Chronic pain (CP) seriously affects the patient’s daily activities and quality of life, but few studies on CP have considered its effects on the patient’s social and family environment. In this work, through a review of the literature, we assessed several aspects of how CP influences the patient’s daily activities and quality of life, as well as its repercussions in the workplace, and on the family and social environment. Finally, the consequences of pain on the health care system are discussed. On the basis of the results, we concluded that in addition to the serious consequences on the patient’s life, CP has a severe detrimental effect on their social and family environment, as well as on health care services. Thus, we want to emphasize on the need to adopt a multidisciplinary approach to treatment so as to obtain more comprehensive improvements for patients in familial and social contexts. Accordingly, it would be beneficial to promote more social- and family-oriented research initiatives.

Pain affects an important proportion of the Spanish adult population and that it has a strong personal impact. Two pain groups were clearly distinguished by their clinical characteristics.

These results highlight the importance of taking into account the type of pain when assessing cognitive performance in CP patients and demonstrate the influence of the emotional state of the patient, especially if depression is present.

To assess the impact of chronic pain on the family environment from the patient's, relative's and caregiver's perspective, we undertook cross-sectional study on a representative sample of Spanish adults who suffered pain at least 4 days a week for ≥3 months and on relatives and caregivers of patients that fulfilled these criteria. The characteristics of pain and the perception of its impact on the family environment were assessed, using logistic regression models to reveal the variables associated with the impact of pain on the family. From a total of 1,957 subjects, 325 experienced chronic pain and 34.6% of them perceived that their pain affected their family environment. These patients recognized a stronger impact when their relatives were sad (OR = 3.61; CI:1.57, 8.27) and had modified the leisure activities because of the pain (OR = 3.62; CI:1.56, 8.38). Among the 131 relatives, 51.2% perceived that pain was affecting the family, causing changes in their leisure activities (OR = 1.17; CI:1.04, 9.94) and sleep disturbance (OR = 1.40; CI:1.32, 12.58). Of the 36 caregivers, mainly women over 50 years of age, 66.7% indicated that pain affected the family, although 72.8% were satisfied with the help they provided. Chronic pain has a very strong impact on the family, although this is perceived distinctly by patients, relatives, and caregivers. Recognizing that factors related to pain affect the family's well-being, and adopting a global approach to pain that takes into consideration the family's experiences, should improve the therapeutic response, and enhance the patient's and relative's quality of life. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Missing data are a common problem in clinical and epidemiological research, especially in longitudinal studies. Despite many methodological advances in recent decades, many papers on clinical trials and epidemiological studies do not report using principled statistical methods to accommodate missing data or use ineffective or inappropriate techniques. Two refined techniques are presented here: generalized estimating equations (GEEs) and weighted generalized estimating equations (WGEEs). These techniques are an extension of generalized linear models to longitudinal or clustered data, where observations are no longer independent. They can appropriately handle missing data when the missingness is completely at random (GEE and WGEE) or at random (WGEE) and do not require the outcome to be normally distributed. Our aim is to describe and illustrate with a real example, in a simple and accessible way to researchers, these techniques for handling missing data in the context of longitudinal studies subject to dropout and show how to implement them in R. We apply them to assess the evolution of health-related quality of life in coronary patients in a data set subject to dropout. Copyright © 2016 John Wiley & Sons, Ltd.