Temporal instability of evidence base: A threat to policy making?

A shift towards evidence-based conservation and environmental managementover the last two decades has resulted in an increased use of systematic reviewsand meta-analyses as tools to combine existing scientific evidence. However, toguide policy making decisions in conservation and management, the conclu-sions of meta-analyses need to remain stable for at least some years. Alarmingly,numerous recent studies indicate that the magnitude, statistical significance,and even the sign of the effects reported in the literature might change overrelatively short time periods. We argue that such rapid temporal changes incumulative evidence represent a real threat to policy making in conservationand environmental management and call for systematic monitoring of temporalchanges in evidence and exploration of their causes

Statins: figures on the pulse, in: The Actuary (August 2018)

Medical advances are the major drivers in the longevity increase. But how to quantify this relationship? Our research, funded by the Actuarial Research Centre (ARC), uses The Health Improvement Network (THIN) primary care data to develop statistical models of longevity, given a particular chronic medical condition or intervention

How medical advances and health interventions will shape future longevity

Medicine-related research includes numerous studies on the hazards of mortality and what risk factors are associated with these hazards, such as diseases and treatments. These hazards are estimated in a sample of people and summarised over the observed period. From these observations, inferences can be made about the underlying population and consequently inform medical guidelines for intervention. New health interventions are usually based on these estimated hazards obtained from clinical trials. A lengthy lead time would be needed to observe their effect on population longevity. This paper shows how estimated mortality hazards can be translated to hypothetical changes in life expectancies at the individual and population levels. For an individual, the relative hazards are translated into the number of years gained or lost in “effective age”, which is the average chronological age with the same risk profile. This translation from hazard ratio to effective age could be used to explain to individuals the consequences of various diseases and lifestyle choices and as a result persuade clients in life and health insurance to pursue a healthier lifestyle. At the population level, a period life expectancy is a weighted average of component life expectancies associated with the particular risk profiles, with the weights defined by the prevalences of the risk factor of interest and the uptake of the relevant intervention. Splitting the overall life expectancy into these components allows us to estimate hypothetical changes in life expectancy at the population level at different morbidity and uptake scenarios. These calculations are illustrated by two examples of medical interventions and their impact on life expectancy, which are beta blockers in heart attack survivors and blood pressure treatment in hypertensive patients. The second example also illustrates the dangers of applying the results from clinical trials to much wider populations

Estimation in meta-analyses of mean difference and standardized mean difference

Methods for random-effects meta-analysis require an estimate of the between-study variance, τ 2. The performance of estimators of τ 2 (measured by bias and coverage) affects their usefulness in assessing heterogeneity of study-level effects and also the performance of related estimators of the overall effect. However, as we show, the performance of the methods varies widely among effect measures. For the effect measures mean difference (MD) and standardized MD (SMD), we use improved effect-measure-specific approximations to the expected value of Q for both MD and SMD to introduce two new methods of point estimation of τ 2 for MD (Welch-type and corrected DerSimonian-Laird) and one WT interval method. We also introduce one point estimator and one interval estimator for τ 2 in SMD. Extensive simulations compare our methods with four point estimators of τ 2 (the popular methods of DerSimonian-Laird, restricted maximum likelihood, and Mandel and Paule, and the less-familiar method of Jackson) and four interval estimators for τ 2 (profile likelihood, Q-profile, Biggerstaff and Jackson, and Jackson). We also study related point and interval estimators of the overall effect, including an estimator whose weights use only study-level sample sizes. We provide measure-specific recommendations from our comprehensive simulation study and discuss an example

Survival benefits of statin therapy in primary care: landmark analyses

Statins have been widely prescribed for primary and secondary prevention of cardiovascular disease since clinical trials have demonstrated the survival benefits. However, the threshold of cardiac risk at which to prescribe statins is still controversial, especially at older ages when everyone would be eligible solely due to their age. Our study aim was to dynamically predict the survival benefits associated with statin therapy over the course of 25 years in patients aged 60 residential in England or Wales

Estimation in meta-analyses of response ratios

BACKGROUND: For outcomes that studies report as the means in the treatment and control groups, some medical applications and nearly half of meta-analyses in ecology express the effect as the ratio of means (RoM), also called the response ratio (RR), analyzed in the logarithmic scale as the log-response-ratio, LRR. METHODS: In random-effects meta-analysis of LRR, with normal and lognormal data, we studied the performance of estimators of the between-study variance, τ2, (measured by bias and coverage) in assessing heterogeneity of study-level effects, and also the performance of related estimators of the overall effect in the log scale, λ. We obtained additional empirical evidence from two examples. RESULTS: The results of our extensive simulations showed several challenges in using LRR as an effect measure. Point estimators of τ2 had considerable bias or were unreliable, and interval estimators of τ2 seldom had the intended 95% coverage for small to moderate-sized samples (n<40). Results for estimating λ differed between lognormal and normal data. CONCLUSIONS: For lognormal data, we can recommend only SSW, a weighted average in which a study's weight is proportional to its effective sample size, (when n≥40) and its companion interval (when n≥10). Normal data posed greater challenges. When the means were far enough from 0 (more than one standard deviation, 4 in our simulations), SSW was practically unbiased, and its companion interval was the only option