Proper analysis in clinical trials: how to report and adjust for missing outcome data

Improving symptom management for survivors of young adult cancer: rationale and study protocol for a pilot randomized controlled trial

BACKGROUND

Young adult (YA) cancer survivors are a growing, yet underserved population who often face significant and long-lasting cancer-related physical (e.g., pain, fatigue) and emotional (e.g., psychological distress) symptoms. Post-treatment symptoms can persist, disrupting YA's abilities to complete goals consistent with their developmental stage (e.g., completing their education, achieving autonomy and independence, building their careers, establishing peer and romantic relationships, building their families). While symptom management has been identified as a significant issue in YA's transitions to survivorship, the symptom management needs of this population largely go unmet.

METHODS

We developed an eight-session, group-based behavioral intervention that is delivered using videoconferencing to address the unique symptom management needs of YA cancer survivors. The intervention was developed in conjunction with YA survivors, leading to the novel combination of traditional behavioral symptom coping strategies, home-based physical activity, strategies from contemporary cognitive-behavioral approaches (e.g., those derived from acceptance and commitment therapy, strategies to foster self-compassion), concepts from meaning centered psychotherapy, and behavioral strategies to improve communication and health care engagement. Participants receive printed intervention materials and access to a study-specific mobile application, both of which are used throughout the program. Herein, we report on a pilot study that is in progress. Recruitment has been completed. YA cancer survivors were recruited in cohorts of n = 10 or n = 11 (n = 61) and randomized to the intervention or waitlist control arms. All participants completed a baseline assessment and four additional assessments over 1 year, with each involving a battery of self-report measures.

DISCUSSION

The primary objective is to evaluate intervention feasibility and acceptability. As a secondary objective, we will examine patterns of change in intervention targets (i.e., pain, fatigue, emotional distress, symptom interference). Changes from baseline among intervention targets will be estimated for each patient and compared between arms using unadjusted statistical testing. Unadjusted and adjusted multilevel modeling will be used to estimate the effect of the intervention on changes in intervention targets. Interaction models will be used to compare the trajectory of change over time between arms. We expect that this pilot trial will inform our future approach to identify, recruit, and retain participants and provide preliminary data to support a larger, fully powered randomized controlled trial evaluating the intervention.

TRIAL REGISTRATION

NCT04035447 at clinicaltrials.gov; registered July 29, 2019.

Pregnancy of unknown location: external validation of the hCG-based M6NP and M4 prediction models in an emergency gynaecology unit

OBJECTIVE

To investigate if M6NP predicting ectopic pregnancy (EP) among women with pregnancy of unknown location (PUL) is valid in an emergency gynaecology setting and comparing it with its predecessor M4.

DESIGN

Retrospective cohort study.

SETTING

University Hospital.

PARTICIPANTS

Women with PUL.

METHODS

All consecutive women with a PUL during a study period of 3 years were screened for inclusion. Risk prediction of an EP was based on two serum human chorionic gonadotropin (hCG) levels taken at least 24 hours and no longer than 72 hours apart.

MAIN OUTCOME MEASURES

The area under the ROC curve (AUC) expressed the ability of a model to distinguish an EP from a non-EP (discrimination). Calibration assessed the agreement between the predicted risk of an EP and the true risk (proportion) of EP. The proportion of EPs and non-EPs classified as high risk assessed the model's sensitivity and false positive rate (FPR). The proportion of non-EPs among women classified as low risk was the model's negative predictive value (NPV). The clinical utility of a model was evaluated with decision curve analysis.

RESULTS

1061 women were included in the study, of which 238 (22%) had a final diagnosis of EP. The AUC for EP was 0.85 for M6NP and 0.81 for M4. M6NP made accurate risk predictions of EP up to predictions of 20% but thereafter risks were underestimated. M4 was poorly calibrated up to risk predictions of 40%. With a 5% threshold for high risk classification the sensitivity for EP was 95% for M6NP, the FPR 50% and NPV 97%. M6NP had higher sensitivity and NPV than M4 but also a higher FPR. M6NP had utility at all thresholds as opposed to M4 that had no utility at thresholds≤5%.

CONCLUSIONS

M6NP had better predictive performance than M4 and is valid in women with PUL attending an emergency gynaecology unit. Our results can encourage implementation of M6NP in related yet untested clinical settings to effectively support clinical decision-making.

Based on biomedical index data: Risk prediction model for prostate cancer

ABSTRACT

To explore the influencing factors of prostate cancer occurrence, set up risk prediction model, require reference for the preliminary diagnosis of clinical doctors, this model searched database through the data of prostate cancer patients and prostate hyperplasia patients National Clinical Medical Science Data Center.With the help of Stata SE 12.0 and SPSS 25.0 software, the biases between groups were balanced by propensity score matching. Based on the matched data, the relevant factors were further screened by stepwise logistic regression analysis, the key variable and artificial neural network model are established. The prediction accuracy of the model is evaluated by combining the probability of test set with the area under receiver operating characteristic curve (ROC).After 1:2 PSM, 339 pairs were matched successfully. There are 159 cases in testing groups and 407 cases in training groups. And the regression model was P = 1 / (1 + e (0.122 ∗ age + 0.083 ∗ Apo lipoprotein C3 + 0.371 ∗ total prostate specific antigen (tPSA) -0.227 ∗ Apo lipoprotein C2-6.093 ∗ free calcium (iCa) + 0.428 ∗ Apo lipoprotein E-1.246 ∗ triglyceride-1.919 ∗ HDL cholesterol + 0.083 ∗ creatine kinase isoenzyme [CKMB])). The logistic regression model performed very well (ROC, 0.963; 95% confidence interval, 0.951 to 0.978) and artificial neural network model (ROC, 0.983; 95% confidence interval, 0.964 to 0.997). High degree of Apo lipoprotein E (Apo E) (Odds Ratio, [OR], 1.535) in blood test is a risk factor and high triglyceride (TG) (OR, 0.288) is a protective factor.It takes the biochemical examination of the case as variables to establish a risk prediction model, which can initially reflect the risk of prostate cancer and bring some references for diagnosis and treatment.

Effectiveness of embedding a specialist preventive care clinician in a community mental health service in increasing preventive care provision: A randomised controlled trial

OBJECTIVE

Clinical practice guidelines recommend that community mental health services provide preventive care for clients' chronic disease risk behaviours; however, such care is often not routinely provided. This study aimed to assess the effectiveness of offering clients an additional consultation with a specialist clinician embedded within a community mental health service, in increasing client-reported receipt of, and satisfaction with, preventive care.

METHOD

A randomised controlled trial was undertaken in one Australian community mental health service. Participants (N = 811) were randomised to receive usual care (preventive care in routine consultations; n = 405) or usual care plus the offer of an additional consultation with a specialist preventive care clinician (n = 406). Blinded interviewers assessed at baseline and 1-month follow-up the client-reported receipt of preventive care (assessment, advice and referral) for four key risk behaviours individually (smoking, poor nutrition, alcohol overconsumption and physical inactivity) and all applicable risks combined, acceptance of referrals and satisfaction with preventive care received.

RESULTS

Analyses indicated significantly greater increases in 12 of the 18 preventive care delivery outcomes in the intervention compared to the usual care condition from baseline to follow-up, including assessment for all risks combined (risk ratio = 4.00; 95% confidence interval = [1.57, 10.22]), advice for all applicable risks combined (risk ratio = 2.40; 95% confidence interval = [1.89, 6.47]) and offer of referral to applicable telephone services combined (risk ratio = 20.13; 95% confidence interval = [2.56, 158.04]). For each component of care, there was a significant intervention effect for at least one of the individual risk behaviours. Participants reported high levels of satisfaction with preventive care received, ranging from 77% (assessment) to 87% (referral), with no significant differences between conditions.

CONCLUSION

The intervention had a significant effect on the provision of the majority of recommended elements of preventive care. Further research is needed to maximise its impact, including identifying strategies to increase client uptake.

How Effective Are Dietitians in Weight Management? A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Effective, evidence-based strategies to prevent and treat obesity are urgently required. Dietitians have provided individualized weight management counselling for decades, yet evidence of the effectiveness of this intervention has never been synthesized. The aim of this study was to examine the effectiveness of individualized nutrition care for weight management provided by dietitians to adults in comparison to minimal or no intervention. Databases (Cochrane, CINAHL plus, MedLine ovid, ProQuest family health, PubMed, Scopus) were searched for terms analogous with patient, dietetics and consultation with no date restrictions. The search yielded 5796 unique articles, with 14 randomized controlled trials meeting inclusion criteria. The risk of bias for the included studies ranged from unclear to high. Six studies found a significant intervention effect for the dietitian consultation, and a further four found significant positive change for both the intervention and control groups. Data were synthesized through random effects meta-analysis from five studies (n = 1598) with weight loss as the outcome, and from four studies (n = 1224) with Body Mass Index (BMI) decrease as the outcome. Groups receiving the dietitian intervention lost an additional 1.03 kg (95% CI:−1.40; −0.66, p < 0.0001) of weight and 0.43 kg/m² (95% CI:−0.59, −0.26; p < 0.0001) of BMI than those receiving usual care. Heterogeneity was low for both weight loss and BMI, with the pooled means varying from 1.26 to −0.93 kg and −0.4 kg/m² for weight and BMI, respectively, with the removal of single studies. This study is the first to synthesize evidence on the effectiveness of individualized nutrition care delivered by a dietitian. Well-controlled studies that include cost-effectiveness measures are needed to strengthen the evidence base.