Increasing value and reducing waste by optimizing the development of complex interventions: Enriching the development phase of the Medical Research Council (MRC) Framework

This is the final version of the article. Available from Elsevier via the DOI in this record.Background In recent years there has been much emphasis on ‘research waste’ caused by poor question selection, insufficient attention to previous research results, and avoidable weakness in research design, conduct and analysis. Little attention has been paid to the effect of inadequate development of interventions before proceeding to a full clinical trial. Objective We therefore propose to enrich the development phase of the MRC Framework by adding crucial elements to improve the likelihood of success and enhance the fit with clinical practice Methods Based on existing intervention development guidance and synthesis, a comprehensive iterative intervention development approach is proposed. Examples from published reports are presented to illustrate the methodology that can be applied within each element to enhance the intervention design. Results A comprehensive iterative approach is presented by combining the elements of the MRC Framework development phase with essential elements from existing guidance including: problem identification, the systematic identification of evidence, identification or development of theory, determination of needs, the examination of current practice and context, modelling the process and expected outcomes leading to final element: the intervention design. All elements are drawn from existing models to provide intervention developers with a greater chance of producing an intervention that is well adopted, effective and fitted to the context. Conclusion This comprehensive approach of developing interventions will strengthen the internal and external validity, minimize research waste and add value to health care research. In complex interventions in health care research, flaws in the development process immediately impact the chances of success. Knowledge regarding the causal mechanisms and interactions within the intended clinical context is needed to develop interventions that fit daily practice and are beneficial for the end-user

Long-range temporal correlations of broadband EEG oscillations for depressed subjects following different hemispheric cerebral infarction

Abnormal long-range temporal correlation (LRTC) in EEG oscillation has been observed in several brain pathologies and mental disorders. This study examined the relationship between the LRTC of broadband EEG oscillation and depression following cerebral infarction with different hemispheric lesions to provide a novel insight into such depressive disorders. Resting EEGs of 16 channels in 18 depressed (9 left and 9 right lesions) and 21 non-depressed (11 left and 10 right lesions) subjects following cerebral infarction and 19 healthy control subjects were analysed by means of detrended fluctuation analysis, a quantitative measurement of LRTC. The difference among groups and the correlation between the severity of depression and LRTC in EEG oscillation were investigated by statistical analysis. The results showed that LRTC of broadband EEG oscillations in depressive subjects was still preserved but attenuated in right hemispheric lesion subjects especially in left pre-frontal and right inferior frontal and posterior temporal regions. Moreover, an association between the severity of psychiatric symptoms and the attenuation of the LRTC was found in frontal, central and temporal regions for stroke subjects with right lesions. A high discriminating ability of the LRTC in the frontal and central regions to distinguish depressive from non-depressive subjects suggested potential feasibility for LRTC as an assessment indicator for depression following right hemispheric cerebral infarction. Different performance of temporal correlation in depressed subjects following the two hemispheric lesions implied complex association between depression and stroke lesion location.</p

A case study of an individual participant data meta-analysis of diagnostic accuracy showed that prediction regions represented heterogeneity well

The diagnostic accuracy of a screening tool is often characterized by its sensitivity and specificity. An analysis of these measures must consider their intrinsic correlation. In the context of an individual participant data meta-analysis, heterogeneity is one of the main components of the analysis. When using a random-effects meta-analytic model, prediction regions provide deeper insight into the effect of heterogeneity on the variability of estimated accuracy measures across the entire studied population, not just the average. This study aimed to investigate heterogeneity via prediction regions in an individual participant data meta-analysis of the sensitivity and specificity of the Patient Health Questionnaire-9 for screening to detect major depression. From the total number of studies in the pool, four dates were selected containing roughly 25%, 50%, 75% and 100% of the total number of participants. A bivariate random-effects model was fitted to studies up to and including each of these dates to jointly estimate sensitivity and specificity. Two-dimensional prediction regions were plotted in ROC-space. Subgroup analyses were carried out on sex and age, regardless of the date of the study. The dataset comprised 17,436 participants from 58 primary studies of which 2322 (13.3%) presented cases of major depression. Point estimates of sensitivity and specificity did not differ importantly as more studies were added to the model. However, correlation of the measures increased. As expected, standard errors of the logit pooled TPR and FPR consistently decreased as more studies were used, while standard deviations of the random-effects did not decrease monotonically. Subgroup analysis by sex did not reveal important contributions for observed heterogeneity; however, the shape of the prediction regions differed. Subgroup analysis by age did not reveal meaningful contributions to the heterogeneity and the prediction regions were similar in shape. Prediction intervals and regions reveal previously unseen trends in a dataset. In the context of a meta-analysis of diagnostic test accuracy, prediction regions can display the range of accuracy measures in different populations and settings

An approach to developing a prediction model of fertility intent among HIV-positive women and men in Cape Town, South Africa: a case study

As a ‘case-study’ to demonstrate an approach to establishing a fertility-intent prediction model, we used data collected from recently diagnosed HIV-positive women (N = 69) and men (N = 55) who reported inconsistent condom use and were enrolled in a sexual and reproductive health intervention in public sector HIV care clinics in Cape Town, South Africa. Three theoretically-driven prediction models showed reasonable sensitivity (0.70–1.00), specificity (0.66–0.94), and area under the receiver operating characteristic curve (0.79–0.89) for predicting fertility intent at the 6-month visit. A k-fold cross-validation approach was employed to reduce bias due to over-fitting of data in estimating sensitivity, specificity, and area under the curve. We discuss how the methods presented might be used in future studies to develop a clinical screening tool to identify HIV-positive individuals likely to have future fertility intent and who could therefore benefit from sexual and reproductive health counselling around fertility options

Probability of Major Depression Classification Based on the SCID, CIDI and MINI Diagnostic Interviews : A Synthesis of Three Individual Participant Data Meta-Analyses

Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results.To compare the odds of the major depression classification based on the SCID, CIDI, and MINI.We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis.In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80).Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics

An Efficient Way to Detect Poststroke Depression by Subsequent Administration of a 9-Item and a 2-Item Patient Health Questionnaire

Background and Purpose-The early detection of poststroke depression is essential for optimizing recovery after stroke. A prospective study was conducted to investigate the diagnostic value of the 9-item and the 2-item Patient Health Questionnaire (PHQ-9, PHQ-2). Methods-One hundred seventy-one consecutive patients with stroke who could communicate adequately were included. In the 6th to 8th weeks after stroke, depression was measured using the PHQ-9 and PHQ-2 and diagnosed using the Composite International Diagnostic Interview. Results-Of the participating patients, 20 (12.2%) were depressed. The PHQ- 9 performed best at a score >= 10, a sensitivity of 0.80 (95% CI, 0.62-0.98), and a specificity of 0.78 (95% CI, 0.72-0.85) and the PHQ-2 at a score >= 2 with a sensitivity of 0.75 (95% CI, 0.56-0.94) and a specificity of 0.76 (95% CI, 0.69-0.83). Administering the PHQ- 9 only to patients who scored >= 2 on the PHQ-2 improved the identification of depression (sensitivity, 0.87; 95% CI, 0.69-1.04). Conclusions-The diagnostic value is acceptable to good for PHQ-9 scores >= 10 and PHQ-2 scores >= 2. Conducting a PHQ-9 only in patients with a PHQ-2 score >2 generates the best results. (Stroke. 2012;43:854-856.)