Two-stage or not two-stage? That is the question for IPD meta-analysis projects

Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g., treatment effect estimates and standard errors); then, in the second stage, these aggregate data are combined in a standard meta-analysis model (e.g., common-effect or random-effects). In a one-stage approach, the IPD from all studies are analysed in a single step using an appropriate model that accounts for clustering of participants within studies and, potentially, between-study heterogeneity (e.g., a general or generalised linear mixed model). The best approach to take is debated in the literature, and so here we provide clearer guidance for a broad audience. Both approaches are important tools for IPDMA researchers and neither are a panacea. If most studies in the IPDMA are small (few participants or events), a one-stage approach is recommended due to using a more exact likelihood. However, in other situations, researchers can choose either approach, carefully following best practice. Some previous claims recommending to always use a one-stage approach are misleading, and the two-stage approach will often suffice for most researchers. When differences do arise between the two approaches, often it is caused by researchers using different modelling assumptions or estimation methods, rather than using one or two stages per se

Lettuce be happy: A longitudinal UK study on the relationship between fruit and vegetable consumption and well-being

Rationale: While the role of diet in influencing physical health is now well-established, some recent research suggests that increased consumption of fruits and vegetables could play a role in enhancing mental well-being. A limitation with much of this existing research is its reliance on cross-sectional correlations, convenience samples, and/or lack of adequate controls. Objective: We aim to add to the emerging literature on the relationship between fruit and vegetable consumption and well-being by using longitudinal data from a study in the United Kingdom (UK). Method: We employ panel data analytical techniques on three waves collected between 2010 and 2017 (i.e., following the same individuals over time) in the UK Household Longitudinal Survey. We also control for time-variant confounders such as diet, health, and lifestyle behaviours. Results: Fixed effects regressions show that mental well-being (GHQ-12) responds in a dose-response fashion to increases in both the quantity and the frequency of fruit and vegetables consumed. This relationship is robust to the use of subjective well-being (life satisfaction) instead of mental well-being. We also document a hump-shaped relationship between fruit and vegetable consumption and age. Conclusion: Our findings provide further evidence that persuading people to consume more fruits and vegetables may not only benefit their physical health in the long-run, but also their mental well-being in the short-run

2004-2005 International Whaling Commission-Southern Ocean Whale and Ecosystem Research (IWC-SOWER) Cruise, Area III

We conducted the 27th annual IWC-SOWER (formerly IDCR) Cruise in Area III (000°-070°E) aboard the Japanese Research Vessels Shonan Maru and Shonan Maru No.2. The 65-day cruise departed Cape Town, South Africa on 4 January 2005 and returned to Fremantle, Australia on 9 March 2005. After transiting to the study area, we carried out a minke whale survey and several research experiments from 12 January to 25 February. A systematic minke whale survey was conducted in Area IIIW (000°-035°E) from 12 January until 8 February. The survey design was intentionally similar to that used during the IWC/IDCR second circumpolar series of cruises (CPII) to provide information towards addressing the effect of changing cruise track design on Antarctic minke whale abundance estimates. 000°-020°E was surveyed in two contiguous strata (Northern and Southern), from 64°30'S to the ice edge. Poor weather limited the coverage 020°E-035°E to the Southern Stratum only. A total of 1788.2 nmiles was surveyed (000°-035°E) including 935.5 nmiles in closing mode and 930.3 nmiles in independent observer mode, and a total of 466 minke whales were sighted. Minke whale visual dive time experiments were conducted during the minke whale survey. 35 trials were completed, recording surfacing cues for a total of 45.81 hours. From 10-22 February the ships conducted collaborative studies with the Japanese icebreaker, Shirase to investigate the relationship between minke whale abundance and the sea ice. During this study the SOWER vessels surveyed for minke whales in the near-ice area from 035°-050°E. 575.3 nmiles were covered and a total of 22 minke whales were detected. The Shirase surveyed in the pack ice zone 040°-050°E from 12-15 February. Two methods-testing experiments were carried out during the cruise: Adaptive Line Transect Sampling and ‘BT Mode.’ Adaptive Line Transect Sampling was tested during survey in Area IIIW. BT Mode trials were conducted 22-25 February in the area between 050° and 065°E. A direct electronic data acquisition program was evaluated during the cruise on both ships. Sightings for the entire cruise included: minke whales (237 groups/515 animals); blue whales (13 groups/46 individuals) of which 6 groups (28 individuals) were identified as true blue whales and 3 groups (3 individuals) were identified as pygmy blue whales; fin whales (14/132); humpback whales (251/646); sperm whales (35/49); killer whales (23/217); southern bottlenose whales (32/60); Gray’s beaked whales (1/7); Layard’s beaked whales (2/3); pilot whales (4/265); hourglass dolphins (4/17); striped dolphins (3/435) and common bottlenose dolphins (1/20). Opportunistic research during the cruise included blue whale research on 8 groups/29 animals resulting in 5 biopsies and images of 23 individuals for photo-identification studies. Biopsy samples and photo-ID images were also obtained opportunistically from other species. Biopsies were collected from 6 humpback whales and 1 southern right whale. Photo-ID images were collected from 45 humpback whales, 1 southern right whale and 8 groups of killer whales. Estimated Angle and Distance Training Exercise and Experiment were each completed on both vessels

Minimum sample size for developing a multivariable prediction model using multinomial logistic regression

Aims Multinomial logistic regression models allow one to predict the risk of a categorical outcome with > 2 categories. When developing such a model, researchers should ensure the number of participants (n)) is appropriate relative to the number of events (Ek)) and the number of predictor parameters (pk) for each category k. We propose three criteria to determine the minimum n required in light of existing criteria developed for binary outcomes. Proposed criteria The first criterion aims to minimise the model overfitting. The second aims to minimise the difference between the observed and adjusted R2 Nagelkerke. The third criterion aims to ensure the overall risk is estimated precisely. For criterion (i), we show the sample size must be based on the anticipated Cox-snell R2 of distinct ‘one-to-one’ logistic regression models corresponding to the sub-models of the multinomial logistic regression, rather than on the overall Cox-snell R2 of the multinomial logistic regression. Evaluation of criteria We tested the performance of the proposed criteria (i) through a simulation study and found that it resulted in the desired level of overfitting. Criterion (ii) and (iii) were natural extensions from previously proposed criteria for binary outcomes and did not require evaluation through simulation. Summary We illustrated how to implement the sample size criteria through a worked example considering the development of a multinomial risk prediction model for tumour type when presented with an ovarian mass. Code is provided for the simulation and worked example. We will embed our proposed criteria within the pmsampsize R library and Stata modules

Evaluation of clinical prediction models (part 2): how to undertake an external validation study

External validation studies are an important but often neglected part of prediction model research. In this article, the second in a series on model evaluation, Riley and colleagues explain what an external validation study entails and describe the key steps involved, from establishing a high quality dataset to evaluating a model’s predictive performance and clinical usefulness

Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia

BACKGROUND: Allocating national resources to regions based on need is a key policy issue in most health systems. Many systems utilise proxy measures of need as the basis for allocation formulae. Increasingly these are underpinned by complex statistical methods to separate need from supplier induced utilisation. Assessment of need is then used to allocate existing global budgets to geographic areas. Many low and middle income countries are beginning to use formula methods for funding however these attempts are often hampered by a lack of information on utilisation, relative needs and whether the budgets allocated bear any relationship to cost. An alternative is to develop bottom-up estimates of the cost of providing for local need. This method is viable where public funding is focused on a relatively small number of targeted services. We describe a bottom-up approach to developing a formula for the allocation of resources. The method is illustrated in the context of the state minimum service package mandated to be provided by the Indonesian public health system. METHODS: A standardised costing methodology was developed that is sensitive to the main expected drivers of local cost variation including demographic structure, epidemiology and location. Essential package costing is often undertaken at a country level. It is less usual to utilise the methods across different parts of a country in a way that takes account of variation in population needs and location. Costing was based on best clinical practice in Indonesia and province specific data on distribution and costs of facilities. The resulting model was used to estimate essential package costs in a representative district in each province of the country. FINDINGS: Substantial differences in the costs of providing basic services ranging from USD 15 in urban Yogyakarta to USD 48 in sparsely populated North Maluku. These costs are driven largely by the structure of the population, particularly numbers of births, infants and children and also key diseases with high cost/prevalence and variation, most notably the level of malnutrition. The approach to resource allocation was implemented using existing data sources and permitted the rapid construction of a needs based formula that is highly specific to the package mandated across the country. Refinement could focus more on resources required to finance demand side costs and expansion of the service package to include priority non-communicable services

India's JSY cash transfer program for maternal health: Who participates and who doesn't - a report from Ujjain district

<p>Abstract</p> <p>Background</p> <p>India launched a national conditional cash transfer program, Janani Suraksha Yojana (JSY), aimed at reducing maternal mortality by promoting institutional delivery in 2005. It provides a cash incentive to women who give birth in public health facilities. This paper studies the extent of program uptake, reasons for participation/non participation, factors associated with non uptake of the program, and the role played by a program volunteer, accredited social health activist (ASHA), among mothers in Ujjain district in Madhya Pradesh, India.</p> <p>Methods</p> <p>A cross-sectional study was conducted from January to May 2011 among women giving birth in 30 villages in Ujjain district. A semi-structured questionnaire was administered to 418 women who delivered in 2009. Socio-demographic and pregnancy related characteristics, role of the ASHA during delivery, receipt of the incentive, and reasons for place of delivery were collected. Multinomial regression analysis was used to identify predictors for the outcome variables; program delivery, private facility delivery, or a home delivery.</p> <p>Results</p> <p>The majority of deliveries (318/418; 76%) took place within the JSY program; 81% of all mothers below poverty line delivered in the program. Ninety percent of the women had prior knowledge of the program. Most program mothers reported receiving the cash incentive within two weeks of delivery. The ASHA's influence on the mother's decision on where to deliver appeared limited. Women who were uneducated, multiparious or lacked prior knowledge of the JSY program were significantly more likely to deliver at home.</p> <p>Conclusion</p> <p>In this study, a large proportion of women delivered under the program. Most mothers reporting timely receipt of the cash transfer. Nevertheless, there is still a subset of mothers delivering at home, who do not or cannot access emergency obstetric care under the program and remain at risk of maternal death.</p

Evaluation of clinical prediction models (part 2):how to undertake an external validation study

External validation studies are an important but often neglected part of prediction model research. In this article, the second in a series on model evaluation, Riley and colleagues explain what an external validation study entails and describe the key steps involved, from establishing a high quality dataset to evaluating a model’s predictive performance and clinical usefulness.</p

How do patient characteristics influence informal payments for inpatient and outpatient health care in Albania: Results of logit and OLS models using Albanian LSMS 2005

Abstract Background Informal payments for health care are common in most former communist countries. This paper explores the demand side of these payments in Albania. By using data from the Living Standard Measurement Survey 2005 we control for individual determinants of informal payments in inpatient and outpatient health care. We use these results to explain the main factors contributing to the occurrence and extent of informal payments in Albania. Methods Using multivariate methods (logit and OLS) we test three models to explain informal payments: the cultural, economic and governance model. The results of logit models are presented here as odds ratios (OR) and results from OLS models as regression coefficients (RC). Results Our findings suggest differences in determinants of informal payments in inpatient and outpatient care. Generally our results show that informal payments are dependent on certain characteristics of patients, including age, area of residence, education, health status and health insurance. However, they are less dependent on income, suggesting homogeneity of payments across income categories. Conclusions We have found more evidence for the validity of governance and economic models than for the cultural model.</p