Background In the hierarchy of research designs, the results of randomized, controlled trials are considered to be evidence of the highest grade, whereas observational studies are viewed as having less validity because they reportedly overestimate treatment effects. We used published meta-analyses to identify randomized clinical trials and observational studies that examined the same clinical topics. We then compared the results of the original reports according to the type of research design. Methods A search of the Medline data base for articles published in five major medical journals from 1991 to 1995 identified meta-analyses of randomized, controlled trials and meta-analyses of either cohort or case–control studies that assessed the same intervention. For each of five topics, summary estimates and 95 percent confidence intervals were calculated on the basis of data from the individual randomized, controlled trials and the individual observational studies. Results For the five clinical topics and 99 r...

Randomized, controlled trials, observational studies, and the hierarchy of research designs.

A method for assessing the quality of a randomized control trial.

Preface. List of examples. 1. Introduction. 1.1 What are Bayesian methods? 1.2 What do we mean by 'health--care evaluation'? 1.3 A Bayesian approach to evaluation. 1.4 The aim of this book and the intended audience. 1.5 Structure of the book. 2. Basic Concepts from Traditional Statistical Analysis. 2.1 Probability. 2.1.1 What is probability? 2.1.2 Odds and log--odds. 2.1.3 Bayes theorem for simple events. 2.2 Random variables, parameters and likelihood. 2.2.1 Random variables and their distributions. 2.2.2 Expectation, variance, covariance and correlation. 2.2.3 Parametric distributions and conditional independence. 2.2.4 Likelihoods. 2.3 The normal distribution. 2.4 Normal likelihoods. 2.4.1 Normal approximations for binary data. 2.4.2 Normal likelihoods for survival data. 2.4.3 Normal likelihoods for count responses. 2.4.4 Normal likelihoods for continuous responses. 2.5 Classical inference. 2.6 A catalogue of useful distributionsaeo. 2.6.1 Binomial and Bernoulli. 2.6.2 Poisson. 2.6.3 Beta. 2.6.4 Uniform. 2.6.5 Gamma. 2.6.6 Root--inverse--gamma. 2.6.7 Half--normal. 2.6.8 Log--normal. 2.6.9 Student's t. 2.6.10 Bivariate normal. 2.7 Key points. Exercises. 3. An Overview of the Bayesian Approach. 3.1 Subjectivity and context. 3.2 Bayes theorem for two hypotheses. 3.3 Comparing simple hypotheses: likelihood ratios and Bayes factors. 3.4 Exchangeability and parametric modellingaeo. 3.5 Bayes theorem for general quantities. 3.6 Bayesian analysis with binary data. 3.6.1 Binary data with a discrete prior distribution. 3.6.2 Conjugate analysis for binary data. 3.7 Bayesian analysis with normal distributions. 3.8 Point estimation, interval estimation and interval hypotheses. 3.9 The prior distribution. 3.10 How to use Bayes theorem to interpret trial results. 3.11 The 'credibility' of significant trial resultsaeo. 3.12 Sequential use of Bayes theoremaeo. 3.13 Predictions. 3.13.1 Predictions in the Bayesian framework. 3.13.2 Predictions for binary dataaeo. 3.13.3 Predictions for normal data. 3.14 Decision--making. 3.15 Design. 3.16 Use of historical data. 3.17 Multiplicity, exchangeability and hierarchical models. 3.18 Dealing with nuisance parametersaeo. 3.18.1 Alternative methods for eliminating nuisance parametersaeo. 3.18.2 Profile likelihood in a hierarchical modelaeo. 3.19 Computational issues. 3.19.1 Monte Carlo methods. 3.19.2 Markov chain Monte Carlo methods. 3.19.3 WinBUGS. 3.20 Schools of Bayesians. 3.21 A Bayesian checklist. 3.22 Further reading. 3.23 Key points. Exercises. 4. Comparison of Alternative Approaches to Inference. 4.1 A structure for alternative approaches. 4.2 Conventional statistical methods used in health--care evaluation. 4.3 The likelihood principle, sequential analysis and types of error. 4.3.1 The likelihood principle. 4.3.2 Sequential analysis. 4.3.3 Type I and Type II error. 4.4 P--values and Bayes factorsaeo. 4.4.1 Criticism of P--values. 4.4.2 Bayes factors as an alternative to P--values: simple hypotheses. 4.4.3 Bayes factors as an alternative to P--values: composite hypotheses. 4.4.4 Bayes factors in preference studies. 4.4.5 Lindley's paradox. 4.5 Key points. Exercises. 5. Prior Distributions. 5.1 Introduction. 5.2 Elicitation of opinion: a brief review. 5.2.1 Background to elicitation. 5.2.2 Elicitation techniques. 5.2.3 Elicitation from multiple experts. 5.3 Critique of prior elicitation. 5.4 Summary of external evidenceaeo. 5.5 Default priors. 5.5.1 'Non--informative' or 'reference' priors: 5.5.2 'Sceptical' priors. 5.5.3 'Enthusiastic' priors. 5.5.4 Priors with a point mass at the null hypothesis ('lump--and--smear' priors)aeo. 5.6 Sensitivity analysis and 'robust' priors. 5.7 Hierarchical priors. 5.7.1 The judgement of exchangeability. 5.7.2 The form for the random--effects distribution. 5.7.3 The prior for the standard deviation of the random effectsaeo. 5.8 Empirical criticism of priors. 5.9 Key points. Exercises. 6. Randomised Controlled Trials. 6.1 Introduction. 6.2 Use of a loss function: is a clinical trial for inference or decision? 6.3 Specification of null hypotheses. 6.4 Ethics and randomisation: a brief review. 6.4.1 Is randomisation necessary? 6.4.2 When is it ethical to randomise? 6.5 Sample size of non--sequential trials. 6.5.1 Alternative approaches to sample--size assessment. 6.5.2 'Classical power': hybrid classical--Bayesian methods assuming normality. 6.5.3 'Bayesian power'. 6.5.4 Adjusting formulae for different hypotheses. 6.5.5 Predictive distribution of power and necessary sample size. 6.6 Monitoring of sequential trials. 6.6.1 Introduction. 6.6.2 Monitoring using the posterior distribution. 6.6.3 Monitoring using predictions: 'interim power'. 6.6.4 Monitoring using a formal loss function. 6.6.5 Frequentist properties of sequential Bayesian methods. 6.6.6 Bayesian methods and data monitoring committees. 6.7 The role of 'scepticism' in confirmatory studies. 6.8 Multiplicity in randomised trials. 6.8.1 Subset analysis. 6.8.2 Multi--centre analysis. 6.8.3 Cluster randomization. 6.8.4 Multiple endpoints and treatments. 6.9 Using historical controlsaeo. 6.10 Data--dependent allocation. 6.11 Trial designs other than two parallel groups. 6.12 Other aspects of drug development. 6.13 Further reading. 6.14 Key points. Exercises. 7. Observational Studies. 7.1 Introduction. 7.2 Alternative study designs. 7.3 Explicit modelling of biases. 7.4 Institutional comparisons. 7.5 Key points. Exercises. 8. Evidence Synthesis. 8.1 Introduction. 8.2 'Standard' meta--analysis. 8.2.1 A Bayesian perspective. 8.2.2 Some delicate issues in Bayesian meta--analysis. 8.2.3 The relationship between treatment effect and underlying risk. 8.3 Indirect comparison studies. 8.4 Generalised evidence synthesis. 8.5 Further reading. 8.6 Key points. Exercises. 9. Cost--effectiveness, Policy--Making and Regulation. 9.1 Introduction. 9.2 Contexts. 9.3 'Standard' cost--effectiveness analysis without uncertainty. 9.4 'Two--stage' and integrated approaches to uncertainty in cost--effectiveness modeling. 9.5 Probabilistic analysis of sensitivity to uncertainty about parameters: two--stage approach. 9.6 Cost--effectiveness analyses of a single study: integrated approach. 9.7 Levels of uncertainty in cost--effectiveness models. 9.8 Complex cost--effectiveness models. 9.8.1 Discrete--time, discrete--state Markov models. 9.8.2 Micro--simulation in cost--effectiveness models. 9.8.3 Micro--simulation and probabilistic sensitivity analysis. 9.8.4 Comprehensive decision modeling. 9.9 Simultaneous evidence synthesis and complex cost--effectiveness modeling. 9.9.1 Generalised meta--analysis of evidence. 9.9.2 Comparison of integrated Bayesian and two--stage approach. 9.10 Cost--effectiveness of carrying out research: payback models. 9.10.1 Research planning in the public sector. 9.10.2 Research planning in the pharmaceutical industry. 9.10.3 Value of information. 9.11 Decision theory in cost--effectiveness analysis, regulation and policy. 9.12 Regulation and health policy. 9.12.1 The regulatory context. 9.12.2 Regulation of pharmaceuticals. 9.12.3 Regulation of medical devices. 9.13 Conclusions. 9.14 Key points. Exercises. 10. Conclusions and Implications for Future Research. 10.1 Introduction. 10.2 General advantages and problems of a Bayesian approach. 10.3 Future research and development. Appendix: Websites and Software. A.1 The site for this book. A.2 Bayesian methods in health--care evaluation. A.3 Bayesian software. A.4 General Bayesian sites. References. Index.

Bayesian approaches to clinical trials and health-care evaluation

https://www.thelancet.com/pdfs/journals/landia/PIIS2213-8587(13)70165-7.pdf

Vitamin D status and ill health: a systematic review

Efficacy and Effectiveness Trials (and Other Phases of Research) in the Development of Health Promotion Programs

In spite of the controversy over the role of randomized clinical trials in medical research, the rationale underlying such trials remains persuasive as compared to recent suggestions for alternative non-randomized studies such as those relying on the use of historical controls and adjustment technics Others have suggested that recent statistical innovations for improving clinical trials, including adaptive allocation of treatment to patients and sequential stopping procedures, are underutilized These innovations, though theoretically interesting, are not easily adapted to large-scale, complex medical trials in which there may be multiple end points and delayed response times Ethical considerations suggest that randomized trials are more suitable than uncontrolled experimentation in protecting the interests of patients Randomized clinical trials remain the most reliable method for evaluating the efficacy of therapies

Randomized clinical trials. Perspectives on some recent ideas.

We treated 93 children with nephropathic cystinosis with oral cysteamine (mean dose, 51.3 mg per kilogram of body weight per day) for up to 73 months. This agent is known to be effective in depleting cells of cystine. In our study, the mean cystine depletion from leukocytes was 82 percent. A historical control group of 55 children received either ascorbic acid (27 children) or placebo (28). At age six, 2 of 17 controls had a serum creatinine level less than 1.0 mg per deciliter, as compared with 17 of 27 patients treated with cysteamine for at least one year (odds ratio, 12.8; 95 percent confidence interval, 2.1 to 33.9). At the end of the study, creatinine clearance was higher in the cysteamine group than in the control group (38.5 vs. 29.7 ml per minute per 1.73 m2; 95 percent confidence limits on the difference, 1.8 and 15.8), even though the cysteamine group was on average 1.4 years older than the control group. Cysteamine also improved growth; those in the cysteamine group between two and three years of age grew at 93 percent of the normal velocity, as compared with 54 percent in the control group. Fourteen percent of the patients could not tolerate the taste and smell of cysteamine. Concurrent controls treated in a blinded fashion with a placebo were not included in this study. With this limitation in mind, we conclude that oral cysteamine, by depleting cells of cystine, helps maintain renal glomerular function, improves growth, and constitutes the current treatment of choice for nephropathic cystinosis.

Cysteamine Therapy for Children with Nephropathic Cystinosis

Because high concentrations of ascorbic acid (0.57 mM) lower the free (nonprotein) cystine content of cultured cystinotic skin fibroblasts by over 50 per cent, we did a double-blind clinical trial to establish whether this drug would benefit cystinotic children. Sixty-four patients were randomized into the study; 32 received ascorbic acid (200 mg per kilogram of body weight per day), and 32 placebo. The study was terminated after approximately two years because there was no indication that vitamin C was beneficial and accumulating evidence that it might be harmful. Of 11 patients who left the study because of death or the requirement for dialysis or renal transplantation, eight were receiving ascorbic acid. The estimated relative risk (treatment vs. control) of an adverse event was R = 2.7, with a 90 per cent confidence interval of (0.8, 11.5). The serum creatinine concentration increased 0.53 mg per deciliter per year in patients receiving vitamin C and 0.24 mg per deciliter per year in patient...

https://www.nejm.org/doi/pdf/10.1056/NEJM197904053001403?listPDF=true

Ineffectiveness of ascorbic acid therapy in nephropathic cystinosis.

In case-control studies, exposure to a risk factor often occurs at several levels, so the attributable risk at each level is of interest. In this paper, estimation for the 2 X 2 table (case-control status versus dichotomous exposure) and the 2 X k table (case-control status versus exposure at several levels) are reviewed along with an example. A method for finding confidence intervals for attributable risk in the 2 X k table is proposed, and its application to estimates adjusted across strata (the 2 X k X s case) is indicated. The results of a Monte Carlo study of the procedure demonstrate that the nominal and actual coverage probabilities agree satisfactorily for practical applications.

James J. Schlesselman

Papers

Randomized clinical trials. Perspectives on some recent ideas.

Cysteamine Therapy for Children with Nephropathic Cystinosis

Ineffectiveness of ascorbic acid therapy in nephropathic cystinosis.

Interval estimation of the attributable risk for multiple exposure levels in case-control studies.