Assessment of proportional hazard assumption in aggregate data: a systematic review on statistical methodology in clinical trials using time-to-event endpoint

Interpretable deep learning survival predictions in sporadic Creutzfeldt-Jakob disease

BACKGROUND

Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive and fatal prion disease with significant public health implications. Survival is heterogenous, posing challenges for prognostication and care planning. We developed a survival model using diagnostic data from comprehensive UK sCJD surveillance.

METHODS

Using national CJD surveillance data from the United Kingdom (UK), we included 655 cases of probable or definite sCJD according to 2017 international consensus diagnostic criteria between 01/2017 and 01/2022. Data included symptoms at diagnosis, CSF RT-QuIC and 14-3-3, MRI and EEG findings, as well as sex, age, PRNP codon 129 polymorphism, CSF total protein and S100b. An artificial neural network based multitask logistic regression was used for survival analysis. Model-agnostic interpretation methods was used to assess the contribution of individual features on model outcome.

RESULTS

Our algorithm had a c-index of 0.732, IBS of 0.079, and AUC at 5 and 10 months of 0.866 and 0.872, respectively. This modestly improved on Cox proportional hazard model (c-index 0.730, IBS 0.083, AUC 0.852 and 0863) but was not statistically significant. Both models identified codon 129 polymorphism and CSF 14-3-3 to be significant predictive features.

CONCLUSIONS

sCJD survival can be predicted using routinely collected clinical data at diagnosis. Our analysis pipeline has similar levels of performance to classical methods and provide clinically meaningful interpretation which help deepen clinical understanding of the condition. Further development and clinical validation will facilitate improvements in prognostication, care planning, and stratification to clinical trials.

Survival causal rule ensemble method considering the main effect for estimating heterogeneous treatment effects

With an increasing focus on precision medicine in medical research, numerous studies have been conducted in recent years to clarify the relationship between treatment effects and patient characteristics. The treatment effects for patients with different characteristics are always heterogeneous, and therefore, various heterogeneous treatment effect machine learning estimation methods have been proposed owing to their flexibility and high estimation accuracy. However, most machine learning methods rely on black-box models, preventing direct interpretation of the relationship between patient characteristics and treatment effects. Moreover, most of these studies have focused on continuous or binary outcomes, although survival outcomes are also important in medical research. To address these challenges, we propose a heterogeneous treatment effect estimation method for survival data based on RuleFit, an interpretable machine learning method. Numerical simulation results confirmed that the prediction performance of the proposed method was comparable to that of existing methods. We also applied a dataset from an HIV study, the AIDS Clinical Trials Group Protocol 175 dataset, to illustrate the interpretability of the proposed method using real data. Consequently, the proposed survival causal rule ensemble method provides an interpretable model with sufficient estimation accuracy.

Exploring the characteristics, methods and reporting of systematic reviews with meta-analyses of time-to-event outcomes: a meta-epidemiological study

BACKGROUND

Time-to-event analysis is associated with methodological complexities. Previous research identified flaws in the reporting of time-to-event analyses in randomized trial publications. These hardships impose challenges for meta-analyses of time-to-event outcomes based on aggregate data. We examined the characteristics, reporting and methods of systematic reviews including such analyses.

METHODS

Through a systematic search (02/2017-08/2020), we identified 50 Cochrane Reviews with ≥ 1 meta-analysis based on the hazard ratio (HR) and a corresponding random sample (n = 50) from core clinical journals (Medline; 08/02/2021). Data was extracted in duplicate and included outcome definitions, general and time-to-event specific methods and handling of time-to-event relevant trial characteristics.

RESULTS

The included reviews analyzed 217 time-to-event outcomes (Median: 2; IQR 1-2), most frequently overall survival (41%). Outcome definitions were provided for less than half of time-to-event outcomes (48%). Few reviews specified general methods, e.g., included analysis types (intention-to-treat, per protocol) (35%) and adjustment of effect estimates (12%). Sources that review authors used for retrieval of time-to-event summary data from publications varied substantially. Most frequently reported were direct inclusion of HRs (64%) and reference to established guidance without further specification (46%). Study characteristics important to time-to-event analysis, such as variable follow-up, informative censoring or proportional hazards, were rarely reported. If presented, complementary absolute effect estimates calculated based on the pooled HR were incorrectly calculated (14%) or correct but falsely labeled (11%) in several reviews.

CONCLUSIONS

Our findings indicate that limitations in reporting of trial time-to-event analyses translate to the review level as well. Inconsistent reporting of meta-analyses of time-to-event outcomes necessitates additional reporting standards.

Proportional Hazards Violations in Phase III Cancer Clinical Trials: A Potential Source of Trial Misinterpretation

PURPOSE

Survival analyses of novel agents with long-term responders often exhibit differential hazard rates over time. Such proportional hazards violations (PHV) may reduce the power of the log-rank test and lead to misinterpretation of trial results. We aimed to characterize the incidence and study attributes associated with PHVs in phase III oncology trials and assess the utility of restricted mean survival time and maximum combination test as additional analyses.

EXPERIMENTAL DESIGN

Clinicaltrials.gov and PubMed were searched to identify two-arm, randomized, phase III superiority-design cancer trials with time-to-event primary endpoints and published results through 2020. Patient-level data were reconstructed from published Kaplan-Meier curves. PHVs were assessed using Schoenfeld residuals.

RESULTS

Three hundred fifty-seven Kaplan-Meier comparisons across 341 trials were analyzed, encompassing 292,831 enrolled patients. PHVs were identified in 85/357 [23.8%; 95% confidence interval (CI), 19.7%, 28.5%] comparisons. In multivariable analysis, non-overall survival endpoints [OR, 2.16 (95% CI, 1.21, 3.87); P = 0.009] were associated with higher odds of PHVs, and immunotherapy comparisons [OR 1.94 (95% CI, 0.98, 3.86); P = 0.058] were weakly suggestive of higher odds of PHVs. Few trials with PHVs (25/85, 29.4%) prespecified a statistical plan to account for PHVs. Fourteen trials with PHVs exhibited discordant statistical signals with restricted mean survival time or maximum combination test, of which 10 (71%) reported negative results.

CONCLUSIONS

PHVs are common across therapy types, and attempts to account for PHVs in statistical design are lacking despite the potential for results exhibiting nonproportional hazards to be misinterpreted.

Time to Functional Loss as an Endpoint in Huntington's Disease Trials: Enrichment and Sample Size

BACKGROUND

Clinical trial scenarios can be modeled using data from observational studies, providing critical information for design of real-world trials. The Huntington's Disease Integrated Staging System (HD-ISS) characterizes disease progression over an individual's lifespan and allows for flexibility in the design of trials with the goal of delaying progression. Enrichment methods can be applied to the HD-ISS to identify subgroups requiring smaller estimated sample sizes.

OBJECTIVE

Investigate time to the event of functional decline (HD-ISS Stage 3) as an endpoint for trials in HD and present sample size estimates after enrichment.

METHODS

We classified individuals from observational studies according to the HD-ISS. We assessed the ability of the prognostic index normed (PIN) and its components to predict time to HD-ISS Stage 3. For enrichment, we formed groups from deciles of the baseline PIN distribution for HD-ISS Stage 2 participants. We selected enrichment subgroups closer to Stage 3 transition and estimated sample sizes, using delay in the transition time as the effect size.

RESULTS

In predicting time to HD-ISS Stage 3, PIN outperforms its components. Survival curves for each PIN decile show that groups with PIN from 1.48 to 2.74 have median time to Stage 3 of approximately 2 years and these are combined to create enrichment subgroups. Sample size estimates are presented by enrichment subgroup.

CONCLUSIONS

PIN is predictive of functional decline. A delay of 9 months or more in the transition to Stage 3 for an enriched sample yields feasible sample size estimates, demonstrating that this approach can aid in planning future trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

LOCC: a novel visualization and scoring of cutoffs for continuous variables with hepatocellular carcinoma prognosis as an example

BACKGROUND

The interpretation of large datasets, such as The Cancer Genome Atlas (TCGA), for scientific and research purposes, remains challenging despite their public availability. In this study, we focused on identifying gene expression profiles most relevant to patient prognosis and aimed to develop a method and database to address this issue. To achieve this, we introduced Luo's Optimization Categorization Curve (LOCC), an innovative tool for visualizing and scoring continuous variables against dichotomous outcomes. To demonstrate the efficacy of LOCC using real-world data, we analyzed gene expression profiles and patient data from TCGA hepatocellular carcinoma samples.

RESULTS

To showcase LOCC, we demonstrate an optimal cutoff for E2F1 expression in hepatocellular carcinoma, which was subsequently validated in an independent cohort. Compared to ROC curves and their AUC, LOCC offered a superior description of the predictive value of E2F1 expression across various cancer types. The LOCC score, comprised of factors representing significance, range, and impact of the biomarker, facilitated the ranking of all gene expression profiles in hepatocellular carcinoma, aiding in the evaluation and understanding of previously published prognostic gene signatures. We also demonstrate that LOCC does not have the same assumptions required of Cox proportional hazards modeling for accurate analysis. Repeated sampling demonstrated that LOCC scores outperformed ROC's AUC in discriminating predictors from non-predictors. Additionally, gene set enrichment analysis revealed significant associations between certain genes and prognosis, such as E2F target genes and G2M checkpoint with poor prognosis, and bile acid metabolism and oxidative phosphorylation with good prognosis.

CONCLUSION

In summary, we present LOCC as a novel visualization tool for the analysis of gene expression in cancer, particularly for understanding and selecting cutoffs. Our findings suggest that LOCC scores, which effectively rank genes based on their prognostic potential, represent a more suitable approach than ROC curves and Cox proportional hazard for prognostic modeling and understanding in cancer gene expression analysis. LOCC holds promise as an invaluable tool for advancing precision medicine and furthering biomarker research. Further research regarding multivariable integration and validation will help LOCC reach its full potential and establish its utility across diverse cancer types and clinical settings.

Environmental exposures associated with early childhood recurrent wheezing in the mother and child in the environment birth cohort: a time-to-event study

BACKGROUND

Antenatal factors and environmental exposures contribute to recurrent wheezing in early childhood.

AIM

To identify antenatal and environmental factors associated with recurrent wheezing in children from birth to 48 months in the mother and child in the environment cohort, using time-to-event analysis.

METHOD

Maternal interviews were administered during pregnancy and postnatally and children were followed up from birth to 48 months (May 2013-October 2019). Hybrid land-use regression and dispersion modelling described residential antenatal exposure to nitrogen dioxide (NO2) and particulate matter of 2.5 µm diameter (PM2.5). Wheezing status was assessed by a clinician. The Kaplan-Meier hazard function and Cox-proportional hazard models provided estimates of risk, adjusting for exposure to environmental tobacco smoke (ETS), maternal smoking, biomass fuel use and indoor environmental factors.

RESULTS

Among 520 mother-child pairs, 85 (16%) children, had a single wheeze episode and 57 (11%) had recurrent wheeze. Time to recurrent wheeze (42.9 months) and single wheeze (37.8 months) among children exposed to biomass cooking fuels was significantly shorter compared with children with mothers using electricity (45.9 and 38.9 months, respectively (p=0.03)). Children with mothers exposed to antenatal ETS were 3.8 times more likely to have had recurrent wheeze compared with those not exposed (adjusted HR 3.8, 95% CI 1.3 to 10.7). Mean birth month NO2 was significantly higher among the recurrent wheeze category compared with those without wheeze. NO2 and PM2.5 were associated with a 2%-4% adjusted increased wheezing risk.

CONCLUSION

Control of exposure to ETS and biomass fuels in the antenatal period is likely to delay the onset of recurrent wheeze in children from birth to 48 months.

Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling

This study investigates the heterogeneity of a biomarker's discriminative performance for predicting subsequent time-to-event outcomes across different patient subgroups. While the area under the curve (AUC) for the time-dependent receiver operating characteristic curve is commonly used to assess biomarker performance, the partial time-dependent AUC (PAUC) provides insights that are often more pertinent for population screening and diagnostic testing. To achieve this objective, we propose a regression model tailored for PAUC and develop two distinct estimation procedures for discrete and continuous covariates, employing a pseudo-partial likelihood method. Simulation studies are conducted to assess the performance of these procedures across various scenarios. We apply our model and inference procedure to the Alzheimer's Disease Neuroimaging Initiative data set to evaluate potential heterogeneities in the discriminative performance of biomarkers for early Alzheimer's disease diagnosis based on patients' characteristics.

Mortality among veterans with epilepsy: Temporal significance of traumatic brain injury exposure

OBJECTIVE

Epilepsy is associated with significant mortality risk. There is limited research examining how traumatic brain injury (TBI) timing affects mortality in relation to the onset of epilepsy. We aimed to assess the temporal relationship between epilepsy and TBI regarding mortality in a cohort of post-9/11 veterans.

METHODS

This retrospective cohort study included veterans who received health care in the Defense Health Agency and the Veterans Health Administration between 2000 and 2019. For those diagnosed with epilepsy, the index date was the date of first antiseizure medication or first seizure; we simulated the index date for those without epilepsy. We created the study groups by the index date and first documented TBI: (1) controls (no TBI, no epilepsy), (2) TBI only, (3) epilepsy only, (4) TBI before epilepsy, (5) TBI within 6 months after epilepsy, and (6) TBI >6 months after epilepsy. Kaplan-Meier estimates of all-cause mortality were calculated, and log-rank tests were used to compare unadjusted cumulative mortality rates among groups compared to controls. Cox proportional hazard models were used to compute hazard ratios (HRs) with 95% confidence intervals (CIs).

RESULTS

Among 938 890 veterans, 27 436 (2.92%) met epilepsy criteria, and 264 890 (28.22%) had a TBI diagnosis. Mortality was higher for veterans with epilepsy than controls (6.26% vs. 1.12%; p < .01). Veterans with TBI diagnosed ≤6 months after epilepsy had the highest mortality hazard (HR = 5.02, 95% CI = 4.21-5.99) compared to controls, followed by those with TBI before epilepsy (HR = 4.25, 95% CI = 3.89-4.58), epilepsy only (HR = 4.00, 95% CI = 3.67-4.36), and TBI >6 months after epilepsy (HR = 2.49, 95% CI = 2.17-2.85). These differences were significant across groups.

SIGNIFICANCE

TBI timing relative to epilepsy affects time to mortality; TBI within 6 months after epilepsy or before epilepsy diagnosis was associated with earlier time to death compared to those with epilepsy only or TBI >6 months after epilepsy.

A Critical Analysis of All-Cause Deaths during COVID-19 Vaccination in an Italian Province

Immortal time bias (ITB) is common in cohort studies and distorts the association estimates between the treated and untreated. We used data from an Italian study on COVID-19 vaccine effectiveness, with a large cohort, long follow-up, and adjustment for confounding factors, affected by ITB, with the aim to verify the real impact of the vaccination campaign by comparing the risk of all-cause death between the vaccinated population and the unvaccinated population. We aligned all subjects on a single index date and considered the "all-cause deaths" outcome to compare the survival distributions of the unvaccinated group versus various vaccination statuses. The all-cause-death hazard ratios in univariate analysis for vaccinated people with 1, 2, and 3/4 doses versus unvaccinated people were 0.88, 1.23, and 1.21, respectively. The multivariate values were 2.40, 1.98, and 0.99. Possible explanations of this trend of the hazard ratios as vaccinations increase could be a harvesting effect; a calendar-time bias, accounting for seasonality and pandemic waves; a case-counting window bias; a healthy-vaccinee bias; or some combination of these factors. With 2 and even with 3/4 doses, the calculated Restricted Mean Survival Time and Restricted Mean Time Lost have shown a small but significant downside for the vaccinated populations.

Clinical association between plan complexity and the local-recurrence-free-survival of non-small-cell lung cancer patients receiving stereotactic body radiation therapy

PURPOSE

To investigate the clinical impact of plan complexity on the local recurrence-free survival (LRFS) of non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).

METHODS

Data from 123 treatment plans for 113 NSCLC patients were analyzed. Plan-averaged beam modulation (PM), plan beam irregularity (PI), monitor unit/Gy (MU/Gy) and spherical disproportion (SD) were calculated. The γ passing rates (GPR) were measured using ArcCHECK 3D phantom with 2 %/2mm criteria. High complexity (HC) and low complexity (LC) groups were statistically stratified based on the aforementioned metrics, using cutoffs determined by their significance in correlation with survival time, as calculated using the R-3.6.1 packages. Kaplan-Meier analysis, Cox regression, and Random Survival Forest (RSF) models were employed for the analysis of local recurrence-free survival (LRFS). Propensity-score-matched pairs were generated to minimize bias in the analysis.

RESULTS

The median follow-up time for all patients was 25.5 months (interquartile range 13.4-41.2). The prognostic capacity of PM was suggested using RSF, based on Variable Importance and Minimal Depth methods. The 1-, 2-, and 3-year LRFS rates in the HC group were significantly lower than those in the LC group (p = 0.023), when plan complexity was defined by PM. However, no significant difference was observed between the HC and LC groups when defined by other metrics (p > 0.05). All γ passing rates exceeded 90.5 %.

CONCLUSIONS

This study revealed a significant association between higher PM and worse LRFS in NSCLC patients treated with SBRT. This finding offers additional clinical evidence supporting the potential optimization of pre-treatment quality assurance protocols.

Clinical and pathological factors associated with survival in patients with pancreatic cancer who receive adjuvant therapy after neoadjuvant therapy: A retrospective multi-institutional analysis

BACKGROUND

Neoadjuvant therapy is being increasingly used for patients with pancreatic cancer. The role of adjuvant therapy in these patients is unclear. The purpose of this study was to identify clinical and pathologic characteristics that are associated with longer overall survival in patients with pancreatic cancer who receive adjuvant therapy after neoadjuvant therapy.

METHODS

This study was conducted using multi-institutional data. All patients underwent surgery after at least 1 cycle of neoadjuvant therapy for pancreatic cancer. Patients who died within 3 months after surgery and were known to have distant metastasis or macroscopic residual disease were excluded. Mann-Whitney U test, χ2 analysis, Kaplan-Meier plot, and univariate and multivariate Cox regression analysis were performed as statistical analyses.

RESULTS

In the present study, 529 patients with resected pancreatic cancer after neoadjuvant therapy were reviewed. For neoadjuvant therapy, 177 (33.5%) patients received neoadjuvant chemotherapy, and 352 (66.5%) patients received neoadjuvant chemoradiotherapy. The median duration of neoadjuvant therapy was 7.0 months (interquartile range, 5.0-8.7). Patients were followed for a median of 23.0 months after surgery. Adjuvant therapy was administered to 297 (56.1%) patients and was not associated with longer overall survival for the entire cohort (24 vs 22 months, P = .31). Interaction analysis showed that adjuvant therapy was associated with longer overall survival in patients who received less than 4 months neoadjuvant therapy (hazard ratio 0.40; 95% confidence interval 0.17-0.95; P = .03) or who had microscopic margin positive surgical resections (hazard ratio 0.56; 95% confidence interval 0.33-0.93; P = .03).

CONCLUSION

In this retrospective study, there was a survival benefit associated with adjuvant therapy for patients who received less than 4 months of neoadjuvant therapy or had microscopic positive margins.

Determining optimal cutoff scores of Cognitive Abilities Screening Instrument to identify dementia and mild cognitive impairment in Taiwan

BACKGROUND

The early detection of dementia depends on efficient methods for the assessment of cognitive capacity. Existing cognitive screening tools are ill-suited to the differentiation of cognitive status, particularly when dealing with early-stage impairment.

METHODS

The study included 8,979 individuals (> 50 years) with unimpaired cognitive functions, mild cognitive impairment (MCI), or dementia. This study sought to determine optimal cutoffs values for the Cognitive Abilities Screening Instrument (CASI) aimed at differentiating between individuals with or without dementia as well as between individuals with or without mild cognitive impairment. Cox proportional hazards models were used to evaluate the value of CASI tasks in predicting conversion from MCI to all-cause dementia, dementia of Alzheimer's type (DAT), or to vascular dementia (VaD).

RESULTS

Our optimized cutoff scores achieved high accuracy in differentiating between individuals with or without dementia (AUC = 0.87-0.93) and moderate accuracy in differentiating between CU and MCI individuals (AUC = 0.67 - 0.74). Among individuals without cognitive impairment, scores that were at least 1.5 × the standard deviation below the mean scores on CASI memory tasks were predictive of conversion to dementia within roughly 2 years after the first assessment (all-cause dementia: hazard ratio [HR] = 2.81 - 3.53; DAT: 1.28 - 1.49; VaD: 1.58). Note that the cutoff scores derived in this study were lower than those reported in previous studies.

CONCLUSION

Our results in this study underline the importance of establishing optimal cutoff scores for individuals with specific demographic characteristics and establishing profiles by which to guide CASI analysis.

Case-control matching-guided exposure-efficacy relationship for avelumab in patients with urothelial carcinoma

Exposure-response (E-R) analyses are an integral component of understanding the benefit/risk profile of novel oncology therapeutics. These analyses are typically conducted using data from the treatment arm to characterize the relationship between drug exposure (low vs. high) and efficacy or safety outcomes. For example, outcomes of patients with lower exposure in the treatment arm (e.g., Q1) might be compared to outcomes of those with higher drug exposure (Q2, Q3, and Q4). Outcomes from the lowest exposure quartile may be also compared to the control arm to evaluate whether the Q1 subgroup derived clinical benefit. However, the sample size and the distribution of patient baseline characteristics and disease risk factors are not balanced in such a comparison (Q1 vs. control), which may bias the analysis and causal interpretation of clinical benefit in the Q1 subgroup. Herein, we report the use of case-control matching to account for this bias and better understand the E-R relationship for avelumab in urothelial carcinoma, a PD-L1 inhibitor approved for the treatment of several cancers. Data from JAVELIN-100 was utilized which is a phase III study of avelumab in first-line maintenance treatment in patients with urothelial carcinoma; this clinical study demonstrated superiority of avelumab versus best-supportive care leading to approval in the United States, Europe, and other countries. A post hoc case-control matching method was implemented to compare the efficacy outcome between Q1 avelumab subgroup and matched patients extracted from the control arm with similar baseline characteristics, which showed a clinically relevant difference in overall survival in favor of the Q1 avelumab subgroup. This analysis demonstrates the importance of accounting for imbalance in important baseline covariates when comparing efficacy outcomes between subgroups within the treatment arm versus the control arm.

DoSurvive: A webtool for investigating the prognostic power of a single or combined cancer biomarker

We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.e. overall survival, disease-specific survival, disease-free interval, and progression-free interval, in 33 cancer types. Notably, the implemented AFT model provides an alternative method for genes/features which failed the proportional hazard assumption in Cox regression. With the unprecedented number of survival models implemented and high flexibility in analysis, DoSurvive is a unique platform for the identification of clinically relevant targets for cancer researcher and practitioners. DoSurvive is freely available at http://dosurvive.lab.nycu.edu.tw/.

Meta-epidemiological review identified variable reporting and handling of time-to-event analyses in publications of trials included in meta-analyses of systematic reviews

OBJECTIVES

Previous findings indicate limited reporting of systematic reviews with meta-analyses of time-to-event (TTE) outcomes. We assessed corresponding available information in trial publications included in such meta-analyses.

STUDY DESIGN AND SETTING

We extracted data from all randomized trials in pairwise, hazard ratio (HR)-based meta-analyses of primary outcomes and overall survival of 50 systematic reviews systematically identified from the Cochrane Database and Core Clinical Journals. Data on methods and characteristics relevant for TTE analysis of reviews, trials, and outcomes were extracted.

RESULTS

Meta-analyses included 235 trials with 315 trial analyses. Most prominently assessed was overall survival (91%). Definitions (61%), censoring reasons (41%), and follow-up specifications (56%) for trial outcomes were often missing. Available TTE data per trial were most frequently survival curves (83%), log-rank P values (76%), and HRs (72%). When trial TTE data recalculation was reported, reviews mostly specified HRs or P values (each 5%). Reviews primarily included intention-to-treat analyses (64%) and analyses not adjusted for covariates (25%). Except for missing outcome data, TTE-relevant trial characteristics, for example, informative censoring, treatment switching, and proportional hazards, were sporadically addressed in trial publications. Reporting limitations in trial publications translate to the review level.

CONCLUSION

TTE (meta)-analyses, in trial and review publications, need clear reporting standards.

Comparison between de novo and metachronous metastatic breast cancer: the presence of a primary tumour is not the only difference-a Dutch population-based study from 2008 to 2018

PURPOSE

The aim of this study was to compare characteristics and survival of patients with de novo and metachronous metastatic breast cancer.

METHODS

Data of patients with metastatic breast cancer were obtained from the Netherlands Cancer Registry. Patients were categorized as having de novo metastatic breast cancer (n = 8656) if they had distant metastases at initial presentation, or metachronous metastatic disease (n = 2374) in case they developed metastases within 5 or 10 years after initial breast cancer diagnosis. Clinicopathological characteristics and treatments of these two groups were compared, after which multiple imputation was performed to account for missing data. Overall survival was compared for patients treated with systemic therapy in the metastatic setting, using Kaplan Meier curves and multivariable Cox proportional hazards models. The hazard ratio for overall survival of de novo versus metachronous metastases was assessed accounting for time-varying effects.

RESULTS

Compared to metachronous patients, patients with de novo metastatic breast cancer were more likely to be ≥ 70 years, to have invasive lobular carcinoma, clinical T3 or T4 tumours, loco-regional lymph node metastases, HER2 positivity, bone only disease and to have received systemic therapy in the metastatic setting. They were less likely to have triple negative tumours and liver or brain metastases. Patients with de novo metastases survived longer (median 34.7 months) than patients with metachronous metastases (median 24.3 months) and the hazard ratio (0.75) varied over time.

CONCLUSIONS

Differences in clinicopathological characteristics and survival between de novo and metachronous metastatic breast cancer highlight that these are distinct patients groups.

The Association between Exposures and Disease Characteristics in Familial Pulmonary Fibrosis

Rationale: Heterogeneous characteristics are observed in familial pulmonary fibrosis (FPF), suggesting that nongenetic factors contribute to disease manifestations. Objectives: To determine the relationship between environmental exposures and disease characteristics of FPF, including the morphological characteristics on chest computed tomography (CT) scan, and timing of FPF symptom onset, lung transplantation, or death. Methods: Subjects with FPF with an exposure questionnaire and chest CT were selected from a prospective cohort at Vanderbilt. Disease characteristics were defined by lung parenchymal findings on chest CT associated with fibrotic hypersensitivity pneumonitis (fHP) or usual interstitial pneumonia (UIP) and by time from birth to symptom onset or a composite of lung transplantation or death. After assessing the potential for confounding by sex or smoking, adjusted logistic or Cox proportional hazards regression models identified exposures associated with fHP or UIP CT findings. Findings were validated in a cohort of patients with sporadic pulmonary fibrosis enrolled in the LTRC (Lung Tissue Research Consortium) study. Results: Among 159 subjects with FPF, 98 (61.6%) were males and 96 (60.4%) were ever-smokers. Males were less likely to have CT features of fHP, including mosaic attenuation (FPF: adjusted [for sex and smoking] odds ratio [aOR], 0.27; 95% confidence interval [CI], 0.09-0.76; P = 0.01; LTRC: aOR, 0.35; 95% CI, 0.21-0.61; P = 0.0002). Organic exposures, however, were not consistently associated with fHP features in either cohort. Smoking was a risk factor for honeycombing in both cohorts (FPF: aOR, 2.19; 95% CI, 1.12-4.28; P = 0.02; LTRC: aOR, 1.69; 95% CI, 1.22-2.33; P = 0.002). Rock dust exposure may also be associated with honeycombing, although the association was not statistically-significant when accounting for sex and smoking (FPF: aOR, 2.27; 95% CI, 0.997-5.15; P = 0.051; LTRC: aOR, 1.51; 95% CI, 0.97-2.33; P = 0.07). In the FPF cohort, ever-smokers experienced a shorter transplant-free survival (adjusted hazard ratio, 1.64; 95% CI, 1.07-2.52; P = 0.02), whereas sex was not associated with differential survival (male adjusted hazard ratio, 0.75; 95% CI, 0.50-1.14; P = 0.18). Conclusions: In FPF, smoking contributes to shortened transplant-free survival and development of honeycombing, a finding that is also likely applicable to sporadic pulmonary fibrosis. Females are more likely to manifest CT features of fHP (mosaic attenuation), a finding that was incompletely explained by sex differences in exposures. These findings may have implications for pulmonary fibrosis classification and management.

On-Admission Anemia and Survival Rate in COVID-19 Patients

Anemia often worsens the severity of respiratory illnesses, and few studies have so far elucidated the impact of anemia on COVID-19 infection. This study aimed to evaluate the effect of anemia at admission on the overall survival of COVID-19 patients using accelerated failure time (AFT) models.

This registry-based, single-center retrospective cohort study was conducted in a university hospital in Ilam, the southwest of Iran, between March 2020 and September 2021. AFT models were applied to set the data of 2,441 COVID-19 patients. Performance of AFT models was assessed using Akaike’s information criterion (AIC) and Cox-Snell residual. On-admission anemia was defined as hemoglobin (Hb) concentration <120 g/l in men, <110 g/l in women, and <100 g/l in pregnant women.

The median in-hospital survival times for anemic and non-anemic patients were 27 and 31 days, respectively. Based on the AIC and Cox-Snell residual graph, the Weibull model had the lowest AIC and it was the best fitted model to the data set among AFT models. In the adjusted model, the results of the Weibull model suggested that the anemia (adjusted time ratio: 1.04; 95% CI: 1.00-1.08; p = 0.03) was the accelerated factor for progression to death in COVID-19 patients. Each unit of increase in hemoglobin in COVID-19 patients enhanced the survival rate by 4%.

Anemia is an independent risk factor associated with the risk of mortality from COVID-19 infection. Therefore, healthcare professionals should be more sensitive to the Hb level of COVID-19 patients upon admission.

Risk of Hepatocellular Carcinoma With Tenofovir vs Entecavir Treatment for Chronic Hepatitis B Virus: A Reconstructed Individual Patient Data Meta-analysis

IMPORTANCE

Conventional meta-analyses with aggregated study-level data have yielded conflicting results for the comparative effectiveness of tenofovir disoproxil fumarate vs entecavir in reducing hepatocellular carcinoma (HCC) risk among patients with chronic hepatitis B virus. Within-study heterogeneity, between-study heterogeneity, and the inability of conventional meta-analyses to capture time-to-event data were associated with these results.

OBJECTIVE

To perform a reconstructed individual patient data meta-analysis of high-quality propensity score-matched studies to provide robust estimates for comparative HCC risk between groups receiving tenofovir or entecavir.

DATA SOURCES

Medline and Embase databases were searched from inception to October 6, 2021.

STUDY SELECTION

The initial search yielded 3435 articles. Fourteen studies that used propensity score matching to balance baseline characteristics were included in the final analysis.

DATA EXTRACTION AND SYNTHESIS

The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was followed. Individual patient data were reconstructed from Kaplan-Meier curves. Risk of HCC was evaluated using random-effects hazard ratios (HRs) via a shared-frailty model and a Cox proportional hazards model stratified by study group. Restricted mean survival time (RMST) analysis was conducted to account for varying estimated treatment effect across time.

MAIN OUTCOMES AND MEASURES

The comparative risk of HCC with tenofovir vs entecavir treatment.

RESULTS

From analysis of 14 studes with 24 269 patients (10 534 receiving tenofovir and 13 735 receiving entecavir; mean age, 49.86 [95% CI, 48.35-51.36] years; 65.05% [95% CI, 58.60%-71.00%] men), tenofovir was associated with decreased HCC incidence compared with entecavir (stratified Cox HR, 0.85 [95% CI, 0.76-0.94] at 5 years; P = .002). However, there was no significant difference in subanalysis of clinical cohort studies (stratified Cox HR, 0.92 [95% CI, 0.80-1.06] at 5 years; P = .24). Among administrative database studies, proportionality was violated, and HRs could not be obtained via Cox proporational hazards-based models. The mean time to HCC development in RMST analysis was 2.8 (95% CI, 1.8-3.7) weeks longer (P < .001) for tenofovir vs entecavir at 5 years. The RMST analyses for other subgroups revealed either insignificant or minimal differences (<3 weeks) in the mean time to HCC at 5 years.

CONCLUSIONS AND RELEVANCE

In this meta-analysis, there was no clinically meaningful difference in the risk of HCC between patients who received entecavir and patients who received tenofovir. There was no difference between tenofovir and entecavir among clinical cohort studies, whereas the mean time to HCC development was less than 3 weeks longer for patients who received tenofovir vs those who received entecavir at year 5 among administrative database studies. The choice between tenofovir or entecavir should be decided based on patient convenience and tolerability.

Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network

BACKGROUND

Synthesis of clinical effectiveness from multiple trials is a well-established component of decision-making. Time-to-event outcomes are often synthesised using the Cox proportional hazards model assuming a constant hazard ratio over time. However, with an increasing proportion of trials reporting treatment effects where hazard ratios vary over time and with differing lengths of follow-up across trials, alternative synthesis methods are needed.

OBJECTIVES

To compare and contrast five modelling approaches for synthesis of time-to-event outcomes and provide guidance on key considerations for choosing between the modelling approaches.

METHODS

The Cox proportional hazards model and five other methods of estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption, were applied to a network of melanoma trials reporting overall survival: restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models.

RESULTS

All models fitted the melanoma network acceptably well. However, there were important differences in extrapolations of the survival curve and interpretability of the modelling constraints demonstrating the potential for different conclusions from different modelling approaches.

CONCLUSION

The restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can accommodate non-proportional hazards and differing lengths of trial follow-up within a network meta-analysis of time-to-event outcomes. We recommend that model choice is informed using available and relevant prior knowledge, model transparency, graphically comparing survival curves alongside observed data to aid consideration of the reliability of the survival estimates, and consideration of how the treatment effect estimates can be incorporated within a decision model.

Associated mortality risk of atypical antipsychotic medication in individuals with dementia

BACKGROUND

Antipsychotic medications such as risperidone, olanzapine and aripiprazole are used to treat psychological and behavioural symptoms among dementia patients. Current evidence indicate prescription rates for antipsychotics vary and wider consensus to evaluate clinical epidemiological outcomes is limited.

AIM

To investigate the potential impact of atypical antipsychotics on the mortality of patients with dementia.

METHODS

A retrospective clinical cohort study was developed to review United Kingdom Clinical Record Interactive Search system based data between January 1, 2013 to December 31, 2017. A descriptive statistical method was used to analyse the data. Mini Mental State Examination (MMSE) scores were used to assess the severity and stage of disease progression. A cox proportional hazards model was developed to evaluate the relationship between survival following diagnosis and other variables.

RESULTS

A total of 1692 patients were identified using natural language processing of which, 587 were prescribed olanzapine, quetiapine or risperidone (common group) whilst 893 (control group) were not prescribed any antipsychotics. Patients prescribed olanzapine showed an increased risk of death [hazard ratio (HR) = 1.32; 95% confidence interval (CI): 1.08-1.60; P < 0.01], as did those with risperidone (HR = 1.35; 95%CI: 1.18-1.54; P < 0.001). Patients prescribed quetiapine showed no significant association (HR = 1.09; 95%CI: 0.90-1.34; P = 0.38). Factors associated with a lower risk of death were: High MMSE score at diagnosis (HR = 0.72; 95%CI: 0.62-0.83; P < 0.001), identifying as female (HR = 0.73; 95%CI: 0.64-0.82; P < 0.001), and being of a White-British ethnic group (HR = 0.82; 95%CI: 0.72-0.94; P < 0.01).

CONCLUSION

A significant mortality risk was identified among those prescribed olanzapine and risperidone which contradicts previous findings although the study designs used were different. Comprehensive research should be conducted to better assess clinical epidemiological outcomes associated with diagnosis and therapies to improve clinical management of these patients.

Associations between Long-Term Air Pollution Exposure and Risk of Osteoporosis-Related Fracture in a Nationwide Cohort Study in South Korea

Bone health is a major concern for aging populations globally. Osteoporosis and bone mineral density are associated with air pollution, but less is known about the impacts of air pollution on osteoporotic fracture. We aimed to assess the associations between long-term air pollution exposure and risk of osteoporotic fracture in seven large Korean cities. We used Cox proportional hazard models to estimate hazard rations (HRs) of time-varying moving window of past exposures of particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3) for osteoporotic fracture in Korean adults (age ≥ 50 years) in the National Health Insurance Service-National Sample Cohort data, followed 2002 to 2015. HRs were calculated for an interquartile range (IQR) increase. Comorbidity and prescription associated with osteoporosis, age, sex, body mass index, health behaviors, and income were adjusted in the models. Effect modification by age, sex, exercise, and income was examined. We assessed 56,467 participants over 535,481 person-years of follow up. Linear and positive exposure-response associations were found for SO2, while PM10 and NO2 showed nonlinear associations. SO2 was associated with osteoporosis-related fracture with marginal significance (HR for an IQR [2 ppb] increase = 1.04, 95% CI: 1.00, 1.09). The SO2 HR estimates were robust in analyses applying various moving windows of exposure (from one to three years of past exposure) and two-pollutant models. The central HR estimate of O3 implied positive associations but was not significant (HR for 0.007 ppm increase = 1.01, 95% CI: 0.97, 1.06). PM10, CO, and NO2 did not show associations. Vulnerable groups by sex, age, exercise, and income varied across air pollutants and there was no evidence of effect modifications. Long-term exposure to SO2, but not PM10, CO, NO2 and O3, was associated with increased osteoporotic fracture risks in Korean adults.

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration.

Pitfalls and perils of survival analysis under incorrect assumptions: the case of COVID-19 data

Non-parametric survival analysis has become a very popular statistical method in current medical research. However, resorting to survival analysis when its fundamental assumptions are not fulfilled can severely bias the results. Currently, hundreds of clinical studies are using survival methods to investigate factors potentially associated with the prognosis of coronavirus disease 2019 (COVID-19) and test new preventive and therapeutic strategies. In the pandemic era, it is more critical than ever to base decision-making on evidence and rely on solid statistical methods, but this is not always the case. Serious methodological errors have been identified in recent seminal studies about COVID-19: One reporting outcomes of patients treated with remdesivir and another one on the epidemiology, clinical course, and outcomes of critically ill patients. High-quality evidence is essential to inform clinicians about optimal COVID-19 therapies and policymakers about the true effect of preventive measures aiming to tackle the pandemic. Though timely evidence is needed, we should encourage the appropriate application of survival analysis methods and careful peer-review to avoid publishing flawed results, which could affect decision-making. In this paper, we recapitulate the basic assumptions underlying non-parametric survival analysis and frequent errors in its application and discuss how to handle data on COVID-19.

The impact of race and comorbid conditions on adult liver transplant outcomes in obese recipients

Many prior studies comparing liver transplant outcomes between obese and nonobese recipients found no significant differences in survival. However, obesity is intrinsically associated with demographic factors such as race and comorbidities. Thus, this work aimed to analyze the effects of obesity, in conjunction with these factors, on liver transplant outcomes. OPTN data was analyzed to identify adult-only, first-time liver transplants between 1995 and 2019. Obesity was defined by the CDC obesity classification. Race, insurance status, age, and comorbidities were analyzed together with patient survival and graft survival using a multivariable Cox Proportional-Hazards model and long-term survival with Kaplan-Meier curves. The multivariable models found that being black, older than 50 years, having diabetes, or having nonprivate insurance were all risk factors for both patient survival and graft survival after liver transplant. Adjusting for obesity class, black recipients had a 20% lower patient survival and 23% lower graft survival compared with nonblack recipients. Survival curves verified that obese black liver transplant recipients had poorer long-term patient survival and graft survival compared with both obese nonblack and nonobese recipients. In conclusion, obesity compounds known factors associated with poor outcomes after liver transplantation. Further work is critical to understand why these discrepancies persist.

External validation and comparison of the predictive performance of 10 different tooth-level prognostic systems

AIM

Tooth-level prognostic systems can be used for treatment planning and risk assessment. This retrospective longitudinal study aimed to evaluate the prognostic performance of 10 different tooth-level risk assessment systems in terms of their ability to predict periodontal-related tooth loss (TLP).

MATERIALS AND METHODS

Data were retrieved retrospectively from patients who received surgical and non-surgical periodontal treatment. Data on medical history and smoking status at baseline and the last maintenance visit were collected. Ten tooth-level prognostic systems were compared using both univariate and multivariate Cox proportional hazard regression models to analyse the prognostic capability of each system for predicting TLP risk.

RESULTS

One-hundred and forty-eight patients with 3787 teeth, followed-up for a mean period of 26.5 ± 7.4 years, were evaluated according to 10 different tooth-level prognostic systems, making up a total of 37,870 individual measurements. All compared prognostic systems were able to stratify the risk of TLP at baseline when different classes of association were compared. After controlling for maintenance, age, and gender, all systems exhibited excellent predictive capacity for TLP with no system scoring a Harrell's C-index less than 0.925.

CONCLUSIONS

All tooth-level prognostic systems displayed excellent predictive capability for TLP. Overall, the Miller and McEntire system may have shown the best discrimination and model fit, followed by the Nunn et al. system.

Impact of a non-constant baseline hazard on detection of time-dependent treatment effects: a simulation study

Background

Non-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard.

Methods

A simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes.

Results

In the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels.

Conclusions

Our results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01372-0.

Prognostic Factors of Survival of Advanced Liver Cancer Patients Treated With Palliative Radiotherapy: A Retrospective Study

Aims

Survival benefit of liver cancer patients who undergo palliative radiotherapy varies from person to person. The present study aims to identify indicators of survival of advanced liver cancer patients receiving palliative radiotherapy.

Patients and Methods

One hundred and fifty-nine patients treated with palliative radiotherapy for advanced liver cancer were retrospectively assessed. Of the 159 patients, 103 patients were included for prediction model construction in training phase, while other 56 patients were analyzed for external validation in validation phase. In model training phase, clinical characteristics of included patients were evaluated by Kaplan-Meier curves and log-rank test. Thereafter, multivariable Cox analysis was taken to further identify characteristics with potential for prediction. In validation phase, a separate dataset including 56 patients was used for external validation. Harrell’s C-index and calibration curve were used for model evaluation. Nomograms were plotted based on the model of multivariable Cox analysis.

Results

Thirty-one characteristics of patients were investigated in model training phase. Based on the results of Kaplan-Meier plots and log-rank tests, 6 factors were considered statistically significant. On multivariable Cox regression analysis, bone metastasis (HR = 1.781, P = 0.026), portal vein tumor thrombus (HR = 2.078, P = 0.015), alpha-fetoprotein (HR = 2.098, P = 0.007), and radiation dose (HR = 0.535, P = 0.023) show significant potential to predict the survival of advanced liver cancer patients treated with palliative radiotherapy. Moreover, nomograms predicting median overall survival, 1- and 2-year survival probability were plotted. The Harrell’s C-index of the predictive model is 0.709(95%CI, 0.649-0.769) and 0.735 (95%CI, 0.666-0.804) for training model and validation model respectively. Calibration curves of the 1- and 2-year overall survival of the predictive model indicate that the predicted probabilities of OS are very close to the actual observed outcomes both in training and validation phase.

Conclusion

Bone metastasis, portal vein tumor thrombus, alpha-fetoprotein and radiation dose are independent prognostic factors for the survival of advanced liver cancer patients treated with palliative radiotherapy.

Prognostic factors related to postoperative survival in the newly classified clinical T4 lung cancer

OBJECTIVES

T4 lung cancer has become a more heterogeneous group since the 8th edition of tumour, node, metastasis classification. The aim of this study was to identify predictive factors related to post-surgical survival in patients with clinical T4 non-small-cell lung cancer (NSCLC), based on the 8th edition of the classification.

METHODS

We retrospectively reviewed consecutive patients with clinical T4 NSCLC who underwent resection between January 2006 and December 2016, to identify factors associated with overall survival.

RESULTS

Ninety-three patients were identified. The criteria for clinical T4 disease included tumours larger than 7 cm (n = 54), great vessels or left atrial invasion (n = 22), mediastinal invasion (n = 11), vertebral invasion (n = 3), tracheal or carina invasion (n = 3), diaphragm invasion (n = 1) and ipsilateral different lobe pulmonary metastasis (n = 2). The postoperative nodal status was 0, 1, 2 and 3 in 59, 18, 15 and 1 patient, respectively. R0 resection was achieved in 80 patients, and the 30-day mortality was 0%. The median follow-up time was 37.6 months, and the 5-year overall survival rate was 56.3%. The multivariable analysis revealed that nodal status and R-status were significant prognostic factors for postoperative survival [hazard ratio (HR) 2.62, 95% confidence interval (CI) 1.20-5.72, P = 0.016 and HR 3.29, 95% CI 1.45-7.44, P = 0.004].

CONCLUSIONS

Surgery provided encouraging survival outcomes for clinical T4 NSCLC based on the 8th edition of classification. The nodal status and R-status were significant prognostic factors for postoperative survival.

A systematic review of the quality of conduct and reporting of survival analyses of tuberculosis outcomes in Africa

BACKGROUND

Survival analyses methods (SAMs) are central to analysing time-to-event outcomes. Appropriate application and reporting of such methods are important to ensure correct interpretation of the data. In this study, we systematically review the application and reporting of SAMs in studies of tuberculosis (TB) patients in Africa. It is the first review to assess the application and reporting of SAMs in this context.

METHODS

Systematic review of studies involving TB patients from Africa published between January 2010 and April 2020 in English language. Studies were eligible if they reported use of SAMs. Application and reporting of SAMs were evaluated based on seven author-defined criteria.

RESULTS

Seventy-six studies were included with patient numbers ranging from 56 to 182,890. Forty-three (57%) studies involved a statistician/epidemiologist. The number of published papers per year applying SAMs increased from two in 2010 to 18 in 2019 (P = 0.004). Sample size estimation was not reported by 67 (88%) studies. A total of 22 (29%) studies did not report summary follow-up time. The survival function was commonly presented using Kaplan-Meier survival curves (n = 51, (67%) studies) and group comparisons were performed using log-rank tests (n = 44, (58%) studies). Sixty seven (91%), 3 (4.1%) and 4 (5.4%) studies reported Cox proportional hazard, competing risk and parametric survival regression models, respectively. A total of 37 (49%) studies had hierarchical clustering, of which 28 (76%) did not adjust for the clustering in the analysis. Reporting was adequate among 4.0, 1.3 and 6.6% studies for sample size estimation, plotting of survival curves and test of survival regression underlying assumptions, respectively. Forty-five (59%), 52 (68%) and 73 (96%) studies adequately reported comparison of survival curves, follow-up time and measures of effect, respectively.

CONCLUSION

The quality of reporting survival analyses remains inadequate despite its increasing application. Because similar reporting deficiencies may be common in other diseases in low- and middle-income countries, reporting guidelines, additional training, and more capacity building are needed along with more vigilance by reviewers and journal editors.

Effects of 1-Year Hospital Volume on Surgical Margin and Biochemical-Failure-Free Survival in Patients Undergoing Robotic versus Nonrobotic Radical Prostatectomy: A Nationwide Cohort Study from the National Taiwan Cancer Database

Simple Summary

Limited evidence exists regarding the effects of hospital volume (i.e., number of patients with PC receiving robotic RP per year) on the oncologic outcomes of biochemical-failure-free survival (BFS) and positive surgical margin (PSM) between patients with prostate cancer (PC) undergoing robotic or nonrobotic radical prostatectomy (RP). This is the first study to include large sample size, long follow-up time, and consistent covariates of patients with PC receiving different surgical techniques for RP and investigate whether hospital volume affects BFS and PSM. Hospital volume significantly improved BFS and PSM rates in robotic RP, but not in nonrobotic RP. When patients with PC wish to receive robotic RP, we suggest that the surgery be performed in a high-volume hospital (>50 patients/year).

Abstract

Purpose: To examine the effect of hospital volume on positive surgical margin (PSM) and biochemical-failure-free survival (BFS) rates in patients with prostate cancer (PC) undergoing robotic-assisted or nonrobotic-assisted radical prostatectomy (RP). Patients and Methods: The patients were men collected in the National Taiwan Cancer Registry diagnosed as having PC without distant metastasis who received RP from 44 multi-institutes in Taiwan. The logistic regression method was used to analyze the risk from RP to PSM in included patients with hospital volume (i.e., number of patients with PC receiving robotic RP per year), and the Cox proportional hazards method was used to analyze the time from the index date to biochemical recurrence. Results: After propensity score adjustment, compared with hospitals with >100 patients/year, the adjusted odds ratios (aORs; 95% confidence intervals) of PSM in the robotic RP group in hospitals with 1–25, 26–50, and 51–100 patients/year were 2.25 (2.10–3.11), 1.42 (1.25–2.23), and 1.33 (1.13–2.04), respectively (type III p < 0.0001). Sensitivity analysis indicated that the aORs of PSM were 1.29 (1.07–1.81), 1.07 (0.70–1.19), and 0.61 (0.56–0.83), respectively, for patients receiving robotic RP compared with nonrobotic RP within hospitals with 1–25, 26–50, and 51–100 patients/year, respectively. Compared with hospitals with >100 patients/year, the adjusted hazard ratios (aHRs) of biochemical failure in the robotic RP group were 1.40 (1.04–1.67), 1.34 (1.06–1.96), and 1.31 (1.05–2.15) in hospitals with 1–25, 26–50, and 51–100 patients/year, respectively. Conclusions: Hospital volume significantly affected PSM and BFS in robotic RP, but not in nonrobotic RP. When patients with PC want to receive robotic RP, it should be performed in a relatively high-volume hospital (>100 patients/year).

Survival analysis using a 5-step stratified testing and amalgamation routine (5-STAR) in randomized clinical trials

Randomized clinical trials are often designed to assess whether a test treatment prolongs survival relative to a control treatment. Increased patient heterogeneity, while desirable for generalizability of results, can weaken the ability of common statistical approaches to detect treatment differences, potentially hampering the regulatory approval of safe and efficacious therapies. A novel solution to this problem is proposed. A list of baseline covariates that have the potential to be prognostic for survival under either treatment is pre-specified in the analysis plan. At the analysis stage, using all observed survival times but blinded to patient-level treatment assignment, "noise" covariates are removed with elastic net Cox regression. The shortened covariate list is used by a conditional inference tree algorithm to segment the heterogeneous trial population into subpopulations of prognostically homogeneous patients (risk strata). After patient-level treatment unblinding, a treatment comparison is done within each formed risk stratum and stratum-level results are combined for overall statistical inference. The impressive power-boosting performance of our proposed 5-step stratified testing and amalgamation routine (5-STAR), relative to that of the logrank test and other common approaches that do not leverage inherently structured patient heterogeneity, is illustrated using a hypothetical and two real datasets along with simulation results. Furthermore, the importance of reporting stratum-level comparative treatment effects (time ratios from accelerated failure time model fits in conjunction with model averaging and, as needed, hazard ratios from Cox proportional hazard model fits) is highlighted as a potential enabler of personalized medicine. An R package is available at https://github.com/rmarceauwest/fiveSTAR.

Sex-specific incident dementia in patients with central nervous system trauma

Introduction

Despite evidence that central nervous system (CNS) trauma, including traumatic brain injury and spinal cord injury, can cause sustained neurocognitive impairment, it remains unclear whether trauma-related variables are associated with incident dementia independently of other known risk factors.

Methods

All adults without dementia entering the health-care system with diagnoses of CNS trauma were examined for occurrence of dementia. All trauma-related variables were examined as predictors in sex-specific Cox regression models, controlling for other known risk factors.

Results

Over a median follow-up of 52 months, 32,834 of 712,708 patients (4.6%) developed dementia. Traumatic brain injury severity and spinal cord injury interacted with age to influence dementia onset; women were at a greater risk of developing dementia earlier than men, all other factors being equal.

Discussion

Risk stratification of patients with CNS trauma by sex is vital in identifying those most likely to develop dementia and in understanding the course and modifying factors.