Biostatistics

15. Can you explain the difference between a Type I and Type II error, and how you would minimize the risk of each type of error in a study

A Type I error occurs when the null hypothesis is rejected when it is actually true, while a Type II error occurs when the null hypothesis is not rejected when it is actually false. To minimize the risk of a Type I error, the significance level can be set lower, while to minimize the risk of a Type II error, the sample size can be increased.

86. Can you explain the difference between a chi-squared test and a Fisher's exact test, and when you would use each test in a statistical analysis

A chi-squared test is used to test the association between two categorical variables, while a Fisher's exact test is used when the sample size is small or when the assumptions of the chi-squared test are not met. The chi-squared test is appropriate when the expected cell frequencies are greater than 5 and the sample size is sufficiently large, while the Fisher's exact test is appropriate when the sample size is small or when the expected cell frequencies are less than 5.

31. What is the difference between a confidence interval and a prediction interval, and when you would use each method

A confidence interval estimates the range of values that a population parameter is likely to fall within, based on a sample from that population. A prediction interval estimates the range of values that an individual observation is likely to fall within, given a certain level of confidence. We use confidence intervals to estimate population parameters, and prediction intervals to make predictions about future observations.

60. Can you explain the difference between a decision tree and a random forest, and how you would use each method in a machine learning analysis

A decision tree is a hierarchical tree-like model that partitions the feature space into regions and makes predictions based on the majority class in each region. A random forest is an ensemble method that combines multiple decision trees and aggregates their predictions to improve accuracy and reduce overfitting. In a machine learning analysis, decision trees and random forests can be used for classification or regression tasks when the relationship between the features and the outcome is nonlinear or complex.

39. How would you approach designing a factorial randomized trial for a new drug treatment

A factorial randomized trial involves testing multiple interventions or factors simultaneously, by randomly assigning participants to different combinations of treatments. I would design a factorial randomized trial by first identifying the relevant interventions or factors, and then using appropriate statistical software or formulas to calculate the necessary sample size and randomization scheme.

95. Can you explain the difference between a random effects model and a fixed effects model, and how you would use each model in a research study

A fixed effects model assumes that the coefficients are constant across all individuals or units, while a random effects model allows for variation in the coefficients across individuals or units. I would use a fixed effects model when the main focus is on the within-unit or within-individual variation, and a random effects model when the main focus is on the between-unit or between-individual variation.

80. Can you explain the difference between a mixed-effects model and a fixed-effects model, and when you would use each model in a research study

A fixed-effects model assumes that the effects of predictors are constant across all units or individuals in the sample, while a mixed-effects model allows for random variation in the effects of predictors across different levels of a grouping variable. Fixed-effects models are appropriate when the focus is on the effects of variables on the units in the sample, while mixed-effects models are appropriate when the focus is on generalizing results to a larger population.

63. Can you explain the difference between a network centrality measure and a community detection algorithm, and how you would use these methods in a network analysis

A linear mixed-effects model is used when the dependent variable is continuous and the data have a hierarchical structure, such as repeated measures within individuals or data collected from multiple sites. A generalized linear mixed-effects model is used when the dependent variable is not normally distributed, such as in the case of count or binary data. The model includes both fixed and random effects, and it accounts for the correlation among observations within the same group. The choice of model depends on the nature of the dependent variable and the structure of the data.

101. Can you explain the difference between a longitudinal analysis and a panel analysis, and how you would use each analysis in a research study

A longitudinal analysis involves tracking individuals or groups over time to examine how variables change or develop over time, while a panel analysis involves using a fixed set of individuals or groups to study the relationship between variables at a single point in time. Longitudinal analysis is used when researchers want to examine the within-subject change over time, while panel analysis is used when the focus is on between-subjects variation at one point in time.

40. Can you explain the difference between a null hypothesis and an alternative hypothesis, and how you would test a hypothesis in a research study

A null hypothesis is a statement that there is no difference or effect between groups or variables, while an alternative hypothesis is a statement that there is a difference or effect between groups or variables. I would test a hypothesis in a research study by first formulating the null and alternative hypotheses based on the research question, and then using appropriate statistical tests, such as t-tests or ANOVA, to assess whether the data support the null or alternative hypothesis.

57. Can you explain the difference between a receiver operating characteristic curve and a precision-recall curve, and how you would use each curve to evaluate a classifier

A receiver operating characteristic (ROC) curve is a plot of the true positive rate against the false positive rate for a binary classifier, where the threshold for classification is varied. A precision-recall (PR) curve is a plot of the precision against the recall for a binary classifier, where the threshold for classification is also varied. ROC curves are useful for evaluating classifiers when the class distribution is balanced, while PR curves are useful when the class distribution is imbalanced. In both cases, a better classifier will have a curve that is closer to the top left corner of the plot.

45. Can you explain the difference between a stratified analysis and a subgroup analysis, and when you would use each method

A stratified analysis is a statistical analysis that is performed separately within subgroups of the study population defined by a particular variable, such as age, sex, or race. A subgroup analysis is a statistical analysis that is performed separately within subgroups of the study population defined by a variable that was not a primary factor in the study design, such as smoking status or income. Stratified analyses are pre-planned and can help to control for potential confounding variables, while subgroup analyses are exploratory and should be interpreted with caution.

1. Can you explain the difference between a t-test and an ANOVA, and when you would use each method

A t-test is used to compare means between two groups, while an ANOVA is used to compare means between three or more groups. You would use a t-test when you have only two groups to compare, and an ANOVA when you have three or more groups.

3. Can you describe your experience using SAS and/or R for statistical analysis, and which software you prefer

Absolutely, SAS was the main programming language I learned when getting my Masters degree. all of my assignments, projects, and practicum experience used it. I also have used SAS to two projects here at St. Jude. A) Independent Logistic Regression Model on my current study's HCP survey completion rates B) Qualitive analysis on the internalized stigma in the Snapout survey for the HPV prevention program. I also took LinkedIn learning courses to improve my SAS skills. My R knowledge is self taught and I am still learning it. Currently I have used R to do the same projects I did in SAS. I prefer SAS right now but I believe as I strengthen my R skills I want to use that language more often.

90. Can you describe your experience with Bayesian hierarchical modeling, and how you would use this method in a research study

Bayesian hierarchical modeling is a statistical method for modeling complex data structures and incorporating prior knowledge into the analysis. It involves specifying a prior distribution for the parameters of interest, and updating the prior distribution based on the observed data to obtain a posterior distribution. Bayesian hierarchical modeling is useful when there is prior knowledge or uncertainty about the parameters, or when the data have a complex structure with multiple levels.

13. Can you describe your experience with Bayesian statistics, and how you would use Bayesian methods in a research study

Bayesian statistics is an approach to statistical inference that uses Bayes' theorem to update prior beliefs about the probability of a hypothesis in light of new data. Bayesian methods can be used in a research study to estimate parameters, model complex relationships, and make predictions.

17. What is bootstrapping, and how would you use it to estimate confidence intervals

Bootstrapping is a resampling method that involves generating new datasets by sampling with replacement from the original dataset, and using these datasets to estimate confidence intervals. Bootstrapping can be used when traditional methods of estimating confidence intervals are not appropriate or when the sample size is small.

58. Can you describe your experience with causal inference, and how you would use causal inference methods in a research study

Causal inference is the process of drawing conclusions about causal relationships between variables from observational data. I have experience using propensity score matching and instrumental variable methods to control for confounding variables and estimate causal effects in observational studies. In a research study, I would use causal inference methods to identify the effects of interventions or exposures on outcomes of interest, while accounting for potential confounding variables.

23. How would you approach designing a cluster-randomized trial for a public health intervention

Cluster-randomized trials are a type of randomized controlled trial where interventions are delivered at the group level, rather than the individual level. When designing a cluster-randomized trial for a public health intervention, it is important to consider the size and number of clusters, the potential for contamination between clusters, and the need for statistical adjustment for clustering effects.

9. How would you approach conducting a meta-analysis, and what are some common challenges in meta-analytic research

Conducting a meta-analysis involves combining the results of multiple studies to obtain an overall estimate of an effect. Common challenges in meta-analytic research include heterogeneity among studies, publication bias, and the quality of the included studies.

14. How would you handle confounding variables in a study dataset

Confounding variables are variables that are associated with both the predictor and response variables and can obscure the true relationship between them. Confounding variables can be handled by stratification, matching, or multivariable regression analysis.

25. What is the difference between a correlation and a regression analysis, and when you would use each method

Correlation analysis is used to investigate the relationship between two continuous variables. Regression analysis, on the other hand, is used to model the relationship between a continuous dependent variable and one or more independent variables. Correlation analysis is used when there is no clear causal relationship between the variables, while regression analysis is used when the goal is to predict or estimate the effect of one or more independent variables on a dependent variable.

6. How would you approach data quality control and data cleaning in a research study

Data quality control involves verifying that the data collected is accurate and reliable, while data cleaning involves identifying and correcting errors or inconsistencies in the data. A thorough approach to data quality control and data cleaning involves careful planning, standardized protocols, and multiple levels of review.

41. Can you describe your experience with data visualization, and how you would use data visualization in a research study

Data visualization is an important tool for exploring and communicating patterns and trends in data. I have experience creating visualizations using a variety of software tools such as Python's Matplotlib, Seaborn, and Plotly libraries, as well as Tableau, Excel, and other tools. In a research study, I would use data visualization to explore patterns and trends in the data, identify outliers, and assess the distribution of the data. I would also use data visualization to communicate the results of the analysis, making it easier for others to understand the findings.

77. Can you explain the difference between a deep neural network and a convolutional neural network, and how you would use each method in a machine learning analysis

Deep neural networks (DNNs) are a type of machine learning model that consists of multiple layers of interconnected nodes, while convolutional neural networks (CNNs) are a type of DNN that are particularly well-suited for analyzing image data. In a machine learning analysis, DNNs may be used for a wide range of applications, while CNNs may be used specifically for image classification tasks.

68. How would you approach designing a Bayesian adaptive trial for a new cancer treatment

Designing a Bayesian adaptive trial for a new cancer treatment involves selecting a prior distribution for the treatment effect based on prior information or expert opinion, and then updating the prior distribution as data are collected during the trial. The trial design includes rules for modifying the sample size and stopping the trial early if the data provide strong evidence for or against the treatment. The adaptive design allows for efficient use of resources and can improve the chances of finding an effective treatment.

18. How would you approach designing a case-control study for a rare disease

Designing a case-control study for a rare disease involves careful selection of cases and controls, using appropriate matching or stratification techniques, and ensuring that cases and controls are comparable in terms of potential confounding variables.

62. How would you approach designing a case-crossover study for an environmental exposure

Designing a case-crossover study for an environmental exposure typically involves selecting a sample of individuals who have experienced a health event of interest and then comparing their exposure to the environmental factor of interest during the time period immediately preceding the event (the case period) to their exposure during a control period, which is a time period that occurred at the same time of day, day of the week, and season as the case period but in a different time period when the individual did not experience the event. The design aims to control for time-invariant confounding factors by using each individual as their own control, and it is useful for investigating the short-term effects of environmental exposures.

12. How would you approach designing a randomized controlled trial for a new cancer treatment

Designing a randomized controlled trial for a new cancer treatment involves careful consideration of factors such as the target population, the intervention and control groups, the primary outcome measure, and the sample size needed to achieve adequate statistical power. The trial should be designed to minimize bias and maximize precision in estimating treatment effects.

85. How would you approach designing a randomized controlled trial for a new vaccine

Designing a randomized controlled trial for a new vaccine involves selecting an appropriate study population, determining the sample size and allocation of participants to treatment and control groups, developing a study protocol and procedures, and implementing appropriate measures to ensure the safety and efficacy of the vaccine.

73. Can you describe your experience with fuzzy set theory, and how you would use this method in a research study

Fuzzy set theory is a mathematical framework used to represent uncertainty and imprecision in data. In a research study, fuzzy set theory can be used to analyze data that does not fit well into traditional statistical models.

55. Can you describe your experience with geospatial data analysis, and how you would use geospatial analysis in a research study

Geospatial data analysis involves analyzing and visualizing data that are associated with geographic locations. It can be used to identify spatial patterns and relationships between variables, such as the relationship between disease incidence and environmental factors. Geospatial analysis techniques include geographic information systems (GIS), spatial statistics, and spatial data visualization. I have experience using GIS software to create maps and analyze spatial patterns in data.

65. How would you approach handling missing data in a cross-lagged panel model

Handling missing data in a cross-lagged panel model involves addressing the possibility that missing data may be related to both the dependent and independent variables in the model. One approach is to use multiple imputation, which involves creating multiple plausible imputed datasets based on the observed data and then combining the results from the analyses conducted on each imputed dataset to obtain a single set of estimates. Another approach is to use full information maximum likelihood estimation, which involves using all available data to estimate the model parameters.

36. How would you approach handling missing data in a longitudinal study dataset

Handling missing data in a longitudinal study dataset can be challenging, as missing data can result in biased estimates and reduced statistical power. I would first examine the pattern of missing data, and then use appropriate methods, such as multiple imputation or maximum likelihood estimation, to account for missing data and obtain unbiased estimates.

83. Can you explain the difference between a hierarchical clustering algorithm and a k-means clustering algorithm, and how you would use each method in a cluster analysis

Hierarchical clustering algorithms group similar units or individuals into clusters based on a hierarchical structure, while k-means clustering algorithms partition the sample into a specified number of clusters based on similarity of values on a set of variables. Hierarchical clustering is useful when there is no prior knowledge of the number of clusters or when the data are nested, while k-means clustering is useful when the number of clusters is known or can be estimated.

99. Can you describe your experience with Bayesian model averaging, and how you would use this method in a research study

In Bayesian model averaging, multiple models are fit to the data and the model weights are calculated based on the posterior probabilities of each model. I would use this method when I want to assess the uncertainty in the model selection process and estimate the model-averaged effects of the predictors.

54. Can you explain the difference between a likelihood function and a posterior distribution, and how you would use these concepts in Bayesian statistics

In Bayesian statistics, the likelihood function represents the probability of observing the data given the parameters of the model, whereas the posterior distribution represents the updated probability of the parameters given the observed data and prior beliefs. The likelihood function is the product of the probability density function of each data point given the parameters, while the posterior distribution is proportional to the product of the likelihood function and the prior distribution of the parameters. Bayesian statistics use the posterior distribution to make inferences about the parameters and predictions about future data.

44. How would you approach designing a case-cohort study for a rare disease

In a case-cohort study for a rare disease, a random sample of individuals is selected from the study population to serve as the subcohort, while all individuals who develop the disease during follow-up are included as cases. The subcohort is used as a comparison group to estimate the incidence rate ratio, relative risk, or odds ratio for the disease. To design a case-cohort study, I would first define the study population, select the rare disease of interest, and determine the follow-up period. I would then select a random sample of individuals to serve as the subcohort and identify all cases of the disease during follow-up. Finally, I would calculate the incidence rate ratio, relative risk, or odds ratio and adjust for confounding variables using appropriate statistical methods.

47. How would you approach handling missing data in a case-control study dataset

In a case-control study dataset, missing data can arise due to various reasons, such as incomplete data collection, participant dropouts, or measurement errors. One common approach to handling missing data in case-control studies is to use multiple imputation methods, such as the fully conditional specification (FCS) method. FCS imputes missing data based on conditional distributions of the variables, allowing for more accurate imputations than other methods. Another approach is to use weighting methods to adjust for potential bias caused by missing data. Weighting methods, such as inverse probability weighting or propensity score weighting, assign weights to participants based on the probability of having complete data, which can help account for the missing data in the analysis.

42. How would you approach handling missing data in a cross-sectional study dataset

In a cross-sectional study dataset, missing data can occur when participants fail to answer certain questions or when data are lost during data collection or entry. To handle missing data in a cross-sectional study dataset, I would first assess the pattern of missing data to determine if it is missing completely at random, missing at random, or missing not at random. I would then use appropriate techniques to impute missing data, such as mean imputation, regression imputation, or multiple imputation. Finally, I would assess the sensitivity of the results to the missing data by performing a sensitivity analysis.

79. How would you approach designing a matched case-control study for a genetic association study

In a matched case-control study for a genetic association study, cases and controls are matched on important variables, such as age and sex, to control for potential confounding variables. Controls are then selected based on their similarity to cases with respect to these variables. This design can help to minimize bias and improve the accuracy of the estimated genetic effects.

59. How would you approach handling missing data in a mixed-effects model

In a mixed-effects model, missing data can be handled using methods such as maximum likelihood estimation or multiple imputation. Maximum likelihood estimation involves fitting the model to the observed data and maximizing the likelihood function to estimate the model parameters. Multiple imputation involves creating multiple plausible imputed datasets based on the observed data and using these datasets to estimate the model parameters.

74. How would you approach designing a nested case-control study for a rare disease

In a nested case-control study for a rare disease, controls are selected from individuals who are at risk of developing the disease but have not yet been diagnosed. Cases are then identified from among the controls based on their subsequent diagnosis of the disease. This design allows for efficient use of resources in studying a rare outcome.

43. Can you explain the difference between a survival function and a hazard function, and how you would calculate these functions in a survival analysis

In a survival analysis, the survival function is the probability that an individual will survive up to a given time point, while the hazard function is the instantaneous rate at which events (e.g., death) occur at a given time point, given that the individual has survived up to that point. The survival function is calculated using the Kaplan-Meier method, while the hazard function is calculated using the Cox proportional hazards model.

96. Can you describe your experience with mediation analysis using causal mediation analysis, and how you would use this method in a research study

In causal mediation analysis, the goal is to estimate the direct and indirect effects of a treatment on an outcome through one or more mediator variables. I would use this method when I want to assess the underlying mechanisms of a treatment effect. To perform causal mediation analysis, I would use appropriate statistical methods, such as the counterfactual framework or structural equation modeling.

93. Can you describe your experience with complex survey data analysis, and how you would use this method in a research study

In complex survey data analysis, the sample is selected using a complex sampling design, such as stratified sampling or cluster sampling. To analyze such data, I would use appropriate weighting and variance estimation techniques to account for the complex design. I would also account for clustering and stratification in the analysis by using appropriate statistical methods, such as generalized estimating equations or multilevel modeling.

78. Can you describe your experience with instrumental variable regression, and how you would use this method in a research study

Instrumental variable regression is a statistical method used to estimate the causal effect of a treatment or exposure variable on an outcome of interest, while accounting for unobserved confounding variables. In a research study, instrumental variable regression can be used to estimate the causal effect of a treatment or exposure variable when randomized controlled trials are not feasible.

61. Can you describe your experience with interrupted time series analysis, and how you would use this method in a research study

Interrupted time series analysis is a statistical method used to evaluate the effect of an intervention or policy change on a time series. It involves modeling the time series both before and after the intervention, and comparing the observed values to the predicted values based on the model. Interrupted time series analysis can be used to estimate the magnitude and direction of the intervention effect, as well as the time frame over which the effect occurs.

70. Can you describe your experience with item response theory, and how you would use this method in a research study

Item response theory (IRT) is a statistical framework used to analyze the relationship between individual responses to test items and the underlying construct being measured. In a research study, IRT can be used to evaluate the psychometric properties of a test, such as its reliability and validity.

75. Can you describe your experience with joint modeling, and how you would use this method in a research study

Joint modeling is a method used to analyze longitudinal data and account for the correlation between repeated measures over time. In a research study, joint modeling can be used to analyze longitudinal data while accounting for potential confounding variables.

88. How would you approach handling missing data in a latent growth curve model

Latent growth curve models are used to analyze change over time in a set of variables that are not directly observed, but are inferred based on measurements of related observable variables. Missing data in a latent growth curve model can be handled using maximum likelihood estimation or multiple imputation.

66. Can you explain the difference between a linear mixed-effects model and a generalized linear mixed-effects model, and when you would use each model in a research study

Latent variable modeling is a statistical method used to model complex relationships between observed variables by assuming that there are one or more underlying, unobserved variables (latent variables) that explain the relationships among the observed variables. Examples include factor analysis, latent class analysis, and structural equation modeling. Latent variable modeling is useful when there are many observed variables that may be related to an underlying construct, and it allows researchers to test complex hypotheses about the relationships among these variables.

67. Can you describe your experience with latent variable modeling, and how you would use this method in a research study

Latent variable modeling is a statistical method used to model complex relationships between observed variables by assuming that there are one or more underlying, unobserved variables (latent variables) that explain the relationships among the observed variables. Examples include factor analysis, latent class analysis, and structural equation modeling. Latent variable modeling is useful when there are many observed variables that may be related to an underlying construct, and it allows researchers to test complex hypotheses about the relationships among these variables.

71. How would you approach handling missing data in an instrumental variable analysis

Linear discriminant analysis (LDA) is a method used to classify individuals into predefined groups based on their measured characteristics, while quadratic discriminant analysis (QDA) is a similar method that allows for non-linear boundaries between groups. In a classification analysis, LDA may be used when the relationship between predictor variables and group membership is linear, while QDA may be used when the relationship is more complex.

72. Can you explain the difference between a linear discriminant analysis and a quadratic discriminant analysis, and how you would use each method in a classification analysis

Linear discriminant analysis (LDA) is a method used to classify individuals into predefined groups based on their measured characteristics, while quadratic discriminant analysis (QDA) is a similar method that allows for non-linear boundaries between groups. In a classification analysis, LDA may be used when the relationship between predictor variables and group membership is linear, while QDA may be used when the relationship is more complex.

29. Can you describe your experience with longitudinal data analysis, and how you would use longitudinal data analysis in a research study

Longitudinal data analysis is a statistical technique used to analyze data collected from the same subjects over multiple time points. It is commonly used in medical research to investigate the effect of treatments over time. Longitudinal data analysis methods include repeated measures ANOVA, mixed-effects models, and growth curve models.

81. Can you describe your experience with longitudinal mediation analysis, and how you would use this method in a research study

Longitudinal mediation analysis examines the causal relationship between a predictor variable and an outcome variable, with a mediator variable acting as a mechanism that links the predictor and outcome variables. It is used to investigate how changes in the predictor variable over time lead to changes in the mediator variable, which in turn lead to changes in the outcome variable.

35. Can you describe your experience with meta-analysis, and how you would perform a meta-analysis for a research study

Meta-analysis involves combining the results from multiple studies to estimate the overall effect size or treatment effect across all studies. I would conduct a systematic review to identify relevant studies, assess their quality and validity, and then use statistical software, such as RevMan or Comprehensive Meta-Analysis, to perform a meta-analysis and estimate the overall effect size.

76. How would you approach handling missing data in a longitudinal data analysis

Missing data in a longitudinal data analysis can be handled using methods such as multiple imputation or mixed-effects modeling. These methods can help to account for the correlation between repeated measures and can improve the accuracy of the estimated treatment effects.

16. Can you describe your experience with mixed-effects models, and when you would use a mixed-effects model in a research study

Mixed-effects models are used to model data that has both fixed and random effects. They are useful in research studies when there are repeated measurements on the same individuals or when there is clustering of observations within groups.

52. Can you describe your experience with mixed-methods research, and how you would use mixed-methods research in a study

Mixed-methods research is a research approach that combines both qualitative and quantitative methods to address a research question. Qualitative methods, such as interviews or focus groups, can be used to explore complex issues and generate hypotheses, while quantitative methods, such as surveys or statistical analysis, can be used to test hypotheses and quantify relationships. Mixed-methods research can be used in various fields, such as public health or social sciences, to provide a more comprehensive understanding of a phenomenon.

82. How would you approach handling missing data in a multiple imputation analysis

Multiple imputation is a technique for handling missing data in which missing values are imputed based on a model that includes all variables in the analysis. It involves generating multiple complete datasets, analyzing each one separately, and combining the results using appropriate methods.

87. Can you describe your experience with multiple testing correction, and how you would use this method in a research study

Multiple testing correction is a method for controlling the false positive rate in studies that involve multiple hypothesis tests. It involves adjusting the p-values of individual tests to account for the increased probability of false positives when multiple tests are conducted.

38. Can you describe your experience with network analysis, and how you would use network analysis in a research study

Network analysis involves examining the relationships between variables or entities in a complex system, and can be used to identify key players, clusters, and pathways. I would use network analysis in a research study by first identifying the relevant variables or entities and their relationships, and then using appropriate statistical software, such as R or Gephi, to visualize and analyze the network structure.

84. Can you describe your experience with network meta-analysis, and how you would use this method in a research study

Network meta-analysis is a statistical method for combining evidence from multiple studies that compare multiple treatments. It involves modeling the relationships between treatments and outcomes across studies, and using this information to estimate treatment effects and compare the relative effectiveness of different treatments.

28. Can you explain the difference between a one-tailed and a two-tailed hypothesis test, and when you would use each method

One-tailed hypothesis tests are used when the research question involves a specific directional hypothesis, such as whether a treatment will increase or decrease a particular outcome. Two-tailed hypothesis tests are used when the research question does not involve a specific directional hypothesis, such as whether there is a difference between two groups. The choice of one-tailed or two-tailed test depends on the research question and the specific hypothesis being tested.

27. How would you handle outlier data in a study dataset

Outlier data can be handled in several ways, depending on the nature of the data and the research question. Outliers can be removed from the dataset, winsorized to reduce their influence, or treated as missing data. It is important to carefully consider the reasons for the outliers and the potential impact of their removal or treatment on the analysis results.

24. Can you explain the concept of overfitting, and how you would avoid overfitting in a statistical model

Overfitting occurs when a statistical model is too complex and fits the data too closely, resulting in poor performance when applied to new data. To avoid overfitting, it is important to use techniques such as cross-validation, regularization, and selecting simpler models.

19. Can you explain the difference between a parametric and a non-parametric statistical test, and when you would use each method

Parametric statistical tests assume that the data follows a specific distribution (such as normal), while non-parametric tests do not make any assumptions about the distribution of the data. Parametric tests are more powerful when the assumptions are met, while non-parametric tests are more robust to violations of assumptions.

20. Can you describe your experience with machine learning methods, and how you would use machine learning in a research study

Parametric statistical tests assume that the data follows a specific distribution (such as normal), while non-parametric tests do not make any assumptions about the distribution of the data. Parametric tests are more powerful when the assumptions are met, while non-parametric tests are more robust to violations of assumptions.

4. What is your understanding of power analysis, and how would you perform one

Power analysis is used to determine the sample size needed for a study based on the desired statistical power, effect size, and significance level. You would perform a power analysis using software or online calculators, and adjust the sample size accordingly.

34. Can you explain the concept of power, and how you would calculate power for a hypothesis test

Power is the probability of detecting a true effect or difference between groups, given a certain level of statistical significance and sample size. I would calculate power using statistical software or formulas, taking into account the expected effect size, level of significance, sample size, and variability of the data.

64. Can you describe your experience with principal component analysis, and how you would use this method in a data reduction analysis

Principal component analysis (PCA) is a statistical method used for data reduction, which involves finding linear combinations of the original variables that explain the most variation in the data. These linear combinations, known as principal components, can then be used as new variables in subsequent analyses. PCA is useful when there are many correlated variables in the data, and it can help identify underlying patterns and reduce the number of variables needed for subsequent analyses.

21. What is propensity score matching, and how would you use it to control for confounding in a research study

Propensity score matching is a statistical technique used to reduce the effects of confounding in observational studies. It involves creating a score that estimates the probability of a subject receiving a treatment based on their baseline characteristics. The treated and control groups can then be matched based on similar propensity scores, reducing the effects of confounding. Propensity score matching can be used to estimate treatment effects in non-randomized studies.

49. Can you describe your experience with record linkage and data integration, and how you would use these methods in a research study

Record linkage and data integration are methods used to combine data from multiple sources, such as databases or registries, to create a unified dataset for analysis. Record linkage involves matching individual records across different datasets based on common identifying variables, such as name or date of birth. Data integration involves combining data from multiple sources into a single dataset, which may involve cleaning, standardizing, and merging the data. These methods can be used in various research studies, such as epidemiological studies or health services research, to create a more comprehensive dataset for analysis.

5. Can you describe your experience with regression analysis, and how you would interpret regression coefficients and confidence intervals

Regression analysis is used to model the relationship between one or more predictor variables and a response variable. Regression coefficients represent the change in the response variable for each unit change in the predictor variable, while confidence intervals provide a range of values within which the true population coefficient is likely to fall.

32. Can you describe your experience with sample size calculations, and how you would perform a sample size calculation for a research study

Sample size calculations involve determining the number of participants needed to detect a certain effect size or difference between groups with a certain level of statistical power and significance. I would use statistical software, such as G*Power, to perform a sample size calculation based on the study design, outcome of interest, and expected effect size.

37. Can you explain the difference between a sensitivity and a specificity, and how you would calculate sensitivity and specificity for a diagnostic test

Sensitivity is the proportion of true positives (i.e., those with the condition who test positive), while specificity is the proportion of true negatives (i.e., those without the condition who test negative) in a diagnostic test. I would calculate sensitivity and specificity using appropriate statistical software or formulas, taking into account the prevalence of the condition and the accuracy of the test.

46. Can you describe your experience with simulation studies, and how you would use simulation in a research study

Simulation studies involve creating a model of the system or phenomenon of interest and using computer simulations to generate data under different scenarios or assumptions. Simulation studies can be used to assess the statistical power of a study design, evaluate the performance of different statistical methods, or investigate the robustness of the findings to different assumptions. In a research study, I would use simulation studies to test the sensitivity of the results to different assumptions and evaluate the performance of the statistical methods used.

7. What are some common types of biases that can affect statistical analyses, and how would you address them

Some common types of biases that can affect statistical analyses include selection bias, measurement bias, and confounding bias. These biases can be addressed through careful study design, randomization, blinding, and adjustment for confounding variables.

8. Can you explain the concept of statistical significance and how it relates to p-values

Statistical significance refers to the likelihood that a relationship observed in a study is due to chance. P-values represent the probability of obtaining the observed results under the null hypothesis, and a p-value below a specified significance level (typically 0.05) is considered statistically significant.

33. How would you approach designing a stratified sampling scheme for a survey

Stratified sampling involves dividing a population into subgroups, or strata, based on certain characteristics, and then randomly selecting participants from each stratum to obtain a representative sample. I would first identify the relevant strata based on the research question, and then use statistical software or formulas to calculate the appropriate sample size from each stratum.

69. Can you explain the difference between a structural equation model and a path analysis, and how you would use each method in a research study

Structural equation modeling (SEM) is a method used to test a theoretical model that specifies relationships among variables, while path analysis is a specific type of SEM that involves a linear set of causal relationships among variables. In a research study, SEM can be used to test complex theoretical models, while path analysis can be used to evaluate more focused causal relationships between variables.

22. Can you describe your experience with survival analysis, and how you would use survival analysis in a research study

Survival analysis is a statistical technique used to analyze the time-to-event data. It is commonly used in medical research to estimate the probability of an event, such as death or disease progression, occurring over time. Survival analysis takes into account censoring, which occurs when some subjects do not experience the event before the end of the study. Kaplan-Meier curves and Cox proportional hazards models are commonly used in survival analysis. My experience with survival analysis is from the course work I did during my masters degree. I would use survival analysis in a research study by _______________________.

10. Can you describe your experience with survival analysis and how you would handle censored data in a survival analysis study

Survival analysis is used to model the time until an event of interest occurs, and censored data occurs when the event has not occurred by the end of the study period. Censored data can be handled using specialized methods such as Kaplan-Meier estimation or Cox proportional hazards regression.

51. Can you explain the difference between a Kaplan-Meier estimator and a Cox proportional hazards model, and when you would use each method in a survival analysis

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function in a survival analysis. The survival function represents the probability of surviving past a certain time point. The Cox proportional hazards model is a parametric method used to model the hazard function, which represents the instantaneous risk of experiencing the event of interest. The Cox model assumes that the hazard is proportional across different groups or predictors, allowing for the estimation of hazard ratios. The choice of method depends on the research question and the underlying assumptions of the data.

48. Can you explain the difference between a binomial and a Poisson distribution, and when you would use each distribution in a statistical analysis

The binomial and Poisson distributions are both used to model count data. The binomial distribution is used when the outcome of interest is binary (e.g., success or failure), and the count represents the number of successes in a fixed number of independent trials. The Poisson distribution, on the other hand, is used to model the number of events that occur in a fixed time period or space, assuming a constant rate of occurrence. The Poisson distribution is often used when the count is rare and the mean and variance are equal.

89. Can you explain the difference between a bootstrap method and a jackknife method, and how you would use each method in a statistical analysis

The bootstrap method involves repeatedly resampling the data with replacement to estimate the variability of a statistic, while the jackknife method involves systematically leaving out one observation at a time to estimate the variability of a statistic. The bootstrap method is useful when the sample size is small or when the distribution of the data is not known, while the jackknife method is useful when the sample size is large and the distribution of the data is known.

11. What is the central limit theorem, and how does it relate to statistical inference

The central limit theorem states that as the sample size increases, the sampling distribution of the sample mean approaches a normal distribution, regardless of the distribution of the population. This is important for statistical inference because it allows us to use parametric methods that assume normality.

92. Can you explain the difference between a proportional hazards model and an accelerated failure time model, and when you would use each model in a survival analysis

The proportional hazards model assumes that the hazard rate is constant over time, while the accelerated failure time model assumes that the survival time is a function of the underlying risk factors and that the hazard rate increases or decreases with time. I would use the proportional hazards model when I want to estimate hazard ratios or compare the survival times between different groups, and the accelerated failure time model when I want to estimate the actual survival times for a particular group.

2. How would you handle missing data in a study dataset

There are several ways to handle missing data, including imputation, deletion, and using specialized methods such as maximum likelihood estimation. The method used would depend on the reason for the missing data and the assumptions made about the missingness.

26. Can you describe your experience with time-series analysis, and how you would use time-series analysis in a research study

Time-series analysis is a statistical technique used to analyze data that is collected over time. It is used to identify patterns and trends in the data, and to make forecasts and predictions about future values. Time-series analysis often involves smoothing the data to reduce noise and variability, and using autoregressive or moving average models to model the time-dependent structure of the data.

56. How would you approach designing a sample survey using stratified sampling, and how would you calculate sampling weights

To design a sample survey using stratified sampling, I would first divide the population into homogeneous groups or strata based on relevant characteristics such as age, gender, or location. Then, I would randomly select a sample from each stratum proportional to its size in the population. To calculate sampling weights, I would divide the inverse of the probability of selection for each unit in the sample by the total sample size. This is necessary to account for the fact that not all units in the population had an equal chance of being selected in the sample.

100. How would you approach handling missing data in a multiple group confirmatory factor analysis

To handle missing data in a multiple group confirmatory factor analysis, I would use appropriate techniques such as full information maximum likelihood, multiple imputation, or maximum likelihood estimation with robust standard errors. I would also assess the missing data mechanism to determine which method is appropriate to use, and check for measurement invariance across the groups to ensure that the factor structure is the same for all groups.

94. How would you approach handling missing data in a structural equation modeling analysis

To handle missing data in a structural equation modeling analysis, I would use appropriate techniques such as full information maximum likelihood, multiple imputation, or maximum likelihood estimation with robust standard errors. I would also assess the missing data mechanism to determine which method is appropriate to use.

53. How would you approach handling missing data in a survey dataset

To handle missing data in a survey dataset, multiple imputation methods can be used to impute missing values based on the observed data. Weighting methods can also be used to adjust for potential bias caused by missing data, such as non-response bias. Another approach is to use model-based methods, such as maximum likelihood estimation, to account for the missing data in the analysis.

97. How would you approach designing a Bayesian nonparametric model for a clustering analysis

When designing a Bayesian nonparametric model for a clustering analysis, I would start by selecting appropriate priors for the model, such as Dirichlet process priors or Indian buffet process priors. I would also select appropriate hyperparameters for the priors and use appropriate Markov chain Monte Carlo methods to fit the model. 98. Can you explain the difference between a hurdle model and a zero-inflated model, and when you would use each model in a count data analysis? A hurdle model is used when there is excess zero counts in count data, while a zero-inflated model is used when there is overdispersion in count data. I would use a hurdle model when the main focus is on the probability of observing a non-zero count, and a zero-inflated model when the main focus is on the count distribution.

30. How would you approach designing a cross-sectional study for a public health survey

When designing a cross-sectional study for a public health survey, it is important to consider the sampling strategy, the size and representativeness of the sample, the variables to be measured, and the data collection methods. It is also important to consider potential sources of bias, such as non-response bias and selection bias, and to use appropriate statistical methods to account for these biases.

50. How would you approach designing a survey using cluster sampling, and how would you calculate sampling weights

When designing a survey using cluster sampling, the population is divided into clusters, and a random sample of clusters is selected. All individuals in the selected clusters are then surveyed. To calculate sampling weights, the inverse of the probability of selection is multiplied by a factor to adjust for non-response or other biases. The sampling weight for each participant is then used in the analysis to adjust for the clustering and sampling design.

91. How would you approach designing a survival analysis using the Fine and Gray competing risks regression model

When designing a survival analysis using the Fine and Gray competing risks regression model, I would start by identifying the competing risks in the study population. I would then define the outcome of interest, such as time to event or probability of event occurrence, and select appropriate predictors for the model. I would also assess the assumptions of the model, such as the proportionality assumption. Finally, I would use appropriate software to fit the model and interpret the results.