scholarly journals The Effect of Exclusive Breastfeeding on Child Survival Using Modified Kaplan Meier Model (MKMM)

2019 ◽  
pp. 1-10
Author(s):  
Moses Longji Dashal ◽  
Kazeem Eyitayo Lasisi ◽  
Kaneng Eileen Longji
Keyword(s):  
Exclusive Breastfeeding ◽  
Survival Data ◽  
Survival Probability ◽  
Survival Function ◽  
Child Survival ◽  
Food Supplement ◽  
Information Criteria ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Milk Substitute

Background: In Survival analysis, Kaplan-Meier estimator serves as a tool for measuring the frequency or the number of patients surviving medical treatment. Kaplan Meier estimates of survival data have become a better way of analyzing data in cohort study. Kaplan- Meier (K-M) is a non-parametric estimates of survival function that is commonly used to describe survivorship of a study population and to compare two study populations. Aims: This research study is aimed at reducing the morbidity and mortality rate of children less than 6 months. Methodology: 58,609 children less than six months were Exclusive Breastfed from the database. The analysis is done using both K-M and the modified K-M model to examine the effects of Exclusive Breastfeeding. The AIC and BIC was also used as the information criteria. Results: Our results revealed that the K-M model 0.998566822 as the estimated survival probability of children under the ages of six months. Also showing, Exclusively Breastfed children stand the chance of 99% survival. The modified K-M model also revealed 6.98276443909739 as the estimated survival probability, due to initiation of milk substitute and food supplement into the breastfeeding pattern. Showing about 70% chances of survival. Implying about 30% of the existence in one disease or the other or the risk of dying before the age of 5 years. From the information criteria, the AIC (2.3119452169420) and BIC (7.8478797677756) in the Modified K-M are both lower compared to Existing Kaplan Meier (4.0012457354876) and (9.5371847322969) respectively. Modified K-M stand as the best model in knowing the types/amount of food to be added to breastfeeding pattern. Conclusion: So far, the Modified Kaplan Meier Model has been verified and the findings agree that the life expectation will be improved by 99% if children are fed exclusively with breast milk while the life span is been reduced that can lead to death by 30% if the children have a mix feeding which agrees with why Exclusive Breastfeeding should be done.

IPDfromKM: Reconstruct Individual Patient Data from Published Kaplan-Meier Survival Curves

2020 ◽  
Author(s):  
Na Liu ◽  
Yanhong Zhou ◽  
J. Jack Lee
Keyword(s):  
Survival Data ◽  
Individual Patient Data ◽  
Secondary Analysis ◽  
R Package ◽  
Patient Data ◽  
Survival Curves ◽  
Raw Data ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Shiny Application

Abstract BackgroundWhen applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to the IPD. We aim to propose a straightforward and robust approach to help researchers to obtain IPD from published survival curves with a friendly software platform. ResultsImproving upon the existing methods, we proposed an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we develop the R package IPDfromKM and an accompanied web-based Shiny application. Both the R package and Shiny application can be used to extract raw data coordinates from published K-M curves, reconstruct IPD from data coordinates extracted, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, as well as hazard ratios. ConclusionsIPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD data and advance the use of secondary data to make informed decision in medical research.

scholarly journals Hidden Imputations and the Kaplan-Meier Estimator

2020 ◽  
Vol 189 (11) ◽  
pp. 1408-1411 ◽  
Author(s):  
Stephen R Cole ◽  
Jessie K Edwards ◽  
Ashley I Naimi ◽  
Alvaro Muñoz
Keyword(s):  
Survival Data ◽  
Survival Function ◽  
Data Set ◽  
Kaplan Meier ◽  
Event Times

Abstract The Kaplan-Meier (KM) estimator of the survival function imputes event times for right-censored and left-truncated observations, but these imputations are hidden and therefore sometimes unrecognized by applied health scientists. Using a simple example data set and the redistribution algorithm, we illustrate how imputations are made by the KM estimator. We also discuss the assumptions necessary for valid analyses of survival data. Illustrating imputations hidden by the KM estimator helps to clarify these assumptions and therefore may reduce inappropriate inferences.

Cabazitaxel or mitoxantrone with prednisone in patients with metastatic castration-resistant prostate cancer (mCRPC) previously treated with docetaxel: Estimating mean overall survival (OS) for health economic analyses from a phase III trial (TROPIC).

2011 ◽  
Vol 29 (7_suppl) ◽  
pp. 128-128 ◽  
Author(s):  
S. Oudard ◽  
F. Joulain ◽  
A. De Geer ◽  
A. O. Sartor
Keyword(s):  
Goodness Of Fit ◽  
Graphical Method ◽  
Survival Function ◽  
Information Criteria ◽  
Phase Iii ◽  
Point Estimate ◽  
Weibull Function ◽  
Term Survival ◽  
Kaplan Meier ◽  
Previously Treated

128^ Background: TROPIC evaluated the efficacy and safety of the novel taxane cabazitaxel in men with mCRPC previously treated with docetaxel. Median OS was significantly improved, as previously reported (12.7 months in mitoxantrone arm vs 15.1 months in cabazitaxel arm, HR=0.72 [0.61 – 0.84], p<0.0001- updated OS results). Median OS is the most useful descriptive statistic for physicians and patients as it reflects a point estimate in time by which 50% patients may survive regardless of disease status or progression. It avoids assumptions on long-term survival beyond the follow-up period of the clinical trial. Payers however are interested in making a coverage and reimbursement decisions based on the overall therapeutic benefit relative to its risk, and the expected impact on healthcare expenditures. Analyses such as cost-effectiveness analysis therefore require the estimation of mean OS. Methods: Mean OS is only observable when the last patient has died. Its estimation can be derived via an extrapolation of the trial Kaplan-Meier curve using a survival function. Several parametric distributions (exponential, weibull, lognormal, loglogistic and Gompertz) were tested. The parametric distribution that best fitted the trial Kaplan-Meier curves was selected using the Akaike's Information criteria (AIC), the Bayesian Information Criteria (BIC) and graphical method to evaluate the goodness of fit of the distributions. Mean OS from the best fitted model was generated to support payer decision making. Results: Using AIC/BIC and graphical method, the Weibull survival function, S(t)=exp(-l ts) where l is a scale parameter and s a shape parameter, was selected as the distribution that best fitted the TROPIC data. Results of the estimated mean survival assuming a Weibull function are described in the table. Conclusions: Assuming a Weibull distribution, mean OS is estimated at 14.5 months in mitoxantrone arm vs 18.5 months in cabazitaxel arm, leading to 4 months OS difference in favour of cabazitaxel. [Table: see text] [Table: see text]

scholarly journals Tree Based Method for Aggregate Survival Data Modeling

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Asanao Shimokawa ◽  
Yoshitaka Narita ◽  
Soichiro Shibui ◽  
Etsuo Miyaoka
Keyword(s):  
Survival Data ◽  
Survival Function ◽  
Primary Brain Tumor ◽  
Aggregated Data ◽  
Tumor Patient ◽  
Kaplan Meier ◽  
Proposed Model ◽  
Tree Modeling ◽  
Variable Space ◽  
New Interpretation

AbstractIn many scenarios, a patient in medical research is treated as a statistical unit. However, in some scenarios, we are interested in treating aggregate data as a statistical unit. In such situations, each set of aggregated data is considered to be a concept in a symbolic representation, and each concept has a hyperrectangle or multiple points in the variable space. To construct a tree-structured model from these aggregate survival data, we propose a new approach, where a datum can be included in several terminal nodes in a tree. By constructing a model under this condition, we expect to obtain a more flexible model while retaining the interpretive ease of a hierarchical structure. In this approach, the survival function of concepts that are partially included in a node is constructed using the Kaplan-Meier method, where the number of events and risks at each time point is replaced by the expectation value of the number of individual descriptions of concepts. We present an application of this proposed model using primary brain tumor patient data. As a result, we obtained a new interpretation of the data in comparison to the classical survival tree modeling methods.

scholarly journals IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Na Liu ◽  
Yanhong Zhou ◽  
J. Jack Lee
Keyword(s):  
Survival Data ◽  
Individual Patient Data ◽  
Secondary Analysis ◽  
Patient Data ◽  
Survival Curves ◽  
Real World Data ◽  
Raw Data ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Shiny Application

Abstract Background When applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to IPD. We aim to propose a straightforward and robust approach to obtain IPD from published survival curves with a user-friendly software platform. Results Improving upon existing methods, we propose an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we developed the R package IPDfromKM and an accompanying web-based Shiny application. Both the R package and Shiny application have an “all-in-one” feature such that users can use them to extract raw data coordinates from published K-M curves, reconstruct IPD from the extracted data coordinates, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the basis of the reconstructed IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real-world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, and hazard ratios. Conclusions IPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD and advance the use of secondary data to facilitate informed decision making in medical research.

Progression-free survival (PFS) and overall survival (OS) across ethnicities for patients with metastatic renal cell carcinoma (mRCC) receiving targeted therapies (TT) as first-line (1L) treatment.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 645-645
Author(s):  
Nicholas Salgia ◽  
Nazli Dizman ◽  
Paulo Gustavo Bergerot ◽  
Cristiane Decat Bergerot ◽  
Joann Hsu ◽  
...  
Keyword(s):  
Ethnic Groups ◽  
Asian American ◽  
Survival Data ◽  
Survival Function ◽  
Progression Free Survival ◽  
Mtor Inhibitors ◽  
Caucasian American ◽  
Kaplan Meier ◽  
Significant Difference ◽  
The Relationship

645 Background: Recent efforts have sought to characterize differences in clinical and pathological characteristics across ethnicities in mRCC (Batai et al CGUC 2018), however, the relationship between ethnicity and treatment outcomes has yet to be explored. We sought to compare survival outcomes across ethnic groups for patients receiving 1L TT for treatment of mRCC. Methods: Patients receiving 1L systemic treatment for mRCC were retrospectively identified from a single institution database from 2009 to present. Patient ethnicity data were collected from electronic health records. Due to the demographics of the patient population, ethnicity was categorized as Non-Hispanic Caucasian American (CA), Hispanic American (HA), or Asian American (AsA). Patients prescribed tyrosine kinase and/or mTOR inhibitors as 1L therapy were included for analysis. PFS and OS were analyzed across ethnic groups and comparisons were performed using the Kaplan Meier Survival Function in SPSS. Results: Of 294 (77:217 F:M) patients with documented survival data, 183 (62%) were CA, 82 (28%) HA, and 29 (10%) AsA. The most frequently used TTs were sunitinib (63%), temsirolimus (10%), pazopanib (7%), sorafenib (5%), and cabozantinib (4%). Median PFS for CA was 5.6 months (95% Confidence Interval [CI]: 4.1-7.1) vs. 4.7 months (95% CI: 3.1-6.2) for HA vs. 4.7 months (95% CI: 2.1-7.3) for AsA. Median OS was 32.0 months (95% CI: 26.2-37.8) for CA vs. 31.7 months (95% CI: 21.1-42.4) for HA vs. 51.7 months (95% CI: 31.6-71.8) for AsA. No significant difference in PFS or OS was calculated across the three ethnic groups (p=0.652 and p=0.435, respectively). Conclusions: The lack of a statistically significant difference in both PFS and OS across ethnic groups is a promising assessment for the current landscape of health disparities in mRCC. As these data are distinct from recent findings identifying disparities in other malignancies (e.g., prostate cancer), multicenter collaborations should be encouraged to validate these findings.

Aflibercept versus placebo in combination with FOLFIRI in previously treated metastatic colorectal cancer (mCRC): Mean overall survival (OS) estimation from a phase III trial (VELOUR).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 3602-3602 ◽  
Author(s):  
Florence Joulain ◽  
Eric Van Cutsem ◽  
Sheikh Usman Iqbal ◽  
Martin Hoyle ◽  
Carmen Joseph Allegra
Keyword(s):  
Decision Support ◽  
Median Survival ◽  
Survival Data ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Survival Function ◽  
Information Criteria ◽  
Phase Iii ◽  
Economic Decision ◽  
Previously Treated

3602 Background: VELOUR evaluated the efficacy and safety of the novel fusion protein aflibercept (VEGF Trap) in combination with FOLFIRI in mCRC patients previously treated with oxaliplatin. Median OS showed significant improvement with 12.06 months in placebo arm vs 13.50 months in aflibercept arm (p=0.0032; HR=0.82 [95.34% CI: 0.71 to 0.94]) [Van Cutsem, 2011]. Since survival data in oncology are usually right skewed, median survival is preferred for regulatory purposes. However, mean survival estimation can render a more meaningful estimate for long term benefit of interventions, and be effectively applied to clinical and economic decision support. Estimating mean OS also allows payers to derive an estimation of total costs and outcomes in the population. The purpose of this study was to estimate the mean OS for VELOUR trial. Methods: During the trial follow-up period, mean OS is not observable and can be estimated by extrapolating the trial Kaplan-Meier curve using a survival function. Several standard parametric distributions were tested: exponential, weibull, lognormal, loglogistic and gompertz. Akaike’s Information Criteria (AIC), Bayesian Information Criteria (BIC) and graphical method were used to evaluate the goodness of fit of the distributions. Models were run by treatment arm separately or combining the two arms and using treatment as covariate to control for variation. Results: Using AIC/BIC and graphical method, loglogistic function best fit the VELOUR data both with and without treatment as covariate. Weibull distribution is used for sensitivity analysis. Conclusions: Using loglogistic function, mean OS benefit for aflibercept in combination with FOLFIRI is at least 2.9 months (versus 1.4 months difference in median survival). The results have important implications for clinical and economic decision support. Study NCT00561470 was funded by sanofi, in partnership with Regeneron. [Table: see text]

MO818SURVIVAL ON DIALYSIS: SWITZERLAND IN COMPARISON WITH OTHER COUNTRIES – A FOLLOW UP

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Rebecca Winzeler ◽  
Patrice Max Ambühl
Keyword(s):  
Survival Probability ◽  
Survival Rates ◽  
European Countries ◽  
Dialysis Patients ◽  
Survival Status ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Second Year ◽  
First Time

Abstract Background and Aims Survival in dialysis patients is substantially reduced compared to the general population. The aim of the present analysis was to compare the survival of dialysis patients in Switzerland with other countries with an additional year of follow up and a higher number of patients. Method Incident dialysis patients (hemo- or peritoneal dialysis; N=5'406) from the Swiss dialysis registry were followed up from 2014 to December 31, 2019 (median follow up days=658). Deaths occurring during this time (N=1'353) were recorded and survival was examined using the Kaplan Meier method, censored for transplantation. Results Characteristics of the dialysis population stratified according to survival status are provided in Table 1. Dialysis patients in Switzerland have an approximately 8% higher survival in the first and second year and about 10% higher 5 years after start of dialysis, compared to other European countries (Annual ERA-EDTA Report 2017). In the first two years, the proportion in survival rates between genders is similar in Switzerland, as well as in Europe. After 5 years, however, a difference in survival rates between genders becomes apparent, with women having a 5-year survival probability of 56.6%, compared to a lower 5-year survival probability of 49.7% in men. Conclusion The markedly better survival in dialysis patients in Switzerland compared to other European countries could be confirmed with an additional year of follow up and more patients. Also, causes of death vary widely among European countries. 5-year survival was calculated for the first time, with Switzerland showing almost 10% better survival rates than other European countries.

An Analysis of Dental Implants Survival and Success

2018 ◽  
Vol 69 (9) ◽  
pp. 2465-2466
Author(s):  
Iustin Olariu ◽  
Roxana Radu ◽  
Teodora Olariu ◽  
Andrada Christine Serafim ◽  
Ramona Amina Popovici ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Medical History ◽  
Retrospective Cohort Study ◽  
Odds Ratio ◽  
Retrospective Cohort ◽  
Demographic Data ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Health Related

Osseointegration of a dental implant may encounter a variety of problems caused by various factors, as prior health-related problems, patients� habits and the technique of the implant inserting. Retrospective cohort study of 70 patients who received implants between January 2011- April 2016 in one dental unit, with Kaplan-Meier method to calculate the probability of implants�s survival at 60 months. The analysis included demographic data, age, gender, medical history, behavior risk factors, type and location of the implant. For this cohort the implants�survival for the first 6 months was 92.86% compared to the number of patients and 97.56% compared to the number of total implants performed, with a cumulative failure rate of 2.43% after 60 months. Failures were focused exclusively on posterior mandible implants, on the percentage of 6.17%, odds ratio (OR) for these failures being 16.76 (P = 0.05) compared with other localisations of implants, exclusively in men with median age of 42 years.

scholarly journals Sampling strategies to evaluate the prognostic value of a new biomarker on a time-to-event end-point

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Francesca Graziano ◽  
Maria Grazia Valsecchi ◽  
Paola Rebora
Keyword(s):  
Survival Data ◽  
Prognostic Value ◽  
Cox Model ◽  
Lymphoblastic Leukemia ◽  
Fixed Ratio ◽  
Case Control ◽  
Time To Event ◽  
Full Cohort ◽  
Number Of Patients ◽  
Nested Case Control

Abstract Background The availability of large epidemiological or clinical data storing biological samples allow to study the prognostic value of novel biomarkers, but efficient designs are needed to select a subsample on which to measure them, for parsimony and economical reasons. Two-phase stratified sampling is a flexible approach to perform such sub-sampling, but literature on stratification variables to be used in the sampling and power evaluation is lacking especially for survival data. Methods We compared the performance of different sampling designs to assess the prognostic value of a new biomarker on a time-to-event endpoint, applying a Cox model weighted by the inverse of the empirical inclusion probability. Results Our simulation results suggest that case-control stratified (or post stratified) by a surrogate variable of the marker can yield higher performances than simple random, probability proportional to size, and case-control sampling. In the presence of high censoring rate, results showed an advantage of nested case-control and counter-matching designs in term of design effect, although the use of a fixed ratio between cases and controls might be disadvantageous. On real data on childhood acute lymphoblastic leukemia, we found that optimal sampling using pilot data is greatly efficient. Conclusions Our study suggests that, in our sample, case-control stratified by surrogate and nested case-control yield estimates and power comparable to estimates obtained in the full cohort while strongly decreasing the number of patients required. We recommend to plan the sample size and using sampling designs for exploration of novel biomarker in clinical cohort data.

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