case-finding presumptive PTB patients with or without HIV. Jointly conditioning on PTB and HIV, we generated independent results for five diagnostic tests and analyzed using Bayesian LCA with Probit regression, separately for sets of five and three diagnostic tests using four working models allowing: (1) constant PTB prevalence and diagnostic accuracy (2) varying PTB prevalence but constant diagnostic accuracy (3) constant PTB prevalence but varying diagnostic accuracy (4) varying PTB prevalence and diagnostic accuracy across HIV subpopulations. Vague Gaussian priors with mean 1 and unknown variance were assigned to the model parameters with unknown variance assigned Inverse Gamma prior. Results: Models accounting for heterogeneity in diagnostic accuracy produced consistent estimates while the model ignoring it produces biased estimates. The model ignoring heterogeneity in PTB prevalence only is less problematic. With five diagnostic tests, the model assuming homogenous population is robust to violation of the assumptions. Conclusion: Well-chosen covariate-specific adaptations of the model can avoid bias implied by recognized heterogeneity in PTB patient populations generating otherwise dependent test results in LCA.

Trial emulation and survival analysis for disease incidence registers: A case study on the causal effect of pre-emptive kidney transplantation.

When drawing causal inference from observed data, failure time outcomes present additional challenges of censoring often combined with other missing data patterns. In this article, we follow incident cases of end-stage renal disease to examine the effect on all-cause mortality of starting treatment with transplant, so-called pre-emptive kidney transplantation, vs starting with dialysis possibly followed by delayed transplantation. The question is relatively simple: which start-off treatment is expected to bring the best survival for a target population? To address it, we emulate a target trial drawing on the long term Swedish Renal Registry, where a growing common set of baseline covariates was measured nationwide. Several lessons are learned which pertain to long term disease registers more generally. With characteristics of cases and versions of treatment evolving over time, informative censoring is already introduced in unadjusted Kaplan-Meier curves. This leads to misrepresented survival chances in observed treatment groups. The resulting biased treatment association may be aggravated upon implementing IPW for treatment. Aware of additional challenges, we further recall how similar studies to date have selected patients into treatment groups based on events occurring post treatment initiation. Our study reveals the dramatic impact of resulting immortal time bias combined with other typical features of long-term incident disease registers, including missing covariates during the early phases of the register. We discuss feasible ways of accommodating these features when targeting relevant estimands, and demonstrate how more than one causal question can be answered relying on the no unmeasured baseline confounders assumption.

Evaluating the prognostic performance of a polygenic risk score for breast cancer risk stratification.

BACKGROUND: Polygenic risk scores (PRS) could potentially improve breast cancer screening recommendations. Before a PRS can be considered for implementation, it needs rigorous evaluation, using performance measures that can inform about its future clinical value. OBJECTIVES: To evaluate the prognostic performance of a regression model with a previously developed, prevalence-based PRS and age as predictors for breast cancer incidence in women from the Estonian biobank (EstBB) cohort; to compare it to the performance of a model including age only. METHODS: We analyzed data on 30,312 women from the EstBB cohort. They entered the cohort between 2002 and 2011, were between 20 and 89 years, without a history of breast cancer, and with full 5-year follow-up by 2015. We examined PRS and other potential risk factors as possible predictors in Cox regression models for breast cancer incidence. With 10-fold cross-validation we estimated 3- and 5-year breast cancer incidence predicted by age alone and by PRS plus age, fitting models on 90% of the data. Calibration, discrimination, and reclassification were calculated on the left-out folds to express prognostic performance. RESULTS: A total of 101 (3.33‰) and 185 (6.1‰) incident breast cancers were observed within 3 and 5 years, respectively. For women in a defined screening age of 50-62 years, the ratio of observed vs PRS-age modelled 3-year incidence was 0.86 for women in the 75-85% PRS-group, 1.34 for the 85-95% PRS-group, and 1.41 for the top 5% PRS-group. For 5-year incidence, this was respectively 0.94, 1.15, and 1.08. Yet the number of breast cancer events was relatively low in each PRS-subgroup. For all women, the model's AUC was 0.720 (95% CI: 0.675-0.765) for 3-year and 0.704 (95% CI: 0.670-0.737) for 5-year follow-up, respectively, just 0.022 and 0.023 higher than for the model with age alone. Using a 1% risk prediction threshold, the 3-year NRI for the PRS-age model was 0.09, and 0.05 for 5 years. CONCLUSION: The model including PRS had modest incremental performance over one based on age only. A larger, independent study is needed to assess whether and how the PRS can meaningfully contribute to age, for developing more efficient screening strategies.

Prognostic value of high-risk human papillomavirus DNA and p16INK4a immunohistochemistry in patients with anal cancer: An individual patient data meta-analysis.

BACKGROUND: High-risk human papillomavirus (hrHPV) types represent the aetiological agents in a major proportion of anal squamous cell carcinomas (ASCC). Several studies have suggested a prognostic relevance of HPV-related markers, particularly hrHPV DNA and p16INK4a (p16) protein expression, in patients with ASCC. However, broader evaluation of these prognostic marker candidates has been hampered by small cohort sizes and heterogeneous survival data among the individual studies. We conducted an individual patient data (IPD) meta-analysis to determine the prognostic value of hrHPV DNA and p16 in patients with ASCC while controlling for major clinical and tumour covariates. PATIENTS AND METHODS: A systematic literature search was conducted to identify all published studies analysing p16 alone or in combination with hrHPV DNA and reporting survival data in patients with ASCC. Clinical and tumour-related IPD were requested from authors of potentially eligible studies. Survival analyses were performed with a proportional hazard Cox model stratified by study and adjusted for relevant covariates. The study-specific hazard ratios (HRs) for the exposures were pooled using a random-effects model. Kaplan-Meier curves from different studies were pooled per exposure group and weighted by the study's total sample size. RESULTS: Seven studies providing IPD from 693 patients with ASCC could be included in the meta-analysis. Seventy-six percent of patients were p16+/hrHPV DNA+, whereas 11% were negative for both markers. A discordant marker status was observed in 13% of cases. Patients with p16+/hrHPV DNA+ ASCC showed significantly superior overall survival (OS) compared with patients with p16-/hrHPV DNA- tumours (pooled adjusted HR = 0.26 [95% confidence interval {CI}, 0.14-0.50]) with pooled three-year OS rates of 86% (95% CI, 82-90%) versus 39% (95% CI, 24-54%). Patients with discordant p16 and hrHPV DNA status showed intermediate three-year OS rates (75% [95% CI, 56-86%] for p16+/hrHPV DNA- and 55% [95% CI, 35-71%] for p16-/hrHPV DNA+ ASCC). CONCLUSION: This first IPD meta-analysis controlling for confounding variables shows that patients with p16+/hrHPV DNA+ ASCC have a significantly better survival than patients with p16-/hrHPV DNA- tumours.

Checkpoint inhibition in combination with an immunoboost of external beam radiotherapy in solid tumors (CHEERS): study protocol for a phase 2, open-label, randomized controlled trial.

BACKGROUND: While the introduction of checkpoint inhibitors (CPIs) as standard of care treatment for various tumor types has led to considerable improvements in clinical outcome, the majority of patients still fail to respond. Preclinical data suggest that stereotactic body radiotherapy (SBRT) could work synergistically with CPIs by acting as an in situ cancer vaccine, thus potentially increasing response rates and prolonging disease control. Though SBRT administered concurrently with CPIs has been shown to be safe, evidence of its efficacy from large randomized trials is still lacking. The aim of this multicenter randomized phase II trial is to assess whether SBRT administered concurrently with CPIs could prolong progression-free survival as compared to standard of care in patients with advanced solid tumors. METHODS/DESIGN: Ninety-eight patients with locally advanced or metastatic disease will be randomized in a 1:1 fashion to receive CPI treatment combined with SBRT (Arm A) or CPI monotherapy (Arm B). Randomization will be stratified according to tumor histology (melanoma, renal, urothelial, head and neck squamous cell or non-small cell lung carcinoma) and disease burden (≤ or > 3 cancer lesions). The recommended SBRT dose is 24Gy in 3 fractions, which will be administered to a maximum of 3 lesions and is to be completed prior to the second or third CPI cycle (depending on CPI treatment schedule). The study's primary endpoint is progression-free survival as per iRECIST. Secondary endpoints include overall survival, objective response, local control, quality of life and toxicity. Translational analyses will be performed using blood, fecal and tissue samples. DISCUSSION: The CHEERS trial will provide further insights into the clinical and immunological impact of SBRT when combined with CPIs in patients with advanced solid tumors. Furthermore, study results will inform the design of future immuno-radiotherapy trials. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT03511391 . Registered 17 April 2018.

Consistency of causal claims in observational studies: a review of papers published in a general medical journal.

OBJECTIVE: To evaluate the consistency of causal statements in observational studies published in The BMJ. DESIGN: Review of observational studies published in a general medical journal. DATA SOURCE: Cohort and other longitudinal studies describing an exposure-outcome relationship published in The BMJ in 2018. We also had access to the submitted papers and reviewer reports. MAIN OUTCOME MEASURES: Proportion of published research papers with 'inconsistent' use of causal language. Papers where language was consistently causal or non-causal were classified as 'consistently causal' or 'consistently not causal', respectively. For the 'inconsistent' papers, we then compared the published and submitted version. RESULTS: Of 151 published research papers, 60 described eligible studies. Of these 60, we classified the causal language used as 'consistently causal' (48%), 'inconsistent' (20%) and 'consistently not causal'(32%). Eleven out of 12 (92%) of the 'inconsistent' papers were already inconsistent on submission. The inconsistencies found in both submitted and published versions were mainly due to mismatches between objectives and conclusions. One section might be carefully phrased in terms of association while the other presented causal language. When identifying only an association, some authors jumped to recommending acting on the findings as if motivated by the evidence presented. CONCLUSION: Further guidance is necessary for authors on what constitutes a causal statement and how to justify or discuss assumptions involved. Based on screening these papers, we provide a list of expressions beyond the obvious 'cause' word which may inspire a useful more comprehensive compendium on causal language.

Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework.

Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records' analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits and whether a sensitivity analysis regarding the missingness mechanism is required; 2) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data and transparently reporting the potential effect on the study results, therefore increasing the confidence in and reproducibility of research findings.

The Multicenter, Randomized, Phase 2 PEACE V-STORM Trial: Defining the Best Salvage Treatment for Oligorecurrent Nodal Prostate Cancer Metastases.

Optimal local treatment for nodal oligorecurrent prostate cancer is unknown. The randomized phase 2 PEACE V-STORM trial will explore the best treatment approach in this setting. Early results on the acute toxicity profile are projected to be published in quarter 3, 2021.

Motion-Control Shoes Reduce the Risk of Pronation-Related Pathologies in Recreational Runners: A Secondary Analysis of a Randomized Controlled Trial.

OBJECTIVE: To investigate whether motion-control shoes reduce the risk of pronation-related injuries in recreational runners. DESIGN: Secondary analysis of a randomized controlled trial of the effect of shoes on running injuries. METHODS: Three hundred seventy-two recreational runners were randomized to receive either standard neutral or motion-control shoes and were followed up for 6 months regarding running activity and injury. Running injuries that occurred during this period were registered and classified as pronation-related injuries (Achilles tendinopathy, plantar fasciopathy, exercise-related lower-leg pain, and anterior knee pain) or other running-related injuries. With the use of competing risk analysis, the relationship between pronation-related and other running-related injuries and shoe type was evaluated by estimating the cause-specific hazard, controlling for other possible confounders like age, sex, body mass index, previous injury, and sport participation pattern. RESULTS: Twenty-five runners sustained pronation-related running injuries and 68 runners sustained other running-related injuries. Runners wearing the motion-control shoes had a lower risk of pronation-related running injuries compared with runners who wore standard neutral shoes (hazard ratio = 0.41; 95% confidence interval: 0.17, 0.98). There was no effect of shoe type (hazard ratio = 0.68; 95% confidence interval: 0.41, 1.10) on the risk of other running-related injuries. CONCLUSION: Motion-control shoes may reduce the risk of pronation-related running injuries, but did not influence the risk of other running-related injuries. J Orthop Sports Phys Ther 2021;51(3):135-143. Epub 11 Dec 2020. doi:10.2519/jospt.2021.9710.

Formulating causal questions and principled statistical answers.

Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on www.ofcaus.org, where SAS and Stata code for analysis is also provided.

Low-dose hydroxychloroquine therapy and mortality in hospitalised patients with COVID-19: a nationwide observational study of 8075 participants.

Hydroxychloroquine (HCQ) has been largely used and investigated as therapy for COVID-19 across various settings at a total dose usually ranging from 2400 mg to 9600 mg. In Belgium, off-label use of low-dose HCQ (total 2400 mg over 5 days) was recommended for hospitalised patients with COVID-19. We conducted a retrospective analysis of in-hospital mortality in the Belgian national COVID-19 hospital surveillance data. Patients treated either with HCQ monotherapy and supportive care (HCQ group) were compared with patients treated with supportive care only (no-HCQ group) using a competing risks proportional hazards regression with discharge alive as competing risk, adjusted for demographic and clinical features with robust standard errors. Of 8075 patients with complete discharge data on 24 May 2020 and diagnosed before 1 May 2020, 4542 received HCQ in monotherapy and 3533 were in the no-HCQ group. Death was reported in 804/4542 (17.7%) and 957/3533 (27.1%), respectively. In the multivariable analysis, mortality was lower in the HCQ group compared with the no-HCQ group [adjusted hazard ratio (aHR) = 0.684, 95% confidence interval (CI) 0.617-0.758]. Compared with the no-HCQ group, mortality in the HCQ group was reduced both in patients diagnosed ≤5 days (n = 3975) and >5 days (n = 3487) after symptom onset [aHR = 0.701 (95% CI 0.617-0.796) and aHR = 0.647 (95% CI 0.525-0.797), respectively]. Compared with supportive care only, low-dose HCQ monotherapy was independently associated with lower mortality in hospitalised patients with COVID-19 diagnosed and treated early or later after symptom onset.