Mitigating selection bias in organ allocation models

Background The lung allocation system in the U.S. prioritizes lung transplant candidates based on estimated pre- and post-transplant survival via the Lung Allocation Scores (LAS). However, these models do not account for selection bias, which results from individuals being removed from the waitlist due to receipt of transplant, as well as transplanted individuals necessarily having survived long enough to receive a transplant. Such selection biases lead to inaccurate predictions. Methods We used a weighted estimation strategy to account for selection bias in the pre- and post-transplant models used to calculate the LAS. We then created a modified LAS using these weights, and compared its performance to that of the existing LAS via time-dependent receiver operating characteristic (ROC) curves, calibration curves, and Bland-Altman plots. Results The modified LAS exhibited better discrimination and calibration than the existing LAS, and led to changes in patient prioritization. Conclusions Our approach to addressing selection bias is intuitive and can be applied to any organ allocation system that prioritizes patients based on estimated pre- and post-transplant survival. This work is especially relevant to current efforts to ensure more equitable distribution of organs.

Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI

Seasonal climate is the main driver of crop growth and yield in broadacre grain cropping systems. With a 40-year record of 30 m resolution images and 16-day revisits, the Landsat satellite series is ideal for producing long-term records of remotely sensed phenology to build understanding of how climate affects crop growth. However, the time-series of Landsat images exhibits gaps caused by cloud cover, which is common in wet periods when crops reach maximum growth. We propose a novel spatial–temporal approach to gap-filling that avoids data fusion. Crop growth curve estimation is used to perform temporal smoothing and incorporation of spatial weights allows spatial smoothing. We tested our approach using Landsat NDVI data acquired for an 8000 ha study area in Western Australia using a train/test approach where 157 available Landsat-7 images between 2013 and 2019 were used to train the model, and 95 at least 80% cloud-free Landsat-8 images from the same period were used to test its performance. We found that compared to nonspatial estimation, use of spatial weights in growth curve estimation improved correlation between observed and predicted NDVI by 75%, MAE by 31% and RMSE by 75%. For cropland, the correlation is improved by 58%, the MAE by 36% and the RMSE by 76%. We conclude that spatially weighted estimation of crop growth curves can be used to fill spatial and temporal gaps in Landsat NDVI for the purpose of within-field monitoring. Our approach is also applicable to other data sources and vegetation indices.

Simulation results on the performance of statistical methods in cumulative meta analysis

Cumulative meta-analysis (CMA) is a process of updating the results of existing meta-analysis to incorporate new study results. This is a popular way to present time-varying evidence. We investigate the properties of CMA, suggest possible improvements and provide the first in-depth simulation study of the use of CMA and CUSUM methods for detection of temporal trends in random-effects meta-analysis. We use the standardized mean difference (SMD) as an effect measure of interest. For CMA, we compare the standard inverse-variance-weighted estimation of the overall effect using REML-estimated between-study variance $\tau^2$ with the sample-size-weighted estimation of the effect combined with Kulinskaya-Dollinger-Bjørkestøl (2011) (KDB) estimation of $\tau^2$. For all methods, we consider type 1 error under no shift and power under shift in the mean. To ameliorate the lack of power in CMA, we introduce the two-stage CMA, where the heterogeneity variance $\tau^2$ is estimated at stage 1 (first 5-10 studies), and the further CMA monitors a target value of effect, keeping the $\tau^2$ value fixed. We recommend the use of this two-stage CMA combined with cumulative testing for positive shift in $\tau^2$.

Study on the associative effects of different proportions of soybean pod, alfalfa and concentrate on the diets at different ratio of concentrate to roughage in vitro

This study aimed to determine the associative effects (AEs) of 28 feed combinations of concentrate/soybean pod/alfalfa at different concentrate-roughage ratios that were incubated for 72 h in single tubes (120 ml) which were added 30 ml rumen buffered fluid. The gas production (GP) at 0, 2, 4, 6, 9, 12, 24, 36, 48, 72 h was recorded. A single exponential equation was applied to calculate the GP parameters a (rapid GP), b (slow GP), a + b (GP potential) and c (rate constant of slow GP that can reflect the specific process of GP, rapid and slow GP and GP rate). The AEs were calculated by 72 h GP and weighted estimation value of each combination. After 72 h incubation, pH, volatile fatty acids (VFA) and ammonia nitrogen (NH₃–N), dry matter digestibility (DMD), organic matter digestibility (OMD) were determined the incubation fluid and residues. The single-factor AE index (SFAEI) and multiple-factor AE index (MFAEI) were computed. The results showed that the groups 50 : 50 : 0, 40 : 60 : 0, 60 : 20 : 20, 60 : 10 : 30, 50 : 30 : 20, 50 : 20 : 30, 40 : 50 : 10, 30 : 55 : 15, 30 : 40 : 30, 20 : 65 : 15, 20 : 50 : 30 had higher GP_{72 h}, a, b, DMD, OMD, NH₃–N, in addition, higher AE of GP, DMD, OMD, total VFA and NH₃–N than those of the other groups (P < 0.05 or P < 0.01), especially the group 30 : 55 : 15 was optimal. In conclusion, in vitro data reveal reliable fermentability and the highest SFAEI and MFAEI occurred when concentrate, soybean pod and alfalfa were combined at the ratios of 50 : 50 : 0, 40 : 60 : 0, 60 : 20 : 20, 60 : 10 : 30, 50 : 30 : 20, 50 : 20 : 30, 40 : 50 : 10, 30 : 55 : 15, 30 : 40 : 30, 20 : 65 : 15, 20 : 50 : 30.

A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications

Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for marginal causal effects that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the outcome misclassification weighting estimator proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med 2018; 37: 425–436), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function (ipwm) in an existing R package (mecor).