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Quantitative methods for health research
A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community
This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis—the explanation of which go beyond introductory concepts. This second edition of Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods.
A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include:
* Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods
* Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application
* Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice
* Integration of practical data analysis exercises to develop skills and confidence
* Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text
Quantitative Methods for Health Research, Second Edition is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference.
eBook
Prospective longevity : a new vision of population aging
\"The study of aging is not fundamentally about how old people are. It is about people's capabilities and their disabilities. In the field of population aging, measurements have generally been made with instruments devised many decades ago. Those measurements systems did not take the changing characteristics of older people into account. Using them 65-year-olds with a remaining life expectancy of 5 years could not be distinguished from 65-year-olds with a remaining life expectancy of 25 years. Although, in the past, those instruments did help us see better, it is now clear that there is a great deal that they did not allow us to see. Prospective Longevity provide a new view of who is old, how healthy people are in old age, the gender gap in survival at older ages, differences in patterns of survival across Russian regions and United States, the effects on the pace of population aging of medical breakthroughs that allow people to live much longer lives, and how an intergenerationally equitable pension age should change as life expectancy increases\"-- Provided by publisher.
Book
Prediction of Implantable Collamer Lens Vault Based on Preoperative Biometric Factors and Lens Parameters
Purpose:
To establish and validate the accuracy of implantable collamer lens (ICL) vault size prediction formula based on preoperative biometric factors and lens parameters.
Methods:
This study included 300 patients (300 eyes) with Visian ICL V4c (STAAR Surgical) implantation. They were randomly divided into the formula establishment group and formula validation group. Anterior segment measurements, ICL V4c size and power, and vault 1 week postoperatively were collected from all patients. Multiple linear regression analysis was performed to establish the prediction formula. Mean absolute error (MAE), median absolute error (MedAE), root mean square error (RMSE), and Bland-Altman diagrams were used to evaluate the prediction formula.
Results:
Anterior chamber depth (ACD) had the greatest influence on vault 1 week after ICL V4c implantation, followed by ICL V4c size and angle-to-angle distance (ATA). The prediction formula was obtained according to the partial regression coefficient, which was vault (mm) = −1.279 + 0.291 × ACD (mm) + 0.210 × ICL V4c size (mm) – 0.144 × ATA (mm) (R2 = 0.661). In the formula validation group, the mean predictive vault, MAE, MedAE, and RMSE were 628.10, 135.09, 130.42, and 150.46 µm, respectively. The Bland-Altman diagram showed the predictive vault was in good agreement with the actual vault.
Conclusions:
A novel ICL V4c vault prediction formula was developed and shown to be an effective method for predicting the vault to reduce surgical complications.
[J Refract Surg. 2023;39(5):332–339.]
Journal Article
Sensitivity Analysis and Power for Instrumental Variable Studies
In observational studies to estimate treatment effects, unmeasured confounding is often a concern. The instrumental variable (IV) method can control for unmeasured confounding when there is a valid IV. To be a valid IV, a variable needs to be independent of unmeasured confounders and only affect the outcome through affecting the treatment. When applying the IV method, there is often concern that a putative IV is invalid to some degree. We present an approach to sensitivity analysis for the IV method which examines the sensitivity of inferences to violations of IV validity. Specifically, we consider sensitivity when the magnitude of association between the putative IV and the unmeasured confounders and the direct effect of the IV on the outcome are limited in magnitude by a sensitivity parameter. Our approach is based on extending the Anderson-Rubin test and is valid regardless of the strength of the instrument. A power formula for this sensitivity analysis is presented. We illustrate its usage via examples about Mendelian randomization studies and its implications via a comparison of using rare versus common genetic variants as instruments.
Journal Article
Prediction of the true IOL position
PurposeTo develop algorithms for preoperative estimation of the true postoperative intraocular lens (IOL) position to be used for IOL power calculation.SettingMoorfields Eye Hospital NHS Foundation Trust, London, UK.MethodsFifty patients were implanted randomly with a 3-piece IOL model in one eye and a 1-piece model in the other eye. Preoperatively, the IOLMaster was used to determine axial length, anterior chamber depth and mean corneal radius. Lens thickness and corneal width were measured with the ACMaster. Postoperative IOL position was measured with the ACMaster. Partial least squares (PLS) regression analysis of IOL position in terms of preoperative parameters was performed with a commercially available software package.ResultsThe PLS regression analysis showed that age, refraction, corneal width, lens thickness and corneal radius are not significant predictors of postoperative position of the anterior IOL surface, while axial length and in particular anterior chamber depth are. Regression relationships in terms of the above-mentioned predictors were determined for the two models implanted. Surprisingly, it turned out that the position of the posterior IOL surface could be described by a single regression relationship valid for both models. The residual SD for prediction of IOL position was about 0.17 mm for all relationships.ConclusionsAccurate relationships to determine the true postoperative IOL position were obtained. In addition to axial length and corneal radius, which are required for the IOL power calculation as such, they require measurement of preoperative anterior chamber depth only.
Journal Article
Optimal Two–Stage Dynamic Treatment Regimes from a Classification Perspective with Censored Survival Data
Clinicians often make multiple treatment decisions at key points over the course of a patient's disease. A dynamic treatment regime is a sequence of decision rules, each mapping a patient's observed history to the set of available, feasible treatment options at each decision point, and thus formalizes this process. An optimal regime is one leading to the most beneficial outcome on average if used to select treatment for the patient population. We propose a method for estimation of an optimal regime involving two decision points when the outcome of interest is a censored survival time, which is based on maximizing a locally efficient, doubly robust, augmented inverse probability weighted estimator for average outcome over a class of regimes. By casting this optimization as a classification problem, we exploit well-studied classification techniques such as support vector machines to characterize the class of regimes and facilitate implementation via a backward iterative algorithm. Simulation studies of performance and application of the method to data from a sequential, multiple assignment randomized clinical trial in acute leukemia are presented.
Journal Article
Estimation of the Optimal Surrogate Based on a Randomized Trial
A common scientific problem is to determine a surrogate outcome for a long-term outcome so that future randomized studies can restrict themselves to only collecting the surrogate outcome. We consider the setting that we observe independent and identically distributed observations of a random variable consisting of baseline covariates, a treatment, a vector of candidate surrogate outcomes at an intermediate time point, and the final outcome of interest at a final time point. We assume the treatment is randomized, conditional on the baseline covariates. The goal is to use these data to learn a most-promising surrogate for use in future trials for inference about a mean contrast treatment effect on the final outcome. We define an optimal surrogate for the current study as the function of the data generating distribution collected by the intermediate time point that satisfies the Prentice definition of a valid surrogate endpoint and that optimally predicts the final outcome: this optimal surrogate is an unknown parameter. We show that this optimal surrogate is a conditional mean and present super-learner and targeted super-learner based estimators, whose predicted outcomes are used as the surrogate in applications. We demonstrate a number of desirable properties of this optimal surrogate and its estimators, and study the methodology in simulations and an application to dengue vaccine efficacy trials.
Journal Article
An adaptive trial design to optimize dose-schedule regimes with delayed outcomes
This paper proposes a two-stage phase I-II clinical trial design to optimize doseschedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decisionmaking is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design’s performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.
Journal Article
Effect of capsular tension ring implantation on intraocular lens calculation formula selection for long axial myopia
Purpose
The study investigated the effect of capsular tension ring (CTR) implantation on postoperative refractive stability and accuracy of intraocular lens (IOL) formulas for axial length (AL) ≥ 27.0 mm patients.
Methods
Prospective case series. The eyes of patients underwent phacoemulsification extraction combined with IOL implantation were classified as CTR implantation (A-CTR) and without CTR implantation (B-CON) groups. Refractive outcome and anterior chamber depth (ACD) were recorded at 1 week, 1 month, and 3 months post-operation. Prediction refractive error (PE) and absolute refractive error (AE) of each formula were calculated.
Results
A total of 89 eyes (63 patients) were included and randomized into the CTR (A-CTR) and control groups (B-CON). Comparison of refraction at different postoperative times of the CTR group showed no statistical difference (all
P
> 0.05). The ACD in the A-CTR group gradually deepened, and that in the B-CON group gradually shallowed (all
P
> 0.05). The formulas’ AE showed statistically significant differences in CTR and CON groups (
P
< 0.001). The PE of Hill-RBF 2.0 and EVO formulas in the A-CTR group were more hyperopic than that in the B-CON group (all
P
> 0.05), the other five formulas were more myopic in A-CTR group than that in the B-CON group (all
P
> 0.05).
Conclusion
Patients with 13 mm diameter CTR implantation tended to have stable refraction at 1 week post-surgery and 1 month for those without it. CTR of the 13 mm diameter had no effect on the selection of formulas. Additionally, it is found that Kane and EVO formulas were more accurate for patients with AL ≥ 27.0 mm.
Journal Article