When comparing groups in your data, you can have either independent or dependent samples. The type of samples in your design impacts sample size requirements, statistical power, the proper analysis, and even your study’s costs. Understanding the implications of each type of sample can help you design a better study. [Read more…] about Independent and Dependent Samples in Statistics

# conceptual

## Independent and Identically Distributed Data (IID)

Having independent and identically distributed (IID) data is a common assumption for statistical procedures and hypothesis tests. But what does that mouthful of words actually mean? That’s the topic of this post! And, I’ll provide helpful tips for determining whether your data are IID. [Read more…] about Independent and Identically Distributed Data (IID)

## Using Moving Averages to Smooth Time Series Data

Moving averages can smooth time series data, reveal underlying trends, and identify components for use in statistical modeling. Smoothing is the process of removing random variations that appear as coarseness in a plot of raw time series data. It reduces the noise to emphasize the signal that can contain trends and cycles. Analysts also refer to the smoothing process as filtering the data.

Developed in the 1920s, the moving average is the oldest process for smoothing data and continues to be a useful tool today. This method relies on the notion that observations close in time are likely to have similar values. Consequently, the averaging removes random variation, or noise, from the data.

In this post, I look at using moving averages to smooth time series data. This method is the simplest form of smoothing. In future posts, I’ll explore more complex ways of smoothing.

## What are Moving Averages?

Moving averages are a series of averages calculated using sequential segments of data points over a series of values. They have a length, which defines the number of data points to include in each average.

### One-sided moving averages

One-sided moving averages include the current and previous observations for each average. For example, the formula for a moving average (MA) of X at time *t* with a length of 7 is the following:

In the graph, the circled one-sided moving average uses the seven observations that fall within the red interval. The subsequent moving average shifts the interval to the right by one observation. And, so on.

### Centered moving averages

Centered moving averages include both previous and future observations to calculate the average at a given point in time. In other words, centered moving averages use observations that surround it in both directions and, consequently, are also known as two-sided moving averages. The formula for a centered moving average of X at time *t* with a length of 7 is the following:

In the plot below, the circled centered moving average uses the seven observations in the red interval. The next moving average shifts the interval to the right by one.

Centered intervals work out evenly for an odd number of observations because they allow for an equal amount of observations before and after the moving average. However, when you have an even length, the calculations must adjust for that by using a weighted moving average. For example, the formula for a centered moving average with a length of 8 is as follows:

For a length of 8, the calculations incorporate the formula for a length of 7 (*t*-3 through *t*+3). Then, it extends the segment by one observation in both directions (*t*-4 and *t*+4). However, those two observations each have half the weight, which yields the equivalent of 7 + 2*0.5 = 8 data points.

## Using Moving Averages to Reveal Trends

Moving averages can remove seasonal patterns to reveal underlying trends. In future posts, I’ll write more about time series components and incorporating them into models for accurate forecasting. For now, we’ll work through an example to visually assess a trend.

When there is a seasonal pattern in your data and you want to remove it, set the length of your moving average to equal the pattern’s length. If there is no seasonal pattern in your data, choose a length that makes sense. Longer lengths will produce smoother lines.

Note that the term “seasonal” pattern doesn’t necessarily indicate a meteorological season. Instead, it refers to a repeating pattern that has a fixed length in your data.

## Time Series Example: Daily COVID-19 Deaths in Florida

For our example, I’ll use daily COVID-19 deaths in the State of Florida. The time series plot below displays a recurring pattern in the number of daily deaths.

This pattern likely reflects a data artifact. We know the coronavirus does not operate on a seven-day weekly schedule! Instead, it must reflect some human-based scheduling factor that influences when causes of death are determined and recorded. Some of these activities must be less likely to occur on weekends because the lowest day of the week is almost always Sunday, and weekends, in general, tend to be low. Tuesdays are often the highest day of the week. Perhaps that is when the weekend backlog shows up in the data?

Because of this seasonal pattern, the number of recorded deaths for a particular day depends on the day of the week you’re evaluating. Let’s remove this season pattern to reveal the underlying trend component. The original data are from Johns Hopkins University. Download my Excel spreadsheet: Florida Deaths Time Series.

The graph displays one-sided moving averages with a length of 7 days for these data. Notice how the seasonal pattern is gone and the underlying trend is visible. Each moving average point is the daily average of the past seven days. We can look at any date, and the day of the week no longer plays a role. We can see that the trend increases up to April 17, 2020. It plateaus, with a slight decline, until around June 22^{nd}. Since then, there is an upward trend that appears to steepen at the end.

Smoothing time series data helps reveal the underlying trends in your data. That process can aid in the simple visual assessment of the data, as seen in this article. However, it can also help you fit the best time series model to your data. The moving average is a simple but very effective calculation!

## A Tour of Survival Analysis

*Note: this is a guest post by Alexander Moreno, a Computer Science PhD student at the Georgia Institute of Technology. He blogs at www.boostedml.com*

Survival analysis is an important subfield of statistics and biostatistics. These methods involve modeling the time to a first event such as death. In this post we give a brief tour of survival analysis. We first describe the motivation for survival analysis, and then describe the hazard and survival functions. We follow this with non-parametric estimation via the Kaplan Meier estimator. Then we describe Cox’s proportional hazard model and after that Aalen’s additive model. Finally, we conclude with a brief discussion.

## Why Survival Analysis: Right Censoring

Modeling first event times is important in many applications. This could be time to death for severe health conditions or time to failure of a mechanical system. If one always observed the event time and it was guaranteed to occur, one could model the distribution directly. For instance, in the non-parametric setting, one could use the empirical cumulative distribution function to estimate the probability of death by some time. In the parametric setting one could do non-negative regression.

However, in some cases one might not observe the event time: this is generally called *right censoring*. In clinical trials with death as the event, this occurs when one of the following happens. 1) participants drop out of the study 2) the study reaches a pre-determined end time, and some participants have survived until the end 3) the study ends when a certain number of participants have died. In each case, after the surviving participants have left the study, we don’t know what happens to them. We then have the question:

*How can we model the empirical distribution or do non-negative regression when for some individuals, we only observe a lower bound on their event time?*

The above figure illustrates right censoring. For participant 1 we see when they died. Participant 2 dropped out, and we know that they survived until then, but don’t know what happened afterwards. For participant 3, we know that they survived until the pre-determined study end, but again don’t know what happened afterwards.

## The Survival Function and the Hazard

Two of the key tools in survival analysis are the survival function and the hazard. The survival function describes the probability of the event not having happened by a time . The hazard describes the instantaneous rate of the first event at any time .

More formally, let be the event time of interest, such as the death time. Then the survival function is . We can also note that this is related to the cumulative distribution function via .

For the hazard, the probability of the first event time being in the small interval , given survival up to is . This is illustrated in the following figure.

Rearranging terms and taking limits we obtain

where is the density function of and the second equality follows from applying Bayes theorem. By rearranging again and solving a differential equation, we can use the hazard to compute the survival function via

The key question then is how to estimate the hazard and/or survival function.

## Non-Parametric Estimation with Kaplan Meier

In non-parametric survival analysis, we want to estimate the survival function without covariates, and with censoring. If we didn’t have censoring, we could start with the empirical CDF . This equation is a succinct representation of: how many people have died by time ? The survival function would then be: how many people are still alive? However, we can’t answer this question as posed when some people are censored by time .

While we don’t necessarily know how many people have survived by an arbitrary time , we do know how many people in the study are still at risk. We can use this instead. Partition the study time into , where each is either an event time or a censoring time for a participant. Assume that participants can only lapse at observed event times. Let be the number of people at risk at just before time . Assuming no one dies at exactly the same time (no ties), we can look at each time someone died. We say that the probability of dying at that specific time is , and say that the probability of dying at any other time is . We can then say that the probability of surviving at any event time , given survival at previous candidate event times is . The probability of surviving up to a time is then

We call this [1] the Kaplan Meier estimator. Under mild assumptions, including that participants have independent and identically distributed event times and that censoring and event times are independent, this gives an estimator that is consistent. The next figure gives an example of the Kaplan Meier estimator for a simple case.

### Kaplan Meier R Example

In R we can use the Surv and survfit functions from the survival package to fit a Kaplan Meier model. We can also use ggsurvplot from the survminer package to make plots. Here we will use the ovarian cancer dataset from the survival package. We will stratify based on treatment group assignment.

library(survminer) library(survival) kaplan_meier <- Surv(time = ovarian[['futime']], event = ovarian[['fustat']]) kaplan_meier_treatment<-survfit(kaplan_meier~rx,data=ovarian, type='kaplan-meier',conf.type='log') ggsurvplot(kaplan_meier_treatment,conf.int = 'True')

## Semi-Parametric Regression with Cox’s Proportional Hazards Model

Kaplan Meier makes sense when we don’t have covariates, but often we want to model how some covariates affect death risk. For instance, how does one’s weight affect death risk? One way to do this is to assume that covariates have a multiplicative effect on the hazard. This leads us to Cox’s proportional hazard model, which involves the following functional form for the hazard:

The baseline hazard describes how the average person’s risk evolves over time. The relative risk describes how covariates affect the hazard. In particular, a unit increase in leads to an increase of the hazard by a factor of .

Because of the non-parametric nuissance term , it is difficult to maximize the full likelihood for directly. Cox’s insight [2] was that the assignment probabilities given the death times contain most of the information about , and the remaining terms contain most of the information about . The assignment probabilities give the following *partial likelihood*

We can then maximize this to get an estimator of . In [3,4] they show that this estimator is consistent and asymptotically normal.

### Cox Proportional Hazards R Example

In R, we can use the Surv and coxph functions from the survival package. For the ovarian cancer dataset, we notice from the Kaplan Meier example that treatment is not proportional. Under a proportional hazards assumption, the curves would have the same pattern but diverge. However, instead they move apart and then move back together. Further, treatment does seem to lead to different survival patterns over shorter time horizons. We should not use it as a covariate, but we can stratify based on it. In R we can regress on age and presence of residual disease.

cox_fit <- coxph(Surv(futime, fustat) ~ age + ecog.ps+strata(rx), data=ovarian) summary(cox_fit)

which gives the following results

Call: coxph(formula = Surv(futime, fustat) ~ age + ecog.ps + strata(rx), data = ovarian) n= 26, number of events= 12 coef exp(coef) se(coef) z Pr(>|z|) age 0.13853 1.14858 0.04801 2.885 0.00391 ** ecog.ps -0.09670 0.90783 0.62994 -0.154 0.87800 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 exp(coef) exp(-coef) lower .95 upper .95 age 1.1486 0.8706 1.0454 1.262 ecog.ps 0.9078 1.1015 0.2641 3.120 Concordance= 0.819 (se = 0.058 ) Likelihood ratio test= 12.71 on 2 df, p=0.002 Wald test = 8.43 on 2 df, p=0.01 Score (logrank) test = 12.24 on 2 df, p=0.002

this suggests that age has a significant multiplicative effect on death, and that a one year increase in age increases instantaneous risk by a factor of 1.15.

## Aalen’s Additive Model

Cox regression makes two strong assumptions: 1) that covariate effects are constant over time 2) that effects are multiplicative. Aalen’s additive model [5] relaxes the first, and replaces the second with the assumption that effects are additive. Here the hazard takes the form

As this is a linear model, we can estimate the cumulative regression functions using a least squares type procedure.

### Aalen’s Additive Model R Example

In R we can use the timereg package and the aalen function to estimate cumulative regression functions, which we can also plot.

library(timereg) data(sTRACE) # Fits Aalen model out<-aalen(Surv(time,status==9)~age+sex+diabetes+chf+vf, sTRACE,max.time=7,n.sim=100) summary(out) par(mfrow=c(2,3)) plot(out)

This gives us

Additive Aalen Model Test for nonparametric terms Test for non-significant effects Supremum-test of significance p-value H_0: B(t)=0 (Intercept) 7.29 0.00 age 8.63 0.00 sex 2.95 0.01 diabetes 2.31 0.24 chf 5.30 0.00 vf 2.95 0.03 Test for time invariant effects Kolmogorov-Smirnov test (Intercept) 0.57700 age 0.00866 sex 0.11900 diabetes 0.16200 chf 0.12900 vf 0.43500 p-value H_0:constant effect (Intercept) 0.00 age 0.00 sex 0.18 diabetes 0.43 chf 0.06 vf 0.02 Cramer von Mises test (Intercept) 0.875000 age 0.000179 sex 0.017700 diabetes 0.041200 chf 0.053500 vf 0.434000 p-value H_0:constant effect (Intercept) 0.00 age 0.00 sex 0.29 diabetes 0.42 chf 0.02 vf 0.05 Call: aalen(formula = Surv(time, status == 9) ~ age + sex + diabetes + chf + vf, data = sTRACE, max.time = 7, n.sim = 100)

The results first test whether the cumulative regression functions are non-zero, and then whether the effects are constant. The plots of the cumulative regression functions are given below.

## Discussion

In this post we did a brief tour of several methods in survival analysis. We first described why right censoring requires us to develop new tools. We then described the survival function and the hazard. Next we discussed the non-parametric Kaplan Meier estimator and the semi-parametric Cox regression model. We concluded with Aalen’s additive model.

[1] Kaplan, Edward L., and Paul Meier. “Nonparametric estimation from incomplete observations.” Journal of the American statistical association 53, no. 282 (1958): 457-481.

[2] Cox, David R. “Regression models and life-tables.” In *Breakthroughs in statistics*, pp. 527-541. Springer, New York, NY, 1992.

[3] Tsiatis, Anastasios A. “A large sample study of Cox’s regression model.” *The Annals of Statistics* 9, no. 1 (1981): 93-108.

[4] Andersen, Per Kragh, and Richard David Gill. “Cox’s regression model for counting processes: a large sample study.” *The annals of statistics* (1982): 1100-1120.

[5] Aalen, Odd. “A model for nonparametric regression analysis of counting processes.” In *Mathematical statistics and probability theory*, pp. 1-25. Springer, New York, NY, 1980.

## Failing to Reject the Null Hypothesis

Failing to reject the null hypothesis is an odd way to state that the results of your hypothesis test are not statistically significant. Why the peculiar phrasing? “Fail to reject” sounds like one of those double negatives that writing classes taught you to avoid. What does it mean exactly? There’s an excellent reason for the odd wording!

In this post, learn what it means when you fail to reject the null hypothesis and why that’s the correct wording. While accepting the null hypothesis sounds more straightforward, it is not statistically correct! [Read more…] about Failing to Reject the Null Hypothesis

## Understanding Significance Levels in Statistics

Significance levels in statistics are a crucial component of hypothesis testing. However, unlike other values in your statistical output, the significance level is not something that statistical software calculates. Instead, you choose the significance level. Have you ever wondered why?

In this post, I’ll explain the significance level conceptually, why you choose its value, and how to choose a good value. Statisticians also refer to the significance level as alpha (α). [Read more…] about Understanding Significance Levels in Statistics

## Guidelines for Removing and Handling Outliers in Data

Outliers are unusual values in your dataset, and they can distort statistical analyses and violate their assumptions. Unfortunately, all analysts will confront outliers and be forced to make decisions about what to do with them. Given the problems they can cause, you might think that it’s best to remove them from your data. But, that’s not always the case. Removing outliers is legitimate only for specific reasons. [Read more…] about Guidelines for Removing and Handling Outliers in Data

## 5 Ways to Find Outliers in Your Data

Outliers are data points that are far from other data points. In other words, they’re unusual values in a dataset. Outliers are problematic for many statistical analyses because they can cause tests to either miss significant findings or distort real results.

Unfortunately, there are no strict statistical rules for definitively identifying outliers. Finding outliers depends on subject-area knowledge and an understanding of the data collection process. While there is no solid mathematical definition, there are guidelines and statistical tests you can use to find outlier candidates. [Read more…] about 5 Ways to Find Outliers in Your Data

## Low Power Tests Exaggerate Effect Sizes

If your study has low statistical power, it will exaggerate the effect size. What?!

Statistical power is the ability of a hypothesis test to detect an effect that exists in the population. Clearly, a high-powered study is a good thing just for being able to identify these effects. Low power reduces your chances of discovering real findings. However, many analysts don’t realize that low power also inflates the effect size.

In this post, I show how this unexpected relationship between power and exaggerated effect sizes exists. I’ll also tie it to other issues, such as the bias of effects published in journals and other matters about statistical power. I think this post will be eye-opening and thought provoking! As always, I’ll use many graphs rather than equations. [Read more…] about Low Power Tests Exaggerate Effect Sizes

## Revisiting the Monty Hall Problem with Hypothesis Testing

The Monty Hall Problem is where Monty presents you with three doors, one of which contains a prize. He asks you to pick one door, which remains closed. Monty opens one of the other doors that does not have the prize. This process leaves two unopened doors—your original choice and one other. He allows you to switch from your initial choice to the other unopened door. Do you accept the offer?

If you accept his offer to switch doors, you’re twice as likely to win—66% versus 33%—than if you stay with your original choice.

Mind-blowing, right?

The solution to the Monty Hall Problem is tricky and counter-intuitive. It did trip up many experts back in the 1980s. However, the correct answer to the Monty Hall Problem is now well established using a variety of methods. It has been proven mathematically, with computer simulations, and empirical experiments, including on television by both the Mythbusters (CONFIRMED!) and James Mays’ Man Lab. You won’t find any statisticians who disagree with the solution.

In this post, I’ll explore aspects of this problem that have arisen in discussions with some stubborn resisters to the notion that you can increase your chances of winning by switching!

The Monty Hall problem provides a fun way to explore issues that relate to hypothesis testing. I’ve got a lot of fun lined up for this post, including the following!

- Using a computer simulation to play the game 10,000 times.
- Assessing sampling distributions to compare the 66% percent hypothesis to another contender.
- Performing a power and sample size analysis to determine the number of times you need to play the Monty Hall game to get an answer.
- Conducting an experiment by playing the game repeatedly myself, record the results, and use a proportions hypothesis test to draw conclusions! [Read more…] about Revisiting the Monty Hall Problem with Hypothesis Testing

## Causation versus Correlation in Statistics

Causation indicates that an event affects an outcome. Do fatty diets cause heart problems? If you study for a test, does it cause you to get a higher score?

In statistics, causation is a bit tricky. As you’ve no doubt heard, correlation doesn’t necessarily imply causation. An association or correlation between variables simply indicates that the values vary together. It does not necessarily suggest that changes in one variable cause changes in the other variable. Proving causality can be difficult.

If correlation does not prove causation, what statistical test do you use to assess causality? That’s a trick question because no statistical analysis can make that determination. In this post, learn about why you want to determine causation and how to do that. [Read more…] about Causation versus Correlation in Statistics

## Observational Studies Explained

Observational studies use samples to draw conclusions about a population when the researchers do not control the treatment, or independent variable, that relates to the primary research question.

In my previous post, I show how random assignment reduces systematic differences between experimental groups at the beginning of the study, which increases your confidence that the treatments caused any differences between groups you observe at the end of the study.

Unfortunately, using random assignment is not always possible. For these cases, you can conduct an observational study. In this post, learn about observational studies, why these studies must account for confounding variables, and how to do so. I’ll close this post by reviewing a published observational study about vitamin supplement usage. [Read more…] about Observational Studies Explained

## Random Assignment in Experiments

Random assignment uses chance to assign subjects to the control and treatment groups in an experiment. This process helps ensure that the groups are equivalent at the beginning of the study, which makes it safer to assume the treatments caused any differences between groups that the experimenters observe at the end of the study. [Read more…] about Random Assignment in Experiments

## 5 Steps for Conducting Scientific Studies with Statistical Analyses

The scientific method is a proven procedure for expanding knowledge through experimentation and analysis. It is a process that uses careful planning, rigorous methodology, and thorough assessment. Statistical analysis plays an essential role in this process.

In an experiment that includes statistical analysis, the analysis is at the end of a long series of events. To obtain valid results, it’s crucial that you carefully plan and conduct a scientific study for all steps up to and including the analysis. In this blog post, I map out five steps for scientific studies that include statistical analyses. [Read more…] about 5 Steps for Conducting Scientific Studies with Statistical Analyses

## Percentiles: Interpretations and Calculations

Percentiles indicate the percentage of scores that fall below a particular value. They tell you where a score stands relative to other scores. For example, a person with an IQ of 120 is at the 91^{st }percentile, which indicates that their IQ is higher than 91 percent of other scores.

Percentiles are a great tool to use when you need to know the relative standing of a value. Where does a value fall within a distribution of values? While the concept behind percentiles is straight forward, there are different mathematical methods for calculating them. In this post, learn about percentiles, special percentiles and their surprisingly flexible uses, and the various procedures for calculating them. [Read more…] about Percentiles: Interpretations and Calculations

## Using Confidence Intervals to Compare Means

To determine whether the difference between two means is statistically significant, analysts often compare the confidence intervals for those groups. If those intervals overlap, they conclude that the difference between groups is not statistically significant. If there is no overlap, the difference is significant.

While this visual method of assessing the overlap is easy to perform, regrettably it comes at the cost of reducing your ability to detect differences. Fortunately, there is a simple solution to this problem that allows you to perform a simple visual assessment and yet not diminish the power of your analysis.

In this post, I’ll start by showing you the problem in action and explain why it happens. Then, we’ll proceed to an easy alternative method that avoids this problem. [Read more…] about Using Confidence Intervals to Compare Means

## Can High P-values Be Meaningful?

Can high p-values be helpful? What do high p-values mean?

Typically, when you perform a hypothesis test, you want to obtain low p-values that are statistically significant. Low p-values are sexy. They represent exciting findings and can help you get articles published.

However, you might be surprised to learn that higher p-values, the ones that are not statistically significant, are also valuable. In this post, I’ll show you the potential value of a p-value that is greater than 0.05, or whatever significance level you’re using. [Read more…] about Can High P-values Be Meaningful?

## Using Post Hoc Tests with ANOVA

Post hoc tests are an integral part of ANOVA. When you use ANOVA to test the equality of at least three group means, statistically significant results indicate that not all of the group means are equal. However, ANOVA results do not identify which particular differences between pairs of means are significant. Use post hoc tests to explore differences between multiple group means while controlling the experiment-wise error rate.

In this post, I’ll show you what post hoc analyses are, the critical benefits they provide, and help you choose the correct one for your study. Additionally, I’ll show why failure to control the experiment-wise error rate will cause you to have severe doubts about your results. [Read more…] about Using Post Hoc Tests with ANOVA

## When Can I Use One-Tailed Hypothesis Tests?

One-tailed hypothesis tests offer the promise of more statistical power compared to an equivalent two-tailed design. While there is some debate about when you can use a one-tailed test, the general consensus among statisticians is that you should use two-tailed tests unless you have concrete reasons for using a one-tailed test.

In this post, I discuss when you should and should not use one-tailed tests. I’ll cover the different schools of thought and offer my own opinion. [Read more…] about When Can I Use One-Tailed Hypothesis Tests?

## One-Tailed and Two-Tailed Hypothesis Tests Explained

Choosing whether to perform a one-tailed or a two-tailed hypothesis test is one of the methodology decisions you might need to make for your statistical analysis. This choice can have critical implications for the types of effects it can detect, the statistical power of the test, and potential errors.

In this post, you’ll learn about the differences between one-tailed and two-tailed hypothesis tests and their advantages and disadvantages. I include examples of both types of statistical tests. In my next post, I cover the decision between one and two-tailed tests in more detail.

[Read more…] about One-Tailed and Two-Tailed Hypothesis Tests Explained