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\"Whether you are evaluating the effectiveness of a drug, a medical device, a behavioral intervention, a community mobilization, or even a new law, this is the book for you. Written in plain language, it simplifies the process of designing interventions, analyzing the data, and publishing the results. Because the choice of research design depends on the nature of the intervention, the book covers randomized and nonrandomized designs, prospective and retrospective studies, planned clinical trials and observational studies. In addition to reviewing standard statistical analysis, the book has easy-to-follow explanations of cutting edge techniques for evaluating interventions, including propensity score analysis, instrumental variable analysis, interrupted time series analysis and sensitivity analysis. All techniques are illustrated with up-to-date examples from medical and public health literature. This will be essential reading for a wide range of healthcare professionals involved in research as well as those more specifically interested in public health issues and epidemiology\"--Provided by publisher.

Background There is increasing awareness that meta-analyses require a sufficiently large information size to detect or reject an anticipated intervention effect. The required information size in a meta-analysis may be calculated from an anticipated a priori intervention effect or from an intervention effect suggested by trials with low-risk of bias. Methods Information size calculations need to consider the total model variance in a meta-analysis to control type I and type II errors. Here, we derive an adjusting factor for the required information size under any random-effects model meta-analysis. Results We devise a measure of diversity ( D 2 ) in a meta-analysis, which is the relative variance reduction when the meta-analysis model is changed from a random-effects into a fixed-effect model. D 2 is the percentage that the between-trial variability constitutes of the sum of the between-trial variability and a sampling error estimate considering the required information size. D 2 is different from the intuitively obvious adjusting factor based on the common quantification of heterogeneity, the inconsistency ( I 2 ), which may underestimate the required information size. Thus, D 2 and I 2 are compared and interpreted using several simulations and clinical examples. In addition we show mathematically that diversity is equal to or greater than inconsistency, that is D 2 ≥ I 2 , for all meta-analyses. Conclusion We conclude that D 2 seems a better alternative than I 2 to consider model variation in any random-effects meta-analysis despite the choice of the between trial variance estimator that constitutes the model. Furthermore, D 2 can readily adjust the required information size in any random-effects model meta-analysis.

Background Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias. Methods In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards). Results A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided. Conclusions By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics.

Holistic approach to understanding medical statistics. This hands-on guide is much more than a basic medical statistics introduction. It equips you with the statistical tools required for evidence-based clinical research. Each chapter provides a clear step-by-step guide to each statistical test with practical instructions on how to generate and interpret the numbers, and present the results as scientific tables or graphs. Showing you how to:.:.; analyse data with the help of data set examples (Click here to download datasets).; select the correct statistics and report results for publication o.

\"Today the citizens of developed counties have never experienced a large-scale disease outbreak. One reason is the success of the public health community, including epidemiologists and biostatisticians, in tracking and identifying disease outbreaks. Monitoring the Health of Populations by Tracking Disease Outbreaks: Saving Humanity from the Next Plague is the story of the application of statistics for disease detection and tracking. The work of public health officials often critically depends on the use of statistical methods to help discern whether an outbreak may be occurring and, if there is sufficient evidence of an outbreak, then to locate and track it\"-- Provided by publisher.

Abstract Context We previously developed and validated an inexpensive and parsimonious prediction model of 2-year all-cause mortality in real-life patients with type 2 diabetes. Objective This model, now named ENFORCE (EstimatioN oF mORtality risk in type 2 diabetiC patiEnts), was investigated in terms of (i) prediction performance at 6 years, a more clinically useful time-horizon; (ii) further validation in an independent sample; and (iii) performance comparison in a real-life vs a clinical trial setting. Design Observational prospective randomized clinical trial. Setting White patients with type 2 diabetes. Patients Gargano Mortality Study (GMS; n = 1019), Foggia Mortality Study (FMS; n = 1045), and Pisa Mortality Study (PMS; n = 972) as real-life samples and the standard glycemic arm of the ACCORD (Action to Control Cardiovascular Risk in Diabetes) clinical trial (n = 3150). Main Outcome Measure The endpoint was all-cause mortality. Prediction accuracy and calibration were estimated to assess the model's performances. Results ENFORCE yielded 6-year mortality C-statistics of 0.79, 0.78, and 0.75 in GMS, FMS, and PMS, respectively (P heterogeneity = 0.71). Pooling the three cohorts showed a 6-year mortality C-statistic of 0.80. In the ACCORD trial, ENFORCE achieved a C-statistic of 0.68, a value significantly lower than that obtained in the pooled real-life samples (P < 0.0001). This difference resembles that observed with other models comparing real-life vs clinical trial settings, thus suggesting it is a true, replicable phenomenon. Conclusions The time horizon of ENFORCE has been extended to 6 years and validated in three independent samples. ENFORCE is a free and user-friendly risk calculator of all-cause mortality in white patients with type 2 diabetes from a real-life setting. This study extended and validated an inexpensive and parsimonious prediction model of 6-year all-cause mortality in white patients with type 2 diabetes from both real-life and clinical trial settings.

Key Points This Analysis article describes the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The analysis reaffirms that control of physicochemical properties during compound optimization is beneficial in identifying compounds of candidate drug quality. Safety and toxicology are the largest sources of failure within the data set. The link between calculated physicochemical properties and frequent causes of attrition (preclinical toxicology, clinical safety and human pharmacokinetics) is assessed. Analysis of this data set shows that none of the physicochemical descriptors we examined correlates with preclinical toxicology outcomes. This work is the first to indicate a link between lipophilicity and clinical failure owing to safety issues. The utility of this finding in a prospective sense is discussed. Although control of physicochemical properties is clearly important, this analysis suggests that further stringency in this respect is unlikely to have a significant effect on attrition in development and that additional work is required to address safety-related failures. Attempts to reduce the number of efficacy- and safety-related failures that may be linked to the physicochemical properties of small-molecule drug candidates have been inconclusive owing to the limited size of data sets from individual companies. Waring and colleagues analyse the largest data set compiled so far on the causes of attrition for oral, small-molecule drug candidates, derived from a pioneering data-sharing effort by AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The pharmaceutical industry remains under huge pressure to address the high attrition rates in drug development. Attempts to reduce the number of efficacy- and safety-related failures by analysing possible links to the physicochemical properties of small-molecule drug candidates have been inconclusive because of the limited size of data sets from individual companies. Here, we describe the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The analysis reaffirms that control of physicochemical properties during compound optimization is beneficial in identifying compounds of candidate drug quality and indicates for the first time a link between the physicochemical properties of compounds and clinical failure due to safety issues. The results also suggest that further control of physicochemical properties is unlikely to have a significant effect on attrition rates and that additional work is required to address safety-related failures. Further cross-company collaborations will be crucial to future progress in this area.