Statistics in medicine
-
This article summarizes recommendations on the design and conduct of clinical trials of a National Research Council study on missing data in clinical trials. Key findings of the study are that (a) substantial missing data is a serious problem that undermines the scientific credibility of causal conclusions from clinical trials; (b) the assumption that analysis methods can compensate for substantial missing data is not justified; hence (c) clinical trial design, including the choice of key causal estimands, the target population, and the length of the study, should include limiting missing data as one of its goals; (d) missing-data procedures should be discussed explicitly in the clinical trial protocol; (e) clinical trial conduct should take steps to limit the extent of missing data; (f) there is no universal method for handling missing data in the analysis of clinical trials - methods should be justified on the plausibility of the underlying scientific assumptions; and (g) when alternative assumptions are plausible, sensitivity analysis should be conducted to assess robustness of findings to these alternatives. This article focuses on the panel's recommendations on the design and conduct of clinical trials to limit missing data. A companion paper addresses the panel's findings on analysis methods.
-
Statistics in medicine · Nov 2012
Paradigms for adaptive statistical information designs: practical experiences and strategies.
In the last decade or so, interest in adaptive design clinical trials has gradually been directed towards their use in regulatory submissions by pharmaceutical drug sponsors to evaluate investigational new drugs. Methodological advances of adaptive designs are abundant in the statistical literature since the 1970s. The adaptive design paradigm has been enthusiastically perceived to increase the efficiency and to be more cost-effective than the fixed design paradigm for drug development. ⋯ Specifically, a case example is used to illustrate how challenging it is to plan a confirmatory adaptive statistical information design. We highlight the substantial risk of planning the sample size for confirmatory trials when information is very uninformative and stipulate the advantages of adaptive statistical information designs for planning exploratory trials. Practical experiences and strategies as lessons learned from more recent adaptive design proposals will be discussed to pinpoint the improved utilities of adaptive design clinical trials and their potential to increase the chance of a successful drug development.
-
Statistics in medicine · Aug 2012
Adaptive extensions of a two-stage group sequential procedure for testing primary and secondary endpoints (II): sample size re-estimation.
In this part II of the paper on adaptive extensions of a two-stage group sequential procedure (GSP) for testing primary and secondary endpoints, we focus on the second stage sample size re-estimation based on the first stage data. First, we show that if we use the Cui-Huang-Wang statistics at the second stage, then we can use the same primary and secondary boundaries as for the original procedure (without sample size re-estimation) and still control the type I familywise error rate. This extends their result for the single endpoint case. ⋯ We show how to modify the boundaries of the original group sequential procedure to control the familywise error rate. We provide power comparisons between competing procedures. We illustrate the procedures with a clinical trial example.
-
Statistics in medicine · Jul 2012
Comparative StudyMultiple imputation for left-censored biomarker data based on Gibbs sampling method.
Biomarkers, increasingly used in biomedical studies for the diagnosis and prognosis of acute and chronic diseases, provide insight into the effectiveness of treatments and potential pathways that can be used to guide future treatment targets. The measurement of these markers is often limited by the sensitivity of the given assay, resulting in data that are censored either at the lower or at the upper limit of detection. For the Genetic and Inflammatory Markers of Sepsis (GenIMS) study, many different biomarkers were measured to examine the effect of different pathways on the development of sepsis. ⋯ We assume a multivariate normal distribution to account for the correlations between biomarkers and use the Gibbs sampler for the estimation of the distributional parameters and the imputation of the censored markers. We evaluate and compare the proposed methods with some simple imputation methods through simulation. We use a data set of inflammatory and coagulation markers from the GenIMS study for illustration.