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  1. Article ; Online: Shape restricted additive hazards models: Monotone, unimodal, and U-shape hazard functions.

    Chung, Yunro / Ivanova, Anastasia / Fine, Jason P

    Statistics in medicine

    2024  Volume 43, Issue 9, Page(s) 1671–1687

    Abstract: We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a ... ...

    Abstract We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a unimodal hazard function, where the hazard is monotone increasing and monotone decreasing with an unknown mode. A popular approach of the proportional hazards model is limited in such setting due to the complicated structure of the partial likelihood. Our model defines a quadratic loss function, and its simple structure allows a global Hessian matrix that does not involve parameters. Thus, once the global Hessian matrix is computed, a standard quadratic programming method can be applicable by profiling all possible locations of the mode. However, the quadratic programming method may be inefficient to handle a large global Hessian matrix in the profiling algorithm due to a large dimensionality, where the dimension of the global Hessian matrix and number of hypothetical modes are the same order as the sample size. We propose the quadratic pool adjacent violators algorithm to reduce computational costs. The proposed algorithm is extended to the model with a time-dependent covariate with monotone or U-shape hazard function. In simulation studies, our proposed method improves computational speed compared to the quadratic programming method, with bias and mean square error reductions. We analyze data from a recent cardiovascular study.
    MeSH term(s) Humans ; Proportional Hazards Models ; Computer Simulation ; Probability ; Algorithms ; Bias ; Likelihood Functions
    Language English
    Publishing date 2024-02-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.10040
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    Zs.A 1756: Show issues Location:
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  2. Article ; Online: Competing risks predictions with different time scales under the additive risk model.

    Lee, Minjung / Fine, Jason P

    Statistics in medicine

    2022  Volume 41, Issue 20, Page(s) 3941–3957

    Abstract: In the analysis for competing risks data, regression modeling of the cause-specific hazard functions has been usually conducted using the same time scale for all event types. However, when the true time scale is different for each event type, it would be ...

    Abstract In the analysis for competing risks data, regression modeling of the cause-specific hazard functions has been usually conducted using the same time scale for all event types. However, when the true time scale is different for each event type, it would be appropriate to specify regression models for the cause-specific hazards on different time scales for different event types. Often, the proportional hazards model has been used for regression modeling of the cause-specific hazard functions. However, the proportionality assumption may not be appropriate in practice. In this article, we consider the additive risk model as an alternative to the proportional hazards model. We propose predictions of the cumulative incidence functions under the cause-specific additive risk models employing different time scales for different event types. We establish the consistency and asymptotic normality of the predicted cumulative incidence functions under the cause-specific additive risk models specified on different time scales using empirical processes and derive consistent variance estimators of the predicted cumulative incidence functions. Through simulation studies, we show that the proposed prediction methods perform well. We illustrate the methods using stage III breast cancer data obtained from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute.
    MeSH term(s) Breast Neoplasms/epidemiology ; Computer Simulation ; Female ; Humans ; Incidence ; Models, Statistical ; Proportional Hazards Models ; Risk
    Language English
    Publishing date 2022-06-07
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9485
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  3. Article ; Online: CRM and partial order CRM with adaptive rescaling for dose-finding in immunotherapy trials with a continuous outcome.

    Saha, Pooja T / Fine, Jason P / Ivanova, Anastasia

    Statistics in medicine

    2023  Volume 42, Issue 14, Page(s) 2409–2419

    Abstract: In many phase 1 oncology trials of immunotherapies, no dose-limiting toxicities are observed and the maximum tolerated dose cannot be identified. In these settings, dose-finding can be guided by a biomarker of response rather than the occurrences of dose- ...

    Abstract In many phase 1 oncology trials of immunotherapies, no dose-limiting toxicities are observed and the maximum tolerated dose cannot be identified. In these settings, dose-finding can be guided by a biomarker of response rather than the occurrences of dose-limiting toxicity. The recommended phase 2 dose can be defined as the dose with mean response equal to a prespecified value of a continuous response biomarker. To target the mean of a continuous biomarker, we build on the idea of the continual reassessment method and the quasi-Bernoulli likelihood. We extend the design to a problem of finding the recommended phase 2 dose combination in a trial with multiple immunotherapies.
    MeSH term(s) Humans ; Neoplasms/drug therapy ; Maximum Tolerated Dose ; Medical Oncology ; Immunotherapy ; Dose-Response Relationship, Drug ; Research Design ; Computer Simulation
    Language English
    Publishing date 2023-04-03
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9729
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  4. Article ; Online: A parametric approach to relaxing the independence assumption in relative survival analysis.

    Adatorwovor, Reuben / Latouche, Aurelien / Fine, Jason P

    The international journal of biostatistics

    2022  Volume 18, Issue 2, Page(s) 577–592

    Abstract: With known cause of death (CoD), competing risk survival methods are applicable in estimating disease-specific survival. Relative survival analysis may be used to estimate disease-specific survival when cause of death is either unknown or subject to ... ...

    Abstract With known cause of death (CoD), competing risk survival methods are applicable in estimating disease-specific survival. Relative survival analysis may be used to estimate disease-specific survival when cause of death is either unknown or subject to misspecification and not reliable for practical usage. This method is popular for population-based cancer survival studies using registry data and does not require CoD information. The standard estimator is the ratio of all-cause survival in the cancer cohort group to the known expected survival from a general reference population. Disease-specific death competes with other causes of mortality, potentially creating dependence among the CoD. The standard ratio estimate is only valid when death from disease and death from other causes are independent. To relax the independence assumption, we formulate dependence using a copula-based model. Likelihood-based parametric method is used to fit the distribution of disease-specific death without CoD information, where the copula is assumed known and the distribution of other cause of mortality is derived from the reference population. We propose a sensitivity analysis, where the analysis is conducted across a range of assumed dependence structures. We demonstrate the utility of our method through simulation studies and an application to French breast cancer data.
    MeSH term(s) Humans ; Female ; Likelihood Functions ; Survival Analysis ; Computer Simulation ; Cause of Death ; Breast Neoplasms
    Language English
    Publishing date 2022-01-24
    Publishing country Germany
    Document type Journal Article
    ISSN 1557-4679
    ISSN (online) 1557-4679
    DOI 10.1515/ijb-2021-0016
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Efficiency of Naive Estimators for Accelerated Failure Time Models under Length-Biased Sampling.

    Roy, Pourab / Fine, Jason P / Kosorok, Michael R

    Scandinavian journal of statistics, theory and applications

    2021  Volume 49, Issue 2, Page(s) 525–541

    Abstract: In prevalent cohort studies where subjects are recruited at a cross-section, the time to an event may be subject to length-biased sampling, with the observed data being either the forward recurrence time, or the backward recurrence time, or their sum. In ...

    Abstract In prevalent cohort studies where subjects are recruited at a cross-section, the time to an event may be subject to length-biased sampling, with the observed data being either the forward recurrence time, or the backward recurrence time, or their sum. In the regression setting, assuming a semiparametric accelerated failure time model for the underlying event time, where the intercept parameter is absorbed into the nuisance parameter, it has been shown that the model remains invariant under these observed data set-ups and can be fitted using standard methodology for accelerated failure time model estimation, ignoring the length-bias. However, the efficiency of these estimators is unclear, owing to the fact that the observed covariate distribution, which is also length-biased, may contain information about the regression parameter in the accelerated life model. We demonstrate that if the true covariate distribution is completely unspecified, then the naive estimator based on the conditional likelihood given the covariates is fully efficient for the slope.
    Language English
    Publishing date 2021-03-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 1466951-1
    ISSN 1467-9469 ; 0303-6898
    ISSN (online) 1467-9469
    ISSN 0303-6898
    DOI 10.1111/sjos.12526
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  6. Article ; Online: Consistency of the CRM when the dose-toxicity curve is not monotone and its application to the POCRM.

    Saha, Pooja T / Fine, Jason P / Ivanova, Anastasia

    Statistics in medicine

    2021  Volume 40, Issue 8, Page(s) 2073–2082

    Abstract: The continual reassessment method (CRM) is a well-known design for dose-finding trials with the goal of estimating the maximum tolerated dose (MTD), the dose with a given probability of toxicity. The standard assumption is that the probability of ... ...

    Abstract The continual reassessment method (CRM) is a well-known design for dose-finding trials with the goal of estimating the maximum tolerated dose (MTD), the dose with a given probability of toxicity. The standard assumption is that the probability of toxicity monotonically increases with dose. We show that the CRM can still be consistent and correctly identify the MTD even when the dose-toxicity curve is not monotone as long as there is monotonicity of the true toxicity probabilities right below and right above the true MTD. In the case of multiple therapies, where it is unclear how to order combinations of dose levels of multiple therapies, our findings provide insight into the performance of the partial order CRM (POCRM). To select the correct dose combination at the end of a trial, the POCRM does not have to select a monotone ordering of drug combinations. We illustrate the connection between our results for the CRM with a nonmonotone dose-toxicity curve and the POCRM via simulations.
    MeSH term(s) Computer Simulation ; Dose-Response Relationship, Drug ; Humans ; Maximum Tolerated Dose ; Probability ; Research Design
    Language English
    Publishing date 2021-02-15
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.8892
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    Zs.A 1756: Show issues Location:
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  7. Article ; Online: Propensity-score matching with competing risks in survival analysis.

    Austin, Peter C / Fine, Jason P

    Statistics in medicine

    2018  Volume 38, Issue 5, Page(s) 751–777

    Abstract: Propensity-score matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time-to-event outcomes are common in medical research. ... ...

    Abstract Propensity-score matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time-to-event outcomes are common in medical research. Competing risks are outcomes whose occurrence precludes the occurrence of the primary time-to-event outcome of interest. All non-fatal outcomes and all cause-specific mortality outcomes are potentially subject to competing risks. There is a paucity of guidance on the conduct of propensity-score matching in the presence of competing risks. We describe how both relative and absolute measures of treatment effect can be obtained when using propensity-score matching with competing risks data. Estimates of the relative effect of treatment can be obtained by using cause-specific hazard models in the matched sample. Estimates of absolute treatment effects can be obtained by comparing cumulative incidence functions (CIFs) between matched treated and matched control subjects. We conducted a series of Monte Carlo simulations to compare the empirical type I error rate of different statistical methods for testing the equality of CIFs estimated in the matched sample. We also examined the performance of different methods to estimate the marginal subdistribution hazard ratio. We recommend that a marginal subdistribution hazard model that accounts for the within-pair clustering of outcomes be used to test the equality of CIFs and to estimate subdistribution hazard ratios. We illustrate the described methods by using data on patients discharged from hospital with acute myocardial infarction to estimate the effect of discharge prescribing of statins on cardiovascular death.
    MeSH term(s) Computer Simulation ; Humans ; Hydroxymethylglutaryl-CoA Reductase Inhibitors/adverse effects ; Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use ; Monte Carlo Method ; Myocardial Infarction/drug therapy ; Myocardial Infarction/mortality ; Patient Discharge/statistics & numerical data ; Propensity Score ; Research Design ; Risk ; Survival Analysis
    Chemical Substances Hydroxymethylglutaryl-CoA Reductase Inhibitors
    Language English
    Publishing date 2018-10-22
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.8008
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  8. Article ; Online: Accounting for Preinvasive Conditions in Analysis of Invasive Cancer Risk: Application to Breast Cancer.

    Kim, Jung In / Fine, Jason P / Sandler, Dale P / Zhao, Shanshan

    Epidemiology (Cambridge, Mass.)

    2021  Volume 33, Issue 1, Page(s) 48–54

    Abstract: Background: Preinvasive cancer conditions are often actively treated to minimize progression to life-threatening invasive cancers, but this creates challenges for analysis of invasive cancer risk. Conventional methods of treating preinvasive conditions ... ...

    Abstract Background: Preinvasive cancer conditions are often actively treated to minimize progression to life-threatening invasive cancers, but this creates challenges for analysis of invasive cancer risk. Conventional methods of treating preinvasive conditions as censoring events or targeting at the composite outcome could both lead to bias.
    Methods: We propose two solutions: one that provides exact estimates of risk based on distributional assumptions about progression, and one that provides risk bounds corresponding to extreme cases of no or complete progression. We compare these approaches through simulations and an analysis of the Sister Study data in the context of ductal carcinoma in situ (DCIS) and invasive breast cancer.
    Results: Simulations suggested important biases with conventional approaches, whereas the proposed estimate is consistent when progression parameters are correctly specified, and the risk bounds are robust in all scenarios. With Sister Study, the estimated lifetime risks for invasive breast cancer are 0.220 and 0.269 with DCIS censored or combined. Without detailed progression information, a sensitivity analysis suggested lifetime risk falls between the bounds of 0.214 and 0.269 across assumptions of 10%-95% of DCIS patients progressing to invasive cancer in an average of 1-10 years.
    Conclusions: When estimating invasive cancer risk while preinvasive conditions are actively treated, it is important to consider the implied assumptions and potential biases of conventional approaches. Although still not perfect, we proposed two practical solutions that provide improved understanding of the underlying mechanism of invasive cancer.
    MeSH term(s) Breast Neoplasms/metabolism ; Carcinoma in Situ/metabolism ; Carcinoma, Ductal, Breast/pathology ; Carcinoma, Intraductal, Noninfiltrating/metabolism ; Carcinoma, Intraductal, Noninfiltrating/pathology ; Disease Progression ; Female ; Humans
    Language English
    Publishing date 2021-09-24
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Intramural
    ZDB-ID 1053263-8
    ISSN 1531-5487 ; 1044-3983
    ISSN (online) 1531-5487
    ISSN 1044-3983
    DOI 10.1097/EDE.0000000000001423
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 3121: Show issues Location:
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  9. Article: Estimation of causal quantile effects with a binary instrumental variable and censored data.

    Wei, Bo / Peng, Limin / Zhang, Mei-Jie / Fine, Jason P

    Journal of the Royal Statistical Society. Series B, Statistical methodology

    2021  Volume 83, Issue 3, Page(s) 559–578

    Abstract: The causal effect of a treatment is of fundamental interest in the social, biological, and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, ...

    Abstract The causal effect of a treatment is of fundamental interest in the social, biological, and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, we study a new binary IV framework with randomly censored outcomes where we propose to quantify the causal treatment effect by the concept of complier quantile causal effect (CQCE). The CQCE is identifiable under weaker conditions than the complier average causal effect when outcomes are subject to censoring, and it can provide useful insight into the dynamics of the causal treatment effect. Employing the special characteristic of the binary IV and adapting the principle of conditional score, we uncover a simple weighting scheme that can be incorporated into the standard censored quantile regression procedure to estimate CQCE. We develop robust nonparametric estimation of the derived weights in the first stage, which permits stable implementation of the second stage estimation based on existing software. We establish rigorous asymptotic properties for the proposed estimator, and confirm its validity and satisfactory finite-sample performance via extensive simulations. The proposed method is applied to a bone marrow transplant dataset to evaluate the causal effect of rituximab in diffuse large B-cell lymphoma patients.
    Language English
    Publishing date 2021-07-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 1490719-7
    ISSN 1467-9868 ; 1369-7412 ; 0035-9246
    ISSN (online) 1467-9868
    ISSN 1369-7412 ; 0035-9246
    DOI 10.1111/rssb.12431
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  10. Article ; Online: Pregnancy-related factors may signal additional protection or risk of future cardiovascular diseases.

    Reddy, Shivani M / Tsujimoto, Tamy H M / Qaqish, Bajhat F / Fine, Jason P / Nicholson, Wanda K

    BMC women's health

    2022  Volume 22, Issue 1, Page(s) 528

    Abstract: Background: Cardiovascular disease (CVD) guidelines recommend using the Pooled Cohort Equation (PCE) to assess 10-year CVD risk based on traditional risk factors. Pregnancy-related factors have been associated with future CVD. We examined the ... ...

    Abstract Background: Cardiovascular disease (CVD) guidelines recommend using the Pooled Cohort Equation (PCE) to assess 10-year CVD risk based on traditional risk factors. Pregnancy-related factors have been associated with future CVD. We examined the contribution of two pregnancy-related factors, (1) history of a low birthweight (LBW) infant and (2) breastfeeding to CVD risk accounting for traditional risk factors as assessed by the PCE.
    Methods: A nationally representative sample of women, ages 40-79, with a history of pregnancy, but no prior CVD, was identified using NHANES 1999-2006. Outcomes included (1) CVD death and (2) CVD death plus CVD surrogates. We used Cox proportional hazards models to adjust for PCE risk score.
    Results: Among 3,758 women, 479 had a LBW infant and 1,926 reported breastfeeding. Mean follow-up time was 12.1 years. Survival models showed a consistent reduction in CVD outcomes among women with a history of breastfeeding. In cause-specific survival models, breastfeeding was associated with a 24% reduction in risk of CVD deaths (HR 0.76; 95% CI 0.45─1.27, p = 0.30) and a 33% reduction in risk of CVD deaths + surrogate CVD, though not statistically significant. (HR 0.77; 95% CI 0.52─1.14, p = 0.19). Survival models yielded inconclusive results for LBW with wide confidence intervals (CVD death: HR 0.98; 95% CI 0.47─2.05; p = 0.96 and CVD death + surrogate CVD: HR 1.29; 95% CI 0.74─2.25; p = 0.38).
    Conclusion: Pregnancy-related factors may provide important, relevant information about CVD risk beyond traditional risk factors. While further research with more robust datasets is needed, it may be helpful for clinicians to counsel women about the potential impact of pregnancy-related factors, particularly the positive impact of breastfeeding, on cardiovascular health.
    MeSH term(s) Pregnancy ; Infant, Newborn ; Female ; Humans ; Adult ; Middle Aged ; Aged ; Cardiovascular Diseases/epidemiology ; Nutrition Surveys ; Risk Factors ; Proportional Hazards Models ; Infant, Low Birth Weight
    Language English
    Publishing date 2022-12-17
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2050444-5
    ISSN 1472-6874 ; 1472-6874
    ISSN (online) 1472-6874
    ISSN 1472-6874
    DOI 10.1186/s12905-022-02125-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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