Time-dependent predictors in clinical research, performance of a novel method.

Individual patients' predictors of survival may change across time, because people may change their lifestyles. Standard statistical methods do not allow adjustments for time-dependent predictors. In the past decade, time-dependent factor analysis has been introduced as a novel approach adequate for the purpose. Using examples from survival studies, we assess the performance of the novel method. SPSS statistical software is used (SPSS Inc., Chicago, IL). Cox regression is a major simplification of real life; it assumes that the ratio of the risks of dying in parallel groups is constant over time. It is, therefore, inadequate to analyze, for example, the effect of elevated low-density lipoprotein cholesterol on survival, because the relative hazard of dying is different in the first, second, and third decades. The time-dependent Cox regression model allowing for nonproportional hazards is applied and provides a better precision than the usual Cox regression (P = 0.117 versus 0.0001). Elevated blood pressure produces the highest risk at the time it is highest. An overall analysis of the effect of blood pressure on survival is not significant, but after adjustment for the periods with highest blood pressures using the segmented time-dependent Cox regression method, blood pressure is a significant predictor of survival (P = 0.04). In a long-term therapeutic study, treatment modality is a significant predictor of survival, but after the inclusion of the time-dependent low-density lipoprotein cholesterol variable, the precision of the estimate improves from a P value of 0.02 to 0.0001. Predictors of survival may change across time, e.g., the effect of smoking, cholesterol, and increased blood pressure in cardiovascular research and patients' frailty in oncology research. Analytical models for survival analysis adjusting such changes are welcome. The time-dependent and segmented time-dependent predictors are adequate for the purpose. The usual multiple Cox regression model can include both time-dependent and time-independent predictors.

Cardiovascular research: data dispersion issues.

Biological processes are full of variations and so are responses to therapy as measured in clinical research. Estimators of clinical efficacy are, therefore, usually reported with a measure of uncertainty, otherwise called dispersion. This study aimed to review both the flaws of data reports without measure of dispersion and those with over-dispersion.EXAMPLES OF ESTIMATORS COMMONLY REPORTED WITHOUT A MEASURE OF DISPERSION INCLUDE: number needed to treat;reproducibility of quantitative diagnostic tests;sensitivity/specificity;Markov predictors;risk profiles predicted from multiple logistic models.Data with large differences between response magnitudes can be assessed for over-dispersion by goodness of fit tests. The χ(2) goodness of fit test allows adjustment for over-dispersion.For most clinical estimators, the calculation of standard errors or confidence intervals is possible. Sometimes, the choice is deliberately made not to use the data fully, but to skip the standard errors and to use the summary measures only. The problem with this approach is that it may suggest inflated results. We recommend that analytical methods in clinical research should always attempt to include a measure of dispersion in the data. When large differences exist in the data, the presence of over-dispersion should be assessed and appropriate adjustments made.