An Introduction to the Fundamentals of Cohort and Case–Control Studies (original) (raw)

Considerations in the Evaluation of Surrogate Endpoints in Clinical Trials

Controlled Clinical Trials, 2001

We report on recommendations from a National Institutes of Health Workshop on methods for evaluating the use of surrogate endpoints in clinical trials, which was attended by experts in biostatistics and clinical trials from a broad array of disease areas. Recent advances in biosciences and technology have increased the ability to understand, measure, and model biological mechanisms; appropriate application of these advances in clinical research settings requires collaboration of quantitative and laboratory scientists. Biomarkers, new examples of which arise rapidly from new technologies, are used frequently in such areas as early detection of disease and identification of patients most likely to benefit from new therapies. There is also scientific interest in exploring whether, and under what conditions, biomarkers may substitute for clinical endpoints of phase III trials, although workshop participants agreed that these considerations apply primarily to situations where trials using clinical endpoints are not feasible. Evaluating candidate biomarkers in the exploratory phases of drug development and investigating surrogate endpoints in confirmatory trials require the establishment of a statistical and in-Address reprint requests to: Victor G. De Gruttola, D.Sc.V.G. De Gruttola et al.

Observational Studies Are Complementary to Randomized Controlled Trials

Nephron Clinical Practice, 2010

Randomized controlled trials (RCTs) are considered the gold standard study design to investigate the effect of health interventions, including treatment. However, in some situations, it may be unnecessary, inappropriate, impossible, or inadequate to perform an RCT. In these special situations, well-designed observational studies, including cohort and case-control studies, may provide an alternative to doing nothing in order to obtain estimates of treatment effect. It should be noted that such studies should be performed with caution and correctly. The aims of this review are (1) to explain why RCTs are considered the optimal study design to evaluate treatment effects, (2) to describe the situations in which an RCT is not possible and observational studies are an adequate alternative, and (3) to explain when randomization is not needed and can be approximated in observational studies. Examples from the nephrology literature are used for illustration.

Placebo control groups in randomized treatment trials: a statistician’s perspective

Biological Psychiatry, 2000

Because a statistical tie between standard treatment and an innovation is uninterpretable, most trials intended to demonstrate efficacy of innovations in psychopharmacology employ a placebo control group, despite the existence of standard medications for many disorders. In this review I consider the statistical issues that inform the ethics of the decision to use a placebo condition and make the following points: 1) the investigator is relying on the assumption that the effects of delayed standard treatment are neither long-lasting nor harmful; 2) the usual practice of truncating follow-up when a patient ceases to adhere to a study treatment makes it difficult to empirically test that assumption; 3) placebo control trials often suffer from methodological weaknesses (including nonrandom truncation) that reduce their inferential power; 4) these subtleties place a substantial burden on the informed consent process; 5) alternative designs are available but not well explored, due to the dominant role of "regulatory" trial methodology; and 6) researchers should consider other goals besides helping to introduce another treatment that improves on placebos but not the standard treatment.

Statistical Issues in Randomized Controlled Trials: an editorial

Electronic physician, 2018

Randomization is the bedrock of randomized controlled trials, which ensures the elimination of selection bias and also to some extent the homogenous distribution of covariates between the intervention arms. Randomization does not always guarantee the baseline balance, and hence makes the statistical analysis more complex. Several published clinical trials have employed test of significance to compare baseline measures between the groups. However, such practice has been criticized by several authors and CONSORT statement also discourages it. This overview discusses various statistical designs that were employed in published trials. Post intervention data (follow up score) comparison between the arms was common practice in published RCTs. However, this approach fails to adjust baseline imbalance. Both Change score and Percentage change methods adjust the baseline imbalance. Both of the approaches give precise estimates when there is a high correlation between baseline and follow-up score. However, when correlation is low they both give biased and less precise estimates of treatment effect. Analysis of covariance (ANCOVA) is a regression method, which maintains high statistical power and gives less biased and more precise estimates of treatment effect regardless of correlation level. Understanding strengths and limitations of different statistical designs of RCTs will prevent statistical errors, which can yield an accurate estimate of treatment effect.

Statistical challenges in the evaluation of surrogate endpoints in randomized trials

Controlled Clinical Trials, 2002

The validation of surrogate endpoints has been studied by Prentice, who presented a definition as well as a set of criteria that are equivalent if the surrogate and true endpoints are binary. Freedman et al. supplemented these criteria with the so-called proportion explained. Buyse and Molenberghs proposed to replace the proportion explained by two quantities: (1) the relative effect, linking the effect of treatment on both endpoints, and (2) the adjusted association, an individual-level measure of agreement between both endpoints. In a multiunit setting, these quantities can be generalized to a trial-level measure of surrogacy and an individual-level measure of surrogacy. In this paper, we argue that such a multiunit approach should be adopted because it overcomes difficulties that necessarily surround validation efforts based on a single trial. These difficulties are highlighted. 607-625 ARTICLE IN PRESS Prentice [2] proposed a formal definition of surrogate endpoints and outlined how potential surrogate endpoints could be validated. Much debate ensued, for the criteria set out by Prentice are not straightforward to verify [3]. In addition, Prentice's operational criteria are only equivalent to his definition in the case of binary endpoints [4]. Freedman et al. [5] supplemented Prentice's approach by introducing the proportion explained (PE), which is the proportion of the treatment effect mediated by the surrogate. Buyse and Molenberghs [4]