Does increasing access-to-care delay accessing of care? Evidence from kidney transplantation

Policies increasing healthcare availability might decrease the cost of delaying accessing of care, leading to potential negative consequences if patients delay treatment. We analyze a policy designed to increase access to kidney transplantation through the use of time since dialysis inception to prioritize patients for transplant, which was piloted at 26 of the 271 kidney transplant centers in the United States in 2006 and 2007. We model the patient’s optimization problem comparing the benefits and costs of early waitlisting and predict that the policy change will lead to delayed waitlisting. To empirically test this prediction, we use difference-in-differences fixed effects panel regression techniques to analyze data on patients who began dialysis between 1/1/2000 and 12/31/2009. The results support the model’s prediction; patients on dialysis who waitlist for kidney transplantation increase pre-waitlist dialysis duration by 11.6 percent or approximately 76 days from a pre-policy mean of 652 days (SD = 654). With regard to waitlist outcomes, the policy is associated with a 4.5 percentage point decrease in the probability of receiving a deceased donor transplant, somewhat offset by a 3.0 percentage point increase in the probability of receiving a live donor transplant. On the extensive margin, patients on dialysis decrease their likelihood of ever waitlisting by 1.5 percentage points. We find an increase in pre-waitlist dialysis time and a decrease in the likelihood of waitlisting at all, especially among populations likely to have experienced increased access to transplantation through the policy change: patients self-identifying as Black or Hispanic rather than Non-Hispanic White, and patients without private insurance. These results suggest that some individuals may not benefit if their access to care increases, if the increase in access sufficiently decreases the penalty of delaying accessing of care.     

Flexible Estimation of Differences in Treatment-Specific Recurrent Event Means in the Presence of a Terminating Event

Summary .  In this article, we consider the setting where the event of interest can occur repeatedly for the same subject (i.e., a recurrent event; e.g., hospitalization) and may be stopped permanently by a terminating event (e.g., death). Among the different ways to model recurrent/terminal event data, the marginal mean (i.e., averaging over the survival distribution) is of primary interest from a public health or health economics perspective. Often, the difference between treatment-specific recurrent event means will not be constant over time, particularly when treatment-specific differences in survival exist. In such cases, it makes more sense to quantify treatment effect based on the cumulative difference in the recurrent event means, as opposed to the instantaneous difference in the rates. We propose a method that compares treatments by separately estimating the survival probabilities and recurrent event rates given survival, then integrating to get the mean number of events. The proposed method combines an additive model for the conditional recurrent event rate and a proportional hazards model for the terminating event hazard. The treatment effects on survival and on recurrent event rate among survivors are estimated in constructing our measure and explain the mechanism generating the difference under study. The example that motivates this research is the repeated occurrence of hospitalization among kidney transplant recipients, where the effect of expanded criteria donor (ECD) compared to non-ECD kidney transplantation on the mean number of hospitalizations is of interest.     

Comparing mortality in renal patients on hemodialysis versus peritoneal dialysis using a marginal structural model

When comparing the causal effect of peritoneal dialysis (PD) and hemodialysis (HD) treatment on lowering mortality in renal patients, using observational data, it is necessary to adjust for different forms of confounding and informative censoring. Both the type of dialysis treatment that is started with and mortality are affected by baseline covariates. Longitudinal and baseline variables can affect both the probability of switching from one type of dialysis to the other, and mortality. Longitudinal and baseline variables can also affect the probability of receiving a kidney transplant, possibly causing informative censoring. Adjusting for longitudinal variables by including them as covariates in a regression model potentially causes bias, for instance by losing a possible indirect effect of dialysis on mortality via these longitudinal variables. Instead, we fitted a marginal structural model (MSM) to estimate the causal effect of dialysis type, adjusted for confounding and informative censoring. We used the MSM to compare the hazard of death as well as cumulative survival between the potential treatment trajectories "always PD" and "always HD" over time, conditional on age and diabetes mellitus status. We used inverse probability weighting (IPW) to fit the MSM.     

Analysis of a partially observed binary covariate process and a censored failure time in the presence of truncation and competing risks

We develop methods for assessing the association between a binary time-dependent covariate process and a failure time endpoint when the former is observed only at a single time point and the latter is right censored, and when the observations are subject to truncation and competing causes of failure. Using a proportional hazards model for the effect of the covariate process on the failure time of interest, we develop an approach utilizing EM algorithm and profile likelihood for estimating the relative risk parameter and cause-specific hazards for failure. The methods are extended to account for other covariates that can influence the time-dependent covariate process and cause-specific risks of failure. We illustrate the methods with data from a recent study on the association between loss of hepatitis B e antigen and the development of hepatocellular carcinoma in a population of chronic carriers of hepatitis B.     

Trigonostemons G and H,dinorditerpenoid dimers with axially chiral biaryl linkage from Trigonostemon chinensis

Trigonostemons G and H, two novel dimeric dinorditerpenoids, were isolated from the stem barks of Trigonostemon chinensis. Their planar structures and relative configurations were established by extensive analysis of spectroscopic data. Trigonostemons G and H possess a homodimeric biaryl skeleton obtained from two rearranged chiral nonracemic abietane-type dinorditerpenes through an axially chiral biaryl 11,11′-linkage. Torsional scan and computation of the transition states were carried out to estimate the rotational energy barrier, and the axial chirality (aS) was determined by time-dependent density functional theory (TDDFT) electronic circular dichroism (ECD) calculations. The positive n-π* ECD transitions of the isolated carbonyl chromophore above 300 nm could be used to determine the central chirality of trigonostemon G independently by ECD calculations of the diastereomers.     

A note on including time-dependent covariate in regression model for competing risks data

Recently, regression analysis of the cumulative incidence function has gained interest in competing risks data analysis, through the model proposed by Fine and Gray (JASA 1999; 94: 496-509). In this note, we point out that inclusion of time-dependent covariates in this model can lead to serious bias. We illustrate the problems arising in such a context, using bone marrow transplant data as a working example and numerical simulations. Practical advices are given, preventing the misuse of this model.     

Electron capture dissociation mass spectrometry in characterization of post-translational modifications

Electron capture dissociation (ECD) represents a significant advance in tandem mass spectrometry for the identification and characterization of post-translational modifications (PTMs) of polypeptides. In comparison with the conventional fragmentation techniques, such as collisionally induced dissociation and infrared multi-photon dissociation, ECD provides more extensive sequence fragments, while allowing the labile modifications to remain intact during backbone fragmentation. This unique attribute offers ECD as an attractive alternative for detection and localization of PTMs. The success and rapid adoption of ECD recently led to the culmination of The 1st International Uppsala Symposium on Electron Capture Dissociation of Biomolecules and Related Phenomena (October 19-22, 2003, Stockholm, Sweden). Herein, we present a general overview of the ECD technique as well as selected applications in characterization of post-translationally modified polypeptides.     

An estimator for the proportional hazards model with multiple longitudinal covariates measured with error

In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and several time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependent covariate follows a linear mixed effects model and the hazard of failure depends on random effects and time-independent covariates via a proportional hazards relationship. Regression calibration and likelihood or Bayesian methods have been advocated for implementation; however, generalization to more than one time-dependent covariate may become prohibitive. For a single time-dependent covariate, Tsiatis and Davidian (2001) have proposed an approach that is easily implemented and does not require an assumption on the distribution of the random effects. This technique may be generalized to multiple, possibly correlated, time-dependent covariates, as we demonstrate. We illustrate the approach via simulation and by application to data from an HIV clinical trial.     

Pre-Transplant CDKN2A Expression in Kidney Biopsies Predicts Renal Function and Is a Future Component of Donor Scoring Criteria

CDKN2A is a proven and validated biomarker of ageing which acts as an off switch for cell proliferation. We have demonstrated previously that CDKN2A is the most robust and the strongest pre-transplant predictor of post- transplant serum creatinine when compared to “Gold Standard” clinical factors, such as cold ischaemic time and donor chronological age. This report shows that CDKN2A is better than telomere length, the most celebrated biomarker of ageing, as a predictor of post-transplant renal function. It also shows that CDKN2A is as strong a determinant of post-transplant organ function when compared to extended criteria (ECD) kidneys. A multivariate analysis model was able to predict up to 27.1% of eGFR at one year post-transplant (p = 0.008). Significantly, CDKN2A was also able to strongly predict delayed graft function. A pre-transplant donor risk classification system based on CDKN2A and ECD criteria is shown to be feasible and commendable for implementation in the near future.     

Unusual CD couplet pattern observed for the pi*<--n transition of enantiopure (Z)-8-methoxy-4-cyclooctenone: an experimental and theoretical study by electronic and vibrational circular dichroism spectroscopy and density functional theory calculation

Ultraviolet absorption (UV) and electronic circular dichroism (ECD) spectra of enantiopure (Z)-8-methoxy-4-cyclooctenone (MCO) were measured in hexane to give a normal single UV absorption band at 298 nm, which is assigned to the carbonyl's pi*<--n transition. Unexpectedly, the ECD spectrum exhibited an apparent couplet pattern with vibrational fine structures. Obviously, the conventional CD exciton coupling mechanism cannot be applied to this bisignate CD signal observed for single-chromophoric MCO. Variable temperature-ECD and vibrational circular dichroism (VCD) spectral measurements, simultaneous UV and ECD spectral band resolution, and density functional theory (DFT) calculations of energy and structure revealed that this apparent CD couplet originates from a rather complicated spectral overlap of more than three conformers of MCO, two of which exhibit mirror-imaged ECD spectra at appreciably deviated wavelengths. In the simultaneous band-resolution analysis, the observed UV and ECD spectra were best fitted to four overlapping bands. Two major conformers were identified by comparing the experimental IR and VCD spectra with the simulated ones, and the other two by comparing the observed UV and ECD spectra with the theoretical ones obtained by time-dependent DFT calculations. It was shown that the combined use of experimental ECD and VCD spectra and theoretical DFT calculations can give a reasonable interpretation for the Cotton effects of the conformationally flexible molecule MCO.     

Effect of diuretic use on 30-day postdialysis mortality in critically ill patients receiving acute dialysis

Background

The impact of diuretic usage and dosage on the mortality of critically ill patients with acute kidney injury is still unclear.

Methods and Results

In this prospective, multicenter, observational study, 572 patients with postsurgical acute kidney injury receiving hemodialysis were recruited and followed daily. Thirty-day postdialysis mortality was analyzed using Cox''s proportional hazards model with time-dependent covariates. The mean age of the 572 patients was 60.8±16.6 years. Patients with lower serum creatinine (p = 0.031) and blood lactate (p = 0.033) at ICU admission, lower predialysis urine output (p = 0.001) and PaO₂/FiO₂ (p = 0.039), as well as diabetes (p = 0.037) and heart failure (p = 0.049) were more likely to receive diuretics. A total of 280 (49.0%) patients died within 30 days after acute dialysis initiation. The analysis of 30-day postdialysis mortality by fitting propensity score-adjusted Cox''s proportional hazards models with time-dependent covariates showed that higher 3-day accumulated diuretic doses after dialysis initiation (HR = 1.449, p = 0.021) could increase the hazard rate of death. Moreover, higher time-varying 3-day accumulative diuretic doses were associated with hypotension (p<0.001) and less intense hemodialysis (p<0.001) during the acute dialysis period.

Background and Significance

Higher time-varying 3-day accumulative diuretic dose predicts mortality in postsurgical critically ill patients requiring acute dialysis. Higher diuretic doses are associated with hypotension and a lower intensity of dialysis. Caution should be employed before loop diuretics are administered to postsurgical patients during the acute dialysis period.     

Propensity score matching with time-dependent covariates

In observational studies with a time-dependent treatment and time-dependent covariates, it is desirable to balance the distribution of the covariates at every time point. A time-dependent propensity score based on the Cox proportional hazards model is proposed and used in risk set matching. Matching on this propensity score is shown to achieve a balanced distribution of the covariates in both treated and control groups. Optimal matching with various designs is conducted and compared in a study of a surgical treatment, cystoscopy and hydrodistention, given in response to a chronic bladder disease, interstitial cystitis. Simulation studies also suggest that the statistical analysis after matching outperforms the analysis without matching in terms of both point and interval estimations.     

Electron Capture Dissociation Mass Spectrometry in Characterization of Peptides and Proteins

Electron capture dissociation (ECD) represents one of the most recent and significant advancements in tandem mass spectrometry (MS/MS) for the identification and characterization of polypeptides. In comparison with the conventional fragmentation techniques, such as collisionally activated dissociation (CAD), ECD provides more extensive sequence fragments, while allowing the labile modifications to remain intact during backbone fragmentation—an important attribute for characterizing post-translational modifications. Herein, we present a brief overview of the ECD technique as well as selected applications in characterization of peptides and proteins. Case studies including characterization and localization of amino acid glycosylation, methionine oxidation, acylation, and “top–down” protein mass spectrometry using ECD will be presented. A recent technique, coined as electron transfer dissociation (ETD), will be also discussed briefly.     

Censored quantile regression model with time-varying covariates under length-biased sampling

Quantile regression is a flexible and effective tool for modeling survival data and its relationship with important covariates, which often vary over time. Informative right censoring of data from the prevalent cohort within the population often results in length-biased observations. We propose an estimating equation-based approach to obtain consistent estimators of the regression coefficients of interest based on length-biased observations with time-dependent covariates. In addition, inspired by Zeng and Lin 2008, we also develop a more numerically stable procedure for variance estimation. Large sample properties including consistency and asymptotic normality of the proposed estimator are established. Numerical studies presented demonstrate convincing performance of the proposed estimator under various settings. The application of the proposed method is demonstrated using the Oscar dataset.     

Competing Risks and Time‐Dependent Covariates

Time‐dependent covariates are frequently encountered in regression analysis for event history data and competing risks. They are often essential predictors, which cannot be substituted by time‐fixed covariates. This study briefly recalls the different types of time‐dependent covariates, as classified by Kalbfleisch and Prentice [The Statistical Analysis of Failure Time Data, Wiley, New York, 2002] with the intent of clarifying their role and emphasizing the limitations in standard survival models and in the competing risks setting. If random (internal) time‐dependent covariates are to be included in the modeling process, then it is still possible to estimate cause‐specific hazards but prediction of the cumulative incidences and survival probabilities based on these is no longer feasible. This article aims at providing some possible strategies for dealing with these prediction problems. In a multi‐state framework, a first approach uses internal covariates to define additional (intermediate) transient states in the competing risks model. Another approach is to apply the landmark analysis as described by van Houwelingen [Scandinavian Journal of Statistics 2007, 34 , 70–85] in order to study cumulative incidences at different subintervals of the entire study period. The final strategy is to extend the competing risks model by considering all the possible combinations between internal covariate levels and cause‐specific events as final states. In all of those proposals, it is possible to estimate the changes/differences of the cumulative risks associated with simple internal covariates. An illustrative example based on bone marrow transplant data is presented in order to compare the different methods.     

A modification of the redistribution to the right algorithm using disease markers

Disease markers are time-dependent covariates which describeprogression towards development of disease. Traditional methodsin survival analysis do not make use of available data on thesemarkers to recover additional information from censored individuals.Using a heuristic modification of the redistribution to theright algorithm (Efron, 1967), a new approach for recoveringinformation for censored individuals using disease markers isproposed. Additionally, the statistical properties of the proposedmethod are examined. There are two possible advantages to thismodification: (i) bias reduction when censoring is informative,and (ii) an increase in efficiency in the case of truly noninformativecensoring.     

Exploring Migration Data Using Interval-Censored Time-to-Event Models

Abstract: Ecologists and wildlife biologists rely on periodic observation of radiocollared animals to study habitat use, survival, movement, and migration, resulting in response times (e.g., mortality and migration) known only to occur within an interval of time. We illustrate methods for analyzing interval-censored data using data on the timing of fall migration (from spring-summer-fall to winter ranges) for white-tailed deer (Odocoileus virginianus) in northern Minnesota, USA, during years 1991–1992 to 2005–2006. We compare both nonparametric and parametric methods for estimating the cumulative distribution function of migration times, and we suggest a parametric (cure rate) model that accounts for conditional (facultative) migrators as a potential alternative to traditional parametric models. Lastly, we illustrate methods for exploring the effect of environmental covariates on migration timing. Models with time-dependent covariates (snow depth, temp) were sensitive to the treatment of the data (as interval-censored or known event times), suggesting the need to account for interval-censoring when modeling the effect of these covariates.     

Nitrate uptake and respiration in roots and shoots: A model

Respiration in plants is often divided into growth and maintenance components. From the growth respiration it is possible to estimate the efficiency of conversion of substrate to plant material. Analysis of recent experimental data on this basis suggests that the conversion efficiency is considerably lower in roots than in shoots, which conflicts with biochemical analysis. The conventional method for describing respiration data is developed to incorporate root activity and is applied to a set of experimental data. The model provides a means for estimating the respiratory cost of nitrate uptake and also a possible explanation for the inconsistency between experimental observations and theoretical analysis.     

Flexible hazard regression modeling for medical cost data

The modeling of lifetime (i.e. cumulative) medical cost data in the presence of censored follow-up is complicated by induced informative censoring, rendering standard survival analysis tools invalid. With few exceptions, recently proposed nonparametric estimators for such data do not extend easily to handle covariate information. We propose to model the hazard function for lifetime cost endpoints using an adaptation of the HARE methodology (Kooperberg, Stone, and Truong, Journal of the American Statistical Association, 1995, 90, 78-94). Linear splines and their tensor products are used to adaptively build a model that incorporates covariates and covariate-by-cost interactions without restrictive parametric assumptions. The informative censoring problem is handled using inverse probability of censoring weighted estimating equations. The proposed method is illustrated using simulation and also with data on the cost of dialysis for patients with end-stage renal disease.