Tell us about a time when you had to deal with missing or incomplete data. How did you handle it?

During my previous internship at a pharmaceutical company, I encountered a situation where the data I needed for analysis was incomplete. I was tasked with conducting a statistical analysis on a dataset related to a research study. However, upon receiving the dataset, I discovered that some of the variables were missing values. To handle this, I first communicated with the data manager to understand why the data was incomplete. I then used my problem-solving skills to brainstorm potential solutions. In the end, I decided to employ multiple imputation techniques to fill in the missing values. I utilized statistical software such as SAS and R to apply the imputation methods. Once the missing data was imputed, I proceeded with the statistical analysis as planned. By addressing the issue proactively and finding a solution, I was able to successfully complete the analysis and provide meaningful insights to the research team.

During my previous internship at a pharmaceutical company, I encountered a situation where the data I needed for analysis was incomplete. I was tasked with conducting a statistical analysis on a dataset related to a research study investigating the efficacy of a drug. When I received the dataset, I noticed that several variables had missing values. To address this, I first communicated with the data manager to understand the reason behind the missing data. It turned out that there was an issue with data entry, leading to the missing values. I collaborated with the data manager and the research team to identify the variables that were likely to be missing at random. We decided to use multiple imputation techniques, specifically the chained equations method, to impute the missing values. I utilized SAS and R, the statistical software I am proficient in, to implement the imputation process. Once the missing values were imputed, I performed the statistical analysis as planned, including calculating means, conducting t-tests, and fitting regression models to assess the drug's effectiveness. I also conducted sensitivity analyses to evaluate the impact of the imputation on the results. Throughout the process, I maintained clear and frequent communication with the research team, providing updates on the progress and seeking their input on the analysis plan. By successfully handling the issue of missing data, I was able to contribute to the overall research objectives and provide reliable statistical results.

During my previous internship at a pharmaceutical company, I faced a challenge when dealing with missing and incomplete data for a clinical trial. The dataset I received had missing values in various variables, including demographic information and clinical outcomes. To handle this issue, I employed a comprehensive approach. First, I conducted a thorough data quality check to identify the pattern and extent of missingness. I collaborated with the data manager and the research team to gain a better understanding of the potential reasons for the missing data. We discovered that some of the missing values were due to participants dropping out of the study. To account for this, I implemented a statistical technique called inverse probability weighting to adjust for potential bias caused by missing data. This technique allowed us to estimate the missing values based on participants' characteristics and the probability of dropping out. Additionally, I utilized SAS and R to perform sensitivity analyses, such as complete case analysis and multiple imputations using predictive mean matching, to assess the robustness of the results. Throughout the process, I maintained open communication with the research team, providing regular updates on the data cleaning and imputation process, as well as discussing the potential impact of missing data on the study's conclusions. By handling the missing data meticulously and applying advanced statistical techniques, I was able to ensure the integrity of the analysis and provide accurate results that helped inform the clinical trial's outcomes.