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.

Ranking versus rating in peer review of research grant applications.

The allocation of public funds for research has been predominantly based on peer review where reviewers are asked to rate an application on some form of ordinal scale from poor to excellent. Poor reliability and bias of peer review rating has led funding agencies to experiment with different approaches to assess applications. In this study, we compared the reliability and potential sources of bias associated with application rating with those of application ranking in 3,156 applications to the Canadian Institutes of Health Research. Ranking was more reliable than rating and less susceptible to the characteristics of the review panel, such as level of expertise and experience, for both reliability and potential sources of bias. However, both rating and ranking penalized early career investigators and favoured older applicants. Sex bias was only evident for rating and only when the applicant's H-index was at the lower end of the H-index distribution. We conclude that when compared to rating, ranking provides a more reliable assessment of the quality of research applications, is not as influenced by reviewer expertise or experience, and is associated with fewer sources of bias. Research funding agencies should consider adopting ranking methods to improve the quality of funding decisions in health research.

The (Im)precision of Life Expectancy Numbers.

Life expectancy figures for countries and population segments are increasingly being reported to more decimal places and used as indicators of the strengths or failings of countries' health and social systems. Reports seldom quantify their intrinsic statistical imprecision or the age-specific numbers of deaths that determine them. The SE formulas available to compute imprecision are all model based. This note adds a more intuitive data-based SE method and extends the jackknife to the analysis of event rates more generally. It also describes the relationships between the magnitude of the SE and the numbers of person-years and deaths on which it is based. These relationships can help quantify the statistical noise present in published year-to-year differences in life expectancies, as well as in same-year differences between or within countries. Agencies and investigators are encouraged to use one of these SEs to report the imprecision of life expectancy numbers and to tailor the number of decimal places accordingly. (Am J Public Health. 2022;112(8):1151-1160. https://doi.org/10.2105/AJPH.2022.306805).

"Translating" All-Cause Mortality Rate Ratios or Hazard Ratios to Age-, Longevity-, and Probability-Based Measures.

Epidemiologists commonly use an adjusted hazard ratio or incidence density ratio, or a standardized mortality ratio, to measure a difference in all-cause mortality rates. They seldom translate it into an age-, time-, or probability-based measure that would be easier to communicate and to relate to. Several articles have shown how to translate from a standardized mortality ratio or hazard ratio to a longevity difference, a difference in actuarial ages, or a probability of being outlived. In this paper, we describe the settings where these translations are and are not appropriate and provide some of the heuristics behind the formulae. The tools that yield differences in "effective age" and in longevity are applicable when both 1) the mortality rate ratio (hazard ratio) is constant over age and 2) the rates themselves are log-linear in age. The "probability/odds of being outlived" metric is applicable whenever the first condition holds, and thus it provides no direct information on the magnitude of the effective age/longevity difference.

LASSO type penalized spline regression for binary data.

BACKGROUND: Generalized linear mixed models (GLMMs), typically used for analyzing correlated data, can also be used for smoothing by considering the knot coefficients from a regression spline as random effects. The resulting models are called semiparametric mixed models (SPMMs). Allowing the random knot coefficients to follow a normal distribution with mean zero and a constant variance is equivalent to using a penalized spline with a ridge regression type penalty. We introduce the least absolute shrinkage and selection operator (LASSO) type penalty in the SPMM setting by considering the coefficients at the knots to follow a Laplace double exponential distribution with mean zero. METHODS: We adopt a Bayesian approach and use the Markov Chain Monte Carlo (MCMC) algorithm for model fitting. Through simulations, we compare the performance of curve fitting in a SPMM using a LASSO type penalty to that of using ridge penalty for binary data. We apply the proposed method to obtain smooth curves from data on the relationship between the amount of pack years of smoking and the risk of developing chronic obstructive pulmonary disease (COPD). RESULTS: The LASSO penalty performs as well as ridge penalty for simple shapes of association and outperforms the ridge penalty when the shape of association is complex or linear. CONCLUSION: We demonstrated that LASSO penalty captured complex dose-response association better than the Ridge penalty in a SPMM.

Cattle and sheep farmers' opinions on the provision and use of abattoir rejection data in the United Kingdom.

BACKGROUND: Communication between farmers and veterinary surgeons is reported to differ when involving abattoir rejection data on cattle or sheep. METHODS: Using surveys, distributed online and on paper at livestock markets, this study describes the interest and positive opinion of a sample of UK cattle and sheep farmers in receiving abattoir data. RESULTS: Forty-nine per cent of respondents always received abattoir data (n=37/76). Over 80 per cent of respondents were interested in all suggested rejection conditions and particularly liver fluke and respiratory conditions. Eighty-two per cent of farmers were willing to share data with their veterinary surgeon as the information could be used to inform health plans. CONCLUSION: The study findings indicate that having an accurate and consistent data system, which is easily accessible to farmers and veterinary surgeons, appears an essential next step to improve the use of existing abattoir data and enhance animal health, welfare and production.

Modeling perinatal mortality in twins via generalized additive mixed models: a comparison of estimation approaches.

BACKGROUND: The analysis of twin data presents a unique challenge. Second-born twins on average weigh less than first-born twins and have an elevated risk of perinatal mortality. It is not clear whether the risk difference depends on birth order or their relative birth weight. This study evaluates the association between birth order and perinatal mortality by birth order-specific weight difference in twin pregnancies. METHODS: We adopt generalized additive mixed models (GAMMs) which are a flexible version of generalized linear mixed models (GLMMs), to model the association. Estimation of such models for correlated binary data is challenging. We compare both Bayesian and likelihood-based approaches for estimating GAMMs via simulation. We apply the methods to the US matched multiple birth data to evaluate the association between twins' birth order and perinatal mortality. RESULTS: Perinatal mortality depends on both birth order and relative birthweight. Simulation results suggest that the Bayesian method with half-Cauchy priors for variance components performs well in estimating all components of the GAMM. The Bayesian results were sensitive to prior specifications. CONCLUSION: We adopted a flexible statistical model, GAMM, to precisely estimate the perinatal mortality risk differences between first- and second-born twins whereby birthweight and gestational age are nonparametrically modelled to explicitly adjust for their effects. The risk of perinatal mortality in twins was found to depend on both birth order and relative birthweight. We demonstrated that the Bayesian method estimated the GAMM model components more reliably than the frequentist approaches.

Effect of an Electronic Medication Reconciliation Intervention on Adverse Drug Events: A Cluster Randomized Trial.

Importance: Adverse drug events (ADEs) account for up to 16% of emergency department (ED) visits and 7% of hospital admissions. Medication reconciliation is required for hospital accreditation because it can reduce medication discrepancies, but there is no evidence that reducing discrepancies reduces ADEs or other adverse outcomes. Objective: To evaluate whether electronic medication reconciliation reduces ADEs, medication discrepancies, and other adverse outcomes compared with usual care. Design, Setting, and Participants: This cluster randomized trial involved 3491 patients who were discharged from 2 medical units and 2 surgical units at the McGill University Health Centre, Montreal, Quebec, Canada, between October 2014 and November 2016. Data analysis took place from July 2017 to July 2019. Intervention: The RightRx intervention electronically retrieved community drugs from the provincial insurer and aligned them with in-hospital drugs to facilitate reconciliation and communication at care transitions. Main Outcomes and Measures: The primary outcome was ADEs in 30 days after discharge. Secondary outcomes included medication discrepancies, ED visits, hospital readmissions, and a composite outcome of ED visits, readmissions, and death up to 90 days after discharge. Results: Of 4656 eligible patients, 3567 (76.6%) consented to participate (2060 [57.8%] men; mean [SD] age, 69.8 [14.9] years). Overall, 76 patients died during the hospital stay, so 3491 patients were included in the analysis. There was no significant difference in the risk of ADEs between intervention and control groups (76 [4.6%] vs 73 [4.0%]; OR, 0.97; 95% CI, 0.33-1.48), ED visits (433 [26.2%] vs 488 [26.6%]; OR, 0.83; 95% CI, 0.36-1.42), hospital readmission (170 [10.3%] vs 261 [14.2%]; OR, 0.22; 95% CI, 0.06-1.14), or the composite outcome (447 [27.0%] vs 506 [27.6%]; OR, 0.75; 95% CI, 0.34-1.27) at 30 days. Medication discrepancies were significantly reduced in the intervention group compared with the control group (437 [26.4%] vs 1029 [56.0%]; OR, 0.24; 95% CI, 0.12-0.57). Changes made to community medications (OR, 1.05; 95% CI, 1.01-1.10) and new medications (OR, 1.09; 95% CI, 1.01-1.18) were significant risk factors for ADEs. Conclusions and Relevance: Electronic medication reconciliation reduced medication discrepancies but did not reduce ADEs or other adverse outcomes. Hospital accreditation should focus on interventions that reduce the risk of adverse events for patients with multiple changes to community medications. Trial Registration: ClinicalTrials.gov identifier: NCT01179867.

A more intuitive and modern way to compute a small-sample confidence interval for the mean of a Poisson distribution.

Small-sample confidence intervals for the mean of a Poisson distribution have been used since the 1930s. They can be computed by trial and error, or using a computation-saving link that few are aware of and that, even if they are, is neither intuitive nor easy to remember. I trace how and why this link has been used, even if the basis for it has been lost or ignored. I promote a direct and more meaningful link that can be easily used today without having to resort to tables or approximations suited to hand calculators. More importantly, this (time-based) link is instructive and intuitive, and thus more easily derived and understood.

A comparison of technique survival in Canadian peritoneal dialysis and home hemodialysis patients.

BACKGROUND: High discontinuation rates remain a challenge for home hemodialysis (HHD) and peritoneal dialysis (PD). We compared technique failure risks among Canadian patients receiving HHD and PD. METHODS: Using the Canadian Organ Replacement Register, we studied adult patients who initiated HHD or PD within 1 year of beginning dialysis between 2000 and 2012, with follow-up until 31 December 2013. Technique failure was defined as a transfer to any alternative modality for a period of ≥60 days. Technique survival between HHD and PD was compared using a Fine and Gray competing risk model. We also examined the time dependence of technique survival, the association of patient characteristics with technique failure and causes of technique failure. RESULTS: Between 2000 and 2012, 15 314 patients were treated with a home dialysis modality within 1 year of dialysis initiation: 14 461 on PD and 853 on HHD. Crude technique failure rates were highest during the first year of therapy for both home modalities. During the entire period of follow-up, technique failure was lower with HHD compared with PD (adjusted hazard ratio = 0.79; 95% confidence interval 0.69-0.90). However, the relative technique failure risk was not proportional over time and the beneficial association with HHD was only apparent after the first year of dialysis. Comparisons also varied among subgroups and the superior technique survival associated with HHD relative to PD was less pronounced in more recent years and among older patients. Predictors of technique failure also differed between modalities. While obesity, smoking and small facility size were associated with higher technique failure in both PD and HHD, the association with age and gender differed. Furthermore, the majority of discontinuation occurred for medical reasons in PD (38%), while the majority of HHD patients experienced technique failure due to social reasons or inadequate resources (50%). CONCLUSIONS: In this Canadian study of home dialysis patients, HHD was associated with better technique survival compared with PD. However, patterns of technique failure differed significantly among these modalities. Strategies to improve patient retention across all home dialysis modalities are needed.

Ability to Predict New-Onset Psychological Distress Using Routinely Collected Health Data: A Population-Based Cohort Study of Women Diagnosed With Breast Cancer.

Objectives: The primary objective of this study was to identify the predictors of new-onset psychological distress available in routinely collected administrative health databases for women diagnosed with breast cancer. The secondary objective was to explore whether the predictors vary based on the period of cancer care. Methods: A population-based cohort study followed 16,495 female patients with newly diagnosed breast cancer who did not experience psychological distress during the 14 months before breast cancer surgery. The incidence of psychological distress was reported overall and by type of mental health problem. Time-varying Cox proportional hazards models were developed to identify predictors of new-onset psychological distress during 2 key periods of cancer care: (1) hospital-based treatment during which women undergo treatment with breast surgery, chemotherapy, and/or radiation, and (2) 1-year transitional survivorship when women begin follow-up care. Results: The incidence of psychological distress was 16% within each period. Anxiety was present in 85.1% and 65.5% of new cases during hospital-based treatment and transitional survivorship, respectively. Predictors during both periods were younger age, receipt of axillary lymph node dissection, rheumatologic disease, and baseline menopausal symptoms, as well as new opioid dispensations, emergency department visits, and hospital contacts that occurred during follow-up. Other predictors varied based on the period of cancer care. More advanced breast cancer and type of treatment were associated with onset of psychological distress during hospital-based treatment. Psychological distress during transitional survivorship was predicted by diagnosis of localized breast disease, shorter duration of hospital-based treatment, receipt of additional hospital-based treatment in survivorship, and newly diagnosed comorbidities or symptoms. Conclusions: This study identified the predictors of new-onset psychological distress available in routinely collected administrative health databases, and showed how predictors change between hospital-based treatment and transitional survivorship periods. The results highlight the importance of developing predictive models tailored to the period of cancer care.

Individually-matched etiologic studies: classical estimators made new again.

With greater access to regression-based methods for confounder control, the etiologic study with individual matching, analyzed by classical (calculator) methods, lost favor in recent decades. This design was costly, and the data sometimes mis-analyzed. Now, with Big Data, individual matching becomes an economical option. To many, however, conditional logistic regression, commonly used to estimate the incidence density ratio parameter, is somewhat of a black box whose output is not easily checked. An epidemiologist-statistician pair recently proposed a new estimator that is easily applied to data from individually-matched series with a 2:1 ratio (and no other confounding variables) using just a hand calculator or spreadsheet. Surprisingly-or possibly not-they overlooked classical estimators developed in earlier decades. This prompts me to re-introduce some of these, to highlight their considerable flexibility and ease of use, and to update them. Nowadays, for any matching ratio (M:1), the Maximum Likelihood result can be easily computed from data gathered under the matched design in two different ways, each using just the summary data. One is via any binomial regression program that allows offsets, applied to just M 'rows' of data. The other is by hand! The aim of this note is not to save on computation; instead, it is to make connections between classical and regression-based methods, to promote terminology that reflects the concepts and structure of the etiologic study, and to focus attention on what parameter is being estimated.

Assessment of potential bias in research grant peer review in Canada.

BACKGROUND: Peer review is used to determine what research is funded and published, yet little is known about its effectiveness, and it is suspected that there may be biases. We investigated the variability of peer review and factors influencing ratings of grant applications. METHODS: We evaluated all grant applications submitted to the Canadian Institutes of Health Research between 2012 and 2014. The contribution of application, principal applicant and reviewer characteristics to overall application score was assessed after adjusting for the applicant's scientific productivity. RESULTS: Among 11 624 applications, 66.2% of principal applicants were male and 64.1% were in a basic science domain. We found a significant nonlinear association between scientific productivity and final application score that differed by applicant gender and scientific domain, with higher scores associated with past funding success and h-index and lower scores associated with female applicants and those in the applied sciences. Significantly lower application scores were also associated with applicants who were older, evaluated by female reviewers only (v. male reviewers only, -0.05 points, 95% confidence interval [CI] -0.08 to -0.02) or reviewers in scientific domains different from the applicant's (-0.07 points, 95% CI -0.11 to -0.03). Significantly higher application scores were also associated with reviewer agreement in application score (0.23 points, 95% CI 0.20 to 0.26), the existence of reviewer conflicts (0.09 points, 95% CI 0.07 to 0.11), larger budget requests (0.01 points per $100 000, 95% CI 0.007 to 0.02), and resubmissions (0.15 points, 95% CI 0.14 to 0.17). In addition, reviewers with high expertise were more likely than those with less expertise to provide higher scores to applicants with higher past success rates (0.18 points, 95% CI 0.08 to 0.28). INTERPRETATION: There is evidence of bias in peer review of operating grants that is of sufficient magnitude to change application scores from fundable to nonfundable. This should be addressed by training and policy changes in research funding.

Statistical Behaviors: Personal and Computer-Aided Observations.

My early years as a statistician were with the Eastern Co-operative Oncology Group and the Radiation Oncology Therapy Group; three of these years were spent at the Sidney Farber Cancer Institute. Later, I collaborated widely with investigators in many clinical research areas. I reflect on the "statistical interrogations of nature" I saw (and helped some of these) investigators plan and carry out. I look back on their (and my own) statistical behaviors when interpreting the information these interrogations produced and-using a few vignettes and some computer-generated observations-draw some lessons from them. These mainly have to do with making too much of one's data.

Disaggregating the mortality reductions due to cancer screening: model-based estimates from population-based data.

The mortality impact in cancer screening trials and population programs is usually expressed as a single hazard ratio or percentage reduction. This measure ignores the number/spacing of rounds of screening, and the location in follow-up time of the averted deaths vis-a-vis the first and last screens. If screening works as intended, hazard ratios are a strong function of the two Lexis time-dimensions. We show how the number and timing of the rounds of screening can be included in a model that specifies what each round of screening accomplishes. We show how this model can be used to disaggregate the observed reductions (i.e., make them time-and screening-history specific), and to project the impact of other regimens. We use data on breast cancer screening to illustrate this model, which we had already described in technical terms in a statistical journal. Using the numbers of invitations different cohorts received, we fitted the model to the age- and follow-up-year-specific numbers of breast cancer deaths in Funen, Denmark. From November 1993 onwards, women aged 50-69 in Funen were invited to mammography screening every two years, while those in comparison regions were not. Under the proportional hazards model, the overall fitted hazard ratio was 0.82 (average reduction 18%). Using a (non-proportional-hazards) model that included the timing information, the fitted reductions ranged from 0 to 30%, being largest in those Lexis cells that had received the greatest number of invitations and where sufficient time had elapsed for the impacts to manifest. The reductions produced by cancer screening have been underestimated by inattention to their timing. By including the determinants of the hazard ratios in a regression-type model, the proposed approach provides a way to disaggregate the mortality reductions and project the reductions produced by other regimes/durations.

A two-stage model in a Bayesian framework to estimate a survival endpoint in the presence of confounding by indication.

Background Biomarker series can indicate disease progression and predict clinical endpoints. When a treatment is prescribed depending on the biomarker, confounding by indication might be introduced if the treatment modifies the marker profile and risk of failure. Objective Our aim was to highlight the flexibility of a two-stage model fitted within a Bayesian Markov Chain Monte Carlo framework. For this purpose, we monitored the prostate-specific antigens in prostate cancer patients treated with external beam radiation therapy. In the presence of rising prostate-specific antigens after external beam radiation therapy, salvage hormone therapy can be prescribed to reduce both the prostate-specific antigens concentration and the risk of clinical failure, an illustration of confounding by indication. We focused on the assessment of the prognostic value of hormone therapy and prostate-specific antigens trajectory on the risk of failure. Methods We used a two-stage model within a Bayesian framework to assess the role of the prostate-specific antigens profile on clinical failure while accounting for a secondary treatment prescribed by indication. We modeled prostate-specific antigens using a hierarchical piecewise linear trajectory with a random changepoint. Residual prostate-specific antigens variability was expressed as a function of prostate-specific antigens concentration. Covariates in the survival model included hormone therapy, baseline characteristics, and individual predictions of the prostate-specific antigens nadir and timing and prostate-specific antigens slopes before and after the nadir as provided by the longitudinal process. Results We showed positive associations between an increased prostate-specific antigens nadir, an earlier changepoint and a steeper post-nadir slope with an increased risk of failure. Importantly, we highlighted a significant benefit of hormone therapy, an effect that was not observed when the prostate-specific antigens trajectory was not accounted for in the survival model. Conclusion Our modeling strategy was particularly flexible and accounted for multiple complex features of longitudinal and survival data, including the presence of a random changepoint and a time-dependent covariate.