Prognostic model for psychological outcomes in ambulatory surgery patients: A prospective study using a structural equation modeling framework

Best (but oft forgotten) practices: Efficient sample sizes for commonly used trial designs

Designing studies such that they have a high level of power to detect an effect or association of interest is an important tool to improve the quality and reproducibility of findings from such studies. Since resources (research subjects, time, and money) are scarce, it is important to obtain sufficient power with minimum use of such resources. For commonly used randomized trials of the treatment effect on a continuous outcome, designs are presented that minimize the number of subjects or the amount of research budget when aiming for a desired power level. This concerns the optimal allocation of subjects to treatments and, in case of nested designs such as cluster-randomized trials and multicenter trials, also the optimal number of centers versus the number of persons per center. Since such optimal designs require knowledge of parameters of the analysis model that are not known in the design stage, in particular outcome variances, maximin designs are presented. These designs guarantee a prespecified power level for plausible ranges of the unknown parameters and minimize research costs for the worst-case values of these parameters. The focus is on a 2-group parallel design, the AB/BA crossover design, and cluster-randomized and multicenter trials with a continuous outcome. How to calculate sample sizes for maximin designs is illustrated for examples from nutrition. Several computer programs that are helpful in calculating sample sizes for optimal and maximin designs are discussed as well as some results on optimal designs for other types of outcomes.

Role of mental resilience in fear of cancer recurrence in patients undergoing radical gastrectomy: An understanding of moderating effects of social support

BACKGROUND

The fear of cancer recurrence after radical gastrectomy seriously affects the quality of life. Mental resilience can keep patients in a good psychological state to face adversity, which may reduce the impact on fear of cancer recurrence (FCR). However, good social support plays an important role in one's mental health, and a positive understanding of social support can alleviate negative emotions by enhancing mental resilience.

AIM

To investigate the effect of psychological resilience on the FCR in patients with radical gastrectomy and to comprehend the regulatory effect of social support on both.

METHODS

One hundred and twenty patients with radical gastrectomy who were hospitalized at our hospital from February 2018 to August 2019 were selected as the study subjects. The simplified scale of fear of disease progression, psychological resilience scale, and comprehensiveness social support scale of Chinese version of cancer patients were used for investigations.

RESULTS

FCR score for the overall population was 42.3 ± 7.6. FCR score of active patients (41.8 ± 7.2) was significantly higher than that of non-active patients (39.1 ± 6.9) (P < 0.05). FCR scores of patients with heavy medical burden and patients with certain medical burden were (42.9 ± 8.2) and (42.1 ± 6.9), respectively, which were significantly higher than those of patients with no medical burden (38.9 ± 7.1) (P < 0.05). However, FCR scores of patients undergoing radical gastrectomy showed no statistically significant difference with regard to other aspects of the general data (P > 0.05). FCR score in patients with radical gastrectomy showed a negative correlation with the total score of perceived social support and mental resilience (r = -0.144, -0.271, P < 0.05), while the total score of perceived social support and scores of all subscales were positively correlated with mental resilience (r = 0.365, 0.348, 0.323, 0.373, P < 0.05). Stratified multiple linear regression analysis showed that FCR was a negative predictor (β = -0.236, P < 0.01), and that perceived social support mediated the relationship between resilience and FCR (β = -0.182, P < 0.01). Family support (β = -0.164, P = 0.003, △R² = 0.025), friend support (β = -0.175, P = 0.001, △R² = 0.027), and other support (β = -0.141, P = 0.008, △R² = 0.025) all had a moderating effect on mental resilience and FCR.

CONCLUSION

Mental resilience has a direct effect on the FCR in patients with radical gastrectomy. Understanding that increased levels of social support increase the impact of resilience on the FCR can further reduce the FCR.

Correction: Prognostic model for psychological outcomes in ambulatory surgery patients: A prospective study using a structural equation modeling framework

[This corrects the article DOI: 10.1371/journal.pone.0193441.].