Comprehensive Analysis of SARS-CoV-2 Dynamics in Bangladesh: Infection Trends and Variants (2020-2023).

The first case of COVID-19 was detected in Bangladesh on 8 March 2020. Since then, the Government of Bangladesh (GoB) has implemented various measures to limit the transmission of COVID-19, including widespread testing facilities across the nation through a laboratory network for COVID-19 molecular testing. This study aimed to analyze the dynamics of SARS-CoV-2 in Bangladesh by conducting COVID-19 testing and genomic surveillance of the virus variants throughout the pandemic. Nasopharyngeal swabs were collected from authorized GoB collection centers between April 2020 and June 2023. The viral RNA was extracted and subjected to real-time PCR analysis in icddr,b's Virology laboratory. A subset of positive samples underwent whole-genome sequencing to track the evolutionary footprint of SARS-CoV-2 variants. We tested 149,270 suspected COVID-19 cases from Dhaka (n = 81,782) and other districts (n = 67,488). Of these, 63% were male. The highest positivity rate, 27%, was found in the >60 years age group, followed by 26%, 51-60 years, 25% in 41-50 years, and the lowest, 9% in under five children. Notably, the sequencing of 2742 SARS-CoV-2 genomes displayed a pattern of globally circulating variants, Alpha, Beta, Delta, and Omicron, successively replacing each other over time and causing peaks of COVID-19 infection. Regarding the risk of SARS-CoV-2 infection, it was observed that the positivity rate increased with age compared to the under-5 age group in 2020 and 2021. However, these trends did not remain consistent in 2022, where older age groups, particularly those over 60, had a lower positivity rate compared to other age groups due to vaccination. The study findings generated data on the real-time circulation of different SARS-CoV-2 variants and the upsurge of COVID-19 cases in Bangladesh, which impacted identifying hotspots and restricting the virus from further transmission. Even though there is currently a low circulation of SARS-CoV-2 in Bangladesh, similar approaches of genomic surveillance remain essential for monitoring the emergence of new SARS-CoV-2 variants or other potential pathogens that could lead to future pandemics.

A Risk-Difference Meta-Analysis for the Prophylactic Treatments of Chronic Migraine.

Chronic migraine (CM) imposes significant personal, societal, and financial burdens, historically lacking specific prophylactic treatments. Monoclonal antibodies (mAbs) targeting calcitonin gene-related peptide (CGRP) represent a novel, mechanism-based, and migraine-specific prophylactic approach. Four mAbs, namely, erenumab, fremanezumab, galcanezumab, and eptinezumab, have been marketed, although head-to-head trials with standard anti-migraine treatments are absent. This study aimed to compare the efficacy and safety of anti-CGRP mAbs with standard anti-migraine treatments using a cross-trial indirect model of the absolute risk difference (ARD) of a 50% responder rate, in order to express the final results in terms of the number needed to treat (NNT) and number needed to harm (NNH). Phase 3 and 2b randomized controlled trials (RCTs) for CM prophylaxis were searched in the MEDLINE and CENTRAL databases with specific inclusion and exclusion criteria. The ARD between groups for the percentage of trial participants who reported a 50% reduction in monthly migraine days and the differences in the number of adverse events (AEs), serious adverse events (SAEs), and participants who withdrew from each RCT were calculated, and subsequently, the NNT and NNH were calculated for each one of the outcome measures. In total, eight RCTs were considered eligible. A similar efficacy and safety have been demonstrated among CGRP mAbs and all standard CM treatments. The results of the ARD for the total number of studies concerning efficacy, total adverse events, serious adverse events, and dropout from the RCTs ranged from -0.688 (95% confidence interval (CI): -0.821-(-0.513)) to -0.018 (95% CI: -0.044-(0.007)), from 0.032 (95% CI: -0.041, 0.104) to -0.380 (95% CI: -0.589, -0.126), from -0.025 (95% CI: -0.046, -0.006) to 0.014 (95% CI: -0.015, 0.42), from 0.048 (95% CI: -0.112, 0.014) to 0.232 (95% CI: -0.016, 0.458) correspondingly. All anti-CGRP mAbs showed a roughly equal statistically significant ARD and similar NNTs, ranging from 5 to 8, while the ARD of onbotulinum toxin A (oBTA) was not significant with an NNT 56. The two studies of topiramate showed contradictory results, the one significant while the other not, with NNTs 2 and 22, respectively. All four anti-CGRP mAbs showed an invariably high efficacy among their studies, in terms of the ARD and its derivative measure of NNT, in contrast to oBTA, while in topiramate, the results are contradictory between the two studies.

Understanding The Number Needed to Treat.

The number needed to treat (NNT) is the inverse of the absolute risk difference, which is used as a secondary outcome to clinical trials as a measure relevant to a positive trial, supplementing statistical significance. The NNT requires dichotomous outcomes and is influenced by the baseline disease or symptom severity, the particular population, the type and intensity of the interventional, the duration of treatment, the time period to assessment of response, and the comparator response. Confidence intervals should always accompany NNT for the precision of its estimate. In this review, three meta-analyses are reviewed, which included the NNT in the analysis of response.

Covariate adjustment and estimation of difference in proportions in randomized clinical trials.

Difference in proportions is frequently used to measure treatment effect for binary outcomes in randomized clinical trials. The estimation of difference in proportions can be assisted by adjusting for prognostic baseline covariates to enhance precision and bolster statistical power. Standardization or g-computation is a widely used method for covariate adjustment in estimating unconditional difference in proportions, because of its robustness to model misspecification. Various inference methods have been proposed to quantify the uncertainty and confidence intervals based on large-sample theories. However, their performances under small sample sizes and model misspecification have not been comprehensively evaluated. We propose an alternative approach to estimate the unconditional variance of the standardization estimator based on the robust sandwich estimator to further enhance the finite sample performance. Extensive simulations are provided to demonstrate the performances of the proposed method, spanning a wide range of sample sizes, randomization ratios, and model specification. We apply the proposed method in a real data example to illustrate the practical utility.

The performance of marginal structural models for estimating risk differences and relative risks using weighted univariate generalized linear models.

We used Monte Carlo simulations to compare the performance of marginal structural models (MSMs) based on weighted univariate generalized linear models (GLMs) to estimate risk differences and relative risks for binary outcomes in observational studies. We considered four different sets of weights based on the propensity score: inverse probability of treatment weights with the average treatment effect as the target estimand, weights for estimating the average treatment effect in the treated, matching weights and overlap weights. We considered sample sizes ranging from 500 to 10,000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We examined both the robust variance estimator when using generalized estimating equations with an independent working correlation matrix and a bootstrap variance estimator for estimating the standard error of the risk difference and the log-relative risk. The performance of these methods was compared with that of direct weighting. Both the direct weighting approach and MSMs based on weighted univariate GLMs resulted in the identical estimates of risk differences and relative risks. When sample sizes were small to moderate, the use of an MSM with a bootstrap variance estimator tended to result in the most accurate estimates of standard errors. When sample sizes were large, the direct weighting approach and an MSM with a bootstrap variance estimator tended to produce estimates of standard error with similar accuracy. When using a MSM to estimate risk differences and relative risks, in general it is preferable to use a bootstrap variance estimator than the robust variance estimator. We illustrate the application of the different methods for estimating risks differences and relative risks using an observational study on the effect on mortality of discharge prescribing of a beta-blocker in patients hospitalized with acute myocardial infarction.

A new method for vascular age estimation based on relative risk difference in vascular aging.

OBJECTIVE: The current models of estimating vascular age (VA) primarily rely on the regression label expressed with chronological age (CA), which does not account individual differences in vascular aging (IDVA) that are difficult to describe by CA. This may lead to inaccuracies in assessing the risk of cardiovascular disease based on VA. To address this limitation, this work aims to develop a new method for estimating VA by considering IDVA. This method will provide a more accurate assessment of cardiovascular disease risk. METHODS: Relative risk difference in vascular aging (RRDVA) is proposed to replace IDVA, which is represented as the numerical difference between individual predicted age (PA) and the corresponding mean PA of healthy population. RRDVA and CA are regard as the influence factors to acquire VA. In order to acquire PA of all samples, this work takes CA as the dependent variable, and mines the two most representative indicators from arteriosclerosis data as the independent variables, to establish a regression model for obtaining PA. RESULTS: The proposed VA based on RRDVA is significantly correlated with 27 indirect indicators for vascular aging evaluation. Moreover, VA is better than CA by comparing the correlation coefficients between VA, CA and 27 indirect indicators, and RRDVA greater than zero presents a higher risk of disease. CONCLUSION: The proposed VA overcomes the limitation of CA in characterizing IDVA, which may help young groups with high disease risk to promote healthy behaviors.

Interval estimation of common risk difference for stratified unilateral and bilateral data✩.

In clinical trials, unilateral or bilateral data can usually be encountered if a subject contributes one or both of paired organs. For the bilateral data, responses from two paired body parts are correlated. In this paper, we study various confidence intervals of common risk difference in stratified unilateral and bilateral data based on the Dallal's model. Simulation results show that the score method outperforms other methods and provides coverage probability close to the nominal level and satisfactory coverage width. Hence, the method is recommended. In addition, the inverse hyperbolic tangent Wald-type become as optimal as the score method with the increase of sample sizes. An otolaryngology example is used to demonstrate the proposed methods.

Are there sex differences in cardiovascular outcomes in non-dialysis CKD patients?

Background: Sex differences for cardiovascular (CV) risk and outcomes in chronic kidney disease (CKD) patients not on dialysis have been scarcely or never investigated. We therefore studied this important aspect in a cohort of CKD stage 2-5 in the south of Italy. Methods: We tested the relationship between sex and fatal and non-fatal major CV events in a cohort of 759 stage 2-5 CKD patients followed up for a median time of 36 months. Results: Out of 759 patients, 455 were males (60%) and the remaining 304 patients were females (40%). During the follow-up, 42 patients died, and 118 had fatal and non-fatal CV events. On univariate Cox regression analyses, the male sex failed to be associated with all-cause mortality but was strongly related to the incidence rate of fatal and non-fatal major CV events [hazard ratio (HR) 1.75, 95% confidence interval (CI) 1.18-2.60, P = .006]. Data adjustment for a series of major potential confounders did not materially affect the strength of this relationship (HR 1.78, 95% CI 1.03-3.09). Further analysis testing the effect of age on major CV outcomes by sex showed an effect modification by this risk factor on the same outcome (P = .037) because the HR of male versus female CV events increased progressively with aging. Conclusion: Male patients in stage G2-5 CKD had a higher risk for CV events compared with female patients. Age was shown to be a risk modifier for the association between sex and CV events and this risk increased linearly across a wide age spectrum in CKD patients.

Continuity corrected Wilson interval for the difference of two independent proportions.

Confidence interval for the difference of two proportions has been studied for decades. Many methods were developed to improve the approximation of the limiting distribution of test statistics, such as the profile likelihood method, the score method, and the Wilson method. For the Wilson interval developed by Beal (1987), the approximation of the Z test statistic to the standard normal distribution may be further improved by utilizing the continuity correction, in the observation of anti-conservative intervals from the Wilson interval. We theoretically prove that the Wilson interval is nested in the continuity corrected Wilson interval under mild conditions. We compare the continuity corrected Wilson interval with the commonly used methods with regards to coverage probability, interval width, and mean squared error of coverage probability. The proposed interval has good performance in many configurations. An example from a Phase II cancer trial is used to illustrate the application of these methods.

Robust Variance Estimation for Covariate-Adjusted Unconditional Treatment Effect in Randomized Clinical Trials with Binary Outcomes.

To improve precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of covariate adjustment. However, the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest. To fill this gap, we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.

Deaths averted: An unbiased alternative to rate ratios for measuring the performance of cancer screening programs.

INTRODUCTION: Screening trials and meta-analyses emphasize the ratio of cancer death rates in screening and control arms. However, this measure is diluted by the inclusion of deaths from cancers that only became detectable after the end of active screening. METHODS: We review traditional analysis of cancer screening trials and show that ratio estimates are inevitably biased to the null, because follow-up (FU) must continue beyond the end of the screening period and thus includes cases only becoming detectable after screening ends. But because such cases are expected to occur in equal numbers in the two arms, calculation of the difference between the number of cancer deaths in the screening and control arms avoids this dilutional bias. This difference can be set against the number of invitations to screening; we illustrate by reanalyzing data from all trials of tomography screening of lung cancer (LC) using this measure. RESULTS: In nine trials of LC screening from 2000 to 2013, a total of 94,441 high-risk patients were invited to be in screening or control groups, with high participation rates (average 95%). In the older trials comparing computed tomography to chest X-ray, 88,285 invitations averted 83 deaths (1068 per death averted (DA)). In the six more recent trials with no screening in the control group, 69,976 invitations averted 121 deaths (577 invitations per DA). DISCUSSION: Screens per DA is an undiluted measure of screening's effect and it is unperturbed by the arbitrary duration of FU. This estimate can be useful for program planning and informed consent.

Calculating risk and prevalence ratios and differences in R: developing intuition with a hands-on tutorial and code.

Epidemiologic research questions often focus on evaluating binary outcomes, yet curricula and scientific literature do not always provide clear guidance or examples on selecting and calculating an appropriate measure of association in these scenarios. Reporting inappropriate measures may lead to misleading statistical conclusions. We present a hands-on tutorial that includes annotated code written in an open-source statistical programming language (R) showing readers how to apply, compare, and understand four methods used to estimate a risk or prevalence ratio (or difference), rather than presenting an odds ratio. We will provide guidance on when to use each method, discussing the strengths and limitations of each approach, and compare the results obtained across them. Ultimately, we aim to help trainees, public health researchers, and interdisciplinary professionals develop an intuition for these methods and empower them to implement and interpret these methods in their own research.

A practitioner's guide to developing critical appraisal skills: How to interpret the magnitude of clinical study results.

BACKGROUND: In the oral health literature, researchers sometimes report measures of association that are inappropriate for their study design. Clinicians using evidence to inform their practice should be able to interpret clinical study results on the basis of the types of measures of association, independent of what the researchers of a study reported. TYPES OF STUDIES REVIEWED: The authors summarized which measures of association can be derived from experimental and observational studies and how to interpret them in the context of different study designs. They also suggested how inferences can be made on the basis of particular designs. RESULTS: Measures of association derived from randomized controlled trials and cohort studies differ from those of case-control and cross-sectional studies. These differences can be attributed to the temporality between exposures and outcomes inherent in the respective study designs. Different measures of association reported from the same study may lead to different clinical decisions. Furthermore, the same measure of association with the same effect estimate derived from different study designs may contribute to different clinical decisions. CONCLUSIONS AND PRACTICAL IMPLICATIONS: Measures of association should be interpreted in the context of a particular study design. Study designs and specific measures of association should be considered when drawing conclusions from clinical studies. Clinicians using the literature to inform practice should be cognizant of measures of association reported for a particular study design and whether the authors have interpreted the measure of association correctly in the context of their chosen study design.

Variance estimation of the risk difference when using propensity-score matching and weighting with time-to-event outcomes.

Observational studies are increasingly being used in medicine to estimate the effects of treatments or exposures on outcomes. To minimize the potential for confounding when estimating treatment effects, propensity score methods are frequently implemented. Often outcomes are the time to event. While it is common to report the treatment effect as a relative effect, such as the hazard ratio, reporting the effect using an absolute measure of effect is also important. One commonly used absolute measure of effect is the risk difference or difference in probability of the occurrence of an event within a specified duration of follow-up between a treatment and comparison group. We first describe methods for point and variance estimation of the risk difference when using weighting or matching based on the propensity score when outcomes are time-to-event. Next, we conducted Monte Carlo simulations to compare the relative performance of these methods with respect to bias of the point estimate, accuracy of variance estimates, and coverage of estimated confidence intervals. The results of the simulation generally support the use of weighting methods (untrimmed ATT weights and IPTW) or caliper matching when the prevalence of treatment is low for point estimation. For standard error estimation the simulation results support the use of weighted robust standard errors, bootstrap methods, or matching with a naïve standard error (i.e., Greenwood method). The methods considered in the article are illustrated using a real-world example in which we estimate the effect of discharge prescribing of statins on patients hospitalized for acute myocardial infarction.

A more powerful test for three-arm non-inferiority via risk difference: Frequentist and Bayesian approaches.

Necessity for finding improved intervention in many legacy therapeutic areas are of high priority. This has the potential to decrease the expense of medical care and poor outcomes for many patients. Typically, clinical efficacy is the primary evaluating criteria to measure any beneficial effect of a treatment. Albeit, there could be situations when several other factors (e.g. side-effects, cost-burden, less debilitating, less intensive, etc.) which can permit some slightly less efficacious treatment options favorable to a subgroup of patients. This often leads to non-inferiority (NI) testing. NI trials may or may not include a placebo arm due to ethical reasons. However, when included, the resulting three-arm trial is more prudent since it requires less stringent assumptions compared to a two-arm placebo-free trial. In this article, we consider both Frequentist and Bayesian procedures for testing NI in the three-arm trial with binary outcomes when the functional of interest is risk difference. An improved Frequentist approach is proposed first, which is then followed by a Bayesian counterpart. Bayesian methods have a natural advantage in many active-control trials, including NI trial, as it can seamlessly integrate substantial prior information. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms.

The Risk of Endometrial Cancer and Uterine Sarcoma Following Endometriosis or Pelvic Inflammatory Disease.

The relationship between uterine corpus cancer and endometriosis was conflicting. We aimed to determine the risk of uterine cancer in patients with endometriosis or pelvic inflammatory disease (PID). In this population-based cohort study, a total of 135,236 females with endometriosis (n = 20,510) or PID (n = 114,726), as well as 135,236 age-matched controls, were included. Cox regression models estimated the risk of uterine cancer in each group. Sub-outcomes of risk for uterine corpus cancer included endometrial cancer and uterine sarcoma were analyzed. An age subgroup analysis was performed to determine the moderator effect of age. A landmark analysis depicted the time varying effect of endometriosis and PID. A propensity score matching analysis was conducted to validate the findings. Patients with endometriosis had significantly higher risk of endometrial cancer (adjusted hazard ratio, aHR = 2.92; 95% CI = 2.12-4.03) and uterine sarcoma (aHR = 5.83; 95% CI = 2.02-16.89), while PID was not associated with the risk of uterine cancer. The increased risk of uterine cancer in patients with endometriosis persisted after propensity score matching (aHR = 2.83, 95%CI = 1.70-4.71). The greatest risk of endometrial cancer occurred in patients who had endometriosis for 37 to 60 months (adjusted relative risk, aRR = 9.15, 95% CI = 4.40-19.02). Females aged 12 to 35 years were at the greatest risk of endometriosis-associated uterine cancer (RR = 6.97, 95% CI = 3.41-14.26). In conclusion, patients with endometriosis were at great risk of uterine cancer, including endometrial cancer and uterine sarcoma, compared with propensity score-matched populations and compared with patients of PID. Younger females with endometriosis and patients who had endometriosis for three to five years were at the greatest risk of endometriosis-associated uterine cancer.

Risk difference, relative risk, and odds ratio for non-inferiority clinical trials with risk rate endpoint.

Non-inferiority (NI) clinical trials are widely used to evaluate whether the new experimental treatment is not unacceptably worse than the current active-control treatment by more than a pre-specified non-inferiority margin (NI margin). However, choosing either an absolute difference [risk difference (RD)] or a relative difference [relative risk (RR) and odds ratio (OR)] to evaluate efficacy in NI clinical trials is still controversial. In this study, we aim to evaluate the performance of abovementioned three metrics for testing NI clinical trials with risk rate endpoint. Herein, extensive Monte Carlo simulations based on various parameter settings (NI margin as well as risk rates in the experimental group and active-control group) are conducted to compare the Type I error rate, statistical power, and the necessary sample size to achieve a desired power for testing NI using RD, RR, and OR. We show that testing NI using RD not only controls well the Type I error and achieves the highest statistical power but also requires the smallest sample size compared to RR and OR. In practice, however, the choice among three metrics still needs to be based upon clinical interpretations and regulatory perspectives.

Meta-analysis methods for risk difference: A comparison of different models.

Risk difference is a frequently-used effect measure for binary outcomes. In a meta-analysis, commonly-used methods to synthesize risk differences include: (1) the two-step methods that estimate study-specific risk differences first, then followed by the univariate common-effect model, fixed-effects model, or random-effects models; and (2) the one-step methods using bivariate random-effects models to estimate the summary risk difference from study-specific risks. These methods are expected to have similar performance when the number of studies is large and the event rate is not rare. However, studies with zero events are common in meta-analyses, and bias may occur with the conventional two-step methods from excluding zero-event studies or using an artificial continuity correction to zero events. In contrast, zero-event studies can be included and modeled by bivariate random-effects models in a single step. This article compares various methods to estimate risk differences in meta-analyses. Specifically, we present two case studies and three simulation studies to compare the performance of conventional two-step methods and bivariate random-effects models in the presence or absence of zero-event studies. In conclusion, we recommend researchers using bivariate random-effects models to estimate risk differences in meta-analyses, particularly in the presence of zero events.

Rejoinder to discussions on "Instrumental variable estimation of the causal hazard ratio".

In this paper, we respond to comments on our paper, "Instrumental variable estimation of the causal hazard ratio."

An improved score-type confidence interval for stratified risk differences involving rare events.

A stratified analysis of the differences in proportions has been widely employed in epidemiological research, social sciences, and drug development. It provides a useful framework for combining data across strata to produce a common effect. However, for rare events with incidence rates close to zero, popular confidence intervals for risk differences in a stratified analysis may not have appropriate coverage probabilities that approach the nominal confidence levels and the algorithms may fail to produce a valid confidence interval because of zero events in both the arms of a stratum. The main objective of this study is to evaluate the performance of certain methods commonly employed to construct confidence intervals for stratified risk differences when the response probabilities are close to a boundary value of zero or one. Additionally, we propose an improved stratified Miettinen-Nurminen confidence interval that exhibits a superior performance over standard methods while avoiding computational difficulties involving rare events. The proposed method can also be employed when the response probabilities are close to one.