The hazard ratio (HR) is a commonly used summary statistic when comparing time to event (TTE) data between trial arms, but assumes the presence of proportional hazards (PH). Non-proportional hazards (NPH) are increasingly common in NICE technology appraisals (TAs) due to an abundance of novel cancer treatments, which have differing mechanisms of action compared with traditional chemotherapies. The goal of this study is to understand how pharmaceutical companies, evidence review groups (ERGs) and appraisal committees (ACs) test for PH and report clinical effectiveness in the context of NPH.

A thematic analysis of NICE TAs concerning novel cancer treatments published between 1 January 2020 and 31 December 2021 was undertaken. Data on PH testing and clinical effectiveness reporting for overall survival (OS) and progression-free survival (PFS) were obtained from company submissions, ERG reports, and final appraisal determinations (FADs).

NPH were present for OS or PFS in 28/40 appraisals, with log-cumulative hazard plots the most common testing methodology (40/40), supplemented by Schoenfeld residuals (20/40) and/or other statistical methods (6/40). In the context of NPH, the HR was ubiquitously reported by companies, inconsistently critiqued by ERGs (10/28), and commonly reported in FADs (23/28).

There is inconsistency in PH testing methodology used in TAs. ERGs are inconsistent in critiquing use of the HR in the context of NPH, and even when critiqued it remains a commonly reported outcome measure in FADs. Other measures of clinical effectiveness should be considered, along with guidance on clinical effectiveness reporting when NPH are present.

Obsessive-compulsive disorder (OCD) and schizophrenia are often reported as co-morbid conditions. However, the evidence of an association between OCD and the risk of schizophrenia is limited. This study investigated the risk of schizophrenia in patients newly diagnosed with OCD using a nationally representative sample cohort in South Korea.

Data were obtained from the 2002–2013 Korean National Health Insurance Service-National Sample Cohort of the National Health Insurance Service. Using propensity score matching, 2509 patients with OCD and a control group of 7527 patients were included in the analysis. Chi-squared tests were used to investigate and compare the general characteristics of the study population. The risk of schizophrenia was analysed using the Cox proportional hazard model.

The incidence rate was 45.79/10 000 person-year for patients with OCD and 4.19/10 000 person-year for patients without OCD. Patients with OCD had a higher risk of schizophrenia compared to the control group after adjusting for covariates (hazard ratio = 10.46, 95% confidence interval = 6.07–18.00).

This study identified an association between the diagnosis of OCD and the risk of schizophrenia in a South Korean national representative cohort. Further research using a prospective design to clarify the causality of OCD in schizophrenia in a controlled environment should be conducted to validate these findings.

Suicide risk is complex and nuanced, and how place impacts suicide risk when considered alongside detailed individual risk factors remains uncertain. We aimed to examine suicide risk in Denmark with both individual and neighbourhood level risk factors.

We used Danish register-based data to identify individuals born in Denmark from 1972, with full parental information and psychiatric diagnosis history. We fitted a two-level survival model to estimate individual and neighbourhood determinants on suicide risk.

We identified 1723 cases of suicide in Denmark during the follow-up period from 1982 to 2015. Suicide risk was explained mainly by individual determinants. Parental comorbidities, particularly maternal schizophrenia [incidence rate ratio (IRR): 2.29, 95% CI 1.56–3.16] and paternal death (2.29, 95% CI 1.31–3.72) partly explained suicide risk when adjusted for all other determinants. The general contextual effect of suicide risk across neighbourhoods showed a median incidence rate ratio (MRR) of 1.13 (1.01–1.28), which was further reduced with full adjustment. Suicide risk increased in neighbourhoods with a higher proportion of manual workers (IRR: 1.08; 1.03–1.14), and decreased with a higher population density (IRR: 0.89; 0.83–0.96).

Suicide risk varies mainly between individuals, with parental comorbidities having the largest effect on suicide risk. Suicide risk was less impacted by neighbourhood, though, albeit to a lesser extent than individual determinants, some characteristics were associated with suicide risk. Suicide prevention policies might consider targeting interventions towards individuals more vulnerable due to particular parental comorbidities, whilst taking into account that some neighbourhood characteristics might exacerbate this risk further.

Time-dependent Cox proportional hazards regression is a popular statistical method used in kidney disease research to evaluate associations between biomarkers collected serially over time with progression to kidney failure. Typically, biomarkers of interest are considered time-dependent covariates being updated at each new measurement using last observation carried forward (LOCF). Recently, joint modeling has emerged as a flexible alternative for multivariate longitudinal and time-to-event data. This study describes and demonstrates multivariate joint modeling using as an example the association of serial biomarkers (plasma oxalate [POX] and urinary oxalate [UOX]) and kidney function among patients with primary hyperoxaluria in the Rare Kidney Stone Consortium Registry.

Time-to-kidney failure was regressed on serially measured biomarkers in two ways: time-dependent LOCF Cox proportional hazards regression and multivariate joint models.

In time-dependent LOCF Cox regression, higher POX was associated with increased risk of kidney failure (HR = 2.20 per doubling, 95% CI = [1.38-3.51], p < 0.001) whereas UOX was not (HR = 1.08 per doubling, [0.66–1.77], p = 0.77). In multivariate joint models, estimates suggest higher UOX may be associated with lower risk of kidney failure (HR = 0.42 per doubling [0.15–1.04], p = 0.066), though not statistically significant, since impaired urinary excretion of oxalate may reflect worsening kidney function.

Multivariate joint modeling is more flexible than LOCF and may better reflect biological plausibility since biomarkers are not steady-state values between measurements. While LOCF is preferred to naïve methods not accounting for changes in biomarkers over time, results may not accurately reflect flexible relationships that can be captured with multivariate joint modeling.

The survival cox analysis is becoming more popular in time-to-event data analysis. When there are unobserved /unmeasured individual factors, then the results of this model may not be dependable. Hence, this study aimed to determine the factors associated with Covid-19 patients’ survival time with considering frailty factor.

This study was conducted at 1 of the hospitals in Iran, so that hospitalized patients with COVID-19 were included. Epidemiological, clinical, laboratory, and outcome data on admission were extracted from electronic medical records. Gamma-frailty Cox model was used to identify the effects of the risk factors.

A total of 360 patients with COVID-19 enrolled in the study. The median age was 74 years (IQR 61 – 83), 903 (57·7%) were men, and 661 (42·3%) were women; the mortality rate was 17%. The Cox frailty model showed that there is at least a latent factor in the model (P = 0.005). Age and platelet count were negatively associated with the length of stay, while red blood cell count was positively associated with the length of stay of patients.

The Cox frailty model indicates that in addition to age, the frailty factor is a useful predictor of survival in Covid-19 patients.

Previous studies have found that stressful life events (SLEs) are associated with an increased risk of adult depression. However, many studies are observational in nature and limited by methodological issues, such as potential confounding by genetic factors. Genetically informative research, such as the co-twin control design, can strengthen causal inference in observational studies. Discrete-time survival analysis has several benefits and multilevel survival analysis can incorporate frailty terms (random effects) to estimate the components of the biometric model. In the current study, we investigated associations between SLEs and depression risk in a population-based twin sample (N = 2299).

A co-twin control design was used to investigate the influence of the occurrence of SLEs on depression risk. The co-twin control design involves comparing patterns of associations in the full sample and within dizygotic (DZ) and monozygotic twins (MZ). Associations were modelled using discrete-time survival analysis with biometric frailty terms. Data from two time points were used in the analyses. Mean age at Wave 1 was 28 years and mean age at Wave 2 was 38 years.

SLE occurrence was associated with increased depression risk. Co-twin control analyses indicated that this association was at least in part due to the causal influence of SLE exposure on depression risk for event occurrence across all SLEs and for violent SLEs. A minor proportion of the total genetic risk of depression reflected genetic effects related to SLEs.

The results support previous research in implicating SLEs as important risk factors with probable causal influence on depression risk.

Considering that coronavirus disease 2019 (COVID-19) is an emerging disease and results in very different outcomes, from complete recovery to death, it is important to determine the factors affecting the survival of patients. Given the lack of knowledge about effective factors and the existence of differences in the outcome of individuals with similar values of the observed covariates, this study aimed to investigate the factors affecting the survival of patients with COVID-19 by the parametric survival model with the frailty approach.

The data of 139 patients with COVID-19 hospitalized in Imam Reza Hospital in Tabriz were analyzed by the Gompertz survival model with gamma frailty effect. At first, variables with P < 0.1 in univariable analysis were included in the multivariable analysis, and then the stepwise method was used for variable selection.

Diabetes mellitus was significantly related to the survival of hospitalized patients (P = 0.021). The rest of the investigated variables were not significant. The frailty effect was significant (P = 0.019).

In the investigated sample of patients with COVID-19, diabetes was an important variable related to patient survival. Also, the significant frailty effect indicates the existence of unobserved heterogeneity that causes individuals with a similar value of the observed covariates to have different survival distributions.