Estimation Of Survival Function Research Articles

The prognostic predictors of six-month mortality for residents with advanced dementia in long-term care facilities in Taiwan: A prospective cohort study

BackgroundAlthough hospice or palliative care for patients with advanced dementia has been implemented for more than 30 years, few studies have investigated the prognostic predictors of 6-month mortality in these patients. Prognostication has been a major obstacle, and 6-month prognostic predictors for patients with advanced dementia are still considered elusive. ObjectivesTo better understand the dying trajectories of patients with advanced dementia, we performed a longitudinal prospective cohort study to investigate the predictors of 6-month mortality for residents with advanced dementia in long-term care facilities in Taiwan. DesignProspective cohort study. SettingThis study took place in 32 long-term care facilities selected from northern, central, and southern Taiwan. ParticipantsThe study included 320 residents with advanced dementia. MeasurementsMeasurements were obtained after determining study eligibility; the participants underwent follow-up assessments once every 3 months for 6 months or died during follow-up. The assessments included demographic characteristics, health and medical status characteristics, and death-related information. The Kaplan-Meier survival function estimation and the Cox proportional hazards model were used to estimate the survival rate and predict the prognostic factors. ResultsBaseline data from 320 residents with advanced dementia in long-term care facilities were obtained. The mean age was 82.7 years and 61.6% were female. The 6-month survival rate was 78.1%. The major cause of death was multiple organ failure related to pneumonia. The 6-month prognostic predictors were pneumonia (adjusted hazard ratio, 5.56; 95% confidence interval, 2.46–12.6; p-value < .001), reduction in nutrient intake >25% (adjusted hazard ratio, 5.05; 95% confidence interval, 2.37–10.8; p-value < .001), oxygen dependency (adjusted hazard ratio, 2.58; 95% confidence interval, 1.51–4.39; p-value = .001), treatment for electrolyte abnormalities (adjusted hazard ratio, 2.14; 95% confidence interval, 1.10–4.14; p-value = .025), severe pressure injuries (adjusted hazard ratio, 2.04; 95% confidence interval, 1.13–3.67; p-value = .018), and long-term indwelling urinary catheters (adjusted hazard ratio, 1.80; 95% confidence interval, 1.09–2.96; p-value = .021). ConclusionOur results identified six prognostic predictors of 6-month mortality among residents with advanced dementia in Taiwan. These predictors may serve as risk assessment indicators for nursing staff who provide clinical care and can enable the identification of patients in recognized terminal decline, thereby allowing access to hospice palliative services.

Estimation of the joint survival function for three successive duration times under double truncation and right censoring

ABSTRACTIn incident cohort studies, it is common to include subjects who have experienced a certain event within a calendar time window. For all the included individuals, the time of the previous events is retrospectively confirmed and the occurrence of subsequent events is observed during the follow-up periods. During the follow-up periods, subjects may undergo three successive events. Since the second/third duration process becomes observable only if the first/second event has occurred, the data is subject to double truncation and right censoring. We consider two cases: the case when the first event time is subject to double truncation and the case when the second event time is subject to double truncation. Using the inverse-probability-weighted approach, we propose nonparametric and semiparametric estimators for the estimation of the joint survival function of three successive duration times. We establish the asymptotic properties of the proposed estimators and conduct a simulation study to investigate the finite sample properties of the proposed estimators.

Fully nonparametric estimation of the marginal survival function based on case-control clustered data

Fully nonparametric estimation of the marginal survival function based on case-control clustered data

Uso del Método “Combinación de Riesgos” para estimar la función de supervivencia en presencia de riesgos competitivos dependientes: Un estudio de simulación.

&#x0D; &#x0D; &#x0D; En este trabajo se comparan distintas estructuras de dependencia para los riegos que compiten en un mode- lo de riesgos competitivos dependientes trivariado, haciendo uso de las técnicas C-Vines y D-Vines cópulas, vía simulación estadística. Los Vines permiten obtener flexibilidad multivariada y son capaces de capturar todo el rango posible de dependencias entre los riegos competitivos, las cuales son de gran interés en los mercados financieros, problemas sociales, genéticos, entre otros. Seguidamente, se estima la función de sobrevivencia para el tiempo mínimo, tanto para el caso independiente, por medio del estimador Kaplan Meier, como para el caso dependiente, en el que usaremos el método de combinación de riesgos, el cual es una extensión del estimador cópula gráfico. Los C-D Vines cópulas, trabajan con una variedad de cópulas bivariadas, las cuales se pueden seleccionar de manera independiente y permiten tener una amplia gama de posibilidades para la caracterización de la dependencia de los riesgos que compiten, se estudian casos particulares donde dos de los tres riesgos tienen igual dependencia y el riesgo restante es independiente a los anteriores. También se estudia el caso donde dos riesgos presentan igual dependencia y el otro con de- pendencia alta. Además se analiza un caso particular donde los tres riesgos presentan distinta dependencia. En todos los casos estudiados, el método de combinación de riesgos es una buena alternativa para estimar las funciones de distribución marginal y la función de sobrevivencia cuando se tiene dependencia entre los riesgos de un modelo de riesgos competitivos dependientes.&#x0D; &#x0D; &#x0D;

Cox Proportional Hazards Model for Identification of the Prognostic Factors in the Survival of Acute Liver Failure Patients in India

Background: Acute Liver Failure (ALF) is a kind of dangerous rare liver injury among all liver diseases. Different statistical methods such as Logistic regression, Kaplan-Meier estimate of survival function followed by Log-rank test and semi-parametric approaches of survival analysis has been applied in order to identify the significant risk factors of ALF patients. In most of the studies, regression models used in this setup has not been evaluated by model assumptions and their goodness of fit tests.Objective: To apply appropriate survival analysis technique to identify the prognostic factors in the survival of ALF patients, to develop prognostic index, and to predict survival probability for different scenario.Materials and Methods: The study is based on the retrospective cohort study design with altogether 1099 ALF patients taken from the liver clinic, All India Institute of Medical Sciences, New Delhi India. Cox regression has been considered as the suitable model for handling this time to event data, and the assumptions of the model, goodness of fit of the model was assessed and survival probabilities were predicted.Results: This study has identified six prognostic factors namely age, prothrombin time, cerebral edema, total serum bilirubin, serum creatinine and etiology for ALF patients. The hazards of mortality [HR: 2.38; 95% C.I.: (1.99, 2.85), p &lt; 0.001] is the highest for cerebral edema among all these prognostic factors. Nearly 9%, 26%, 39%, 50%, 59% and 63% of ALF patients with a PI of 1, 3, 5, 7, 9 and 10 respectively die by 3 days of hospital stay.Conclusion: The developed Cox Proportional Hazards model with six prognostic factors has satisfied the model assumptions and goodness of fit tests. The risk score and the predicted survival probabilities will be immensely helpful to the hepatologists to make a quick decision regarding the likely prognosis of a patient at admission and helpful in triaging the ALF patients for liver transplant.Nepalese Journal of Statistics, Vol. 2, 53-74

Non-parametric estimation of survival in age-dependent genetic disease and application to the transthyretin-related hereditary amyloidosis

In genetic diseases with variable age of onset, survival function estimation for the mutation carriers as well as estimation of the modifying factors effects are essential to provide individual risk assessment, both for mutation carriers management and prevention strategies. In practice, this survival function is classically estimated from pedigrees data where most genotypes are unobserved. In this article, we present a unifying Expectation-Maximization (EM) framework combining probabilistic computations in Bayesian networks with standard statistical survival procedures in order to provide mutation carrier survival estimates. The proposed approach allows to obtain previously published parametric estimates (e.g. Weibull survival) as particular cases as well as more general Kaplan-Meier non-parametric estimates, which is the main contribution. Note that covariates can also be taken into account using a proportional hazard model. The whole methodology is both validated on simulated data and applied to family samples with transthyretin-related hereditary amyloidosis (a rare autosomal dominant disease with highly variable age of onset), showing very promising results.

Instrumental variable estimation in semi-parametric additive hazards models.

SummaryInstrumental variable methods allow unbiased estimation in the presence of unmeasured confounders when an appropriate instrumental variable is available. Two‐stage least‐squares and residual inclusion methods have recently been adapted to additive hazard models for censored survival data. The semi‐parametric additive hazard model which can include time‐independent and time‐dependent covariate effects is particularly suited for the two‐stage residual inclusion method, since it allows direct estimation of time‐independent covariate effects without restricting the effect of the residual on the hazard. In this article, we prove asymptotic normality of two‐stage residual inclusion estimators of regression coefficients in a semi‐parametric additive hazard model with time‐independent and time‐dependent covariate effects. We consider the cases of continuous and binary exposure. Estimation of the conditional survival function given observed covariates is discussed and a resampling scheme is proposed to obtain simultaneous confidence bands. The new methods are compared to existing ones in a simulation study and are applied to a real data set. The proposed methods perform favorably especially in cases with exposure‐dependent censoring.

Robust frontier estimation from noisy data: A Tikhonov regularization approach

In stochastic frontier models, the regression function defines the production frontier and the regression errors are assumed to be composite. The actually observed outputs are assumed to be contaminated by a stochastic noise. The additive regression errors are composed from this noise term and the one-sided inefficiency term. The aim is to construct a robust nonparametric estimator for the production function. The main tool is a robust concept of partial, expected maximum production frontier, defined as a special probability-weighted moment. In contrast to the deterministic one-sided error model where robust partial frontier modeling is fruitful, the composite error problem requires a substantial different treatment based on deconvolution techniques. To ensure the identifiability of the model, it is sufficient to assume an independent Gaussian noise. In doing so, the frontier estimation necessitates the computation of a survival function estimator from an ill-posed equation. A Tikhonov regularized solution is constructed and nonparametric frontier estimation is performed. The asymptotic properties of the obtained survival function and frontier estimators are established. Practical guidelines to effect the necessary computations are described via a simulated example. The usefulness of the approach is discussed through two concrete data sets from the sector of Delivery Services.

Using Mixed Distribution for Gamma and Exponential to Estimate of Survival Function (Brain Stroke)

Using Mixed Distribution for Gamma and Exponential to Estimate of Survival Function (Brain Stroke)

A multicenter, randomized, controlled trial comparing the occurrence of major adverse cardiovascular events (MACEs) in patients (pts) with prostate cancer (pc) and cardiovascular disease (CVD) receiving degarelix (GnRH receptor antagonist) or leuprolide (GnRH receptor agonist).

TPS395 Background: Epidemiological studies showed an association between GnRH agonists and a long-term increased risk of CVD, early after treatment initiation and with a higher risk seen in pts with pre-existing CVD. Retrospective pooled safety analyses of 6 randomized trials showed that significantly fewer pts treated with the GnRH receptor antagonists, degarelix, had a CV event or death compared with pts receiving a GnRH receptor agonist. In those studies showing an increased CV risk, Androgen-Deprivation Therapy (ADT) was primarily with GnRH receptor agonists. The mechanistic differences between GnRH antagonists and agonists, including testosterone surge and time to suppression at initiation, effect on follicle-stimulating hormone and on GnRH receptors e.g. T-lymphocytes in atherosclerotic plaque, raises the possibility of different CV risk profiles. The PRONOUNCE trial is the first to prospectively assess whether a GnRH agonist/antagonist can worsen pre-existing CVD; assess the impact of GnRH agonist/antagonist on CV risk biomarkers; and effects of hormonal therapy on immune system. Methods: PRONOUNCE is a multi-center, randomized, controlled trial of 900 men with pc and concomitant CVD, assessing adjudicated MACEs, i.e. myocardial infarction (fatal, non-fatal), stroke (fatal, non-fatal), or death in pts randomized 1:1 to either degarelix or leuprolide according to label recommendations for up to one year. Eligibility include pre-defined CVD, metastatic or locally advanced pc; high-risk disease with plan for definitive radiation therapy (RT); recurrence after local therapy with PSA doubling time &lt;12 months; or salvage RT with neoadjuvant/adjuvant ADT for at least 12 months. Serum samples are collected for the analysis of various CV, inflammatory, and immune biomarkers. The primary endpoint will be based on Kaplan-Meier estimator of survival function and stratified for age group and region. Interim analysis is scheduled when 50% of MACE events have occurred allowing the DSMB to recommend for sample size correction. Clinical trial information: NCT02663908.

Abstract P4-15-01: Integration of clinical and pathological data with the DCIS score to predict the risk of local recurrence

Abstract Background: Prediction of local recurrence (LR) risk after breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS) is needed to guide decisions regarding risks and benefits of adjuvant radiotherapy (RT). We aim to determine the optimal combination of clinical and pathological characteristics with the Oncotype DCIS Score (DS) to predict individualized 10 year risks of local recurrence (LR) after BCS (with or without RT) for DCIS and develop a web-based nomogram / risk calculator. Methods: DS (continuous, categorical risk groups low/intermediate/high) and complete clinico-pathological data (age, tumor size, nuclear grade, comedonecrosis, multifocality, margin width and receipt of breast RT) are available for 1102 cases from the Ontario population cohort of pure DCIS treated by BCS (981 cases with negative margins, 121 cases with positive margins). We examined various categorizations of discrete variables, and transformations of continuous variables, and used model selection procedures to determine the best fitting Cox proportional hazards regression model of LR according to the c-statistic, Akaike Information Criterion, and log-likelihood. We tested all two-way interactions and interactions with time. The 10-year probability of LR was calculated for each woman using the estimate of the baseline survival function and the estimate of the linear predictor, which is a function of the regression parameter estimates and specific covariate values. Model calibration will be explored by comparing observed versus predicted risk of LR, and the model's discriminative ability will be assessed by the concordance index. Model validation will be conducted via bootstrapping approaches. Results: In the best fitting main effects full model, the adjusted hazard ratios (HR) (95% confidence intervals (CI)) for LR included: intermediate/ high risk DS vs. low risk (HR =1.96 (1.39, 2.74)), age &amp;lt; 50 years at diagnosis vs. age&amp;gt;= 50 (HR = 1.62 (1.16, 2.25)), square root of tumor size (HR/mm = 1.24 (1.11, 1.38)), comedonecrosis &amp;gt; 30% vs. &amp;lt;=30% (HR=1.53 (1.08, 2.16)), multifocality ( HR=2.01 (1.45, 2.77)), and receipt of RT ( HR=0.50 (0.37, 0.68)). There was a significant interaction between tumor size and DS but not between DS and RT. Among women with a low risk DS and age &amp;gt;= 50, tumor size &amp;lt;= 10 mm, &amp;lt;= 30% comedo necrosis, no multifocality, low or moderate nuclear grade and negative margins, the average predicted 10 year LR risk = 6.8% (range 6.4% - 7.6%) after treatment by BCS without RT, and 3.6% (range 3.4% - 3.8%) after BCS+RT (an absolute benefit of 3.2% from RT). Among women with intermediate/high risk DS and the same low risk clinical-pathological features, the average predicted 10 year LR risk = 19.0% (range 18.3% - 20.0%) without RT, and 9.5% (range 9.1% - 10.3%) with RT (an absolute benefit of 9.5% from RT). Conclusion: This prediction model combines clinical and pathological features with the DS to improve estimates of local recurrence risk after BCS alone and the absolute benefit with RT, which can improve decision making in DCIS. After calibration and validation, it will be the basis of a web-based nomogram / risk calculator. It also demonstrates the importance of molecular testing for studies of the de-escalation of therapy for DCIS. Citation Format: Paszat L, Sutradhar R, Zhou L, Lalani N, Nofech-Mozes S, Rakovitch E. Integration of clinical and pathological data with the DCIS score to predict the risk of local recurrence [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-15-01.

Management and Invasive Cardiological Review by Comparison of Percutaneous Coronary Intervention in Left Anterior Descending Artery with Drug Eluting and Bare Metal Stents.

Introduction:The dominant global public health challenge are non-communicable diseases. According to World Health Organization (WHO) data. The fifth leading causes of death in FB&H are diseases of the heart and coronary arteries: stroke, acute myocardial infarction, cardiac arrest, cardiomyopathy and essential hypertension. The prevention of these diseases has great importance in improving health in B&H.Objective:The aim of this study is in estimation of one-year survival and left heart systolic function after the treatment. After the data collection and evidence of their statistical value, the results of the research point to the profile of patients with a LAD disease in one-vessel coronary artery disease that should be subjected to PCI DES LAD and PCI BMS LAD, respectively, or creating guidelines for a better and more effective LAD treatment.Material and methods:The study was performed as retrospective/ prospective, clinically controlled for a period of three years. In this study was included 60 patients, which was followed in 12 months period. With the PCI BMS method was treated 63.3% and 36.7% of subjects were treated with the PCI DES in LAD.Conclusion:The number of complications in patients with one-vessel LAD coronary heart disease, treated with PCI DES and PCI BMS was statistically significant. One possible complication (4 patients) is due to the spread of the disease to other blood vessels. Due to possible complications in the treated or LAD with repeated stenosis, the complication in terms of restenosis of the previously placed stent in 75% are with BM stents justifying the use of drug eluting stent, while the progression of disease in patients (2 patients) indicates the need for detection and prevention of risk factors.

Goodness-of-fit test for the parametric proportional hazard regression model with interval-censored data

ABSTRACTInterval-censored data are common in medical research. Fully parametric models provide simple and efficient inference for the estimation of survival functions using interval-censored observations. Inference based on a parametric regression model requires the complete specification of the probability density function, and therefore, correctly specifying the model is crucial, while the regression diagnostic is a very important step. However, regression diagnostic methods for use with the interval-censored data have not been completely developed. Here, we developed a model-checking procedure based on the cumulative martingale residuals for the interval-censored observations. We employed the conditional expectation of residuals for the diagnostics, because the data showing the exact failure time cannot be obtained for the interval-censoring analyses, and developed the formal resampling-based supremum-type test and graphical model-checking techniques. A simulation study demonstrated an excellent performance of the proposed method during the detection of a misspecified functional form of covariates in the finite sample. Furthermore, we used this method for the analysis of the medical checkup data obtained in Japan.

Small sample confidence intervals for survival functions under the proportional hazards model

ABSTRACTWe develop a saddlepoint-based method for generating small sample confidence bands for the population surviival function from the Kaplan-Meier (KM), the product limit (PL), and Abdushukurov-Cheng-Lin (ACL) survival function estimators, under the proportional hazards model. In the process we derive the exact distribution of these estimators and developed mid-ppopulation tolerance bands for said estimators. Our saddlepoint method depends upon the Mellin transform of the zero-truncated survival estimator which we derive for the KM, PL, and ACL estimators. These transforms are inverted via saddlepoint approximations to yield highly accurate approximations to the cumulative distribution functions of the respective cumulative hazard function estimators and these distribution functions are then inverted to produce our saddlepoint confidence bands. For the KM, PL and ACL estimators we compare our saddlepoint confidence bands with those obtained from competing large sample methods as well as those obtained from the exact distribution. In our simulation studies we found that the saddlepoint confidence bands are very close to the confidence bands derived from the exact distribution, while being much easier to compute, and outperform the competing large sample methods in terms of coverage probability.

Parametric Model Based On Imputations Techniques for Partly Interval Censored Data

The term ‘survival analysis’ has been used in a broad sense to describe collection of statistical procedures for data analysis. In this case, outcome variable of interest is time until an event occurs where the time to failure of a specific experimental unit might be censored which can be right, left, interval, and Partly Interval Censored data (PIC). In this paper, analysis of this model was conducted based on parametric Cox model via PIC data. Moreover, several imputation techniques were used, which are: midpoint, left & right point, random, mean, and median. Maximum likelihood estimate was considered to obtain the estimated survival function. These estimations were then compared with the existing model, such as: Turnbull and Cox model based on clinical trial data (breast cancer data), for which it showed the validity of the proposed model. Result of data set indicated that the parametric of Cox model proved to be more superior in terms of estimation of survival functions, likelihood ratio tests, and their P-values. Moreover, based on imputation techniques; the midpoint, random, mean, and median showed better results with respect to the estimation of survival function.

An improved survival estimator for censored medical costs with a kernel approach

ABSTRACTCost assessment serves as an essential part in economic evaluation of medical interventions. In many studies, costs as well as survival data are frequently censored. Standard survival analysis techniques are often invalid for censored costs, due to the induced dependent censoring problem. Owing to high skewness in many cost data, it is desirable to estimate the median costs, which will be available with estimated survival function of costs. We propose a kernel-based survival estimator for costs, which is monotone, consistent, and more efficient than several existing estimators. We conduct numerical studies to examine the finite-sample performance of the proposed estimator.

Estimation of discrete survival function for error-prone diagnostic tests.

The product limit or Kaplan-Meier (KM) estimator is commonly used to estimate the survival function in the presence of incomplete time to event. Application of this method assumes inherently that the occurrence of an event is known with certainty. However, the clinical diagnosis of an event is often subject to misclassification due to assay error or adjudication error, by which the event is assessed with some uncertainty. In the presence of such errors, the true distribution of the time to first event would not be estimated accurately using the KM method. We develop a method to estimate the true survival distribution by incorporating negative predictive values and positive predictive values, into a KM-like method of estimation. This allows us to quantify the bias in the KM survival estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival function and its variance. Asymptotic properties of the proposed estimators are provided, and these properties are examined through simulations. We demonstrate our methods using data from the Viral Resistance to Antiviral Therapy of Hepatitis C study.

A dynamic system for Gompertz model

ABSTRACTTwo-parameter Gompertz distribution has been introduced as a lifetime model for reliability inference recently. In this paper, the Gompertz distribution is proposed for the baseline lifetimes of components in a composite system. In this composite system, failure of a component induces increased load on the surviving components and thus increases component hazard rate via a power-trend process. Point estimates of the composite system parameters are obtained by the method of maximum likelihood. Interval estimates of the baseline survival function are obtained by using the maximum-likelihood estimator via a bootstrap percentile method. Two parametric bootstrap procedures are proposed to test whether the hazard rate function changes with the number of failed components. Intensive simulations are carried out to evaluate the performance of the proposed estimation procedure.

Proteomic profiling of breast cancer metabolism identifies SHMT2 and ASCT2 as prognostic factors

BackgroundBreast cancer tumors are known to be highly heterogeneous and differences in their metabolic phenotypes, especially at protein level, are less well-understood. Profiling of metabolism-related proteins harbors the potential to establish new patient stratification regimes and biomarkers promoting individualized therapy. In our study, we aimed to examine the relationship between metabolism-associated protein expression profiles and clinicopathological characteristics in a large cohort of breast cancer patients.MethodsBreast cancer specimens from 801 consecutive patients, diagnosed between 2009 and 2011, were investigated using reverse phase protein arrays (RPPA). Patients were treated in accordance with national guidelines in five certified German breast centers. To obtain quantitative expression data, 37 antibodies detecting proteins relevant to cancer metabolism, were applied. Hierarchical cluster analysis and individual target characterization were performed. Clustering results and individual protein expression patterns were associated with clinical data. The Kaplan-Meier method was used to estimate survival functions. Univariate and multivariate Cox regression models were applied to assess the impact of protein expression and other clinicopathological features on survival.ResultsWe identified three metabolic clusters of breast cancer, which do not reflect the receptor-defined subtypes, but are significantly correlated with overall survival (OS, p ≤ 0.03) and recurrence-free survival (RFS, p ≤ 0.01). Furthermore, univariate and multivariate analysis of individual protein expression profiles demonstrated the central role of serine hydroxymethyltransferase 2 (SHMT2) and amino acid transporter ASCT2 (SLC1A5) as independent prognostic factors in breast cancer patients. High SHMT2 protein expression was significantly correlated with poor OS (hazard ratio (HR) = 1.53, 95% confidence interval (CI) = 1.10–2.12, p ≤ 0.01) and RFS (HR = 1.54, 95% CI = 1.16–2.04, p ≤ 0.01). High protein expression of ASCT2 was significantly correlated with poor RFS (HR = 1.31, 95% CI = 1.01–1.71, p ≤ 0.05).ConclusionsOur data confirm the heterogeneity of breast tumors at a functional proteomic level and dissects the relationship between metabolism-related proteins, pathological features and patient survival. These observations highlight the importance of SHMT2 and ASCT2 as valuable individual prognostic markers and potential targets for personalized breast cancer therapy.Trial registrationClinicalTrials.gov, NCT01592825. Registered on 3 May 2012.

Reply re: Vertically Oriented Femoral Neck Fractures: A Biomechanical Comparison of 3 Fixation Constructs.

OBJECTIVES To compare the static and dynamic mechanical properties of a modified crossed cannulated screw (CS) configuration, the inverted triangle (IT) cannulated screw configuration, and a compression hip screw (CHS) with derotation screw in Pauwels type III femoral neck fractures. METHODS Thirty synthetic femora were divided into 3 groups, and vertical femoral neck osteotomies were made. Ten osteotomized femora were fixed with a CS configuration, 10 were fixed with 3 parallel screws in an IT configuration, and the remaining 10 osteotomized femora were fixed with a CHS construct. All groups were tested using a cyclic (up to 15,000 load cycles) axial loading protocol, and survivors were statically loaded to failure. Cycles to failure, load to failure, and stiffness were calculated. The Kaplan-Meier method was used to estimate survival functions and were compared among fixation methods. The relationship between construct structural properties (maximum load and stiffness) and treatment were assessed using general linear modeling. RESULTS All CHS fixation constructs survived the 15,000 cycle loading protocol. They endured longer (P = 0.034) than the CS fixation constructs (mean failure 13,332 cycles), but were not different from IT fixation constructs (mean failure 13,592 cycles). Maximum loads to failure for CS (3870 N) and IT (3756 N) fixation constructs were not different, but were less (P < 0.0001) than the maximum loads to failure for the CHS fixation constructs (5654 N). These findings parallel the results of the axial stiffness measurements: CS fixation constructs (663.01 N/mm) were not stiffer than IT fixation constructs (620.0 N/mm), but were less (P = 0.0005) than the axial stiffness of the CHS fixation constructs (1241.86 N/mm). CONCLUSIONS The biomechanical performance of the CHS fixation method was superior to both the CS fixation method and the IT fixation method using a synthetic femoral model and this test protocol. Biomechanical performance of the latter 2 groups was indistinguishable. We recommend the use of CHS with the derotational screw construct for Pauwels III femoral neck fractures whenever possible.