Gender differences in PTSD severity and pain outcomes: baseline results from the LAMP trial

Background

Post-traumatic stress disorder (PTSD) and chronic pain are highly prevalent comorbid conditions. Veterans dually burdened by PTSD and chronic pain experience more severe outcomes compared to either disorder alone. Few studies have enrolled enough women Veterans to test gender differences in pain outcomes [catastrophizing, intensity, interference] by the severity of PTSD.

Aim

Examine gender differences in the association between PTSD symptoms and pain outcomes among Veterans enrolled in a chronic pain clinical trial.

Methods

Participants were 421 men and 386 women Veterans with chronic pain who provided complete data on PTSD symptoms and pain outcomes. We used hierarchical linear regression models to examine gender differences in pain outcomes by PTSD symptoms.

Results

Adjusted multivariable models indicated that PTSD symptoms were associated with higher levels of pain catastrophizing (0.57, 95% CI [0.51, 0.63]), pain intensity (0.30, 95% CI [0.24, 0.37]), and pain interference (0.46, 95% CI [0.39, 0.52]). No evidence suggesting differences in this association were found in either the crude or adjusted models (all interaction p-values<0.05).

Conclusion

These findings may reflect the underlying mutual maintenance of these conditions whereby the sensation of pain could trigger PTSD symptoms, particularly if the trauma and pain are associated with the same event. Clinical implications and opportunities testing relevant treatments that may benefit both chronic pain and PTSD are discussed.

On a Hybrid Method in Dose Finding Studies

Objectives: Combination of multiple testing and modeling techniques in dose-response studies. Use of hypotheses tests to assess the significance of the dose-response signal associated with a given candidate dose-response model. Estimation of target dose(s) following the previous model selection step. Illustration of the method with a real data example.

Methods: We assume a set of candidate models potentially reflecting the data generating process. The appropriateness of each individual model is evaluated in terms of contrast tests, where each set of contrast weights describes a specific dose-response shape. Optimum contrast weights are computed, which maxi-mize the non-centrality parameters associated with the contrast tests. A reference set of appropriate candidate models is obtained while controlling the familywise error rate. A single model is then selected from this reference set using standard model selection criteria. The final step is devoted to dose finding by applying inverse regression techniques. This is illustrated for estimating the minimum effective dose.

Results: The method is as powerful as competing standard dose-response tests to detect an overall dose-related trend. In addition, the possibility is given to estimate one or more target doses of interest. The analysis of a real data example confirms the advantages of the proposed hybrid method.

Conclusions: Combining multiple testing and modeling techniques leads to a powerful tool, which uses the advantages of both approaches: Rigid error control at the significance testing step and flexibility at the dose estimation step. The method can be extended to handle more general linear models including covariates and factorial treatment structures.

Correcting overall survival for the impact of crossover via a rank-preserving structural failure time (RPSFT) model in the RECORD-1 trial of everolimus in metastatic renal-cell carcinoma

Clinical trials in oncology often allow patients randomized to placebo to cross over to the active treatment arm after disease progression, leading to underestimation of the treatment effect on overall survival as per the intention-to-treat analysis. We illustrate the statistical aspects and practical use of the rank-preserving structural failure time (RPSFT) model with the Fleming-Harrington family of tests to estimate the crossover-corrected treatment effect, and to assess its sensitivity to various weighting schemes in the RECORD-1 trial. The results suggest that the benefit demonstrated in progression-free survival is likely to translate into a robust overall survival benefit.

DAME: planetary-prototype drilling automation

We describe results from the Drilling Automation for Mars Exploration (DAME) project, including those of the summer 2006 tests from an Arctic analog site. The drill hardware is a hardened, evolved version of the Advanced Deep Drill by Honeybee Robotics. DAME has developed diagnostic and executive software for hands-off surface operations of the evolved version of this drill. The DAME drill automation tested from 2004 through 2006 included adaptively controlled drilling operations and the downhole diagnosis of drilling faults. It also included dynamic recovery capabilities when unexpected failures or drilling conditions were discovered. DAME has developed and tested drill automation software and hardware under stressful operating conditions during its Arctic field testing campaigns at a Mars analog site.

Combining Multiple Comparisons and Modeling Techniques in Dose-Response Studies

The analysis of data from dose-response studies has long been divided according to two major strategies: multiple comparison procedures and model-based approaches. Model-based approaches assume a functional relationship between the response and the dose, taken as a quantitative factor, according to a prespecified parametric model. The fitted model is then used to estimate an adequate dose to achieve a desired response but the validity of its conclusions will highly depend on the correct choice of the a priori unknown dose-response model. Multiple comparison procedures regard the dose as a qualitative factor and make very few, if any, assumptions about the underlying dose-response model. The primary goal is often to identify the minimum effective dose that is statistically significant and produces a relevant biological effect. One approach is to evaluate the significance of contrasts between different dose levels, while preserving the family-wise error rate. Such procedures are relatively robust but inference is confined to the selection of the target dose among the dose levels under investigation. We describe a unified strategy to the analysis of data from dose-response studies which combines multiple comparison and modeling techniques. We assume the existence of several candidate parametric models and use multiple comparison techniques to choose the one most likely to represent the true underlying dose-response curve, while preserving the family-wise error rate. The selected model is then used to provide inference on adequate doses.

On a hybrid method in dose finding studies

OBJECTIVES

Combination of multiple testing and modeling techniques in dose-response studies. Use of hypotheses tests to assess the significance of the dose-response signal associated with a given candidate dose-response model. Estimation of target dose(s) following the previous model selection step. Illustration of the method with a real data example.

METHODS

We assume a set of candidate models potentially reflecting the data generating process. The appropriateness of each individual model is evaluated in terms of contrast tests, where each set of contrast weights describes a specific dose-response shape. Optimum contrast weights are computed, which maximize the non-centrality parameters associated with the contrast tests. A reference set of appropriate candidate models is obtained while controlling the familywise error rate. A single model is then selected from this reference set using standard model selection criteria. The final step is devoted to dose finding by applying inverse regression techniques. This is illustrated for estimating the minimum effective dose.

RESULTS

The method is as powerful as competing standard dose-response tests to detect an overall dose-related trend. In addition, the possibility is given to estimate one or more target doses of interest. The analysis of a real data example confirms the advantages of the proposed hybrid method.

CONCLUSIONS

Combining multiple testing and modeling techniques leads to a powerful tool, which uses the advantages of both approaches: Rigid error control at the significance testing step and flexibility at the dose estimation step. The method can be extended to handle more general linear models including covariates and factorial treatment structures.

Reconsidering some aspects of the two-trials paradigm

A common standard for the demonstration of efficacy in a clinical submission is a statistically significant outcome in at least two pivotal trials ("two-trials convention"). When the data structures in different trials are sufficiently similar to allow pooling of the data across trials for a combined analysis, we argue here that such an analysis is a more logical and efficient basis for a judgment regarding efficacy. Criteria for combined analyses may be established, which ensure the same false positive rate protection as the two-trials convention. A combined analysis will generally have much more power than the corresponding application of the two-trials approach that has the same false positive rate protection. In addition, we describe the behavior of modified versions of pure combined analysis, which incorporate a formal standard for reproducibility of trial results by limiting the larger of the individual trial p-values. These modifications are shown to maintain the desirable behavior of the pure combined analysis, namely, higher power compared to the two-trials convention.

Hazards of sharps disposal

The disposal of sharps continues to be one of the most hazardous procedures for clinical staff. This article looks at the dangers encountered and the precautions necessary to ensure the safe disposal of sharps.

Evaluation and application of continuity measures in primary care settings

Continuity of contact between patients and physicians has become an important criterion of quality primary care. Using three measures of continuity that have appeared in the literature, this article examines, through the use of simulated data and through application to data from five primary care settings, the differences and utility of these approaches for measuring continuity. Further, these measures are applied to four selected diagnoses from each of the five sites, and the observed continuity scores afforded patients with these diagnoses are compared with those expected based on the population. Finally, the scores are correlated with the number of return visits prescribed and kept and with the rate at which laboratory studies are ordered. The findings indicate that site-specific differences in continuity prevail even after adjustments in the number of visits. Continuity based on selected diagnoses is greater, for the most part, than continuity afforded the patient population. Finally, continuity is related to the number of return visits prescribed but not to the number kept or the rate at which laboratory studies are ordered. The implications of continuity for other aspects of quality patient care are discussed.