Good Control Of Type Research Articles

Multiple Comparison Procedures, Trimmed Means And Transformed Statistics

Multiple Comparison Procedures, Trimmed Means And Transformed Statistics

The effect of correlation structure on treatment contrasts estimated from incomplete clinical trial data with likelihood-based repeated measures compared with last observation carried forward ANOVA

Valid analyses of longitudinal data can be problematic, particularly when subjects dropout prior to completing the trial for reasons related to the outcome. Regulatory agencies often favor the last observation carried forward (LOCF) approach for imputing missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood-based analyses developed under the missing at random framework provide viable alternatives. The within-subject error correlation structure is often the means by which such methods account for the bias from missing data. The objective of this study was to extend previous work that used only one correlation structure by including several common correlation structures in order to assess the effect of the correlation structure in the data, and how it is modeled, on Type I error rates and power from a likelihood-based repeated measures analysis (MMRM), using LOCF for comparison. Data from four realistic clinical trial scenarios were simulated using autoregressive, compound symmetric and unstructured correlation structures. When the correct correlation structure was fit, MMRM provided better control of Type I error and power than LOCF. Although misfitting the correlation structure in MMRM inflated Type I error and altered power, misfitting the structure was typically less deleterious than using LOCF. In fact, simply specifying an unstructured matrix for use in MMRM, regardless of the true correlation structure, yielded superior control of Type I error than LOCF in every scenario. The present and previous investigations have shown that the bias in LOCF is influenced by several factors and interactions between them. Hence, it is difficult to precisely anticipate the direction and magnitude of bias from LOCF in practical situations. However, in scenarios where the overall tendency is for patient improvement, LOCF tends to: 1) overestimate a drug's advantage when dropout is higher in the comparator and underestimate the advantage when dropout is lower in the comparator; 2) overestimate a drug's advantage when the advantage is maximum at intermediate time points and underestimate the advantage when the advantage increases over time; and 3) have a greater likelihood of overestimating a drug's advantage when the advantage is small. In scenarios in which the overall tendency is for patient worsening, the above biases are reversed. In the simulation scenarios considered in this study, which were patterned after acute phase neuropsychiatric clinical trials, the likelihood-based repeated measures approach, implemented with standard software, was more robust to the bias from missing data than LOCF, and choice of correlation structure was not an impediment to its implementation.

Choice of the primary analysis in longitudinal clinical trials

AbstractMissing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.

The Effects of Nonnormality on Parametric, Nonparametric, and Model Comparison Approaches to Pairwise Comparisons

Researchers in the behavioral sciences are often interested in comparing the means of several treatment conditions on a specific dependent measure. When scores on the dependent measure are not normally distributed, researchers must make important decisions regarding the multiple comparison strategy that is implemented. Although researchers commonly rely on the potential robustness of traditional parametric test statistics (e.g., t and F), these test statistics may not be robust under all nonnormal data conditions. This article compared strategies for performing multiple comparisons with nonnormal data under various data conditions, including simultaneous violations of the assumptions of normality and variance homogeneity. The results confirmed that when variances are unequal, use of the traditional two-sample t test can result in severely biased Type I and/or Type II error rates. However, the use of Welch’s two-sample test statistic with the REGWQ procedure, with either the usual means and variances or with trimmed means and Winsorized variances, resulted in good control of Type I error rates. The Kruskal-Wallis nonparametric statistic provided good Type I error control and power when variances were equal, although Type I error rates became severely inflated when variances were unequal. Furthermore, for researchers interested in eliminating intransitive decisions or comparing potential mean configuration models, a protected model-testing procedure suggested by Dayton provided good overall results.

Intensive Glucose Control and Complications in Type 2 Diabetes.

Two long-awaited reports from the U.K. Prospective Diabetes Study (UKPDS) shed light on the question of whether good control of type 2 diabetes with

Sampling behaviour of kappa and weighted kappa in the null case

Kappa and weighted kappa are widely used statistics for measuring inter‐rater agreement. New formulas for their asymptotic standard errors are proposed under the condition that their population values are zero, and their behaviours compared to that of previous formulas used to test the null hypothesis that these population measures are zero. The results indicate that critical ratios based on the new formulas provide better control of Type I errors. The implications of these findings for the prediction analysis of cross‐classifications are also considered.

Personality traits as predictors of good diabetic control

To identify personality characteristics that might contribute to overall good control of type I diabetes mellitus, we used a biological correlate of control, glycosylated hemoglobin A 1c values, as a means of selecting patients. Patients with evidence of good control (HbA lc < 7.5%) were compared with patients with evidence of poor control (HbA lc > 10.4%). All patients were administered the Personality Research Form E. Need for achievement and a socially desirable response style were associated with good glycemic control. This finding is placed in the context of the complexity of health care behaviors required for adequate self-management.

Comparative study of the therapeutic effects of glibenclamide or the fixed combination of glibenclamide-phenformin with those of gliclazide or chlorpropamide

This study was designed to compare the therapeutic effects of glibenclamide or the fixed combination of glibenclamide-phenformin with those of gliclazide, chlorpropamide or biguanides in non-insulin-dependent diabetes. It is divided into two parts: a) in the retrospective study (473 subjects), glucose control of patients who were transferred from chlorpropamide, gliclazide, glibenclamide, glibenclamide + biguanide or metformin to the fixed combination glibenclamide-phenformin in the same tablet (2.5 mg and 25 mg, respectively) was monitored. A statistically significant decrease of blood glucose and glycosylated hemoglobin values was found under the combination of glibenclamide-phenformin contained in the same tablet in contrast to the values obtained with the treatment with glibenclamide, gliclazide, chlorpropamide, combination of glibenclamide and biguanides, metformin, and insulin. b) In the prospective study (57 subjects), the patients were transferred from chlorpropamide or gliclazide to glibenclamide for 3 months and then reallocated to the previous treatment for 3 additional months. It was found that under glibenclamide, glucose control was significantly better than under chlorpropamide or gliclazide. In conclusion, glibenclamide, a second generation sulfonylurea, and the fixed combination glibenclamide-phenformin in the same tablet are more effective compared to the other antidiabetic agents here studied and lead to a better control of type II diabetic patients. There was no increase in plasma lactic acid concentration in all patients studied before and after having received the fixed combination of glibenclamide-phenformin in the single tablet form.