Sharon Love

DEPRESSION AND ANXIETY IN WOMEN WITH EARLY BREAST CANCER: FIVE YEAR OBSERVATIONAL COHORT STUDY

Abstract Objective To examine the prevalence of, and risk factors for, depression and anxiety in women with early breast cancer in the five years after diagnosis. Design Observational cohort study. Setting NHS breast clinic, London. Participants 222 women with early breast cancer: 170 (77%) provided complete interview data up to either five years after diagnosis or recurrence. Main outcome measures Prevalence of clinically important depression and anxiety (structured psychiatric interview with standardised diagnostic criteria) and clinical and patient risk factors, including stressful life experiences (Bedford College life events and difficulties schedule). Results Nearly 50% of the women with early breast cancer had depression, anxiety, or both in the year after diagnosis, 25% in the second, third, and fourth years, and 15% in the fifth year. Point prevalence was 33% at diagnosis, falling to 15% after one year. 45% of those with recurrence experienced depression, anxiety, or both within three months of the diagnosis. Previous psychological treatment predicted depression, anxiety, or both in the period around diagnosis (one month before diagnosis to four months after diagnosis). Longer term depression and anxiety, were associated with previous psychological treatment, lack of an intimate confiding relationship, younger age, and severely stressful non-cancer life experiences. Clinical factors were not associated with depression and anxiety, at any time. Lack of intimate confiding support also predicted more protracted episodes of depression and anxiety. Conclusion Increased levels of depression, anxiety, or both in the first year after a diagnosis of early breast cancer highlight the need for dedicated service provision during this time. Psychological interventions for women with breast cancer who remain disease free should take account of the broader social context in which the cancer occurs, with a focus on improving social support.

Influence of delay on survival in patients with breast cancer: a systematic review

BACKGROUND Most patients with breast cancer are detected after symptoms occur rather than through screening. The impact on survival of delays between the onset of symptoms and the start of treatment is controversial and cannot be studied in randomised controlled trials. We did a systematic review of observational studies (worldwide) of duration of symptoms and survival. METHODS We identified 87 studies (101,954 patients) with direct data linking delay (including delay by patients) and survival. We classified studies for analysis by type of data in the original reports: category I studies had actual 5-year survival data (38 studies, 53,912 patients); category II used actuarial or multivariate analyses (21 studies, 25,102 patients); and category III was all other types of data (28 studies, 22,940 patients). We tested the main hypothesis that longer delays would be associated with lower survival, and a secondary hypothesis that longer delays were associated with more advanced stage, which would account for lower survival. FINDINGS In category I studies, patients with delays of 3 months or more had 12% lower 5-year survival than those with shorter delays (odds ratio for death 1.47 [95% CI 1.42-1.53]) and those with delays of 3-6 months had 7% lower survival than those with shorter delays (1.24 [1.17-1.30]). In category II, 13 of 14 studies with unrestricted samples showed a significant adverse relation between longer delays and survival, whereas four of five studies of only patients with operable disease showed no significant relation. In category III, all three studies with unrestricted samples supported the primary hypothesis. The 13 informative studies showed that longer delays were associated with more advanced stage. In studies that controlled for stage, longer delay was not associated with shorter survival when the effect of stage on survival was taken into account. INTERPRETATION Delays of 3-6 months are associated with lower survival. These effects cannot be accounted for by lead-time bias. Efforts should be made to keep delays by patients and providers to a minimum.

A NEW PROGNOSTIC CLASSIFICATION OF RECTAL CANCER

Only 60% of patients having radical surgery for rectal cancer are cured of their disease. The ideal system of classification would identify just two categories--the cured and those who will die of their disease. Specimens from 379 patients who had undergone radical surgery for rectal cancer more than 20 years ago were re-examined in order to identify discrete pathological variables that independently influence long-term survival. The selected variables were given weighted scores and the score range was divided to provide four prognostic groups. The model was tested on a second data set comprising 331 patients and gave similar results. The new prognostic classification is simple to use and is superior to staging by the method of Dukes because it places twice as many patients into groups that provide a confident prediction of clinical outcome.

Survival Analysis Part I: Basic concepts and first analyses

Survival analysis is a collection of statistical procedures for data analysis where the outcome variable of interest is time until an event occurs. Because of censoring–the nonobservation of the event of interest after a period of follow-up–a proportion of the survival times of interest will often be unknown. It is assumed that those patients who are censored have the same survival prospects as those who continue to be followed, that is, the censoring is uninformative. Survival data are generally described and modelled in terms of two related functions, the survivor function and the hazard function. The survivor function represents the probability that an individual survives from the time of origin to some time beyond time t. It directly describes the survival experience of a study cohort, and is usually estimated by the KM method. The logrank test may be used to test for differences between survival curves for groups, such as treatment arms. The hazard function gives the instantaneous potential of having an event at a time, given survival up to that time. It is used primarily as a diagnostic tool or for specifying a mathematical model for survival analysis. In comparing treatments or prognostic groups in terms of survival, it is often necessary to adjust for patient-related factors that could potentially affect the survival time of a patient. Failure to adjust for confounders may result in spurious effects. Multivariate survival analysis, a form of multiple regression, provides a way of doing this adjustment, and is the subject the next paper in this series.

Survival Analysis Part II: Multivariate data analysis – an introduction to concepts and methods

Survival Analysis Part II: Multivariate data analysis – an introduction to concepts and methods

The prognostic importance of peritoneal involvement in colonic cancer: A prospective evaluation

BACKGROUND & AIMS Prognostic parameters specific to the colon have been somewhat neglected compared with the rectum. This study was instituted to assess the influence of local peritoneal involvement (LPI) on pelvic and intraperitoneal recurrence and prognosis in an unselected, prospective series of colonic cancer resections. METHODS Meticulous examination of 412 resections included evaluation of the relation of the tumor to the peritoneal surface. Histological assessment was as follows: 1, peritoneal involvement absent (81 resections, 20%); 2, inflammatory reaction with tumor close but not present at the surface (89 resections, 22%); 3, peritoneal surface unequivocally infiltrated (112 resections, 27%); and 4, peritoneal involvement with ulceration and tumor cells lying apparently free in the peritoneum (130 resections, 32%). RESULTS LPI showed strong independent prognostic influence in both curative surgery groups and in all patients. In multivariate analysis in curative surgery, LPI was the most powerful prognostic indicator. It was significantly associated with palliative surgery, extent of local spread, and mucinous subtype and predicted cases with subsequent intraperitoneal recurrence and/or persistence. CONCLUSIONS LPI is a common event in colonic cancer and is a consistent predictor of subsequent intraperitoneal recurrence. It is an important independent pathological prognostic parameter and may supersede other parameters in current usage in colonic cancer prognosis.

Who and what influences delayed presentation in breast cancer

This study aimed to examine the extent and determinants of patient and general practitioner delay in the presentation of breast cancer. One hundred and eighty-five cancer patients attending a breast unit were interviewed 2 months after diagnosis. The main outcome measures were patient delay in presentation to the general practitioner and non-referral by the general practitioner to hospital after the patients first visit. Nineteen per cent of patients delayed > or = 12 weeks. Patient delay was related to clinical tumour size > or = 4 cm (P = 0.0002) and with a higher incidence of locally advanced and metastatic disease (P = 0.01). A number of factors predicted patient delay: initial breast symptom(s) that did not include a lump (OR 4.5, P = 0.003), not disclosing discovery of the breast symptom immediately to someone else (OR 6.0, P < 0.001), seeking help only after being prompted by others (OR 4.4, P = 0.007) and presenting to the general practitioner with a non-breast problem (OR 3.5, P = 0.03). Eighty-three per cent of patients were referred to hospital directly after their first general practitioner visit. Presenting to the GP with a breast symptom that did not include a lump independently predicted general practitioner delay (OR 3.6, P = 0.002). In view of the increasing evidence that delay adversely affects survival, a large multicentre study is now warranted to confirm these findings that may have implications for public and medical education.

Survival Analysis Part III: Multivariate data analysis - choosing a model and assessing its adequacy and fit

Survival Analysis Part III: Multivariate data analysis – choosing a model and assessing its adequacy and fit

Effect of mitoxantrone on outcome of children with first relapse of acute lymphoblastic leukaemia (ALL R3): an open-label randomised trial

Summary Background Although survival of children with acute lymphoblastic leukaemia has improved greatly in the past two decades, the outcome of those who relapse has remained static. We investigated the outcome of children with acute lymphoblastic leukaemia who relapsed on present therapeutic regimens. Methods This open-label randomised trial was undertaken in 22 centres in the UK and Ireland and nine in Australia and New Zealand. Patients aged 1–18 years with first relapse of acute lymphoblastic leukaemia were stratified into high-risk, intermediate-risk, and standard-risk groups on the basis of duration of first complete remission, site of relapse, and immunophenotype. All patients were allocated to receive either idarubicin or mitoxantrone in induction by stratified concealed randomisation. Neither patients nor those giving interventions were masked. After three blocks of therapy, all high-risk group patients and those from the intermediate group with postinduction high minimal residual disease (≥10−4 cells) received an allogenic stem-cell transplant. Standard-risk and intermediate-risk patients with postinduction low minimal residual disease (<10−4 cells) continued chemotherapy. The primary outcome was progression-free survival and the method of analysis was intention-to-treat. Randomisation was stopped in December, 2007 because of differences in progression-free and overall survival between the two groups. This trial is registered, reference number ISCRTN45724312. Findings Of 239 registered patients, 216 were randomly assigned to either idarubicin (109 analysed) or mitoxantrone (103 analysed). Estimated 3-year progression-free survival was 35·9% (95% CI 25·9–45·9) in the idarubicin group versus 64·6% (54·2–73·2) in the mitoxantrone group (p=0·0004), and 3-year overall survival was 45·2% (34·5–55·3) versus 69·0% (58·5–77·3; p=0·004). Differences in progression-free survival between groups were mainly related to a decrease in disease events (progression, second relapse, disease-related deaths; HR 0·56, 0·34–0·92, p=0·007) rather than an increase in adverse treatment effects (treatment death, second malignancy; HR 0·52, 0·24–1·11, p=0·11). Interpretation As compared with idarubicin, mitoxantrone conferred a significant benefit in progression-free and overall survival in children with relapsed acute lymphobastic leukaemia, a potentially useful clinical finding that warrants further investigation. Funding Cancer Research UK, Leukaemia and Lymphoma Research, Cancer Council NSW, and Sporting Chance Cancer Foundation.

Survival analysis part IV: further concepts and methods in survival analysis.

Most analyses of survival data use primarily Kaplan–Meier plots, logrank tests and Cox models. We have described the rationale and interpretation of each method in previous papers of this series, but here we have sought to highlight some of their limitations. We have also suggested alternative methods that can be applied when either the data or a given model is deficient, or when more difficult or specific problems are to be addressed. For example, analysis of recurrent events can make an important contribution to the understanding of the survival process, and so investigating repeat cancer relapses may be more informative than concentrating only on the time until the first. More fundamentally, missing data are a common issue in data collection that in some cases can seriously flaw a proposed analysis. Such considerations may be highly relevant to the analysis of a data set, but are rarely mentioned in the analysis of survival data. One possible reason for this is a perceived lack of computer software, but many of the approaches discussed here are currently incorporated into existing commercial statistical packages (e.g. SAS, S-Plus, Stata) and freeware (e.g. R). On the other hand, the desire may be to ‘keep things simple for the readership’. This view is reasonable, but is valid only where a simple analysis adequately represents the survival experience of patients in the study. Ensuring the analyses are appropriate is therefore crucial. More advanced survival methods can derive more information from the collected data; their use may admittedly convey a less straightforward message, but at the same time could allow a better understanding of the survival process. The aim of this series has been to aid awareness, understanding and interpretation of the many and varied methods that constitute the analysis of survival data. It is paramount that analyses are performed in the knowledge of the assumptions that are made therein, and the more complex methods, in particular, are best applied by a statistician.