Incorporating external trial data to improve survival extrapolations: a pilot study of the COU-AA-301 trial

Survival extrapolation plays a key role within cost effectiveness analysis and is often subject to substantial uncertainty. Use of external data to improve extrapolations has been identified as a key research priority. We present findings from a pilot study using data from the COU-AA-301 trial of abiraterone acetate for metastatic castration-resistant prostate cancer, to explore how external trial data may be incorporated into survival extrapolations. External trial data were identified via a targeted search of technology assessment reports. Four methods using external data were compared to simple parametric models (SPMs): informal reference to external data to select appropriate SPMs, piecewise models with, and without, hazard ratio adjustment, and Bayesian models fitted with a prior on the shape parameter(s). Survival and hazard plots were compared, and summary metrics (point estimate accuracy and restricted mean survival time) were calculated. Without consideration of external data, several SPMs may have been selected as the ‘best-fitting’ model. The range of survival probability estimates was generally reduced when external data were included in model estimation, and external hazard plots aided model selection. Different methods yielded varied results, even with the same data source, highlighting potential issues when integrating external trial data within model estimation. By using external trial data, the most (in)appropriate models may be more easily identified. However, benefits of using external data are contingent upon their applicability to the research question, and the choice of method can have a large impact on extrapolations.

Prevalence of Frailty in the Middle East: Systematic Review and Meta-Analysis

(1) We aimed to systematically search available data on the prevalence of frailty among community-dwelling elders in Middle Eastern countries. The results from available studies are cumulated to provide comprehensive evidence for the prevalence of frailty. (2) Methods: A meta-analysis was done. A literature search was carried out using PRISMA guidelines in PubMed, Web of Science, and SCOPUS websites for studies up to 2020. Inclusion criteria entailed all primary studies conducted in Middle Eastern countries on frailty in community-dwelling older adults aged 60 years and older. (3) Results: A total of 10 studies were selected for this study. Random-effects meta-analysis of nine studies indicated there was a pooled prevalence rate of 0.3924 with a standard error of 0.037. This pooled prevalence point estimate of 0.3924 was statistically significant (p < 0.001). The Egger’s regression test and the trim-fill method for detecting publication bias did not detect any evidence of publication bias in the sample of included studies. The Egger’s regression test was not statistically significant. The trim-fill method indicated zero studies were missing on either side; (4) Conclusions: The study’s findings indicate that the prevalence of frailty is higher in Middle Eastern nations. Despite indications that many of these nations’ populations are rapidly ageing, we presently lack information on the incidence of frailty in these populations; this information is essential to health, policymakers, and social care planning.

Comparison of Radiation Risk of Cancer Incidence among PA Mayak Workers Hired at Different Calendar Periods

Purpose: comparative epidemiological analysis of cancer incidence rates among PA Mayak workers hired at different calendar periods. Material and Methods: The cohort of PA Mayak workers hired at reactors, radiochemical, plutonium, water preparing and repair plants in 1948-1982 was selected for analysis of solid cancer (except lung, liver and bone) incidence. The cohort was divided into two subcohorts 1948-1958 and 1959-1982 years of hire. Regression models of relative risk included the description of background rates and excess radiation risk was used. Results: The radiation relative risk per 1 Gy was (ERR/Gy: 0.11; 95 % CI: 0.02; 0.21) in the subcohort of workers hired in 1948–1958 and was close to the estimates from previous studies for the whole cohort. In the subcohort of workers hired in 1959–1982 the point estimate of ERR/Gy was 3 times higher than in the subcohort 1948–1958 years of hire, but it was not statistically significant in the whole dose range. Restriction of doses up to 2 Gy allowed to get significant estimate of ERR/Gy (0.45; 95 % CI: 0.04; 0.95), which 4 times higher the estimate in the first subcohort calculated with the same restriction (0.11; 95 % CI: -0.01; 0.25). All subcohorts differences were not statistically significant and we can say about point distinctions only.

Suprathreshold perceptual decisions constrain models of confidence

Perceptual confidence is an important internal signal about the certainty of our decisions and there is a substantial debate on how it is computed. We highlight three confidence metric types from the literature: observers either use 1) the full probability distribution to compute probability correct (Probability metrics), 2) point estimates from the perceptual decision process to estimate uncertainty (Evidence-Strength metrics), or 3) heuristic confidence from stimulus-based cues to uncertainty (Heuristic metrics). These metrics are rarely tested against one another, so we examined models of all three types on a suprathreshold spatial discrimination task. Observers were shown a cloud of dots sampled from a dot generating distribution and judged if the mean of the distribution was left or right of centre. In addition to varying the horizontal position of the mean, there were two sensory uncertainty manipulations: the number of dots sampled and the spread of the generating distribution. After every two perceptual decisions, observers made a confidence forced-choice judgement whether they were more confident in the first or second decision. Model results showed that observers were on average best-fit by a Heuristic model that used dot cloud position, spread, and number of dots as cues. However, almost half of the observers were best-fit by an Evidence-Strength model that uses the distance between the discrimination criterion and a point estimate, scaled according to sensory uncertainty, to compute confidence. This signal-to-noise ratio model outperformed the standard unscaled distance from criterion model favoured by many researchers and suggests that this latter simple model may not be suitable for mixed-difficulty designs. An accidental repetition of some sessions also allowed for the measurement of confidence agreement for identical pairs of stimuli. This N-pass analysis revealed that human observers were more consistent than their best-fitting model would predict, indicating there are still aspects of confidence that are not captured by our model. As such, we propose confidence agreement as a useful technique for computational studies of confidence. Taken together, these findings highlight the idiosyncratic nature of confidence computations for complex decision contexts and the need to consider different potential metrics and transformations in the confidence computation.

Long-term Health Ailments among COVID-19 recovered patients in South Asian Countries: A Descriptive Cross-sectional Study

Introduction: COVID-19 pandemic declared as Global Health Emergency by World Health Organization. The novel SARS-CoV-2 virus is the major cause of COVID-19.Although the recovery rate of COVID-19 is higher, the recovered patients experience mild to severe health ailments post recovery. These health ailments affect their routine day to day life and also their quality of life. The key objective of this study is to find out the prevalence of various health ailments among COVID recovered population from south Asian countries.Methods: A descriptive cross-section study was conducted among 384 COVID-19 recovered population in South Asian Countries through randomized survey. Ethical approval of the institution was obtained and a convenient sampling technique was done. Statistical package for Social Sciences is used for the analysis of the data. . Point estimate at 95% Confidence Interval was calculated along with frequency and proportion for binary dataResults: Among 384 samples, 68% of patients had post COVID-19 long term extreme tiredness and 64% of patients reported with sleepless ness. 73% of patients had fever and smell loss during the COVID19. 64% had reported body pain and cough when they had the infection. 42% of the patients were healthy ones without any comorbidity prior to COVID.Conclusions: The study concludes that there was high prevalence of long term illness among COVID-19 recovered patients and the prevalence was reported even in patients who had no comorbidities prior to COVID19 are the dominant health disorders prevalent among COVID-19 recovered population.

Obtaining Interpretable Parameters From Reparameterized Longitudinal Models: Transformation Matrices Between Growth Factors in Two Parameter Spaces

This study proposes transformation functions and matrices between coefficients in the original and reparameterized parameter spaces for an existing linear-linear piecewise model to derive the interpretable coefficients directly related to the underlying change pattern. Additionally, the study extends the existing model to allow individual measurement occasions and investigates predictors for individual differences in change patterns. We present the proposed methods with simulation studies and a real-world data analysis. Our simulation study demonstrates that the method can generally provide an unbiased and accurate point estimate and appropriate confidence interval coverage for each parameter. The empirical analysis shows that the model can estimate the growth factor coefficients and path coefficients directly related to the underlying developmental process, thereby providing meaningful interpretation.

Bayesian estimate of system availability for consecutive k-out-of-n:F system

In the efficient design and functionality of complex systems, redundancy problems in systems play a key role. The consecutive-k-out-of-n:F structure, which has broad application in street light arrangements, vacuum systems in an accelerator, sliding window detection, relay stations for a communication system. Availability is one of the significant measures for a maintained device because availability accounts for the repair capability. A very significant feature is the steady-state availability of a repairable device. For the repairable consecutive k-out-of-n:F system with independent and identically distributed components, the Bayesian point estimate (B.P.E) of steady-state availability under squared error loss function (SELF) and confidence interval are obtained.

Mutation, selection, and the prevalence of the C. elegans mortal germline phenotype

C. elegans strains with the mortal germline (Mrt) phenotype become progressively sterile over the course of a few tens of generations. Mrt is proximately controlled epigenetically, and is typically temperature-dependent, being penetrant at temperatures near the upper range of C. elegans' tolerance. Previous studies have suggested that Mrt presents a relatively large mutational target, and that Mrt is not uncommon in natural populations of C. elegans. The Mrt phenotype is not monolithic. Some strains exhibit a strong Mrt phenotype, in which individuals invariably become sterile over a few generations, whereas other strains show a weaker (less penetrant) phenotype in which the onset of sterility is slower and more stochastic. We present results in which we (1) quantify the rate of mutation to the Mrt phenotype, and (2) quantify the frequency of Mrt in a collection of 95 wild isolates. Over the course of ~16,000 meioses, we detected one mutation to a strong Mrt phenotype, resulting in a point estimate of the mutation rate UMrt 6 10-5/genome/generation. We detected no mutations to a weak Mrt phenotype. 5/95 wild isolates had a strong Mrt phenotype, and although quantification of the weak Mrt phenotype is inexact, the weak Mrt phenotype is not rare in nature. We estimate a strength of selection against mutations conferring the strong Mrt phenotype 0.1%, similar to selection against mutations affecting competitive fitness. The appreciable frequency of weak Mrt variants in nature combined with the low mutation rate suggests that Mrt may be maintained by balancing selection.

A likelihood ratio test for the homogeneity of between-study variance in network meta-analysis

Background Network meta-analysis (NMA) is a statistical method used to combine results from several clinical trials and simultaneously compare multiple treatments using direct and indirect evidence. Statistical heterogeneity is a characteristic describing the variability in the intervention effects being evaluated in the different studies in network meta-analysis. One approach to dealing with statistical heterogeneity is to perform a random effects network meta-analysis that incorporates a between-study variance into the statistical model. A common assumption in the random effects model for network meta-analysis is the homogeneity of between-study variance across all interventions. However, there are applications of NMA where the single between-study assumption is potentially incorrect and instead the model should incorporate more than one between-study variances. Methods In this paper, we develop an approach to testing the homogeneity of between-study variance assumption based on a likelihood ratio test. A simulation study was conducted to assess the type I error and power of the proposed test. This method is then applied to a network meta-analysis of antibiotic treatments for Bovine respiratory disease (BRD). Results The type I error rate was well controlled in the Monte Carlo simulation. We found statistical evidence (p value = 0.052) against the homogeneous between-study variance assumption in the network meta-analysis BRD. The point estimate and confidence interval of relative effect sizes are strongly influenced by this assumption. Conclusions Since homogeneous between-study variance assumption is a strong assumption, it is crucial to test the validity of this assumption before conducting a network meta-analysis. Here we propose and validate a method for testing this single between-study variance assumption which is widely used for many NMA.