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The main purpose of this study is to evaluate the extent of dropout students and identify the relationship between risk factors of dropout and the survival time of students.

The Kaplan–Meier estimator (KM), also known as the product-limit technique, is a nonparametric model function that is commonly used in estimating survival function events (Kaplan and Meier, 1958). The survival function's Kaplan–Meier estimators are used to estimate and graph survival probabilities as a function of time, as well as explanatory data analysis (EDA) for the survival data, including the median survival time, and compare for two or more of the survival events. In addition, Cox proportional hazards model is employed for modelling purpose.

Results of the Kaplan–Meier curves show that male students have lower survival rates than female, researchers have found that there is a difference between the survival times of the student's school types, results show students from English-based schools are higher than Arabic-based schools as suggested by the survival curve. Similarly, there is a difference between the survival times of students aging equal or greater than 25 and students aging less than 25 and survival function estimates of dropout according to high school grade marks has huge difference. These results were confirmed using log rank test as age, school type and marks were statistically significantly different while gender is not statistically significant.

There is no study of this kind from the Somalia context about the student's dropout. Subsequent to the outbreak of civil war in 1988 and the collapse of the central government in 1991, all public social services in Somalia including education centers were severely disrupted.

The statistical methods discussed in the previous section will be applied on a real dataset obtained from different offices of the university; most of the data were extracted from faculty of economics office and admission and record office. The data set comprised of 70 students from SIMAD university, consists of full-time faculty of economics students who enrolled at the university in the academic year of 2017–2018 until two years of diploma, students either complete 24 months of diploma or leave the university and that is the event of interest.

Survival analysis is statistical technique that uses longitudinal data to model the process that allows an individual or firm to survive to a particular point in time. Despite a large number of studies that use survival analysis to model the duration of time that precedes financial distress, some criticism has suggested that the application of survival analysis to financial distress research provides limited incremental knowledge. This study uses survival analysis to model the duration of time that precedes a firm's initial payment default. The data set consists of firm financial information obtained from a large credit information company in Finland for a five‐year period split into estimation and holdout samples. Financial ratios, size, industry, and age are used as covariates to model the survival process preceding the initial payment default. The hazard is compared to a logistic risk measure estimated from data one year prior to default. The proportional hazards model is shown to give a more accurate forecast of default for the earlier years prior to the onset of financial distress.

The aim of this paper is to examine biological, maternal and socioeconomic determinants of infant mortality in Sierra Leone.

It uses an analytical framework and Cox proportional hazards regression to break down the effects of factors determining infant mortality. Factors utilized in the empirical investigation include sex of the child, birth size, birth spacing, mother's working status, age of mother, antenatal care, postnatal care, mother's anemia level, religion, mother's education and wealth status.

Results suggest that birth spacing of three years and above associated with a reduced risk of infant mortality contrasted with short birth intervals. Children born to nonanemic mothers have a lower hazard (22%) of infant mortality compared to those born to anemic mothers (HR = 0.78; 95% CI: 0.64–0.96). At least one antenatal care visit by mothers lowers infant mortality rate by 41% compared to no antenatal visits at all ( HR = 0.59; 95% CI: 0.36–0.96). Similarly, infants whose mothers have received postnatal care are at lower risk (31%) of dying than those whose mothers have not received (HR = 0.69; 95% CI: 0.52, 0.93). Infant mortality is likely to decrease with the increase in the birth order.

The family health and planning programs should aim at educating men and women about the usefulness of birth spacing methods.

This paper might be the first attempt to analyze the determinants of infant mortality by utilizing a methodological framework and Cox regression.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2019-0478.

Entrepreneurs nurture their ambitions of founding tech start-ups that facilitate significant innovations despite vulnerability and considerable uncertainty by resolutely addressing multiple challenges to avert failures. The paper aims to answer how soon do tech start-ups fail, given their lifecycle comprising multiple stages of formation and what attributes hasten failure of tech start-ups over their lifecycle? These questions have not been answered adequately, particularly in the context of India's emerging economy, where an aspiring start-up ecosystem is striving to flourish at an exceptional rate.

The study addressed two specific objectives: (1) Does life expectancy vary between life-cycle stages? and (2) What attributes impact tech start-ups' failures? Primary data were gathered from 151 cofounders (101 who have experienced failure and 50 who are successful and continuing their operations) from India's 6 leading start-up hubs. The survival analysis techniques were used, including non-parametric Kaplan–Meier estimator, to study the first objective and semi-parametric Cox proportional hazard regression to explore the second objective.

The survival probability log-rank statistics ascertain that life expectancy is different across the life-cycle stages, namely emergence, stability and growth. The hazard ratios (HRs) throw light on attributes like stage, revenue, conflict with investors, number of current start-ups, cofounder experience, level of confidence (LoC) and educational qualifications as the key attributes that influence start-up life expectancy over its lifecycle.

The empirical study on tech start-ups' life expectancy has practical implications for entrepreneurs and investors besides guiding the ecosystem's policymakers. First, the study helps entrepreneurs plan for resources and be aware of their start-up journey's potential pitfalls. Second, the study helps investors to establish the engagement framework and plan their future funding strategy. Third, the study helps policymakers to design and establish progressive support mechanisms that can prevent a start-up's failure.

First and foremost, start-up life expectancy study by life-cycle stages provide detailed insights on start-ups' failures. The theoretical framework defined is replicable, scalable and distinctly measurable for studying the start-up failure phenomenon. The life expectancy of tech start-ups by life-cycle stage is a critical empirical contribution. Next, the attributes impacting start-up life expectancy are identified in the context of an emerging economy.

This study investigates the association of various corporate governance attributes and financial characteristics with the survival likelihood of distressed firms. To address the manner in which firms evolve over time, we employ survival analysis techniques by incorporating Cox Proportional Hazards regressions. We longitudinally track an ex ante sample of 176 financially distressed firms. The results suggest that firms that replaced their CEO with an outsider, were more than twice as likely to experience bankruptcy. Furthermore, larger levels of blockholder and insider ownership over the sample period are positively associated with the likelihood of firm survival.

We previously examined the efficacy of rTMS for major depressive disorder in an applied clinical practice. Clinical response was related to severity of depression as well as the rTMS instrument utilized suggesting a relationship to instrument or magnetic field parameters and individual factors. The effectiveness of repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder was further evaluated using Log-Rank statistics for time to remission outcomes. A follow-up retrospective medical records study was carried out on patients with major depressive disorder undergoing rTMS therapy at AwakeningsKC Clinical Neuroscience Institute (CNI), a suburban tertiary psychiatric clinic. Cox Proportional Hazard with Log-Rank statistics were applied and the time course to clinical remission was evaluated over a 6-week period with respect to age, gender, and depression severity. Clinical response was observed referencing two different rTMS instruments (MagVenture; NeuroStar). Time to remission studies of 247 case reports (N=98 males; N=149 females) showed consistently greater clinically defined remission rates after 6 weeks of rTMS treatment for patients using the MagVenture vs NeuroStar instrument. Patients previously admitted for inpatient psychiatric hospitalization exhibited higher response rates when treated with the MagVenture rTMS unit. Stepwise Cox Proportional Hazards Regression final model of time to remission included rTMS unit, inpatient psychiatric hospitalization and obese body habitus. Response to rTMS in applied clinical practice is related to severity of psychiatric illness and may require consideration of magnetic field parameters of the rTMS unit with respect to individual factors such as sex or body composition.

The purpose of this paper is to assess the capacity of two methodological approaches – discrete choice and survival analysis models – to investigate the relationship between socio-economic characteristics and turnover in a retailing company. A comparison of the estimation results under each model and their interpretation is carried out. The study provides a guide to determine, assess and interpret the effects of different driving factors behind turnover.

The authors use a data set containing information about 1,199 workers followed up between January 2007 and December 2009. First, not distinguishing voluntary and involuntary resignation, a binary logistic regression model and a Cox proportional hazards (PH) model for univariate survival data are set up and estimated. Second, distinguishing voluntary and involuntary resignation, a multinomial logistic regression model and a Cox PH model for competing risk data are set up and estimated.

When no distinction is made, the results point that wage and age exert a negative effect on turnover. Risk of resignation is higher for male, single, not married and Spanish nationals. When the distinction is made, previous results hold for voluntary turnover: wage, age, gender, marital status and nationality are significant. However, when explaining involuntary turnover, all variables except wage lose explaining power. The survival analysis approach is better suited as it measures risk of resignation in a longitudinal way. Discrete choice models only study the risk at a particular cut-off point (24 months in case of this study).

This paper is a systematic application, evaluation and comparison of four different statistical models for analysing employee turnover in a single firm. This work is original because no systematic comparison has been done in the context of turnover.

Dynamic predictions of financial distress of the firms have received less attention in finance literature rather than static prediction, specifically in Malaysia. This study, therefore, investigates dynamic symptoms of the financial distress event a few years before it happened to the firms by using neural network method. Cox Proportional Hazard regression models are used to estimate the survival probabilities of Malaysian PN17 and GN3 listed firms. Forecast accuracy is evaluated using receiver operating characteristics curve. From the findings, it shown that the independent directors’ ownership has negative association with the financial distress likelihood. In addition, this study modeled a mix of corporate financial distress predictors for Malaysian firms. The combination of financial and non-financial ratios which pressure-sensitive institutional ownership, independent director ownership, and Earnings Before Interest and Taxes to Total Asset shown a negative relationship with financial distress likelihood specifically one year before the firms being listed in PN 17 and GN 3 status. However, Retained Earnings to Total Asset, Interest Coverage, and Market Value of Debt have positive relationship with firm financial distress likelihood. These research findings also contribute to the policy implications to the Securities Commission and specifically to Bursa Malaysia. Furthermore, one of the initial goals in introducing the PN17 and GN3 status is to alleviate the information asymmetry between distressed firms, the regulators, and investors. Therefore, the regulator would be able to monitor effectively distressed firms, and investors can protect from imprudent investment.

The purpose of this study is to investigate the association between the marriage among blood relatives and resulting adverse pregnancy outcomes.

This study uses data from India Human Development Survey in 2005. The methods of analyses include bivariate, trivariate estimates and Cox proportional hazard regression model.

The results reveal that the occurrence of consanguineous marriages is more predominant in southern India and among socioeconomically disadvantageous groups. Moreover, women in consanguineous unions are more likely to have adverse pregnancy outcomes including stillbirths (RR=1.59, p‐value<0.01), abortions (RR=3.03, p‐value<0.01), miscarriages (RR=1.94, p‐value<0.01) and spontaneous miscarriages (RR=1.70, p‐value<0.01). Consanguineous marriages continue to be a critical predictor of adverse pregnancy outcomes in India.

In order to avoid loss of pregnancy and related reproductive health problems in India, it is imperative to create awareness regarding the adverse effects of consanguineous marriages, focusing on the regions with high prevalence.

This unique study comprehensively examines the occurrence of consanguineous marriages and their association with adverse pregnancy outcomes by using advanced statistical analyses and nationally representative data.

When making going concern assessments, Statement on Auditing Standards No. 59 (Auditing Standards Board 1988) directs auditors to consider the nature of management's plans and ability to mitigate periods of financial distress successfully. Corporate governance factors reflect attributes of control, oversight, and/or support of management's plans and actions intended to overcome financial distress. Correspondingly, this study investigates the impact of certain corporate governance factors on the likelihood of a going concern modification. Using survival analysis techniques, we examine a sample of 161 financially distressed firms for the time period 1988–1996. We find that auditors are twice as likely to issue a going concern modification when the CEO is replaced. We also find that going concern modifications are inversely associated with blockholder ownership. We also confirm Carcello and Neal's (2000) findings with respect to the association between an independent audit committee and an increased likelihood of modification. In a repeated events setting, we find that insider ownership and board independence are inversely associated with repeated going concern modifications. Our study concludes by proposing implications for the current financial reporting environment (including the Sarbanes‐Oxley Act of 2002) and future research avenues.